Leveraging Einstein's / Amazon Personalize Capabilities to Observe Behavior, Build preference profiles, and surfaced the Predictive Trending Products.

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KWeb recommendation, leverages Einstein / Amazon Personalize Engagement Scoring feature on the ordered data over a period of time (configurable parameter) and builds Product category / catalog (Region, Season etc.) to observe the trending product behavior, preference of the customers etc. and provide predictive trending product catalog insights to the B2B buyers. This facilitates the salesperson to show the trending products to the potential B2B buyers based on the category the buyer falls under (Region, Season etc.) Salesperson can choose the appropriate product category and show the trending products to their potential B2B customers. The possibilities are limitless with applications that include:

Expand the ways that your customers can discover your products and increase sales.

  1. Provide facility to define multiple recommendations with each recommendation having with multiple fields key-value pairs.
  2. Allows to define the recommendation period / duration for each recommendation.
  3. Activate or deactivate the recommendations.

Monitor your brand across all your channels to increase your marketing reach and preserve brand integrity.

  1. Better understand customer preferences and lifestyle through their order data.
  2. Evaluate banner advertisement exposure during broadcast events to drive higher ROI.

Increase the ways that you can identify your products to streamline sales processes and customer service.

  1. Identify the products which are out of trend or trending to streamline inventory restocking.
  2. Measure retail shelf-share to optimize product mix and represent top-selling products among competitors.

Tech Kasetti, facilitates in setting up of Einstein / Amazon Personalize Engagement Scoring on your order data and discover new insights about your customers and predict outcomes that lead to smarter decisions.

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