AI-powered deal evaluation with Vtiger Calculus
Posted: Sat Dec 21, 2024 4:44 am
Does your organization use a deal evaluation process where you manually assign probabilities to predict deals to close? Then you know that the process can be flawed and how “unpredictable” the predictions can be.
Every business needs a reliable way to predict the outcome of a deal. Knowing the probability of a deal winning is useful for two reasons:
Taking Corrective Actions - Sales reps can take actions to increase the chances of winning if the probability is low.
Improve forecast reliability - Use reliable estimates to create more accurate forecasts.
Transaction Evaluation
While every deal is unique, there are many factors such as buy Phone Number List customer and contact needs, behavior, and demographics that influence the outcome of a deal. For example, the industry your client is in is an important factor.
In addition, there are also dynamic factors such as engagement patterns. If customer engagement is high, it usually results in a higher conversion rate. Just remember that while you may have a higher conversion rate in one industry, the same factors or formulas may not work in another.
Based on these factors, you can calculate the probability of winning a deal in one of the following ways:
Traditional assessment
AI-based assessment
Traditional assessment
The traditional approach uses rules (or conditions) set up in the CRM to assign points.
Consider these examples:
You can assign 5 points if the client is from the Manufacturing industry and 7 points for a client from Telecom. Similarly, you can award 3 points for each email and 6 points for each call.
You can set up a rule to directly assign a deal stage probability. The probability increases with each step. For example, a deal at the Value Proposition stage might be 50%, while at the Agreement Review stage it might be 80%.
With traditional scoring, the sales leader is responsible for defining the scoring rules. In addition to being tedious, this process is also error-prone and usually results in inaccurate scores. Administrators typically spend time reviewing the results and going back to change the rules.
AI-powered deal evaluation
Vtiger has applied AI-based learning models to overcome the problems of traditional estimating. AI-based models determine estimating rules based on historical data. And the estimating process improves over time as the data used for training grows.
We recommend AI-based estimating over others any day. We may start with the cliche “data doesn’t lie.” But here are the real reasons.
AI can detect patterns in data that humans might miss.
AI predictions are based on data, not emotion. Therefore, the calculations are accurate. (However, we can also make AI take sentimental data into account.)
The AI's score gets better over time as historical data increases.
AI assessment does not require manual intervention; therefore, no “external influences” will disrupt the predictions.
Recommendations for improving the transaction evaluation
Deal scoring is only useful to sales reps when the system can provide recommendations to improve the score.
Here are some examples of recommendations your sales reps will see if they use Calculus.
A reminder to take actions such as identifying decision makers.
Recommendations for documents that have previously helped advance similar transactions.
A reminder to respond when the time comes.
We suggest the best time to contact to increase your winning percentage.
Reliable forecasts with AI-powered trade evaluation
Accurate deal scoring helps sales reps make reliable forecasts. Forecasts improve over time because the deal scoring itself gets better with more data. Managers can relax because forecasts are not based solely on the rep’s “gut feeling.” Instead, AI-powered forecasts are based on available signals that influence the outcome of the deal.
Try Vtiger Calculus — it’s time to put AI in the hands of your sales team.
Deal scoring, deal recommendations, email assistant, call analysis, training features are all part of Vtiger Calculus.
Vtiger Calculus is an add-on available for Vtiger Sales and Vtiger One editions (Professional and Enterprise levels).
Every business needs a reliable way to predict the outcome of a deal. Knowing the probability of a deal winning is useful for two reasons:
Taking Corrective Actions - Sales reps can take actions to increase the chances of winning if the probability is low.
Improve forecast reliability - Use reliable estimates to create more accurate forecasts.
Transaction Evaluation
While every deal is unique, there are many factors such as buy Phone Number List customer and contact needs, behavior, and demographics that influence the outcome of a deal. For example, the industry your client is in is an important factor.
In addition, there are also dynamic factors such as engagement patterns. If customer engagement is high, it usually results in a higher conversion rate. Just remember that while you may have a higher conversion rate in one industry, the same factors or formulas may not work in another.
Based on these factors, you can calculate the probability of winning a deal in one of the following ways:
Traditional assessment
AI-based assessment
Traditional assessment
The traditional approach uses rules (or conditions) set up in the CRM to assign points.
Consider these examples:
You can assign 5 points if the client is from the Manufacturing industry and 7 points for a client from Telecom. Similarly, you can award 3 points for each email and 6 points for each call.
You can set up a rule to directly assign a deal stage probability. The probability increases with each step. For example, a deal at the Value Proposition stage might be 50%, while at the Agreement Review stage it might be 80%.
With traditional scoring, the sales leader is responsible for defining the scoring rules. In addition to being tedious, this process is also error-prone and usually results in inaccurate scores. Administrators typically spend time reviewing the results and going back to change the rules.
AI-powered deal evaluation
Vtiger has applied AI-based learning models to overcome the problems of traditional estimating. AI-based models determine estimating rules based on historical data. And the estimating process improves over time as the data used for training grows.
We recommend AI-based estimating over others any day. We may start with the cliche “data doesn’t lie.” But here are the real reasons.
AI can detect patterns in data that humans might miss.
AI predictions are based on data, not emotion. Therefore, the calculations are accurate. (However, we can also make AI take sentimental data into account.)
The AI's score gets better over time as historical data increases.
AI assessment does not require manual intervention; therefore, no “external influences” will disrupt the predictions.
Recommendations for improving the transaction evaluation
Deal scoring is only useful to sales reps when the system can provide recommendations to improve the score.
Here are some examples of recommendations your sales reps will see if they use Calculus.
A reminder to take actions such as identifying decision makers.
Recommendations for documents that have previously helped advance similar transactions.
A reminder to respond when the time comes.
We suggest the best time to contact to increase your winning percentage.
Reliable forecasts with AI-powered trade evaluation
Accurate deal scoring helps sales reps make reliable forecasts. Forecasts improve over time because the deal scoring itself gets better with more data. Managers can relax because forecasts are not based solely on the rep’s “gut feeling.” Instead, AI-powered forecasts are based on available signals that influence the outcome of the deal.
Try Vtiger Calculus — it’s time to put AI in the hands of your sales team.
Deal scoring, deal recommendations, email assistant, call analysis, training features are all part of Vtiger Calculus.
Vtiger Calculus is an add-on available for Vtiger Sales and Vtiger One editions (Professional and Enterprise levels).