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Artificial intelligence plays a vital role in improving customer relationship management’s productivity and quality. It revolutionizes the strategies, processes, and technologies that help organizations manage and analyze customer interactions.
The challenge for today’s businesses is to put the rapidly advancing AI technology to constructive use. One company leading the way in helping organizations capitalize on this goal is marketing technology firm SAS.
SAS Customer Intelligence 360 is a multichannel marketing hub that enables organizations to seamlessly collect, enhance, extend, and activate customer data, he explained. Powerful audience targeting and management, comprehensive identity resolution incorporating online and offline data, and a unique hybrid data architecture enable marketers to create journeys to deploy messages and personalize experiences across the entire customer lifecycle.
Consumer demands are evolving, and customer service resolution expectations have increased significantly. To keep up, brands must ensure the technologies they adopt enable speed. AI-powered chatbots provide customers with a near-instant response, assisting them in self-serving and problem-solving no matter the time of day or what human resources are available.
“The most significant contribution to date is from a generative AI perspective. A lot of marketing technology/CRM vendors are doing really cool things with it. However, we will see more AI-related capabilities infused into CRM platforms for front-end customer experience in the future,” SAS Head of Martech Solutions Marketing Jonathan Moran told CRM Buyer.
AI techniques to do this expand beyond generative AI. A variety of AI-powered tools are used in front-end CRM. These include natural language understanding (NLU) and natural language generation (NLG), beacons and geofencing, based optimization and customer routing, plus many more.
According to Moran, these AI-powered technologies make CRM more efficient across four key pillars:
With generative AI, brands can automate CRM processes to deliver campaigns, content, messages, and interactions to market faster. The most obvious benefit of AI to CRM is increased productivity.
Demand for better customer experience (CX) is increasing, and CRM platforms and processes must scale to meet it. AI- and analytics-powered technologies like customer routing and enterprise decision-making enable organizations to process numerous engagements and interactions concurrently. These capabilities support businesses in delivering better personalized CX at scale across CRM engagement channels, he explained.
“AI and analytics drive CRM initiatives forward by collecting relevant customer data, applying insight to that data, and then leveraging the data to derive insights to provide an individual-based level of customer understanding. When leveraged properly, these technologies have the power to improve business metrics around revenue, profit, margin, loyalty, trust, and customer lifetime value,” Moran said.
AI-powered CRM features include text analytics, natural language processing, sentiment analysis, visual and voice recognition through biometrics, real-time decisions, optimization, and customer routing.
One of the pressing problems CRM has not overcome with AI solutions so far is that machine learning has not been as seamlessly integrated into CRM systems as it could be.
We asked Jonathan Moran to share his insight and lengthy experience integrating various forms of AI to improve CRM efficiency.
Moran: Many martech vendors are focusing on incorporating generative AI but are neglecting other types of AI. While generative AI solves many menial marketing tasks, it does not generate the level of insight that predictive analytics-based machine learning and other AI techniques can.
Moran: AI can do several things to help fix inefficiencies in CRM software. For starters, AI can automate data entry but can also create data to augment data sets through the use of large language models (LLMs) and synthetic data generation.
Additionally, AI algorithms can enhance data quality by removing errors, inconsistencies, and duplicates. As we all know, improved data leads to better outcomes from CRM systems.
Artificial intelligence is rooted in predictive analytics. So, AI can be used to analyze CRM data to identify trends, patterns, and behavior signals that inform future actions.
Moran: AI embedded directly into the journey creation capability of CRM systems allows users to uncover insights they previously might not have had.
For example, a user can analyze audience or segment data within a journey to understand what net-new audiences they could engage via a journey similar to the one they are currently creating.
Known as automated segment discovery, it allows users to uncover previously unknown cross-sections of the customer base for different types of interaction.
AI can also be used to analyze previous actions from a cohort of customers to then predict and suggest the best path for future prospects when engaging in a customer journey.
Moran: This use of reinforcement learning allows brands to essentially set guardrails to guide customers to end conversion events. It is a better approach than trying to force them to a conversion event through certain channels or with specific messages.
AI, particularly generative AI, can automate routine tasks such as creating content, copy, text, e-mail subject lines, and other marketing needs. Similarly, RPA, or robotic process automation, can automate low-level routine tasks, such as lead scoring, reminders and notifications, scheduling, and other more menial activities. These automations increase productivity and free up resources to focus on other, more strategic actions.
Moran: I think that depends on the type of AI. For generative AI, it is the need to make marketers and other users more efficient and effective in their work. Whereas for AI, such as machine learning and reinforcement learning, it is to better understand prospects and customers [uncovering patterns and behaviors] in order to better engage with them to drive metrics like conversion, cross-sell/up-sell, etc.
Moran: Company workers want a CRM solution that is easy to use, allows them to complete their work efficiently, and gives them access to the data and insight that they need to be effective marketers. CRM platforms are keeping a strong eye on usability and the decrease in the overall complexity of the solutions.
Retail customers want offers that are anticipated, personalized, and relevant. If CRM platforms can deliver this type of offer or interaction, disfavor decreases.
Moran: It should not have to be an either-or scenario. Generative AI can be used with the main goal being to improve the efficiency of company-facing processes such as marketing, advertising, and sales, while other types of AI, such as machine learning techniques that improve personalized recommendations and real-time personalization, can be used to improve specific customer experiences.
Additionally, you have AI techniques such as natural language processing, sentiment analysis, and text analytics that improve front-end customer experience capabilities such as chatbots and conversational marketing.
Mistrust in how online retailers handle their customers’ personal information is at an all-time high, costing merchants lost revenue and follow-up orders.
Over a quarter (26%) of consumers have abandoned a brand in the past 12 months due to privacy concerns. Establishing a higher trust level is no longer an either/or alternative; it is becoming an increasingly important basis for digital retailers’ success if not survival.
Technology and security provider firm Thales released its 2024 Digital Trust Index Ranking in February. The report revealed that only 8% of consumers feel comfortable sharing their personal details with online vendors. Based on responses from 12,426 consumers globally about their relationship with online brands and services, the findings challenge the growing popularity of online shopping.
The survey results cover more than just food and soft goods purchased online. Media and entertainment, social media, and logistics companies are languishing at the bottom of the industry rankings.
Thales found that most retail customers now demand a good balance of security and positive digital experience from all online business encounters. Research also revealed that newer forms of online engagement pose barriers to securing customers’ trust.
When sharing their information, consumers place more trust in banking, health care, and government services. According to Thales Vice President of Identity and Access Management, Danny de Vreeze, this is a universal trend across all the markets surveyed.
“This is perhaps unsurprising when considering how highly regulated these industries are, the types of information they are responsible for handling, and the measures they have put in place to keep consumer data secure,” he said.
This year’s report ranked retail and hospitality as the fourth least trusted sectors. This aligned with the 2022 report findings, in which the retail industry was still among the least trusted industries, advised Haider Iqbal, director of product marketing for Thales IAM business.
“But interestingly, while retail wasn’t among the most trusted in either 2022 or 2024, consumers were significantly more trusting of retail organizations in 2022 (20%) when compared to this year’s findings (8%),” Iqbal told the E-Commerce Times.
With such a high percentage of consumers not trusting data security, the problem seems to be falling on deaf ears. While a clear realization of the need for data privacy exists, he noted that realization does not always lead to actionable results.
“Regional dynamics appear to be the biggest driver behind taking action. With strong enforcement of GDPR by the regulatory authorities, the retail players in Europe are taking more serious steps towards better practices and controls for implementing consumer data privacy,” Iqbal offered.
The H&M data protection violation fine was an eye-opener for the industry. However, as the report points out, retail companies should not just be looking at data privacy because regulators want to enforce it.
“They should be looking at it because their customers are demanding it,” Iqbal advised.
The report findings reinforce that the right to privacy and security is non-negotiable. The majority of customers (89%) are willing to share their data with organizations.
However, that comes with some non-negotiable caveats. For instance, over a quarter of consumers (29%) have abandoned a brand in the past 12 months because it demanded too much personal information.
“While businesses are subject to international data privacy laws regardless of sector, those further down the rankings have been subjected to fewer directives directly addressing both data security and privacy,” offered de Vreeze.
As more businesses grow their digital presence, lessons can be learned for non-regulated industries as consumer preferences evolve.
More than four in five (87%) expect some level of privacy rights from the companies with whom they interact online. The biggest expectation is the right to be informed that their data is being collected (55%), closely followed by the right to have their personal details erased (53%).
Online customers also expect more concessions on privacy standards businesses follow. For instance, 39% expect the right to correct their data, 33% expect the right to request a copy of it, and 26% expect the right to move data from one platform to another.
The Thales report also highlighted the role a well-oiled website experience plays in cementing customer allegiance, regardless of the privacy factors they encounter. Customers’ concerns go deeper than how online services use their data.
In addition to privacy demands, organizations must also deliver a seamless online experience to earn the trust of their customers. Today’s consumers are increasingly time-conscious, with over a fifth (22%) stating they would instantly give up on an online interaction when facing a frustrating experience.
Respondents also named advertising pop-ups as their number one frustration (71%), closely followed by password resets (64%) and having to re-enter personal information (64%). The study also named complex cookie options a top frustration by 59% of those surveyed.
“Our findings unveiled that 93% of consumers give up on an online brand after five minutes or less if they encounter a frustrating experience. In fact, 25% give up within just the first one or two minutes. This means companies only have a small window of time to ensure they provide users with the digital experiences they want,” said Iqbal.
