"In God We Trust. All Others Bring Data"

 In B2B Marketing, Data Science, Predictive Intelligence

If you have been in the data business long enough, you have likely heard this quip attributed to Dr. W. Edwards Deming, an advisor, professor, and consultant well-known for economics and management theory: “In God We Trust. All Others Bring Data.”

There’s an awful lot of talk about data, and CMOs and CIOs of “intelligent enterprises” are wise to say, “Show me.” Data can prove or disprove hunches and shine a light on bad strategy or poor execution. Yet, it’s early days, and there’s still fear and resistance to change.

A few fears we’ve heard:

1) I am already overloaded with data. At Thursday’s great “DB8” among the leading predictive vendors, one marketer said: “I already have too much data about my prospects. Why do I need more?”

2) My data is cr*p. In 2014, Econsultancy tallied the costs of bad data to organizations.

“Research from Experian Data Quality shows that inaccurate data has a direct impact on the bottom line of 88% of companies, with the average company losing 12% of its revenues.”

3) I will be replaced by a robot. I have already written about this, but seriously, many are worried that the “knowledge worker” will be replaced by machines.

If I embrace data, will I become a casualty of it?

Well folks, you can’t have it both ways. You can stay still and let your competitors steal your business. Or you can educate yourself. Part of that is thinking through your goals and where you lack information.

A few goals we’ve heard:

  • I must get in front of my prospects earlier because my competitors are getting there first. We have heard this many, many times. Companies that have made the switch to 6sense from basic lead scoring or predictive lead scoring solutions are now tapping into so much more information, much earlier, about prospects. Since most buyer activity is anonymous and 70% of the buyer’s journey takes place before a prospect hits your web site or fills out a lead gen form, only 6sense taps into these early signals to help customers detect and fast-track the best leads to sales. Too much data is not the problem; it’s just the wrong kind.
  • I need to improve my conversion rates and reduce costs. According to the Econsultancy study, companies’ 88% “loss of revenue comes from wasted marketing spend, wasted resources, and wasted staff time.” If there’s one thing I would love to see, it’s the end of the silo-ed marketing. No buyer cares about your email newsletter, your phone call, your special offer unless they are in market to buy what you offer and need to know more. What about all those buyers you’re not reaching because they don’t want to be chased by you? To solve the cr*p data problem, you need to a get a better solution to get a full picture of the current and potential prospects in your funnel. You don’t need more cr*p data, you need intelligent data to see who in the buying committee you should be talking to; the products they are looking at; and how close each prospect is to a purchase.
  • I want more information about my target accounts so I can make sure that sales is targeting the right companies and contacts with the right offers. Data is a commodity today, and not all data is equal. There’s static data about your buyers and there’s time-based activity data that shows us what buyers’ intentions are. Even intent data has wide variances. Think about it like a shopping mall versus a trunk show. One has a huge mass of potential people who buy stuff, and the other has people who make a point to show up at a trunk show, because they like the brand and the products. It’s the same thing in the B2B world. You want to know who is in your store, what items they’re likely to buy, and then you can deliver the goods. As a “knowledge worker,” it’s your duty to connect those dots and personalize the sale. Oddly, that’s where machine learning comes in. It provides the massive processing and self-learning from a constant ingestion of new data to give you the tools to target and make that sale. Fear not!

If you are still in the dark, check out our 14 Questions to Ask guide to help you evaluate predictive vendors and think about a solution that will suit your needs.

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