Ultimately marketers want to get the best possible return on investment on their campaigns. Delivering promotions that match with an audience's previous behavior is more likely to drive conversions than ones that are generic. With Predictive Purchase Data (PPD), companies can target consumers based on previous behaviors resulting in a rise in new business, repeat customers, engagement, and other key metrics.
By expanding on the traditional use of machine learning to classify consumers, PPD uses causal analysis to determine what it will take to motivate an individual consumer to convert.
In compliance with data privacy laws, PPD utilizes a predictive engine built without the use of any sensitive personal information or any private consumer data.
Increase conversions, loyalty and sales with predictive behavioral targeting
Improve consumer journeys through motivation-based personalization
Realize the full potential of your audience data and determine ideal promotion strategies
Price Sensitivity: Helps marketers predict how price sensitive a new or existing customer is within a product category.
Full Price Buyer: A simplified version of Price Sensitivity that helps marketers predict if a new or existing customer is a full-price buyer within a product category.
Retailer Preference Rank: Helps brands or retailers understand how important each retailer is to a given customer for a specific category of spend.
Retailer Class of Trade Preference: Tells a retailer or CPG which type of retailer a given consumer is likely to purchase a category of products (i.e. baby care) at.
Retailer Preference: Helps retailers understand where else a given customer is spending money on a certain category of product and create tailored messaging for those customers to win those purchases.