As Iqbal sees retailers’ responses, they will not soon have the choice of just paying lip service to data privacy and security. With GDPR as the predecessor to any U.S. legislation — and following actions like California’s CCPA and Virginia’s VCPA — he sees more regulatory action taken for consumer data rights.
“With more scrutiny than ever on large language models (LLMs) and where they get their training data, this conversation will only become more important and will make it so legislation ensures that the right to privacy and security is as non-negotiable as the report respondents are requesting,” he predicted.
The report highlights that email (40%) and phone calls (28%) are not just the preferred channel of communication for consumers in retail but also in other industries, according to Iqbal. The important trend to note is that there are now significantly more channels and touchpoints for retail services to handle.
The notion of having a good strategy for omnichannel experiences isn’t just limited to the banking industry, for example. In-store/in-person communication is still significantly important as a mode of communication (32%) in retail. The industry needs to embrace this reality and gear itself toward building a consistent omnichannel experience for its consumers,” he urged.
Iqbal maintains that trust is not a monolithic concept, especially in the digital world. Organizations must devise their own formula for measuring trust.
“The perception of trusting a retail brand is likely to be very different from the perception of trusting a bank or an insurance company,” he noted, adding that Thales “understands that this notion of trust can be very nuanced, not just from one industry to the other, but also within an industry.”
This understanding is why organizations must have fundamental capabilities in their digital channels to tweak data security and data privacy controls, Iqbal explained. For example, when a company discovers the reasons for consumer abandonment but lacks the means or agility to address them, it must be ready for a steady churn of consumers.
“If you are still relying on legacy and often monolithic systems to address the modern and fast-evolving needs of consumers and regulators alike, you are not geared for the future,” concluded Iqbal.
The adage “different strokes for different folks” now guides public relations strategies, particularly in maintaining engagement with diverse generational clientele.
Generation Z, sometimes called Zoomers, is the new power kid on the buying block. This demographic follows Generation Y, or millennials, born between 1981 and 1994 and precedes Generation Alpha, which includes those 10 years old and younger. Born between 1997 and 2012, members of Generation Z are currently between the ages of 11 and 26.
Each targeted segment comes with its own message delivery demands. When creating PR content for Gen Z, for instance, retailers have to stay on or ahead of the trends, cautioned Danielle Gober, account strategist at public relations firm Otter PR.
“If it is not TikTok-worthy, Gen Zers could lose interest more easily. They like quick, funny, engaging content from the brands they follow and love,” she told the E-Commerce Times.
Attracting Gen Z consumers now and sustaining ongoing sales as they grow means rethinking traditional PR strategies. Successful sales marketing requires companies to help the world rather than hurt it. So, your company’s PR must first win over its brand’s saving graces to close the purchase to a growing number of consumer tiers.
“Gen Z is the first generation in my eyes that cares and is concerned about what big corporations are doing when it comes to climate change, wage gaps, and what philanthropy they are taking part in,” Gober shared about the difficulties in creating such specialized PR.
That often means PR content cannot come with the old tropes that PR is used to using. Gen Zers are big on doing their homework. If the media is trying to spin something a certain way, they can dig up the truth in mere seconds since they grew up with smartphones in the palms of their hands, she offered.
“Lastly, you have to keep in mind the current climate. You can’t promote a weight loss product and not be body-positive in your pitch. You can’t promote self-made millionaires without them checking to see if they came from wealthy parents, and you can’t promote equality in any area without them checking who they donate to as a company,” Gober added.
This is not to say Gen Z is trying to catch people in lies. They are not, she insisted.
“They are just the first generation that fully holds every person or company in the media to a certain standard. If that isn’t met, they will let you know,” she counseled.
Employing a PR agency can help companies increase sales and expand their customer base. We asked Danielle Gober for further insights on utilizing PR effectively, especially for reaching Generation Z.
“Sales and public relations do not directly correlate in most cases. Thinking they do is one of the big mistakes companies make in dealing with PR. However, the more eyes on your product, the more product reviews — and more exposure will always aid in sales efforts. That is what PR can accomplish,” she opined.
“It also can add a layer of credibility to your brand. That will encourage consumers to be more receptive to buying or at least trying your product. PR can make potential customers more responsive because they are familiar with the messages about the firm and its brand.”
Danielle Gober: Knowing your demographic is key to a successful campaign. Targeting the people who would use or most benefit from your product is the goal when it comes to marketing. But it also aids in being aware of other sub-targets and angling press or ads to those people as well to give yourself a bigger reach.
Gober: Thinking that PR success is guaranteed. It is not. Take dealing with product reviews as an example. Most reporters expect free products to give an honest review.
They cannot promise coverage in exchange for the product. Be prepared to send out a lot of products without the guarantee that it will be published.
Gober: Gen Z is not impossible to market to. You just have to speak their language and get along with the current times. Companies need to showcase the good they are doing either through their product or because of their product.
They also need to be as inclusive as possible and be aware of Gen Z’s budget. Knowing ahead of time what they are willing to spend in this economy keeps budget and PR expectations in proper alignment.
Gober: You wouldn’t market to a boomer the same way you would to Gen Z and everyone in between. Each generation has its own motives for purchasing and desires for buying. Each segment pays attention to trends in its own way.
The usual pathway for this is through social media platforms. You have to pivot when targeting certain demographics, but you also must stay on message consistently. It’s a fine balance to achieve.
Gober: Take as many opportunities out there that fit your brand. Limiting yourself to certain publications will limit eyes on you. Even mid-tier publications can do wonders for a campaign if it is the right audience.
Gober: Traditional retail is becoming a thing of the past. It is not something I have even encountered in a working environment because even when I worked retail through college at a local boutique, we utilized social media and website development daily, weekly, and monthly to stay on trend and get people to stop by the store.
We’re at a turning point in business, technology, and society, and I don’t think there’s been anything quite like it since the 1960s.
I can’t get into everything in one column, but what impresses me so far is how stately this revolution has been. If you think these days are chaotic, all I can say is you should have been around in 1968. My only disappointment today is the music. Sorry, but I knew the Beatles, Motown, and folk rock.
The thing that grabs my attention, at least in tech, is that we’re not exactly throwing everything away and beginning again with a blank slate.
The mini-computer era started that way in the late 60s. Although mini stood for smaller and less cumbersome than a mainframe, the cultural upheaval brought by companies like Digital Equipment Corporation, Data General, and Wang upset everyone’s apple carts.
Suddenly, we had the ability to bring computing power much closer to where it was needed; that was when we first added statistical controls to making things. The result was that we made things better and satisfied more customers in the process.
Perhaps we are not throwing everything away now because there’s really nothing to discard. Today’s revolution is toward artificial intelligence, which is like nothing that came before, so we are actually starting from scratch anyway.
Most interesting to me is that a small cluster of businesses from about the mini-computer era are reinventing themselves as they have in almost every decade since they were founded.
My list includes HP, IBM, Microsoft, and Oracle, though a few others, like Salesforce, are in the same boat. Salesforce seems to reinvent itself on a whim and has for 25 years.
So, why is this important? After all, Schumpeter and Kondratiev long ago articulated the boom-and-bust cycle, which Schumpeter famously described as creative destruction.
It’s important because true inflection points are infrequent. I just referenced 1968, but I’ll bet that most people reading this were either too young to remember that year or weren’t even born yet. That’s one of the reasons that major changes seem so chaotic and disturbing — they are so far apart in time that few people have a memory to compare to the present.
I am nearing the end of my career, so I have an interesting perspective because I can see a whole cycle, which provides some perspective few of us have. Perhaps the most salient part of that perspective is the comforting realization that there are vanishingly few truly hard problems.
Hard problems involve science, often including developing new science. Most of our problems are engineering problems. Science shows the way, and engineering levels the road, making it possible for all to pass. Unlike other eras, so much science is available today that we can spend most of our attention on engineering.
Cloud computing was a science problem until Amazon, the bookseller, figured out how to host millions of shoppers simultaneously. That cleared the way for Salesforce and a small cadre of innovative companies to invent cloud computing.
It does not diminish the founders of SaaS to say they solved an engineering problem rather than one of science. Most early entrants failed to understand that although the technology was revolutionary, the changes to business were even more so. Early entrants also had to solve an economic/finance/business model problem in the process.
We should linger here over the idea of solving engineering problems because much of the stasis I see in the world stems from not tackling engineering problems. CRM seems to be an exception; we revel in solving problems, and it shows. There is no finer place to work than in CRM.
Solving the engineering problems associated with CRM has improved life in many ways. Of course, it has created jobs, and those jobs have generated literally trillions of dollars worth of economic activity. The follow-on impacts are incalculable.
So here we are at the beginning once again. The field is wide open, and there are new fortunes to be made as we take on the latest engineering challenges. There will be successes and, unfortunately, setbacks along the way. But as long as we continue striving to solve the engineering problems before us, we’ll be more than fine.
The only real failure would come from saying something is too hard, or it’s not worth doing. Since the Industrial Revolution, no successful entrepreneur ever said that.
Contrary to what some folks think about the “recent” discovery of artificial intelligence (AI) for business uses, AI as a computing function is not novel. What is new is its growing adoption for expanded uses and the ability to transform data into actionable business strategies.
AI has been around for a long time, observed Daniel Ziv, VP for experience management and analytics, GTM Strategy at workforce management firm Verint. AI is not one thing. It has a variety of capabilities depending on what it is designed to do.
