The Predictive Marketing Analytics for B2B Marketers Forrester Wave

When my colleague Laura Ramos and I set out to “Wave” the B2B predictive marketing analytics space, we knew that while there are impressive results across the board here, marketers struggle to identify differentiation among its vendors. We’re thrilled to have published Forrester’s first Wave that provides clarification to B2B marketers who seek to connect their business requirements to a predictive marketing analytics solution.

The Wave process begins by screening dozens of interested vendors and each participating vendor – household name or not – brings exceptional capabilities to the market.

We included 11 vendors in the assessment: 6sense, BrightTarget, EverString, Infer, Lattice, Leadspace, Mintigo, MRP, Radius, The Big Willow, and Versium.

Forrester Waves have a track record of delivering objective guidance to technology buyers of all stripes, supported with an interactive tool that marketers can use to zoom in on the capabilities that are important to them. We chose to focus on how well these offerings give marketers the ability to deploy predictive marketing analytics across the customer life cycle, to integrate with other popular martech solutions, and leverage a variety of data sources. Laura and I prioritized the following core principles as we built and assigned weights to each of the 28 criteria:

  • The extent to which solutions can reliably predict an outcome in a specific time frame.
  • The ease with which marketing and sales can execute campaigns using model output.
  • The degree to which model output supports engagement across the customer life cycle.
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Link Marketing Results To Decision Making And Revenue Outcomes

Link B2B Marketing Results to Decision Making and Revenue OutcomesIf you took your company name and logo off of your marketing collateral, would the reader still know that it came from your firm? How strongly does your brand shine through in unique language, perspective, and distinct value? 

You can ask the same about your marketing performance reports. Are the metrics that you use to demonstrate success unique? Do they reflect your firm’s core priorities? Without these distinctions, you may be providing a collection of data that is ultimately disconnected from decision making.

If your marketing reports don’t enable performance management, it’s time that they do. Eighty-two percent of CMOs report that their goals directly align to revenue targets. But many practitioners acknowledge that marketing performance management programs require significant tuning to link marketing results to decision making and revenue outcomes.

The window of opportunity for you to lead your marketing department toward revenue relevance is closing. I suggest that you do the following before it shuts:

  1. Uncover your firm’s explicit path to revenue. You might be satisfied to make your numbers, but switch your focus to supporting a revenue mix that reflects your firm’s most strategic priorities. Get in lockstep with your organization’s explicit path to revenue. Perhaps this is net new customer acquisition from a specific industry, building out the market presence of a specific segment of the product portfolio, or customer service proficiency for up-sell and renewal revenue in a specific business line.
     
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Predictive Marketing Analytics and The Link to Customer Engagement Action

Since 73% of companies understand the business value of data and aspire to be data-driven, but just 29% confirm that they are actually turning data into action – it’s not a leap to suspect that organizations are at risk of collecting data without deploying them in ways that support deeper customer engagement.

This concept – linking insights to action – is an example of a mission-critical imperative that transcends client roles. Consider:

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Three Use Cases Illustrate the Power of Predictive Analytics In B2B Marketing

There are a number of predictive analytics firms dedicated to helping B2B revenue leaders examine their own successes and losses to inform everything from account selection to next-step action analysis and recommendation.

Last year, Laura Ramos introduced them to us in her report, New Technologies Emerge To Help Unearth Buyer Insight From Mountains Of B2B Data. Laura concluded this report with a recommendation to prepare to take the predictive analytics plunge.

Well, many of you have "taken the plunge," or are about to. Nearly two thirds of marketing decision makers plan to implement or upgrade predictive analytics solutions during the next 12 months. Since I joined Forrester a few months ago, I've spoken to many of you that wonder what lessons early adopters have learned and how to consider predictive marketing analytics in the context of your specific go-to-market strategies and organizational goals.  

In my first Forrester report, What’s Really Possible With Predictive Marketing Right Now, Laura and I collaborated to look more closely at the trends driving predictive marketing and the common attributes among early successes.   

What we found is that three categories of use cases dominate the current landscape, not only laying the foundation for more complex use of predictive marketing analytics, but also supporting the full scope of the customer lifecycle, from net-new prospect identification to account expansion: 

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M&A in the Predictive Marketing Space: eBay Acquires SalesPredict in an Unexpected-But-Perfectly-Logical-Move

It’s not infrequent that a merger or acquisition takes place in a particular coverage area and, as an analyst, I’d typically expect to be ready to discuss the event at a moment’s notice.  

Not so when it came to last Monday’s news about eBay acquiring predictive analytics startup SalesPredict.

There are a little over 20 vendors vying to provide predictive modeling solutions to B2B marketers and sales professionals. It’s a “new-ish” technology, and one might reasonably expect consolidation or merger activity. But for most folks, this particular collaboration was an eyebrow raiser and I needed to talk to some people first.  

Founded in 2012, SalesPredict builds predictive algorithms that help B2B firms identify correlative relationships between the presence of various attributes and/or buyer behaviors to positive or negative outcomes. I had met with Yaron Zakai-Or, CEO and co-founder, and Sahil Mansuri, VP of Marketing, several times in my role as analyst. I imagine Sahil’s background in marketing helped them to grow their base within 6 months to 60 customers. But it didn't hurt that at SalesPredict, it was always about powerful technology without bounds. Co-founder Kira Radinsky, a self-proclaimed “data scientist at heart,” says that that founding SalesPredict was part of her vision “to bring about a major change in how business is conducted by unifying micro- and macro-economic predictions.”

This didn’t go unnoticed by eBay, with its own goal of increased sophistication in artificial intelligence, machine learning, and data science to support its structured data plan. In fact, that is exactly how eBay described the acquisition - frankly reminding me of how broad the use cases of predictive technology really are.

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