As marketers and sales pros, we find irresistible the promise that technology tools will make our jobs easier and help us win more business faster. Which is why so many of us are turning to companies that are focused on predictive analytics tools that support account-based marketing, and scaling all of the different aspects of the ABM process.
Take key account selection, for example. Way back when, before we had access to the data we have now, companies typically defined their “hot” lists based on some broad criteria, often defaulting to company size, product line, or even the Fortune 500. But these lists can be way off target when it actually comes to producing sales.
Start ups hawking predictive analytics tools say they can help you better target the accounts most likely to need and buy what you’re selling. They look at all of the information around your sales history to identify common data points.
Mintigo’s Chief Product Officer Atul Kumar explained the process in a recent interview, noting, “When you build a predictive model using data records of your closed-won customers, the model will then identify all the commonly shared characteristics of these companies who purchased from you previously.”
Kumar says the aggregation of these data points provides an ideal customer profile, which you can use to compare with other companies to identify a fit. With several predictive analytics marketing providers tacking millions of organizations, all the ones that match your ideal customer profile should pop up—and that, he said, should be your target account list.
Sounds like a silver bullet, right?
Maybe. But for what it’s worth, here’s some advice from someone who has been around the block more than a few times. Whenever I hear phrases like “silver bullet” or “our solution will help you automate and scale [fill in the blank]” a red flag goes up. And that’s because it usually turns out to be a “too good to be true” situation.
Don’t get me wrong, I’m not a luddite. There is certainly a place for these tools. For one thing they can predict buyer intent based on looking at customer data. But I believe they can lull you into a false sense of security and cause you to miss out on opportunities—or lead you down the wrong path. Assuming that predictive analytics have uncovered your perfect account list means you are reliant on pure data. But there’s actually so much more that predictive analytics can’t account for. Like the words and intentions of the people running the companies to which you are selling—stuff that would never get captured in the data.
Let’s look at some examples:
- The CEO of a company on your list just informs analysts during an earnings call that the company will spend 100% of its IT budget this year on personalization initiatives—which does not match up with your product portfolio.• A company on the list announces it is acquiring a competitor and its #1 priority for the year is the successful integration of the two companies.
• A new CEO has just been named to a targeted company and in an interview she reveals a major strategy shift and a new set of priorities. Historical data—however recent it might be—will not reflect any of these critical nuggets.
None of this revelatory information would appear in the data. Given that the data can’t tell the full story, you can miss out on opportunities if you solely rely on the data.
So, while you may have a predictive analytics tool telling you that the company is well-positioned to buy your product, you would have a much stronger case for a sale if you told a customer, “Your CFO said on the last earnings call that almost all of your IT spend will be dedicated to driving personalization initiatives. I would like to speak with you about how our product has helped many companies like yours drive personalization…”
Additionally, having insight into executive priorities enables you to fill in the blanks between the data. Sometimes this knowledge eliminates a company as a target customer so that you don’t waste time on a non-starter. Or perhaps it gives you a new and compelling “hook” with a company on the list. Or maybe the analytics didn’t surface a company that should be on your list. Reading—and keeping your eyes and ears open—can help you uncover new sales opportunities that even the best analytics can’t unearth.
Does this mean you don’t use the tools? Of course not. But don’t use them exclusively or in a vacuum. Use them to supplement what you’re learning, as a jumping off point. Then go beyond the analytics to discover what key players at these companies are saying. Their words matter. With solid research your team can refine their data-driven list and have talking points to boot.
By smartly using predictive analytics with research, your team can confidently connect the dots between the data, what company execs are saying and planning, and what you’re selling. The match is clearer; the strategy becomes smarter. And you have an exponentially larger chance in making a sale.
Sharon Gillenwater is the founder and editor-in-chief of Boardroom Insiders.