For example, one of the chief components of AI’s various elements is large language models (LLMs), which have a longstanding presence in the field. Innovation in their capabilities resulted from the emergence of advancements that exposed the power of natural language understanding and natural language generation about 18 months ago.
“That work has been evolving and building for many years,” Ziv told the E-Commerce Times. “It exposed awareness because it was publicly accessible for anyone to try.”
A meaningful turning point is accelerating both the need and the opportunity for automation platforms that organizations can leverage in new ways, Ziv noted. For instance, generative AI is evolving and getting smarter and more proficient at understanding language.
One key element in AI’s growing business adoption is cloud computing, which can process more data much faster and at a lower cost. Ten years ago, companies deployed most AI software on-premises. Adopters had to buy hardware, provision it, install software and train everybody.
“It would take months — sometimes years — to get the value that now you can get sometimes in days or weeks,” Ziv said.
Today’s challenge is learning how to leverage AI’s advancements over the last two years to transform massive data for speedy analysis and recommendations. Data transformation has many approaches depending on the types of data collected, such as structured and unstructured data.
“Structured data tends to be numbers, and computers have been running on structured data. Computers are very good at building models and doing things based on numbers,” he said.
The transformation process becomes more complicated with unstructured and semi-structured data, which includes unstructured elements like text, audio, or video and some metadata associated with it.
“In the past, that was more challenging for computers. Today, with generative AI, the technology has caught up and can do it much faster,” Ziv explained.
Verint has used AI for decades to help companies get a handle on using their data more effectively. It has helped its customers work with a range of accuracy issues.
“In our industry, I think people might perceive that transformative data is not so accurate because we’ve taken general LLMs trained on internet data that is not specific to your business. It is not behavioral data. So, what it learned to do is kind of like babies as they learn to speak,” Ziv suggested.
So far, we have trained our AI to understand language in general and to be able to respond to some level. But the AI’s comprehension is much like a baby still lacking the right knowledge, information, and experiences to give educated answers on things that directly relate to the desired results, he added.
AI developers are continuing to learn how to make that baby grow into an effective adult. The solution, according to Ziv, is to take that ability to understand language and generate language with the correct behavioral data specific to interactions you have with your customers or organizations have with their customers.
“We are at the beginning of this transformative phase. But I do believe that the competence to write data with an open platform and the power of generative AI will allow us to see things that are very compelling and will allow us to automate,” he observed.
SoundCommerce is an example of why using data to predict actionable results is not a one-size-fits-all process. The company takes a different approach than other data management providers by using a no-code environment accessible to everyone.
The company’s CEO, Eric Best, noted that the data transformation pathway is littered with challenges. The process involves extracting data from a source system and customer data from the client’s CRM platform.
Then, the data has to be validated to contain reasonable quality. According to Best, the next step is applying the data to address a particular problem that SoundCommerce is working to solve: ascribe meaning to the data as it flows.
“That is important because, by the time you get to the data warehouse, where your analysis is going to happen, you are going to make these all-important business decisions,” Best told the E-Commerce Times.
To make that happen accurately, the data must be converted from one format to another to create compatibility and similarity. For example, for most retail brands, orders come from multiple sources in addition to a cash register or point-of-sale system. These vending sites often include an e-commerce storefront, an Amazon Marketplace business, and a proprietary mobile app.
“Seeing all four of the order data records in a common format and schema is an area where AI can be helpful,” Best said.
To get accurate results from the combined data feeds, you have to be able to describe the data in natural language terms. So, in order to get the AI to help with this data mapping problem, you need to tell the AI in very verbose, natural language terms what data you want and how you want to define the data.
The solution is having the AI write the software to effect that change that transformation on the data. So instead of being a really good software engineer, you need to become a prompt engineer, Best explained.
“People have to be very good at describing what they want, not in coding terms but in natural language terms. Accuracy in speech and writing becomes super important.”
SoundCommerce customers are just beginning to experiment with these generative AI algorithms. Some of that AI enablement is done by the company using its own proprietary algorithms around things that are very specialized for its customers, Best noted.
One proprietary code example is the ability to forecast the lifetime value of an individual customer or shopper. When it comes to the generic capabilities, the generative AI work innovation comes from Microsoft, Google, Amazon Web Services, and an independent specialty data warehousing company called Snowflake that Best’s company works.
Those cloud platform companies are generally building their own generative AI tooling with their own proprietary large language models.
How cost-effective and practical is this high-tech AI decision-making capability for business? The answer to that question depends, quipped Best.
For less technical organizations, the new technology’s practicality for smaller companies increases the more tightly you can define a use case. SoundCommerce had to learn this the hard way, he admitted.
Best uses an age-old reality to answer the practicality versus cost-effectiveness question. For more than a century, people have been figuring out where to spend advertising dollars effectively.
“So, the questions and answers are not new. The ability to automate the answers at scale is definitely new,” Best concluded.
As technologies like ChatGPT exemplify, generative AI (gen AI) is rapidly evolving, prompting businesses across industries to refine their application strategies. The challenge in 2024 is to leverage these new technologies to drive positive business outcomes and enhance customer satisfaction effectively.
Since its introduction, one of the main revelations has been the distinct roles this new generation of AI can fulfill, transitioning from the traditional focus on analysis and classification to creative content generation. Generative AI uses complex algorithms and neural networks to mimic human creativity, producing diverse outputs such as text, images, and music.
Distinct from artificial general intelligence (AGI), which seeks to replicate full human intellectual capabilities, generative AI is task-specific. It provides practical solutions within its trained areas, adeptly handling various tasks and adapting to new situations based on incoming data.
In practice, generative AI is a potent productivity tool, enabling rapid content generation across mediums such as text, images, sounds, animations, and 3D models. It not only learns and retains patterns and nuances in language but also remembers past interactions, leading to more coherent and contextually relevant exchanges with users.
However, gen AI currently falls short in decisions involving numerous complex factors, particularly those requiring deep contextual or emotional understanding. While it excels at data-driven suggestions, integrating and managing nuanced human factors remains beyond its reach, at least for now.
According to Will Devlin, vice president of marketing at customer engagement platform firm MessageGears, business and industry adopters can leverage AI without fear of failure.
“Any marketer who has ever conducted a standard A/B test can tell you that failure isn’t always something to be avoided. In our careers, we constantly learn new tools, technology, and techniques. Fear of failure is always going to be a necessary part of that learning and growing process. As with anything new, there are concerns around AI that are relevant and real,” he told TechNewsWorld.
Michael Fisher, chief product officer at digital compliance and data management firm Complykey (formerly Waterfield Technologies), has four predictions addressing those areas.
Over the past year, contact centers, primary adopters of this technology, have rapidly integrated generative AI. Fisher predicts that in 2024, the focus will shift towards a deeper understanding of generative AI’s ROI.
He expects contact center leaders and other AI adopters to increasingly focus on calculating the cost of AI more meaningfully. This effort includes a better understanding of how the deployment cost can be optimized related to scale and cost per transaction.
Gen AI will continue to be adopted the fastest this year in marketing and customer prospecting, which is cross-industry, Fisher offered as a second prediction. In the lead generation business, you must consider the value, the cost, and the risks.
The inherent risks are slowing adoption in highly regulated industries like health care, government, and finance. The back end of the contact center in these industries will be aggressive about using generative AI for summarizing data and reporting.
“But on the customer-facing front end, those verticals will all move slower and more deliberately. The further you get away from industries that are already highly regulated, like retail, the faster generative AI adoption we’ll see,” he observed.
Many companies have continued offering on-premises and cloud-based contact center solutions catering to customer preferences. However, keeping both solutions live creates a technology cost drain for vendors. So, leverage one over the other.
Fisher’s third prediction was that “in 2024, more companies will sunset their on-premises solutions or raise the price significantly to make an on-premises solution commercially unviable for customers — essentially forcing cloud adoption and innovation on customers.”
The insurance industry uniquely uses video-based communications for things like collaborative document signing or showing accident damage to a vehicle. Most industries have been slow to adopt video as a customer service channel.
“This will change in 2024. We expect video to be more broadly deployed as a customer service channel across industries, especially for companies that sell a physical product that benefits from a show-and-tell,” Fisher noted as his fourth leveraging prediction.
Specific use cases will help drive demand for this feature. Changing consumer preferences, led by Gen Z’s comfort and familiarity with video-based content, may also help, he shared.
MessageGear’s Devlin thinks it is vital that as brands start to harness AI — particularly generative AI — they put guardrails in place and develop standard operating procedures and guidelines for their teams to follow.
That will be a learning process. Companies must realize that Gen AI is not a one-size-fits-all solution.
“I expect that AI technology will only get better as we get more hands-on with it,” he cautioned, adding, “Because AI is such a new technology, brands are still navigating how to manage it and ensure they use it responsibly and to its fullest potential.”
A recently conducted survey by MessageGears of marketers at enterprise brands showed that the most significant challenges brands face when implementing AI solutions are limited expertise, staff training, and integration complexity.
“AI modeling is only as good as the data you put into it. Conversely, AI can be a powerful tool, helping brands improve conversions and ROI, save time, reduce time-to-value, and improve testing and learning,” Devlin told TechNewsWorld.
Shahid Ahmed, group EVP for new ventures and innovation at digital consulting firm NTT Data, revealed that his company’s 2023 Global Customer Experience Report found that the majority of CX interactions still require a form of human intervention.
