Why Predictive Marketing Has a New Role in the Market

October 8, 2018

Predictive Marketing

Editor’s Note: This guest post was contributed by Justin Gray, CEO, LeadMD.

Predictive marketing barreled into the marketing lexicon as only buzz worthy trends can and quickly became a buzzword (or buzz term). For marketers, its promise was enticing, pledging to deliver ease of use, scalability, time savings and ROI.

Who could resist the urge to believe in what predictive marketing would deliver and what it would transform? But as we marketers scratched below the surface and our pilot programs fizzled, it became clear that predictive marketing was really just a branded word for business intelligence based on data science.

Business intelligence can be incredibly reliable, but the data marketers pumped into their predictive marketing tools was not. There were plenty of issues: the data sets weren’t large enough. Solid and consistent data were lacking. Professional data scientists were also in short supply.

Predictive sounds complex — that’s because it is. This complexity ultimately became a problem in sales cycles, which moved too fast for predictive marketing to have an immediately measurable impact. Cutting their losses, marketers began to latch onto predictive marketing’s value as lead generation and qualification tool. That’s how many marketers still use it today. But that’s never what it was meant to be, at least not solely, and we marketers have to leave that mentality in the past.

Here’s how we should be using the power of predictive — it’s a multi-faceted tool that can help you define Total Addressable Markets (TAM) and within them, Ideal Customer Profiles (ICP). It can also help you prioritize accounts or leads, create segmentation, or match the optimal messaging and sales plays to the right buyer. With predictive, we can use the massive amounts of data we collect on our prospects and customers to make more intelligent decisions for our business.

The Roadblocks to Predictive Success

When done well, predictive marketing gathers all your customer data — from online behavior to marketing programs to sales to customer success to financial performance and everything in between. Then, with a 360-degree view of the customer, you can perform analyses, pull out the commonalities that make up your ‘best fit’ customers, and then find and engage more like them. 

But of course, that’s an ideal scenario, and we all know that nothing that yields that type of benefit is easy. One of the challenges with predictive is that it doesn't have a straight line to revenue (a lacking which, as we all know, can make the C-suite queasy). However, it does have a straight line to efficiency and to proper qualification. And the savviest marketers among us realize that, when contextualized, these two things are highly important for solid, sustainable, growing revenue.

Another hurdle for many folks who want to benefit from predictive is that it requires… wait for it ... work. If you’re investing in this methodology, you need to track where customers have gone online, how they engaged with your marketing material, how sales interacted with them, how they bought, how they were onboarded, how they use your offerings and how they pay. It might be a giant pain to gather, but all of this information is critical to developing a reliable data model. If you’re not ready for this level of effort, you’re not ready for predictive. 

The New Face of Qualification

As predictive is being more widely embraced for what it should be (business intelligence) instead of what it shouldn’t be (a method of lead generation), it’s upended the traditional lead qualification process. In the past, qualification was a function of marketing. Especially through an Account-Based Marketing (ABM) lens, this is no longer exclusively the case. Sales is getting more deeply involved in qualification. The more signals (data) we have about our customers — through predictive — the more visibility we have into those prospects who mirror their traits and therefore are truly the best fit, and deserving of our efforts in the first place.

The other thing this kind of analysis does is remove biases around target customers. Most companies, when asked who their ideal customers are, will say something like: “Fortune 500 brands.” But it’s not that simple, and not every single business should be chasing the Fortune 500, because it’s a simple fact that they’re not the best customer fit for every business. You need to go deeper, and wider, and really tap into predictive to figure out who your most qualified companies or accounts are.

What about Customer Performance?

Using predictive the right way also means factoring in customer performance. When you’ve whittled down your list to the most ideal customers, ask yourself, “Are they using our solutions?” and, “What makes them a great customer for us?” You also must consider what types of organizations have become advocates for you, going beyond using your software, product or service, and actually being transformed by it — and then telling their story to others. Applying predictive to this process will help you identify the top tier, target customers you need to engage. It allows us to move qualification to the very beginning of our process and ultimately ensure that our efforts are only targeted to those who have already been demonstrated to exhibit best fit.

It’s One Part of a Greater Whole

One final point to keep in mind is that predictive is not a standalone solution and should never be treated as such. Instead, you need to integrate predictive modeling technology into your technology stack, so it can be fed all available data and evolve the model. This is called training and re-training the data model — it gets more intelligent over time. The more comprehensive the information we provide, along with the volume of that information, the more it increases trust (i.e. predictability) of that model.

Embracing the ‘new’ predictive is guaranteed to take you some time and investment. But when done well, it will become a single source of truth from which you can analyze data and then take informed action across a variety of channels and strategies. As with all aspects of marketing, the more informed you are, the better you can action and the more you will succeed.

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Photo: Mark Skipper