With Data, Oldies Aren’t Always Golden

I spent most of last week in Boston at the Enterprise 2.0 conference, where I was honored to be the sales and marketing track chairman. Next year it will be called “E2 Social” and will bookend the other conference that has been held in Santa Clara, which will become known as “E2 Innovate.” There’s good symmetry here. I can’t think of another purely social show or one focused on innovation. Most shows today are vendor-sponsored, which is good but different.

Our track had some cool presentations on social marketing from IDC mavens Gerry Murray and Joe Farentino, revenue performance management from Phil Fernandez, CEO of Marketo, and an intriguing discussion from Pam Kostka, a fellow Crusader and CMO of VirtuOz, a company that makes virtual agents.

If you are wondering, a virtual agent is a software robot that you can talk to regarding sales, marketing or service issues just like a person. These agents are a happening thing and promise to do away with wait times and improve service.

There were also two panels, one on M&A activity that we put together last minute with the able assistance of Sameer Patel, Josh Greenbaum and Louis Columbus. As is so often the case with these things, serendipity played a role and caused more than a few people to walk away with the idea that this kind of thing ought to happen again. Thanks guys, the panel was outstanding and a good example of the talent pool that lurks in the Enterprise Irregulars, a group with a low profile (that ought to be greater) and an inversely proportional IQ factor.

Out With the Old

The other panel, which I want to focus on, was illuminating to me for an unexpected reason. I invited some of my brain trust, including Thor Johnson, Cary Fulbright, Derek Peplau, Columbus and Murray mentioned above. Toward the end we had a discussion of big data, and someone mentioned a large company that had converted from one CRM system to another and had deleted many years of sales data in the process, rather than bring it along and try to figure it out.

Initially I thought throwing away all that data was folly, but I came to see it as smart — but for reasons that I think are different from the consensus of the panel and audience. One audience member got the analysis right, in my opinion, when he said simply, “There’s nothing in it,” by which he meant there was a great deal of data but that it was devoid of information content. How could this be?

Marketing Data vs. Sales Data

Very simply, most CRM systems either have fields or enable you to create them to capture important data like product interest, deal size, projected close date and much more. All of this is valuable, but CRM’s point of failure is that these fields can be overwritten, and there is no provision for storing historical information.

Now, you’ve heard my sermon on historical data before, most likely. But at E2.0 I had an insight about the difference between sales and marketing that reflects the difference in the data we collect and analyze in each space.

In marketing, we collect data once from a large sample. If you run a program against a list, you collect data from a large number of people one time. You analyze the data and perhaps discover people who are interested in a product now or in the future, and you process accordingly.

Sales is different. The universe of data sources is smaller but the sources give off data constantly through a sales cycle. Sales reports — pipelines and forecasts — show a single cross-section of the data, and they are equivalent to the individual frames of a movie. Most of the time it’s hard to say much about how a film ends by examining a random frame. Sometimes you get lucky and the random frame shows the butler with a knife in the in the library, etc., and you can make a deduction. But most of the time you aren’t that lucky.

Unlike a movie, which is a succession of stills projected in rapid succession to give the illusion of movement, the sales forecast is a one-and-done thing. Worse, making the report necessarily destroys the old frames.

So getting back to the company that threw away old data, I would throw it away too. The old data was simply the last frame depicting the end state of a deal, and usually the end state is a loss.

New Thinking

There’s almost nothing you can discover from the end state, but if you have all the frames that led to the end, then you can apply analytics to it and find out things you didn’t know. Analytics lets you play the movie back and forth to find the aha! moment. But you need to keep all the frames. The point is that in marketing, you can apply analytics to a single state of the market, but if you try to do the same with sales, you’re toast. Sales data is different from marketing data, and so are the ways we analyze it.

In the panel I moderated last week, that idea was not in evidence, and it shows how we need to re-think and maybe find new people who think differently about selling and sales data. Without new thinking, we’re liable to be unable to figure out the importance of social tools, and selling will continue to be a hard nut to crack because it remains more art than science. It doesn’t have to be that way.

Denis Pombriant

Denis Pombriant is the managing principal of the Beagle Research Group, a CRM market research firm and consultancy. Pombriant's research concentrates on evolving product ideas and emerging companies in the sales, marketing and call center disciplines. His research is freely distributed through a blog and website. He is the author of Hello, Ladies! Dispatches from the Social CRM Frontier and can be reached at [email protected].

1 Comment

  • The example company may have said they don’t have any of the data I’m referring to but depending upon what is available it could be quite valuable even if its a point in time snapshot. Here are a few potential ways sales data can be useful even if old.

    You can analyze customer demographics helping improve future marketing & sales efforts

    You can determine seasonality trends – who is buying when?

    You can determine customer purchase patterns – what happens after the first sale?

    Depending on what fields were used you may also be able to trace the deal cycle from campaign to lead to opportunity to close. Understanding the conversion rates along the way is a key way to identify areas for improvement in the sales cycle.

    A common challenge is lack of cross-functional context. IT may do a great job defining requirements for a project but without a deeper understanding of the business they may not be able to make suggestions that would provide greater benefit moving forward. At the same time, many salespeople see CRM as a burden and don’t bother entering what could be valuable data limiting the potential value.

    All that being said, if there’s not a plan in place as to how to effectively use the data, you’re right – its useless.

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