Lattice Engines has taken the pole position in the emerging Predictive Analytics space. In yesterday’s blog, I covered its pricing, value proposition, content, and integrations. Part two covers model building.
When first launched, Lattice Engines and its peers had long deployments and black-boxed models that required data science expertise. The firm now offers 24-hour deployments, simplified model building, and greater transparency around models and recommendations. Furthermore, the system allows marketers to either build their own models or import industry standard PMML files constructed by their data science teams.
Predictive models are built by importing training files which are matched against the Lattice Data Cloud using D&B DUNSMatch logic and Lattice proprietary techniques. Training models contain examples of both positive and negative outcomes (e.g. win / lose, renew / drop). A model is typically available within thirty minutes of the training file upload.
Ideal Buyer Profile scores (Lattice’s term which is similar to Ideal Customer Profile scores) are available to sales and marketing and include both scores and recommendations. Marketing can view the model via a graphical Data Cloud Explorer which highlights the key signals and variables in the model and makes the data available for export to other platforms.
To make the data more actionable for sales reps, Lattice provides Salesforce Talking Points which display recommendations and explanations that include Lattice data, transactional history, and buyer behavior. A Lattice Buyer Insights CRM I-frame contains Lattice recommendations, talking points, company profiles, company fit, engaged contacts, engagement activity, intent analysis (surging topics), web activity, and purchase history tabs.
Future plans include a user interface for segmentation analysis and simplifying intent scoring to high/medium/low.
Part 1: Lattice Engines Overview.
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