According to this report, executives agree this will remain a critical part of customer journeys. Despite 80% of organizations planning to incorporate AI into CX delivery within the next 12 months, the human element will be central to its success.
“As enterprises turn their attention to how automation can complement and enhance human capabilities, they will place greater emphasis on closing the mounting skills shortages that will challenge AI aspirations,” Ahmed told TechNewsWorld.
He cautioned that the fundamentals of AI and big data analytics will become baseline skills for most jobs across industries, and new hires will not be the only pathway.
“Research by NTT Data uncovered that business leaders are more likely to have seen profitability of more than 25% over the last three years because of investments in reskilling and upskilling initiatives. This trend will continue in 2024, with more curated teaching experiences to help close skills gaps and meet the needs of organizations,” he advised.
AI’s best leveraging approach might well be in a managed cloud combination. AI is everywhere today. Adopters should ponder what numbers chart this explosive growth.
A report by cloud security provider Wiz shows a key connection between using AI services via a managed cloud platform. Its analysis of aggregate data related to a large sample of organizations provides a comprehensive overview of how generative AI and machine learning are being used in the cloud and its implications for organizations.
According to that research, AI is rapidly gaining ground in cloud environments. Over 70% of organizations now use managed AI services. At that percentage, the adoption of AI technology rivals the popularity of managed Kubernetes services, which Wiz sees in over 80% of organizations.
Another noteworthy view is many organizations experiment with AI but do not go beyond that step.
Only 10% are power users who deployed 50 or more instances in their environments. While the adoption of AI in the cloud is soaring, many organizations (32%) still appear to be in the experimentation phase with these tools, deploying fewer than 10 instances of AI services in their cloud environments., according to the report.
For most folks, 2023 was the year that AI came into focus, with adopters asking how to utilize it best, observed MessageGear’s Devlin. Now, if they have not already started using AI regularly, most brands are, at the very least, AI-curious.
“They want to test and see how it can help them and are ready to explore. As brands become more comfortable with the idea of AI, I think we’ll see certain roles grow in complexity while others are made more efficient using AI tools,” he noted.
Generative AI becomes especially powerful when paired with insights from predictive AI. Not only do you know when and where a customer wants to hear from you, but you also know the likelihood that they will make a purchase and what language and imagery will likely sway them to act.
“It’s a combination that brands are only beginning to take advantage of, and it has almost infinite potential,” he concluded.
Bigeye’s Dependency-Driven Monitoring platform, announced on March 25, is a data observability solution that allows enterprise data teams to see more trustworthy results from their CRM systems.
The process lets data teams connect analytics dashboards and map every dependency across modern and legacy data sources. It also enables analysts to deploy targeted data observability to stay reliable by default.
According to the Bigeye 2023 State of Data Quality Report, 70% of business leaders do not trust analytics dashboards to make critical decisions due to recurring data quality incidents. The company hopes to change that statistic with its optimized data observability capability for every column, powering essential dashboards of analytics and data products.
“We’ve spoken with hundreds of enterprise data leaders, and despite investing heavily in data quality tools and processes, they still struggle to deliver reliable data analytics to business users,” noted Bigeye CEO and Co-founder Kyle Kirwan.
The data observability industry has not yet solved the problem of handling the complexity and size of large enterprise data pipelines. Enterprise dashboards have a long list of dependencies that span modern and legacy technologies. Data observability platforms have yet to offer genuine support for the types of hybrid environments nearly all Fortune 500 companies have, he explained.
According to Kirwan, data observability enables organizations to understand the state of their data at all times so they can find and fix issues before they impact business operations, which helps customers answer questions like, “Where is data coming from and going within our pipelines?”
It also answers concerns such as “Is it arriving on time and with the volume we expect?” “Is the quality high enough for our use cases?” or “Are there any recent anomalies in the data that indicate it may have a problem?”
Bigeye provides enterprise-grade data observability access for modern and legacy data stacks. Its platform brings together data observability and end-to-end lineage with scalability and security.
The result gives enterprise data teams unmatched insight into the reliability of data powering their business, whether data is stored on-premises, in the cloud, or hybrid.
“All organizations use data to power strategic decision-making, user experience, and efficient operations. Incomplete or incorrect data too often makes it into these processes undetected — significantly impacting business performance, customer satisfaction, and employee trust,” Kirwan told CRM Buyer.
Bigeye helps eliminate these challenges while simultaneously improving efficiency and reducing the cost of data operations, he added.
Data observability in a data pipeline is a complex task that depends on the sequence and dependencies of various processing jobs.
Bigeye addresses this complexity by integrating enterprise-grade lineage technology with data observability. This process enables automatic, column-level tracing of the entire data pipeline, including ETL stages, and across the cloud to on-premises environments, providing a thorough and secure monitoring system that you can rely on to catch any anomalies.
When issues arise, Bigeye immediately alerts the relevant data source owners via Slack or Microsoft Teams and can auto-generate tickets in IT service management tools like JIRA and ServiceNow for streamlined incident management.
Without precise knowledge of which columns are vital for business operations, data engineering teams often implement wide-ranging monitoring across numerous tables and columns to ensure they catch any anomalies. This broad monitoring approach, while thorough, leads to higher computing costs, excessive alert noise, and the burden of unnecessary monitoring.
Bigeye’s solution, however, allows data analysts and business users to initiate data observability from their essential dashboards and focus on monitoring only the crucial columns, thereby reducing overhead and enhancing efficiency.
Kirwan noted that common industries utilizing Bigeye’s approach include financial services, insurance, health care, high-tech, and retail. He emphasized that managing data pipelines is a challenge faced by organizations of all sizes, including smaller companies. “Large enterprise pipelines are particularly tricky because of their size, complexity, and the breadth of data sources and tools they employ,” he clarified.
Bigeye Dependency-Driven Monitoring provides significant benefits compared to other data observability approaches. These include:
For data consumers, Bigeye displays data health updates directly in users’ analytics dashboard to provide instant insight into the reliability of their analytics. Data engineering teams can then use Bigeye’s lineage-powered root cause and impact analysis to quickly trace the data problem to the source for fast triage and resolution.
Data lineage has become a ubiquitous feature for many data operations tools. Due to the complexity of mapping lineage in legacy or on-premises environments, most tools require the customer to use complex, custom APIs or manual entry to try and capture a complete picture of an enterprise pipeline.
Before this launch, Bigeye provided data quality monitoring, data pipeline monitoring, and data lineage for cloud data warehouses such as Snowflake, Databricks, and Google BigQuery and transactional data sources such as MySQL. The launch of Dependency-Driven Monitoring expands Bigeye’s capabilities in two significant areas:
Bigeye’s 2023 State of Data Quality Report revealed the need for a better solution. Respondents told Bigeye researchers that building an in-house data quality monitoring solution would take 37,500 full-time equivalent (FTE) hours.
Roughly, that equates to one year of work for 20 engineers. Other highlights of that report include:
“Insights from this report were part of Bigeye’s primary and secondary research to validate the need for Dependency-Driven Monitoring. In addition, we also spoke with hundreds of data leaders, industry experts, and partners,” Kirwan said about what led the company to develop the new technology.
He added that the Dependency-Driven Monitoring feature is a new capability available as part of Bigeye’s data observability platform.
Bigeye focused on analytics reliability as the first solution. Poor analytics data quality is a pervasive challenge across large enterprises, Kirwan observed. Solving it delivers immediate, demonstrable value to our customers. He said the additional use cases for Dependency-Driven Monitoring are vast and can include monitoring dependencies for everything from ML models to data products and gen AI projects.
Bigeye Lineage Plus is the technology that enables Bigeye Dependency-Driven Monitoring. It is a complete data lineage technology built to handle the largest, most complex enterprise pipelines. It includes 50 connectors for transactional databases, cloud data warehouses, data lakes, ETL platforms, analytics tools, and more.
“Without complete, column-level lineage, we would be unable to trace the dependencies upstream from an analytics dashboard. The ability to maintain column-level precision across enterprise data technologies is uncommon, and only a handful of the largest data vendors offer anything like it,” he concluded.
Artificial intelligence-powered bots used in customer service settings are finally learning to get along with human agents. Or is it the other way around?
Either way, the friction that existed as humans learned to divest some of their more routine service tasks is producing better efficiency at smoothing out CRM encounters, according to Michael Bradford, head of operations, Americas, at HappyOrNot — a provider of customer feedback and analytics tools designed to capture real-time, actionable insights to improve customer satisfaction and business operations.
The battle between bots and humans raged for years in customer service. Now, it seems that many American workers are embracing the benefits of AI.
Recent research by integration platform as a service (iPaaS) provider SnapLogic reveals that 68% of employees want their companies to introduce more AI technologies. Meanwhile, studies from human capital and workforce management solutions provider UKG indicate that 56% of employees already use AI on a daily basis.
CMSWire reports that 60% of customer experience (CX) leaders expect AI to transform or significantly impact addressing obstacles with existing CX operations. Additionally, according to digital consulting firm Deloitte, 81% of contact center execs are investing in agent-enabling AI.
“American workers are increasingly welcoming AI, not out of fear of replacement but because they see its potential to augment their capabilities,” Bradford told CRM Buyer.
He noted that the repetitive, data-heavy tasks that AI excels at are often the most tedious aspects of a job.
By automating these tasks, AI frees human workers to focus on higher-order skills like critical thinking, creativity, and emotional intelligence. In these job areas, humans remain irreplaceable.
