The Broad Scope of AI Implementation for Enhancing CRM Efficiency

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.

Developing the Role of AI in CRM Efficiency

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:

• Automation and speed
• Scale
• Productivity
• Depth of insight

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.

Highlighting the AI-CRM Connection

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.

CRM Buyer: Why is this still a problem?

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.

Aside from that stumbling block, how is AI fixing existing inefficiencies in CRM software?

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.

How does embedding AI into CRM platforms improve performance?

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.

Are there data safety overrides a part of this deep dive into data, or is automation limitless?

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.

What is driving development for AI features in CRM platforms?

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.

How are CRM platforms overcoming the growing disfavor among retail customers and/or company workers?

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.

Is AI’s best role applied to improving efficiency for company-facing processes for its agents or improving specific customer experiences?

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.