<aside> 🚜 Introduction
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<aside> 🤝 Engagement Risk
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<aside> 🔭 Forecast Risk
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<aside> 📊 Risk Analytics
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To develop a world-class sales team, excellent deal execution is essential. Our AI-driven engagement scoring and activity mapping provide real-time visibility into deal-by-deal risk, stakeholder involvement, sentiment analysis, competition mentions, and keyword extraction.
“AI is in a golden age” and is solving problems that were once in the realm of sci-fi — Jeff Bezos
Because Clientell is constantly learning, your AI scoring is constantly modified, and the accuracy continues to improve. Clientell's engagement AI solely takes into account objective activity signals like CRM data changes, not subjective rep conduct. As a result, Clientell's approach produces an extremely realistic image of deal risk without the risk of salespeople altering their health for personal gain.
The engagement risk report's purpose is to notify the deal reviewer whether they are engaging with the client on a frequent basis, as well as to provide an overall picture of how the engagements are progressing. Are the clients responding positively, have they utilized terms that can be understood, and how did the talks go?
It employs risk assessment algorithms based on research to deliver the most realistic picture of the future. Take a look at the data from each customer interaction. It uses a number of algorithms to generate a risk score, which can be used to estimate the importance of a particular deal in the projection.
<aside> ✅ Prospect responsive: **This function is used to see if a prospect is responding to emails and whether or not meetings have been planned. It gives us with a numerical number that is then added to the total Engagement risk score, giving weight to every client sentiment.
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<aside> 😀 Sentiment: **This property analyses the emotion of emails received from the buyer's side, as the name implies. Clientell rates the sentiment of each discussion on a scale of -1 to 1 by closely monitoring the keywords through a self-evolving NLP model. This is then standardized with other variables to provide a more quantitative evaluation for this qualitative metric.
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<aside> 👬 Contacts Engaged: **The number of unique contacts to whom emails were sent or who were added to meetings is examined in this measure. By comparing the number to past data, we can determine the potential risk in the transaction. Clients who include many contacts in a single transaction have a higher conversion rate.
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<aside> 📆 Meetings scheduled: **This property determines whether meetings are planned with any clients engaged in a specific contract, the frequency of meetings, and the meeting's reaction. The score is based on the number of meetings, the time frame in which they are scheduled, and the meeting's end.
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<aside> ☎️ Rep follow-up: **This is to keep an eye on the representative's behavior after a meeting. Are the salespeople following up, what are their follow-up frequencies, and how much engagement are they bringing in with them? All of these factors are combined with previous data and assessed using our best-in-class AI algorithms, which provide us with a subjective number to add to the total engagement score.
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<aside> 🕰️ Recent Meetings: **This is the attribute that determines the activeness of meetings based on not only the numbers but also the trends in meeting frequency. Outliers from the trend are given a lower score and are utilized to help the NLP model learn more.
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The ultimate engagement score is generated by combining all of the metrics and their weighted averages. These weights are never static and are generated based on historical trends. After that, the score is standardized to a scale of one hundred and shown for each opportunity, as well as for each sales rep or management.