Additionally, AI can provide real-time data and insights that empower workers to make better decisions. These capabilities, in turn, ultimately lead to a more fulfilling and productive work experience.
HappyOrNot’s sentiment analysis tools are a striking example of why AI-powered CRM is on a growth spree. Generative AI in CRM applications empowers workers by automating mundane tasks like summarizing customer interactions or generating personalized responses.
This automation frees up time for agents to engage in complex conversations that require empathy and understanding. Bradford noted that generative AI ensures faster response times and a more consistent customer experience. “Ultimately, it is a win-win scenario for workers and customers,” he said.
Recent studies indicate that more than half (56%) of business owners now use AI for customer service tasks. Bradford suggests that this number is likely to rise through continuing feature adoption. Businesses recognize AI’s efficiency and cost-effectiveness for tasks like routing inquiries, providing basic troubleshooting steps, and answering FAQs.
Bradford explained that HappyOrNot’s real-time feedback platform integrates seamlessly with AI chatbots, allowing businesses to identify customer pain points and escalate complex issues to human agents where needed.
However, for greater success in this regard, effective AI integration requires a managerial shift from control to collaboration. He cautioned that managers must focus on creating workflows where human and AI strengths complement each other.
“This requires ongoing training and development to ensure agents can work seamlessly with AI tools,” said Bradford.
For instance, AI handles rote tasks, while human agents leverage their emotional intelligence, communication skills, and problem-solving abilities to tackle complex customer issues.
According to Bradford, the trend of AI bots and humans working together in customer service is not just an American phenomenon. Businesses worldwide are recognizing the benefits of this collaborative approach. His company’s global reach shows this trend unfolding across diverse markets.
Companies are using AI bots and features in various ways based on use cases integrated within the firm’s platform. For example, AI streamlines the initial contact process, routing inquiries to the most appropriate agent based on keywords or sentiment analysis.
AI chatbots handle basic questions and provide 24/7 support, freeing up human agents for more complex issues. Real-time feedback analysis tools integrate with AI to analyze customer sentiment and identify areas for improvement.
“The level of training needed for human employees when implementing AI in customer service varies,” Bradford explained.
For essential platforms, training might focus on understanding how the AI tool works and best practices for collaborating with it. More sophisticated AI may require more profound training in data analysis and interpretation.
“AI is not about phasing out human agents but empowering them,” insisted Bradford.
AI allows agents to focus on building relationships with customers and resolving complex issues. These benefits, in turn, lead to higher customer satisfaction and improved agent morale, he added.
His company found that redesigning the virtual and hybrid workplace with humans at the center is crucial for success. It improves an organization’s worker experience and delivers actual business results.
However, companies need to approach the adoption of AI technology with a clear plan to improve customer services. This strategy could involve providing CX human agents with the resources and training to excel in a human-AI collaborative environment.
“When human well-being is prioritized, it leads to more positive work experience,” counseled Bradford. “This translates into better customer service and, ultimately, stronger business results.”
Investing in agent-enabling AI technology is a proven business collaborative approach that lies in the future of customer service factor, emphasized Bradford. Studies show a direct correlation between AI adoption and improved customer satisfaction, increased agent productivity, and reduced operational costs.
HappyOrNot’s CRM platform is a prime example. By combining real-time customer and employee feedback with AI analysis, businesses can pinpoint areas for improvement and make data-driven decisions that lead to a more positive and productive work environment.
This collaborative approach lies in the future of customer service, and HappyOrNot is at the forefront of this exciting revolution.
HappyOrNot serves 4,000 brands, including health care organizations, Amazon, Google, airports, and retailers across 135 countries. The firm collected and reported on over 1.5 billion feedback responses to provide AI tech that enables other companies to identify and optimize customer and patient experiences through various touchpoints and in-moment feedback data.
Virtual storefronts have become the gateway to global markets. Online retail has transformed shopping by making transactions more convenient and accessible. However, as businesses strive to navigate the digital space, the nuances of e-commerce platforms go beyond the ability to simply add items to a cart.
As soon as users land on your shop page, they should have quick access to everything they need to find what they’re looking for. Although this objective might seem simple, achieving it represents the fundamental baseline for a user-friendly experience. You don’t just want to satisfy your customers — you want the shopping experience to blow them away.
Let’s review some general user experience (UX) insights that outline a few often-overlooked mistakes that have the potential to revive or ruin your e-commerce shop.
When you complete an online purchase, what makes you want to buy from that virtual storefront again? Or what makes you tell someone you know about your experience? When e-commerce companies employ user-centered design, they’re making an effort to empathize with their customers and make the shopping experience as enjoyable as possible.
The following are some qualities that help to facilitate good UX for an online store.
Detailed Product Information
For most e-commerce stores, the products they sell are their bread and butter. Your product offerings are the whole reason your customer is there. They want to buy something — ideally, something you offer. That reasoning alone should be enough to make you want to provide as much information about the offering as possible. A fully fleshed-out product listing includes:
Breadcrumb Navigation
Once the user has viewed all the information available about the product, they may decide it’s not quite what they’re looking for. They’re often willing to continue exploring the site and browse other product options. When they hit the “back” button, they should be able to pick up their shopping right where they left off. That’s the essence of breadcrumb navigation: After experiencing a change of heart, users can easily find their way back.
Vigorous User Testing
Accessibility checklists are a huge blessing for any business looking to optimize its website and test a storefront’s UX. Referencing any number of free checklists, such as those offered by accessiBe, can help achieve Web Content Accessibility Guidelines (WCAG) and a positive customer experience. Usability tests can also involve moderated in-person testing of the user interface (UI), user surveys, or a full-scale UX audit.
Maximum Accessibility
Vigorous user testing is essential because e-commerce websites are responsible for creating an ideal online shopping experience for as many users as possible. Having an accessible website means that virtually anyone of any ability can successfully use and navigate its pages, and the site’s content is universally legible. For example, users with sight or hearing impairments or those who speak a different language should still get the full experience of your UI and extract value from its content.
Conversely, when you intend to make an online purchase, what hindrances cause you to abandon your cart or seek the product elsewhere? The following oversights may at first seem trivial, but these common e-commerce mistakes have the potential to make or break your store’s UX.
Lack of Image Diversity
Potential customers need to be able to see your product in various settings to fully understand its use cases and applications. Once again, e-commerce companies should strive to present as much information about their offering as possible, giving buyers every reason to add it to their cart. Accurately represent the product with multiple images from different angles and in various contexts. Also, consider incorporating dynamic formats like videos or GIFs if feasible.
Confusing Navigation
Even if it’s someone’s first time visiting your site (perhaps especially so), the architecture and design of the page should be straightforward and simple to navigate. No one likes getting lost when looking for something specific, and their path to finding something should be smooth and predictable. For example, many sites feature a prominent search option or a chatbot to answer certain questions, helping the user along in the customer journey.
Complicated Checkout
Checkout should be at the top of the priority list for e-commerce websites looking to create a seamless UX. After all, checkout is the user’s final step before converting to a purchase. For this reason, customers need to be able to easily edit cart contents, whether that means changing the size, quantity, or color of a product. They should have a frictionless checkout experience, from entering the shipping address to hitting “Place Order.”
Limited Payment Options
Offering multiple ways to pay is an excellent way to encourage conversions and an often-overlooked accessibility consideration. Ideally, e-commerce companies want to give their customers diverse payment method options, such as PayPal, Apple Pay, and Venmo, to avoid limiting their buyers to people with access to a debit or credit card.
Missing Meta Tags
To be effective, meta tags and alt text must be descriptive, not assumptive. Thoughtful naming conventions not only boost SEO but also improve a website’s accessibility. Keyword-stuffing in meta tags and using file names as alt text are not recommended.
To ensure the e-commerce customer experience remains fruitful, merchants must stay current by adapting to trends and changes occurring across the industry. Three of these developing trends are:
Increasingly Strict WCAG Standards
Ever-stricter accessibility guidelines mean that today’s website developers prioritize producing content that considers everyone, regardless of cognitive or physical ability. This cultural shift illustrates that more and more websites are paying attention to the overall quality of their content and how it affects user experience.
Website hosts today must consider many accessibility factors — yet another reason free usability checklists are such a valuable tool. Since these standards are constantly being updated, it’s vital that you stay informed on the latest criteria.
Widespread AI Adoption in E-Commerce
From chatbots to content creation, AI’s advanced automation capabilities have allowed businesses in the e-commerce industry to accomplish more in less time. New uses for AI seem to be discovered every day, particularly in sales and retail. Therefore, brands must actively research technologies that will support their bottom line and develop a detailed strategy for integrating them into their tech stack.
Marketing teams, for example, often employ AI-generated smart modules as automation tools for nurturing leads or making product recommendations. These modules show the buyer-related content they may be interested in based on specific actions they take on the site, encouraging additional conversions.
User Behavior Tracking for Lifecycle Optimization
Understanding their unique customer lifecycle in its entirety is a challenge for virtually every marketing and sales team. Optimizing this lifecycle is a matter of determining customer experiences in your industry and then using this information to promote more productive interactions with your brand, both online and in general.
You can access some of this valuable data by strategically tracking the behaviors of people who are already using your site. Modern businesses are leveraging this data to identify at what points in the lifecycle their customers experience friction. Do any of these obstacles stem from subpar UX? By observing the behaviors of your existing customer base, you can capture more value (and ideally more sales) and apply it to future updates of the entire customer lifecycle.
