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The Predictive B2B Sales and Marketing Landscape Continues to Evolve

By Todd Berkowitz | September 08, 2016 | 1 Comment

Technology and Emerging TrendsLead ScoringData and Analytics Strategies


The NFL season starts tonight and my Denver Broncos (the defending Super Bowl Champions!!) have a rematch with the team they vanquished in Super Bowl 50, the Carolina Panthers. I haven’t seen the Swami’s predictions for the game (not even sure Chris Berman even does predictions these days), but I can predict with a high degree of accuracy that I will be watching the game and that most of the people near me will be rooting for the Broncos.

A large number of people (and a few machines) will predict the winner of a given football game. A larger number of people (and machines) will predict the outcome of an election happening in early November. There will no lack of confidence by many of those prognosticators. But predicting whether a company is one that you should target or whether a lead will convert to an opportunity, or an opportunity will close by a given date tends to inspire less confidence on the part of humans. Fortunately, the machines are doing a pretty good job of that.

Gartner’s new Market Guide for SaaS-Based Predictive Analytics Applications for B2B Sales and Marketing (subscription required) has published and it highlights the job that the machines are doing on that front. . A lot has has changed over the last eighteen months since the last market guide. C9 got acquired by, Fliptop got acquired by LinkedIn and SalesPredict got acquired by eBay. The latter two are leveraging their technology for interesting new use cases, but not for the original ones. We’ve seen brand new entrants (or ones that were just getting started in this market in 2014 when I researched the last guide) including Aviso, Clari, Datanyze, DxContinuum, Entylte, Everstring, MarianaIQ, Radius and SalesChoice. Plus we have more things cooking in Europe, especially the UK and France with BrightTarget, GrowthIntel, and IKO System.

We’ve also seen a big change in the use cases for these predictive B2B sales and marketing applications. Predictive lead scoring was far and away the most common use case on the marketing side and opportunity scoring and upsell/cross-sell modeling were big on the sales side. Now, we see a lot of interest from clients around demand generation (both for marketers and SDRs) and with the rise of account-based marketing (ABM), predictive analytics are being used for both account selection and to provide insights to marketers and sales reps who are engaging with those accounts. On the sales side, standalone opportunity scoring still gets used, but predictive forecasting offers a more transformational benefit.

The market for predictive B2B sales and marketing applications is showing signs of maturing as well. While churn still remains high (given short contracts and ease of switching), we’re seeing more stability than in the past. Venture capital investment had been pretty free-flowing (especially in 2015), but now some of the vendors aren’t far from being profitable. High tech (and related industries like professional services and telecom/CSPs) are still the biggest and earliest adopters, but more financial services, business services and even industrial manufacturers are getting in the game.  In our 2016 Hype Cycles, Predictive B2B Marketing Analytics was positioned at the Peak of Inflated Expectations, while Predictive Sales Analytics was positioned at the beginning of the Trough of Disillusionment. (Gartner classifies those as separate technology categories, but combines in one market guide because many vendors offer solutions in both categories).

This is still a fairly small market (we estimate between $100M and $150M by the end of this year), but appears to be on the verge of really taking off. Despite some early challenges and hiccups along the way, the ROI is still really compelling. Vendors may end overselling exactly what their machine learning models are capable of doing and in some cases, individual data points (intent and technographic for example) can deliver significant benefits for far less money. But ultimately, these models are usually pretty good at predicting who’s going to buy, what they are going to buy, why they are going to buy. and when they are going to buy. And the technology is only going to improve and new use cases will arise.

So look for two more posts over the next week talking more about the use cases on the marketing side and the sales side. And if you are a Gartner client, feel free to schedule an inquiry to discuss the market, the technologies or the vendors.

Let me make one prediction of my own. The Denver Broncos will defeat the Carolina Panthers in tonight’s game. I won’t give a score and I’m not going to rely on a model from FiveThirtyEight or anyone else. I just strongly believe that Denver’s defense and home-field advantage will neutralize the Panthers offensive weapons and that Trevor Siemian (the 2nd year quarterback for the Broncos with one regular-season snap under his belt) will not embarrass himself. (Plus the models have the game as a near toss-up, so what do I have to lose).

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1 Comment

  • Derek Kane says:

    Todd, thanks for you entertaining and interesting perspective on the predictive world. I look forward to hearing more from you in upcoming months. It’s unfortunate that your NFL Fandom is so misspent. Thanks for the article. Keep em coming.