How Predictive Analytics & ABM Go Together - Mintigo CPO Breaks It Down

DES-703-Blog-Post-Graphic_Interview-Atul_v0.1.pngIt seems as if you can’t walk into a marketing meeting these days without someone talking about predictive analytics and account-based marketing (ABM). But understanding how these two methodologies should work together is still somewhat vague to most B2B marketers.

We interviewed Atul Kumar, Chief Product Officer at Mintigo, to get some clarification on the state of ABM, the role predictive analytics should have, and some tips for those B2B marketing practitioners beginning their ABM journey.
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David: ABM has received much attention the past year. Why do you think that is? What about the industry has made ABM so important now?

Atul: Most sales organizations have been managing their sales operations by organizing according to territory and accounts. Marketing has traditionally focused on top of the funnel demand generation, i.e. bringing in lots of leads, nurturing them and eventually passing them to sales. This is an incredibly inefficient process because of the high percentage of poor quality leads generated and high wastage of resources to acquire and follow up on those leads.

Oftentimes the result of this is misalignment between marketing and sales. Account-based marketing allows marketers to focus on accounts that sales reps are focused on and thus work on demand gen programs that acquire and nurture qualified target account leads to send to sales. So rather than throwing leads over the wall and waiting for the expected finger-pointing and blaming to ensue, an ABM approach that’s bought in by marketing and sales increases the alignment between the two teams. Sales gets truly qualified leads from the accounts that they’re focused on.

A great idea is one thing – executing that idea is another. How is this playing out with ABM?

Execution for ABM is hard because of many reasons. First of all, most marketing automation systems are designed to work with leads and not accounts for obvious reasons.

Secondly, sales may provide you with a list of accounts (can be in thousands), but this list might not represent the best accounts to target. Simply grabbing the Fortune 100 list does not mean that all of these companies are likely buyers of your product – and finding the truly best accounts that’ll most likely buy from you is not a trivial task.

Lastly, you still need to engage people at target accounts...when selling to enterprises you’re typically working with buying centers as opposed to a single decision maker, so you need to identify the best people and best profiles to engage with at these accounts using various channels such as display, social, content syndication, direct email, etc.  Orchestrating all of these channels seamlessly to sup port dynamic buyer journeys from an account perspective is also not trivial.

Why is predictive analytics so important to a robust ABM strategy? Can you tell us a little about Mintigo’s software and where it fits into the greater ABM picture?

Ask any thought leader on ABM and they’ll tell you that an effective ABM strategy starts with identifying your target accounts.  However, many practitioners simply put together a wish list of companies that they’d like to sell to.  Most of the time, it’s some variation of Inc’s or Forbes’ or Fortune’s list of top companies and possibly filtered by company size and/or a particular vertical that the seller focuses on.

While this is certainly a starting point, the assumption made is that all of these companies in this list are a great fit to purchase their product or service.  This is a big assumption! Great ABM strategies involve a heavy commitment from a resources, time and budgetary standpoint to succeed, and if there are target accounts in this list that aren’t a great fit, then there will potentially be a good amount of wastage.

Predictive marketing solutions (including the company I work for – Mintigo), plays a huge role in an ABM strategy because it uses a data science approach to identify the companies that will most likely buy from you. 

I’m simplifying it here, but how it generally works is that 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.  Depending on data coverage of the predictive provider, these characteristics can be basic firmographic data such as industry and company size, but also other (and possibly more insightful) data points such as the technologies-in-use, hiring trends and organizational make-up, compliancy requirements, and even demonstrated intent data. 

The aggregation of these data points provides an “ideal customer profile,” which you can then use to compare other companies to see how much they “look like” your ideal customer profile.  And because several predictive marketing providers track millions and millions of companies, you’ll be able to identify all the accounts that match your ideal customer profile – this should then be your target account list.

What’s your advice for marketers who’ve just been greenlighted for an ABM pilot? Where should they start?

I’d suggest that marketers look at ABM as a strategic initiative and not just a simplistic mass demand gen program. Start with identifying the best accounts and expand the list to include look-alikes exhibiting purchase intent. Then identify the best persona for those accounts. Once this is done this, they should create a plan to intelligently engage these personas and leads via omni-channel marketing.   

Any advice for experienced ABM marketers who need to ramp up their program results?

Four words: Discover, Engage, Measure, and be Strategic.  Okay, I guess that’s six words!

 

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