Predictive Analytics and CRM

Predictive analytics is using data and quantitative analysis to derive insights, use those insights to shape business decisions and improve business performance. Predictive analysis helps to determine the future outcomes based on data, statistical algorithms and machine learning techniques on historical data.

Common uses of predictive analytics for Customer Relationship Management include:

Optimizing marketing campaigns

By optimizing marketing campaigns predictive models can help to attract retain and grow most profitable customers. By determining customer responses predictive analysis can be used to promote cross-sell opportunities. Check how Automate SMB was able to implement Predictive Analytics for Vtiger using BigML to predict potential customers. 

Product Recommendation 

Product recommendation is based on customer data to create accurate, individualized client profiles and give personal service to every client. It helps to figure out what kind of content and solutions a specific customer might be interested in. Product recommendation engines are predictive offers or next best offers based on  sophisticated algorithms taking into account massive amounts of customer data, including purchase history, preferences, and direct feedback. Predictive analytics  intelligently anticipates the intent of the customer, and then provides a unique recommendation based on what has been observed.

Churn Prediction 

Chrun Prediction is helpful to analyse if customers who are likely to cancel a subscription, product or service. If the customers are at the risk of leaving, churn prediction helps to predict that and hence is very useful in customer retention. Just by assessing how much your customers are using your product compared to how much they were using previously you can figure out the customers who need attention. Expanding revenues from existing clients is a must to achieve hyper positive growth. Churn prediction helps to renew accounts and makes customers continue using the company product.

Sales Forecast

Predictive analysis includes different attributes to determine which deals will win and which will lose. Sales forecast can help to relocate the resources on important deals. Some attributes of sales cycle can help to predict wins. Example sales stage- a newly qualified deal has a lower probability of winning than a deal that is in the negotiate stage. Existing customers are easy to pursue to buy a product or service than new customers, dollar amount, time since last stage transition, time until expected close date. The dollar amount of the product also dictates the probability of it closing. Medium deals tend to have a high probability of winning compared to really large or small deals. The number of days that have elapsed since a deal last changed sales stage is also a key factor in considering if a deal will win.

Other common uses of predictive analysis include –


Detecting fraud

Predictive analytics can be used to improve pattern detection and prevent criminal behavior. High-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud and threats

Calculating Credit Score of Customers 

A credit score is a number which incorporates all data relevant to a person’s creditworthiness. It is used to assess a buyer’s likelihood of default for purchases. It also takes into account risk-related uses like insurance claims and collections.

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