When e-commerce companies and digital storefronts don’t empathize with the user, it shows. We’ve all been there: We visit an online store, but it doesn’t work how we want or expect it to. We get frustrated, and we bounce — likely to a direct competitor. Implementing user-centered design is any virtual storefront’s best bet for avoiding that scenario, encouraging a purchase, and, best of all, delighting the customer.
To better market and cater to customers’ needs, brands can offer digital signage platforms that go beyond hearing and seeing information to touch and engage products in new ways within physical locations.
Digital signage itself is not radically new. The concept has existed for many years to provide targeted information, entertainment, merchandising, and advertising at retail or business locations. What is new, however, are the improved features and artificial intelligence behind the network delivery.
At first, the process involved using a network of interconnected digital displays that business owners managed from a central location. This flexibility allowed them to adjust messaging to match the needs of any given audience or time frame at their physical locations. Now, AI is plugged into the platform to offer personalized sales, customer service, and more immersive shopping experiences on-site.
According to Christian Armstrong, a senior director of business development at the digital signage company Spectrio, digital signs that use AI have content that is 50% more relevant to their target audience and can spur greater interaction.
Sometimes called electronic signage, it refers to display technologies like LED walls, projection, and LCD monitors that vividly display webpages, videos, directions, restaurant menus, marketing messages, or digital images.
These commercial-grade TV screens operate continuously as large video walls, large-format screens, or smaller screens on top of a shelf at a point of purchase.
For digital signage to be effective, it must be compelling enough to grab the attention of on-premises shoppers and keep them off their mobile phones while searching for competitive pricing. When executed correctly, digital signage keeps customers fully engaged in the marketing messages as they walk around the store.
“The focus in a retail store should be keeping the phone in the shoppers’ pockets and engaging with that audience at the point of purchase,” he told the E-Commerce Times.
Armstrong offered that using digital signage in traditional physical stores is gaining popularity. When he first got involved with this marketing method some 20 years ago, most people considered it a foreign concept.
“Much of its use today is experience-driven. As retailers get smarter and the technology improves, we are introducing things like computer vision and analytics that trigger content based on specific scenarios,” he noted.
Quick-serve restaurants provide a prime example of how effective digital signage can be. Think Starbucks, for instance.
While you wait in a long line, Starbucks can change the menu display on the spot to promote products that are faster to make, fulfill orders faster, and get people out the door. Then, when the line gets shorter, the display can change to promote more high-margin items that may take a little longer to make.
In other scenarios, quick-serve establishments can show menu items along with motion graphics, engaging people to buy certain products and services.
“When you have static menu boards, there is really no way to influence that decision at the point of sale. With digital menu boards, our analytics technology lets the merchant see how long the line is in real time using just a simple radar scanner. We can change what content is playing on the screen just on that line,” explained Armstrong.
A vital part of the added feature sets now coming to digital signage platforms is the measurement component. AI lets retailers leverage all the data the digital platforms collect, making intelligent decisions to help them make better content decisions.
Two examples built into Spectrio’s offering are non-obtrusive radar technology and computer vision. The radar component recognizes who walks past the display and how long they stand there engaging with that screen. A camera within the screen identifies the demographics of the individuals, their age ranges, and even their sentiments — as in what mood they are in when looking at a piece of content.
Why is this essential?
The AI extrapolates that data, ties it into what content played at a given time, and provides that dataset to a retailer. Armstrong added that it could include the number of products sold based on people looking at that product on the screen and what content was playing.
“This is very similar to how retailers already measure the effectiveness of their online content. It allows management to influence the decisions in the store in real time,” said Armstrong.
The store owner on-site or a pre-programmed control at corporate headquarters can control how the interactions work because Spectrio’s technology is all cloud-based and accessible through a web browser.
“If you have your content available, if it’s a video or an image, changing the screen display is as quick as dragging and dropping it into your playlist and getting someone who creates the videos,” noted Armstrong.
What makes Spectrio unique compared to other digital signage software providers is the company’s strong focus on the content going to those screens. The company employs a separate creative department that produces about 8,500 assets for a variety of applications involving both media and digital signage.
According to Armstrong, Spectrio can also measure the effectiveness of that content using radar and computer vision on-site. The company then works with its platform subscribers to improve the quality of that content and how it engages customers. The goal is to solve the organization’s communication challenges.
“We are having a lot of conversations with some pretty large retail brands these days. A lot of the focus and the hype around it has been our ability to combine three pillars — the delivery of the content, the actual content itself, and then measuring and improving. We are starting to see a ton of traction from that across many different facets of retail,” concluded Armstrong.
I am seeing a pattern. Last time I looked at how Oracle’s generative AI is integrating into a variety of front- and back-office work areas like supply chain, HR, and, of course, CRM. Today, I want to examine Salesforce’s recently announced AI enhancements for its Field Service Suite.
The pattern that I see involves automating jobs.
In both cases, the vendors are adding automation that improves and possibly invents jobs. Now, no software that I am aware of is going to make a field service call that involves tools and component replacement, but the Salesforce suite aims at the next best thing.
In the process, Salesforce is doing a new job that field service people cannot do well enough, in some cases, because of their mobility. It’s hard to carry enough manuals in your truck, and trying to look up something on your phone is sometimes frustrating. That’s where AI comes in because it can be proactive in several ways.
First Data Cloud knows everything about customers and products, at least at the data level, meaning there’s a high probability a field service person can find an answer if a case is stumping them or, and this is important, find someone within the organization who can.
All of this behind-the-scenes research is a job in itself, and while field service people have usually been able to do that work alone, they may not have been able to achieve first-call resolution to a high degree. This continuation results in lower productivity and higher overhead for the organization if reps need to make return calls.
Another way to achieve higher productivity is to enable better diagnostics. Salesforce achieves this in part through Visual Remote Assistant, which enables the field service tech to see a problem before arriving on the scene, thus getting a jump on the issue.
Finally, there’s Einstein Copilot for Mobile Workers, which can generate job summaries for customers. Technicians can use these summaries as the basis for reviewing the situation before and after, improving the customer experience.
All in all, this is a good example of how AI might be offloading some data-intensive parts of field service, but to me, it’s more because it does things for the worker and the customer that were not done as well earlier, if at all. Right there, you can see the evolution of a job that would never be given to a human simply because it would add unacceptable overhead. But the job needs doing, and automation is doing it.
No workers are displaced in this scenario. Instead, it seems that technology is enabling people to work smarter while raising the quality of service and decreasing costs.
Too often, we lump automation together, and none of it is good for the worker. There are certainly automation efforts that eliminate humans from a process, and I think we’ll see AI doing its part. However, automation can also set a new equilibrium that enhances processes and customer experiences, and that’s what we are witnessing in many respects today using AI.
Oracle on Thursday announced 50 new generative AI apps for its application suite that embed into existing business workflows across finance, supply chain, HR, sales, marketing, and service, as well as an expansion of the Oracle Guided Journeys’ extensibility. It seems like good stuff, but it also neatly demonstrates how you may need to take the good with the “what?” when looking at AI. I can explain.
First, the bad news — which extends well beyond Oracle to almost any vendor in the space working on AI.
Generative AI collects gobs of data from customers and vendors and synthesizes ideas that can help customers, often better than people. Like it or not, AI does things that humans do, and over time, the shift to tireless, precise, and more efficient machines is inevitable.
Knowing everything about the latest product or release is something AI just does, and it is something that people need to get up to speed on. This means that on day one, the machine likely has the advantage. But that’s just day one. Also, machines are not good at empathy.
The key to successfully automating jobs is first to find ways to be successful with people and automation working together, and so far, AI seems to be tuned in. AI is collating data and serving it up to users who can then add their own insights, and if that persists, we should be fine. Along the way, though, we will likely see new jobs open for people that we may not even be imagining right now. That would be a win for all sides.
This is not just me talking.
Back in 2011 a couple of MIT profs, Erik Brynjolfsson and Andrew McAfee, said much the same in their book, “Race Against the Machine.” Ironically, their jumping-off point was the American railroad folk hero, John Henry, who competed with a mechanical drill to bore holes in rock faces to set explosives. My point of interest is that the MIT professors went back nearly two centuries for their comparison, and now I’m doing the same with only about 14 years between forecast and reality.
Now, the good news.
Consider where there might be virgin territory, where a job is just too involved (so far) to be anything other than a human responsibility. I had not considered this until we began climbing out of Covid when supply chains were in a complete tangle, contributing significantly to inflation.
Americans suddenly shifted from buying services to buying products, resulting in too many products awaiting shipment in China and too many empty shipping containers piled up on the other side of the Pacific. Railroads and the trucking industry also had problems, and the only solution available was to work overtime and invest in having more people pull on the threads of various chains. There was a lot of paper involved.
I am not saying that AI would have been a panacea, but I also recall an article from McKinsey around that time that showed how most supply chain pros relied on spreadsheets to manage shipments. So, I can imagine things could have been at least a tiny bit better had supply chain AI been available.
That’s one example, but I think there are many more of how AI can make things better in all kinds of business activities, including, and maybe especially, in the front office. Where? Just ask yourself where you are currently using spreadsheets in places like sales, marketing, service, and supply chain right now and you’ll have a big clue.
In my early days as an analyst, I noticed that many new software niches would spring up around little functions that were dominated by spreadsheets, the most obvious being SFA. It was sound reasoning, too, but replacing spreadsheets is a big job, and we all got mired down in coding for many years.
