Precision recommendations – beyond collaborative filtering
A recommendation engine is the most obvious of eCommerce strategies for any business trying to boost revenue from its existing client base. Collaborative Filtering is the most commonly recognized technique for finding likely customer-product matches, given only sparse purchasing history data. A recent innovation, known as Factorization Machines, far exceeds collaborative filtering in recommendation precision, to the point where a single customer-product pair can be identified to set the recommendation score. In addition, this new technique incorporates cross-feature influences, which may be too important to be overlooked. For example, a viewer may actually dislike romance and comedy movies, but is fascinated by romantic comedies.
Detection of prospect funnel state buyer readiness
The sales funnel is an important marketing concept used to direct sales efforts and organize campaigns. While marketing automation platforms offer prospect scoring, the feature is always set manually. Advanced analytics, such as Hidden Markov Model (HMM), can automatically and optimally score each prospect based on their digital behavior. Digitally intelligent enterprises can leapfrog the competition with aggressive adoption of data-driven innovations and harness these important hidden indicators.
Advanced classification for market segmentation
While fine-grain personalization is well within the capability of today’s app serving platforms, from a management perspective, it is of practical necessity to conduct campaigns at a cluster level. Analytically sophisticated organizations can make guided decisions in choosing among a host of clustering techniques from simple (k-means) to advanced (latent classification) to handle data at the extreme scales of volume and complexity. Mathematically-derived classifications reduce dependency on gut feelings in favor of consistent returns.