The Critical Elements of a Machine Learning System: A Perfect Fit with Clari’s Approach

When I recently read an article in Destination CRM describing six ways machine learning can generate more value from CRM, my first thought was, “This is exactly why we built Clari.”

The author, Jeff Erhardt, highlights a key observation of expensive CRM systems: most just store and report data; they don't make sense of that data. This limits the value of your CRM. Machine learning, however, adds an intelligent layer on top of existing CRM systems and, as the article explains, “Teases out insight from all of your data to tell the full customer story.”

While Jeff highlights the use of machine learning to deliver insights in customer service, we at Clari apply it in sales execution. We apply machine learning to every activity that challenges sales teams— pipeline inspection; risk analysis; forecast management; and territory, resource and milestone planning. Let's examine how we do this using each “critical element” Jeff’s article describes (article topics are in bold below).

1. Gaining insight into the future. Clari collects and analyzes data from critical sources including CRM, email, and calendar. We warn you which deals are at risk, confirm which deals will close, and give you confidence in your forecast.

2. Continually updating predictions. Traditional BI and analytics tools export data and then analyze it, which means those systems are always out of date. Clari’s real-time visibility means speed. This leads to coaching in time to make the upcoming meeting successful, smarter resource allocation in time to make the quarter, and precise forecasting based on up-to-the-minute deal insight.

3. Discovering the "why." Beyond predicting a deal’s likelihood of closing (what we call the “Clari Score”), Clari gives you the ability, like a physician, to diagnose the health of a deal. Drilling down on the Clari score, you know which factors contribute to the score — lack of meetings, insufficient communication, or other deal details.

4. Making predictions at the individual customer level. Machine learning is about patterns. Reps have patterns. Product lines have patterns. We analyze these patterns and tell you, for each of your deals, which next action will lead to a close and which to a stall. Armed with deep insight, you can set deal strategy and evaluate your pipeline’s health.

5. Analyzing unstructured data. Sales reps connect with customers in email and meetings. So their email and calendar is the source of truth for both customer engagement and rep activity. Deep email and calendar analytics show you how engaged customers are, the strength of key relationships, and the level of rep activity. We also comb the web for unstructured public data, like customer and industry news, to speed meeting prep.

6. Encouraging more consistent CRM use. Applying machine learning to your raw CRM data gets you consistent insights into your deal pipeline and dramatically reduces the "gut calls" your sales reps and managers make. If your entire sales team is driven by a common smart engine, you not only get greater productivity from every member of your team, but also gain consistency in how you evaluate deals in the pipeline.

Applying machine learning and predictive analytics to sales execution means an even greater ROI on your CRM investment. If the new insight you can have by combining machine learning and CRM sounds attractive, click here to see it in action!

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