Code generation and platforms changed the equation to the point that today, we can easily contemplate AI bolted onto traditional apps precisely because the actual bolting is dominated by the generators.
When we think about AI or any new technology automating some jobs away while automating others into existence, we’re also thinking about what Oracle calls its Oracle Guided Journeys’ framework. My thought is that the capabilities that Oracle is introducing, like predictive forecast explanations, supplier recommendations, or negotiation summaries, will form the nuclei of new processes and new jobs. As always, it will be the users who figure out the optimum utility for these things.
The growing intersection of artificial intelligence and CRM platforms is helping companies automate processes, accelerate decision-making, and predict future trends, thereby driving real-time customer engagement.
At the core of this transformation, AI is becoming the backbone of CRM systems, integrating analytics, predictive, and generative AI to furnish businesses with actionable insights and foresight into customer behaviors.
Today’s challenge for CRM vendors lies in simplifying the adoption of sophisticated, out-of-the-box AI models over the complexities of DIY projects, which can be daunting. Simplification is crucial, as AI-powered CRM platforms are revolutionizing how businesses deliver customer experiences.
By providing businesses with a holistic view of their customers, these platforms enable automation and personalized interactions at every touchpoint, significantly reducing the workload on customer-facing employees by automating data acquisition.
This approach allows companies to keep the most up-to-date view of the customer. It helps them break down silos between marketing, sales, and customer service. It also makes it easier for companies to anticipate customer needs, tailor their approaches, and deliver exceptional experiences that foster loyalty and growth in a world where speed and accuracy matter, according to Zac Sprackett, chief product and technology officer at SugarCRM.
Historically, achieving a 360-degree view of the customer was impractical due to the manual effort required to sift through a plethora of interaction records. Generative AI now addresses this challenge by enabling businesses to distill and analyze this information quickly, providing a clear understanding of current customer relationships and guiding future actions.
This streamlined approach significantly enhances CRM platforms’ efficiency, Sprackett explained, by reducing redundancy and speeding up data processing.
Companies have built comprehensive data about their customers and target markets. Many have struggled to leverage all of this data effectively. Whether because of organizational silos, data complexity, data quality concerns, or challenges with tools, turning data into business value is time-consuming and relies on skills that are in short supply.
“Businesses are under extreme pressure to deliver an enhanced customer experience while also controlling costs and operating more efficiently,” Sprackett told CRM Buyer.
AI is an essential tool for reducing data entry, bringing structure to unstructured information, performing analysis and segmentation, and personalizing engagement. All of these help to improve outcomes for a business, he added.
People sometimes think of traditional CRM technology as data management tools for organizing contacts, interactions, and transactions. This model of CRM is quite reactive as it is essentially a ledger of past occurrences, Sprackett offered.
Typically, gaining business insight requires manual analysis, specialized skill sets, and time-consuming human interpretation. The new wave of AI-driven capabilities enhances CRM systems beyond just being the system of record into the system of customer insight and engagement.
“Applied effectively, AI unlocks a deeper understanding of your customers, foresight into their behavior, and personalized engagement strategies that improve and evolve over time. It democratizes that insight, putting it directly into the hands of those closest to the customer who can benefit from it the most,” Sprackett said.
AI integration also improves the usage prospects for CRM platforms that are not now possible for companies. It opens up vast possibilities for businesses to enhance their operations.
Two new CRM features only AI can provide bring high-end innovation. Time-aware customer experience (CX) platforms and no-touch information management represent advanced features in the CRM landscape. They are significantly intertwined with AI, according to Sprackett.
Being time-aware means meticulously collecting all of the interactions and changes occurring over time. In many ways, this is an enabler for getting the best results from AI. No-touch is about requiring as little data entry as possible.
“It’s about gathering information from all the different places it exists, like email, calendar, chat, mobile phones, and ERP [enterprise resource planning] systems. It’s also about augmenting that information with firmographic data and relevant news articles and then structuring the end result,” explained Sprackett, emphasizing AI’s critical role in no-touch data management.
Organizations need large volumes of well-curated and classified historical data to benefit from predictive AI. This requirement has historically excluded many small businesses due to insufficient data volume and has posed challenges for larger organizations.
The difficulties for larger businesses stem from the need to curate and maintain a data set amidst decaying data over time and shifting business processes and priorities.
According to Sprackett, this is where generative AI has gained prominence, offering seemingly lower barriers for users to experience value by mitigating these challenges.
Generative AI allows an end user with little or no prior experience to interact with much of the knowledge that exists today. The large language model, or LLM, is pre-trained on a large and diverse collection of text from the internet, encompassing a wide range of topics, sources, and formats.
“Users immediately experience compelling results with comparatively little investment from the first time they try it out, which inspires them to keep coming back,” he added.
Other AI derivatives have a place in upgrading CRM platforms that contribute to improving CRM performance and customer experience. AI, in general, brings a wide range of new tools.
For instance, predictive analytics can make predictions based on historical data. Speech recognition and natural language processing unlock new interaction models for workers and customers.
Computer vision can help classify and identify pictures, which can be helpful in use cases like product support. Sentiment analysis helps companies detect rifts as they form and can also be used to provide both real-time and after-the-fact coaching.
“It is a long list today, and it continues to expand. Large action models are also likely to offer significant benefits to CRM in the near future,” suggested Sprackett.
Businesses face some challenges in implementing AI-powered CRM platforms. Among these, a fundamental necessity is educating employees on the responsible and beneficial use of this new technology.
“Most of us have seen examples where AI has come across as creepy, so how do we ensure that we are enhancing the customer experience rather than detracting from it? Finding the right balance between automation and human touch will be a growing challenge going forward,” Sprackett warned.
An equally high hurdle is the governance angle. Organizations must establish guidelines for ethical and compliant use of AI within the business, and these guidelines must be flexible and nimble as data privacy regulations emerge and evolve.
“Our job as a vendor is to help businesses navigate these challenges effectively so they can reap the benefits,” he said.
An important first step is to show people the art of the possible. Things are changing at an increasing rate, and businesses need education and support to evolve, Sprackett offered.
Second, it is the CRM platform vendor’s job to connect technology to data in a way that helps people do their jobs effectively. Therefore, vendor offerings must be intuitive and simple but flexible enough to cater to the unique needs of their business.
Third, CRM vendors must integrate technology in a way that safeguards business data. This data contains your special sauce. It encodes what differentiates you from your competition and needs to be kept private, secure, and used in compliant ways.
By focusing on these areas, vendors will significantly lower the barriers to AI adoption for businesses, which is SugarCRM’s goal in what features its AI-powered platform offers, he concluded.
If you buy into the exploding hype around generative AI, you see this flavor of artificial intelligence as the best thing to happen to computing since cloud storage. But you could be getting the wrong impression.
As businesses and industries continue to evaluate the pros and cons of ChatGPT, generative AI, and other artificial intelligence species, some adopters praise its time-saving and innovative benefits. Others are hesitant to trust the new technology. Either way, where gen AI is headed is an ongoing conversation.
Talkdesk in January released a report warning that continuing bias and inaccurate data have seeped into retail experiences, already integrating AI, and are impacting consumer attitudes towards the new technology. This belief comes as shoppers chronicle AI-powered interactions gone amok and worry about how businesses may use facial recognition, customer data, and general unethical AI use cases.
Shoppers, already dissatisfied with their customer experiences, are ready to leave behind any brand not practicing responsible AI use, according to the Talkdesk Bias & Ethical AI in Retail Survey. At the same time, corporate officials rave about how targeted their AI results are and backslap one another in excitement that everything is peachy keen.
This mixed sentiment may not bode well for an accelerated expansion of gen AI this year, as some proponents predict. The report reveals some shocking and negative attitude changes in how consumers feel about interfacing with AI, Shannon Flanagan, VP and GM of retail and consumer goods at Talkdesk, told the E-Commerce Times.
Her company provides a cloud contact center platform for AI-powered customer service.
“I have definitely seen an attitude shift. There is some shocking information about how gen AI is being used for product recommendations that people are not using. And then there are high expectations shoppers have on data security and transparency not being met,” she offered.
Artificial intelligence has been quietly deployed with limited capabilities for nearly a decade. Its use cases in recent years have gradually improved thanks to advancements in machine learning (ML) and combination with robotic process automation (RPA).
The release of ChatGPT last year marked a significant breakthrough that enhanced automation for repetitive, rules-based activities requiring minimal human oversight. This advancement has broadened AI’s capabilities to encompass a more comprehensive array of functions.
All AI programs are not the same species. Traditional artificial intelligence focuses on analysis and classification. Generative AI, or gen AI, is a subset of artificial general intelligence technology that uses complex algorithms and neural networks to simulate human creativity and produce new content from models that can include text, images, sounds, animation, 3D models, and other types of data.
Gen AI captures nuances in language and generates output based on the patterns on which it was trained. Its models can remember previous interactions, resulting in more coherent and relevant conversation experiences for users.
However, gen AI cannot make decisions involving many complex factors. At least not yet. It excels at making data-based suggestions but is inept at including and handling the most critical human factor.
Research shows that disconnects exist in how businesses can safely and accurately integrate gen AI skills into their business cycles and avoid unintended consequences. In retail and call center circles, consumers are not unanimous about how AI is affecting their customer experiences (CX).
Flanagan has seen a definite shift in user attitudes as the Talkdesk platform integrated gen AI capabilities. Not all the changes reflected in the company’s numerous surveys favored AI.
