Updated: Apr 22
“Solving strategic problems is more important than handling operational tasks when it comes to achieving critical targets in sales”
According to the most recent Salesforce State of Sales report, high-performing sales teams are 2.4x more likely than underperforming teams to use AI-based Tools for their Sales processes, such as to lead prioritization and opportunities.
KVP’s Einstein solution provides users with sophisticated capabilities that enable businesses to obtain customer insights, forecast outcomes, identify appropriate actions to take, and automate repetitive operations, allowing them to focus more on the customer.
In this post, we’ll explore some specific Einstein projects implemented by KVP for different business scenario. Let's dive deeper into the topic and find out how businesses can benefit from it.
Einstein Use Case #1 - Churn Prediction Insights
Get more predictive about your business and customers. Machine learning uses past data to predict what will happen in the future with minimal programming. With predictive analytics, businesses can take their knowledge of their customers to the next level and anticipate wants and needs
Aligning insights and discovery to predict future churn possibility
Earmarking reasons/attributes for lower customer retentivity
Insights for maximizing customer retentivity - high influence factors
Displaying discovered influence factors on customer records/account management module for next best actions
Business Growth parameterization through GLM algorithm, predicting deviations from norms
Einstein Use Case #2 - Dynamic Deal Price Optimization
Einstein deal insights predicts when opportunities scheduled to close this month are unlikely to be won during the same month.
Understanding ask price, median price, budget and cost correlations along with optimum margin for binary trees with historical data
Insights and discovery stories on each sales order to evaluate markers, top prediction parameters, and showcasing the same on record levels along with deviations / gap analysis
Segmented decision trees based on sales order and product variables
To improve sales productivity and focused interaction with clients using data analytics
Einstein Use Case #3 - Delinquency Prediction
It uses predictive and prescriptive analysis to predict future outcomes, as well as suggests ways in which you can improve predicted outcomes. In order to predict the likelihood of a customer experiencing an incident so that you can deliver proactive service to your customer.
Integrated with core banking application and payment system
Creating a delinquency check point in Einstein through GLM algorithm to predict customer defaults
Correlating historical data and repayment behavior and changes in customer sentiment
Suggesting insights and highlighting prime causes of delinquency
Stories to minimize delinquency and better repayment behavior with preemptive actions / engagements
Salesforce Einstein can give you the edge you need to be more competitive than ever. By getting started with Einstein, you’ll have access to smarter insights that make it easier to predict your customers, allowing you to pivot your offerings and stand out in your industry. For more information on Salesforce Einstein book a free slot with our Einstein experts.