Predictive analytics was introduced to banking software just a decade ago. But, its usage was limited to few niches; fraud detection and credit risk evaluation. Over time, marketers comprehended that to win and retain customers this would provide a competitive advantage. Just look at how smartly Facebook predicts ‘News Feeds’ to predict the user interests. Now imagine how unique your customer communication would be if you customize your banking activities through predictive analytics consulting services in the same way.

Where to Begin with Predictive Analytics in just Four Steps?

#1. Ask a Question: Predictive analytics allow insight into the future unlike traditional Business Intelligence tools, which have a retrospective nature. Therefore, instead of anticipating the reason for something that happened in the past, the bank can focus on understanding upcoming events through predictive analytics and work out the plans in advance.

With steadfast predictive analytics, the bank can ask the following questions:

What is the profit probability of a particular customer over the next two years?

What is the probability of a customer buying product B if he or she purchases product A?

What is the number of leads new marketing campaign will generate?

#2. Collect Data: The next step is to collect the data that one way or another reflects the above answers. The confidence level of every prediction is directly proportional to the quality of data presented for analysis.

According to expert predictive analytics consulting in US companies, data collection is time-consuming. And for some predictive models, customary banking CRM is not enough. In such situations, banks will require additional data sources listed below:

• Channel preferences
• Geolocation
• Consumer ratings and reviews
• Social media insights
• Bill payment behavior
• Current events
• Personal financial management (e.g., customers’ financial goals)

#3. Predictive Model Building

This step is all about data analyst creating a model that defines a probability of happening of an event. Machine Learning (ML) methods are employed along with linear regression to deepen learning for accomplishing successful predictive model building.

After numerous training & testing iterations, the analyst gets a predictive model, explains customer attrition. A right predictive model is a powerful tool that any organization can ever have. One predictive model is enough to handle all your customers and generate a ‘churn score’ detail.

#4. Focus on Assumptions

'The future will be similar to the past', is the major assumption to make in predictive analytics. Indeed, people develop strong behavior pattern and follow that over time, which lead to a reliable predictive model. However, you can notice changes in customer’s behavior pattern and the model once used becomes invalid in such conditions. Besides, market changes have a big impact on predictive model assumptions.

How Predictive Analytics Consulting Boost Cross-Selling?

Cross-selling is a nightmare for retail banks and you can blame increased product commoditization for this. Predictive analysis allows banks to customize communication with customers and offer only the relevant banking services and products. To implement such advanced cross-selling technique, buying standalone predictive solution isn’t essential, because there is in-built predictive analytics competence in various bank CRM platforms.

Wrapping Up

Some banks are already taking advantage of predictive analytics consulting facilities. For example, First Tennessee Bank (U.S.) optimized its marketing strategy that further resulted in an outstanding 600% ROI via more precisely targeted offers within high-value customer parts. For more information on Predictive analytics consulting in United States, UK and Europe contact our experienced team.