“Some of our pre-holiday AI surveys talked about how shoppers are feeling about AI versus retailers. A large part of them aren’t doing it,” she told the E-Commerce Times.
Big brands like Walmart are legitimately using gen AI. However, according to Flanagan, a broad spectrum of her company’s clients do not know how to utilize it.
“Product description copy is kind of a no-brainer. In some places, the use case in customer service is a no-brainer. But there’s still a lot of hesitancy,” she said.
The recent Talkdesk report unveils startling findings on AI’s use in product recommendations, revealing a majority of surveyed individuals are not utilizing them. Additionally, consumers highlighted unexpected demands for data security and transparency.
Flanagan emphasizes the urgent need for a strategic overhaul to engage customers effectively, pointing out the now evident use cases.
Still, there are issues about using AI that must be resolved, she cautioned. That fix should be easy to accomplish, especially in uses that serve as agent assistants rather than customer-facing integration.
“Now the reality is for doing it in anything that’s customer facing. It should be seamless to the customer, but that’s a little bit more risky than I think some of the back office uses like a marketing ops, and then obviously like the agent assistant in our self-service world,” Flanagan explained.
Examples from the Talkdesk report about how shoppers use AI show that:
“As it relates to AI, this is a ton of mistrust,” she observed. “What needs to be done this year is a little bit of a pause and saying, what’s our strategy?”
Yet another prominent AI report gives a much different view. According to a new study from MessageGears, 99% of marketers say that using AI has impacted their ability to understand customer preferences and behavior.
A key takeaway from the survey of enterprise marketers in companies with 500 or more employees is that the vast majority already use AI in their marketing, which is paying off. For today’s marketers, the big goal is making real connections with customers. Doing so strengthens brand recognition and builds trust.
Bottom line: Surveyed enterprise leaders said AI has been particularly useful in boosting customer engagement.
“AI algorithms are like the secret sauce, letting marketers dive deep into customer data,” Will Devlin, VP of marketing at MessageGears, told the E-Commerce Times.
“Then, armed with the inside scoop on preferences, behaviors, and demographics, marketers can fine-tune messages on the fly. With real-time tweaks to content, timing, and more, AI-powered campaigns ensure that the connection between a brand and its audience is spot-on and meaningful.”
Only 53% of marketing pros surveyed by MessageGears said they were very successful at connecting with customers. This statistic leaves significant room for improvement.
Another 53% want to use the tech to identify more accurately who will most likely make a purchase. Half would like AI to help them pinpoint the most effective channels to reach customers.
Fifty-eight percent of marketers in the MessageGears survey use AI in targeted advertising campaigns. Almost half (49%) use the technology for personalized email marketing, customer support and service, and customized product recommendations.
Further, 97% of enterprise marketing experts using AI said they successfully delivered personalized content and recommendations, while 39% said the experience was exceptional, and 99% said AI is making a big difference in figuring out customer preferences and behavior.
A critical use of gen AI from a marketing perspective in 2024 will be to help fix the customer engagement problem. Customer engagement is all about providing value and communicating that value to your customers in a way that makes them feel connected and appreciated, offered Devlin.
“Your customers should be excited about what they’re receiving from you. Messaging should be timely and relevant and be delivered on the channels that are most important to your customers. Businesses already know this, but it’s often a manual guessing game to make it happen,” he told the E-Commerce Times.
Devlin added that marketers should anticipate increased utilization of predictive AI and modeling to determine the most effective communication strategies with customers, eliminating the need for guesswork. Marketers can then pair these predictive AI insights with generative AI to further refine and personalize the message.
ChatGPT’s first anniversary marks the remarkable aspect of the ascent of generative AI, marveled Priya Vijayarajendran, president of technology and CTO at gen AI software developer ASAPP. She stressed that its democratization and ability to unite talents from various corners of the technology landscape allows the best and brightest to harness their skills to collaborate to “get it right.”
“Moving forward, responsible usage of data and investment in AI privacy and assurance are essential so we can unlock the potential of generative Al for enterprise innovation. This innovation must continue. There is no slowing down now,” she told the E-Commerce Times.
Generative AI will continue to deliver incremental innovation across GPUs, LLMs, and compute frameworks; she said of expected progress this year. Data will dominate as the most significant differentiator, applying LLMs in a hybrid domain focus to achieve accuracy, time to value, and scale.
“These vectors coming together will be the key to unlock exponential value [of Gen AI] for enterprises,” Vijayarajendran concluded.
A new era of customer relationship management is emerging, with a focus on innovative design and strategic platform goals. Businesses rethinking their use of these platforms can improve profit margins, enhance customer satisfaction, streamline operations, and foster innovative engagement strategies.
According to Dan Roche, chief marketing officer at Workbooks CRM, two factors are driving this change. First is the realization that CRM is not about features and functions but relationships. The second is the re-evaluation of bloated CRM implementations.
His company is positioning itself at the forefront of this new CRM direction, focusing on CRM platform features it hopes will redefine how businesses navigate the shift towards tailoring efficiency, fostering relationships, and making informed choices for their customers’ better economic success.
The “R” in CRM is usually about the relationship between a company using a CRM system and its customers and prospects. Surprisingly, the connection between the customer and the vendor is an aspect that has been considerably neglected, he argued.
“We already suspected that this had a real impact on the success of a CRM project, and that’s just what our research uncovered,” Roche told CRM Buyer.
Against a tighter economic backdrop, the focus is shifting to seeking efficiency. Roche sees businesses realizing they bought a Rolls-Royce-style CRM equivalent (think Salesforce) when they need a VW with some well-chosen options and excellent aftercare.
“The savings can be enormous, and the usage of the system rises significantly, meaning better value and productivity, as the system is tailored for the users, not the other way around,” he said.
The SaaS platform provider of Workbooks CRM recently conducted a survey that revealed that 64.9% of businesses currently favor a single-vendor approach to CRM deployment. This shift allows them to establish direct relationships with one CRM provider who provides the software and handles the implementation.
Those firms that opt for the multi-party route select software from one vendor and then engage a third-party provider to roll out additional software features. The research revealed that businesses engaging in multi-party deployments reported lower satisfaction levels than those with direct relationships.
One reason for this is that 75% of CRM buyers choosing the multi-party route reported they had incurred unexpected costs during deployment. Within this response group, 14% of buyers incurred significant additional costs.
Roche noted that these findings highlight a critical but not well-understood issue: The multi-party CRM approach often works against the buyer’s interests, leading to increased risks and potential deployment failures.
Also driving this new era of CRM technology is the consumerization of B2B software. Individuals, whether consciously or subconsciously, anticipate a user experience that offers something similar to the App Store’s seamless functionality or Amazon’s effortless purchasing process when engaging with any new software.
“Gone are the days when business software was a specialist-only affair, characterized by clunkiness and solely deployable by IT teams. The landscape has evolved to a stage where non-technical users are actively involved in both procuring and implementing systems that form the backbone of crucial business processes,” he said about the evolving uses for CRM platforms.
Multi-party CRM involves a vendor selling you the software and a partner handling its implementation. This is a prevalent practice in enterprise SaaS, explained Roche. That process, however, involves a misalignment of interests.
On one hand, vendors typically aim to maximize license sales and, as a result, may not invest extensively in deployment, which offers less financial reward.
On the other hand, deployment or consulting partners derive their revenue from time and materials, which, in turn, incentivizes longer projects, often irrespective of the ultimate outcomes.
“This represents a huge conflict of interests, which tends to have consequences for the buyer,” said Roche.
Part of this redefinition of CRM software is the potential for more successful selling in the business-to-business realm. Business buyers possess a clear vision of their desired outcomes.
The challenge lies in navigating all the different routes available to achieve them. The transformation will reshape how these buyers embark on the CRM journey, ushering in a new era of decision-making, suggested Roche.
CRM features that address efficiency, foster better connections between customers and sellers, and provide informed choices will play a key role in bringing about innovation for economic success.
“The more effectively a CRM system is implemented, the higher the user adoption and the better the business process will work. The outcome is improved efficiency and business growth,” said Roche.
Roche has a simple answer to why this new operational approach has not been available or desired until now: Not enough analysis or research has been conducted into the existing relationships between CRM providers and customers.
“We saw this first-hand, speaking with our customers who were unaware that there was a more effective approach out there,” he quipped.
The need for businesses and platform developers to re-evaluate things like feature bloat suggests that “more is better” is no longer pertinent in CRM platforms, agreed Roche.
“A bloated CRM often arises from projects that lacked initial planning or experienced shifts in business requirements since the project’s inception,” he added.
Another common scenario involves the individuals or groups spearheading the CRM project transitioning to different roles or leaving the company. Consequently, the CRM responsibility falls onto someone else’s shoulders, possibly a new entrant to the business.
“This is when challenges arise,” noted Roche.
U.K.-based Workbooks CRM also has U.S. operations. The company takes a unique approach by serving as both a vendor and a deployment partner to provide a relationship-centric strategy that actively mitigates commercial risks by investing alongside its customers.
For instance, the company’s Shared Success program matches its customers’ investment in Workbooks licenses with complimentary consulting days. If a customer’s annual license value is $50k, Workbooks CRM provides an equivalent of 50 free consulting days.
“Our primary goal is to speed up the acquisition of customers for you as cost-effectively as possible so you can witness the intrinsic value of a well-implemented CRM solution, both internally and externally. Subsequently, we work towards implementing a tailored solution that seamlessly aligns with your existing business processes,” said Roche.
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