Predictive Analytics Is Losing Steam as AI Becomes Prevalent across SalesTech & MarTech

On Monday, Radius Intelligence and Leadspace announced their merger and plans to become the “leader in B2B data intelligence.”  The firm, which will continue under the Radius brand, is no longer emphasizing predictive analytics.

The predictive analytics market has failed to develop as a standalone segment. According to Radius Chairman Darian Shirazi, the total investment in the space was over $600 million.  However, Gartner sized the market at $100 million to $150 million in 2016 revenue, suggesting that the promise of predictive analytics was developing slowly.

In his just released 2018 MarTech Landscape, Scott Brinker removed Predictive Analytics as a segment as machine learning is being integrated broadly across marketing products.

For B2B predictive tools to work, they require high quality reference data sets for initial and ongoing enrichment, but the predictive analytics companies black-boxed their data sourcing. Radius was one of the few exception to this opacity as they were transparent about their data acquisition model (web crawling combined with a customer contributed data model), but most of the other firms have been vague about their data models.

The predictive analytics companies were also slow to offer ABM tools and similar company and contact recommendations. These features are now commonly offered by both predictive analytics companies and sales and marketing intelligence firms such as D&B Hoovers, InsideView, DiscoverOrg, and Zoominfo. What’s more, the sales and marketing intelligence firms have all developed light predictive scoring or ranking tools. While none of these firms approaches Radius or Leadspace in predictive capabilities, they all provide company and contact insights for sales reps, ABM tools for sales and marketing, and integrated data enrichment processes.

The predictive analytics firms also initially black boxed their models, preferring to hide complexity. They have since become more transparent and begun displaying the top reasons for recommendations. However, Salesforce Einstein has provided similar functionality with predictive scores and insights.

Todd Berkowitz of Gartner summed up the situation well.

I’ve been covering the market for B2B predictive marketing analytics for almost four years. A few years ago, predictive lead scoring was all the rage. Then it became about fit and intent models for demand generation and prospecting. Then these tools were used for selecting accounts for large-scale ABM programs. But in the end, the standalone market for these applications never fully reached its potential. Many of the original vendors got acquired for their technology (Fliptop, SalesPredict, Infer and others) and predictive scoring became a standard feature of marketing automation and SFA systems.

Just because the standalone market went away, doesn’t mean there isn’t a lot of value here. In fact, the solutions have essentially moved into two other markets (and you’ll see this reflected in our upcoming Hype Cycle reports). On one end, you have the Data Intelligence for Sales market where predictive and AI-driven solutions are competing with traditional data vendors for demand gen, prospecting, and segmentation use cases. On the other end, you have the broader ABM solutions market where these applications not only help with account selection and planning, but are moving towards engagement and orchestration.

Berkowitz predicted that one or two of the remaining predictive analytics vendors will be acquired in the next six months.

With over 6,000 MarTech companies, the market is quite fragmented. Although the MarTech sector continues to expand, there is already momentum towards consolidation as clients look for broad, integrated functionality instead of many point solutions. For example, marketing and sales departments adopting ABM need a broad set of functionality which includes

  • AI scoring and recommendations
  • Real-time, batch, and continuous company and contact enrichment
  • Data hygiene (e.g. de-duplication, data standardization, and verification services)
  • Third-party verticalized data enrichment
  • Programmatic marketing
  • Website visitor id
  • Lead-to-account mapping
  • Look-a-like company and contact prospecting
  • Segmentation, TAM, and pipeline analysis
  • CRM, MAP, and sales engagement connectors
  • Sales triggers
  • Account social media monitoring
  • Company and contact intelligence

At this point, nobody offers a full suite of these ABM capabilities for sales and marketing departments.

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