The newly-released content recommendation engine uses relevant user and deal info to lead reps to the most relevant content for each prospect, available directly within their CRM
Enablix, the sales content enablement solution for revenue facing teams, made the release of its Intelligent Content Recommendation Engine public as part of their Fall 2022 product launch. The ML-based engine flips the typical content paradigm by recommending both internal and external sales and marketing content to the sales rep for any target account. With data inputs from both the CRM as well as the Enablix content engagement database, the recommendations allow sales reps to spend less time searching for the right content and get higher prospect engagement.
The recommendation engine solves a problem endemic to sales content enablement: it’s unrealistic for sales reps to be aware of every piece of content that could potentially be used in every situation, resulting in over half of all content created for the sales team going unused. This ‘dormant’ sales collateral is not just a waste of time for the content marketer creating it, but it makes searching for additional content difficult and encourages reps to continue sending the same documents they’ve sent to prospects in the past.
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“For every opportunity, reps are provided ‘what to show’, or the external content to share with a prospect, but they’re also given a ‘what to know’ panel highlighting training and learning materials that are relevant to that rep and sales opportunity.”
Additionally, Enablix’s recommendations go beyond surfacing only external-facing marketing content to reps, and suggests internal training materials for in-stream learning as well. For every opportunity, reps are given “what to show”, or the external content to share with a prospect, but they’re also given a “what to know” panel highlighting training and learning materials that are relevant to that rep and sales opportunity.
Enablix’s CRM integrations give power to the recommendations by combining data points from each opportunity alongside the content analytics library residing within the Enablix database. Powered by a purpose-built Bayesian Inference Model, the engine uses details like the stage and opportunity amount as well as any specific opportunity details that reside within a user’s CRM, while also weighing how ‘successful’ or ‘popular’ pieces of content are when choosing what to surface to a rep. These recommendations are constantly improving as the engine gathers more information each time users share Enablix content.
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“The thing that excites me most about our content recommendation engine is the ability to surface just-in-time training alongside relevant external-facing collateral for each prospect,” said Gaurav Harode, CEO at Enablix. “By combining both internal and external content into a singular view that’s unique for each opportunity and rep, we continue to reduce the overall content burden on the rep while pushing our enablement capabilities even further”.
When combined with other Enablix features like the content quality checks and internal user/content analytics, the recommendation engine will reduce the bloat that creeps into many content libraries and helps rep close deals quicker through effectively educating prospects.