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B2B Predictive Analytics: Marketing Table Stakes by 2016

Predictive analytics is one of today’s hottest B2B marketing technologies. Fueled by drivers such as big data, SaaS delivery models, and data-driven marketing and sales, predictive analytics garners a tremendous amount of attention, particularly given how few customers are in actual production. While the hype can sometimes be excessive, early adopters are realizing demonstrable ROI as they use statistical modeling, machine learning, and scoring technologies to identify and prioritize accounts, leads, contacts, and customers at specific points in the marketing and sales funnel. It’s this demonstrable ROI that’s causing more and more marketing organizations to look at predictive analytics as a new, non-negotiable element of their marketing technology stacks.

That’s why the we just published The B2B Predictive Analytics Technology Report. The report provides buyers with specific, actionable guidance for understanding, evaluating, and adopting predictive technology. This 30 page report:

  • Is based on dozens of interviews with production customers and leading predictive analytics vendors
  • Analyzes key market trends and dynamics from both the buyer and vendor perspective
  • Provides a framework for identifying specific marketing and sales use cases where predictive can help
  • Offers a specific methodology and tools to use for vendor evaluation and adoption cycles
  • Profiles 7 vendors that made TOPO’s predictive analytics shortlist

B2B Predictive Analytics Trends

The report analyzes the key trends and dynamics that are shaping the predictive analytics market as it evolves from a nascent market to mainstream, must-have technology. While there are numerous trends that buyers and vendors alike should track, a few of the more notable ones include:

B2B predictive analytics is an emerging market with less than a $100M in aggregate vendor revenue. The predictive analytics market for B2B is still in the early market stage of the technology adoption life cycle. Vendors are small, and most have been in business for only a few years. Most importantly, few customers are actually running in production.

We forecast rapid growth, with 36.8% of high growth companies investing in predictive analytics over the next 12 months. While it’s still a nascent market, the promise of using predictive analytics to increase revenue growth is real. Early adopters have seen compelling, demonstrable ROI from their predictive analytics programs. These early adopter wins are spurring investment from a larger swath of the market, particularly among companies with demonstrated marketing automation success and high volume funnels.

As the market accelerates, buyers need a framework to reduce adoption risk and demonstrate ROI. Given the number of vendors, conflicting messages, and use cases in this market, it’s critical that buyers use a framework to: 1) identify priority use cases (start with lead scoring and ICP) and requirements; 2) shortlist and select a vendor; and 3) drive successful adoption in marketing and sales.

predictive analytics

Evaluating Predictive Analytics Vendors

For prospective buyers, the B2B predictive analytics vendor landscape can be confusing. A number of dynamics make understanding the overall landscape and specific vendor capabilities a challenge. First, a large number of vendors currently have a predictive offering. Second, marketing and sales can apply predictive to many different use cases. Third, very few vendors are able to articulate how they are different from the competition. Finally, the vendor landscape will undergo rapid change over the next five years. To overcome these challenges, buyers should use a number of evaluation best practices and tactics, a handful of which are described below:

Demonstrate success at a specific point in the funnel, such as lead scoring or ICPs. For most organizations, the best predictive analytics starting point is lead scoring and building ideal customer profiles (ICPs) for target account selection. We recommend demonstrating success in one of these two areas before moving to other areas in the revenue chain.

Compare vendors against your starting use case requirements and talk to customers that are in production in that specific area. Marketing organizations should develop requirements against their starting point use case and evaluate vendors against those requirements. Buyers should also work with vendors to speak with customers that are in production on a use case that closely mirrors yours. As part of this research effort, the TOPO Analyst team performed detailed analysis on vendors such as 6sense, EverString, Fliptop, Infer, Lattice Engines, Leadspace, and Mintigo.

Marketing teams must be prepared for rapid iteration during the pilot and production phases. Despite SaaS delivery models, predictive analytics does require implementation and adoption resources. The most successful early adopters rapidly iterate on data inputs and scoring models during the pilot. Marketing should be familiar with data modeling and scoring methodologies. Most importantly, they should be prepared to work iteratively with vendors on building and maintaining models.

We anticipate vendor consolidation so buyers must consider the long term viability of vendors. Over the next 3-5 years we expect to see market consolidation as products and datasets commoditize, larger marketing technology vendors get acquisitive and many predictive analytics vendors either pivot or go out of business. We anticipate that 2-3 market leaders will emerge. When selecting a vendor, buyers must be aware of long term issues such as contract length and model/data ownership.

If you’d like access to The B2B Predictive Analytics Technology Report, contact TOPO at www.topohq.com.

About the author: Craig Rosenberg is a co-founder and Chief Analyst at TOPO and blogger on the Funnelholic sales and marketing blog.

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