Predictive Analytics

Using Predictive Analytics in your Business

Your customers are providing you with information that can assist you in forecasting the future. Predictive analytics is now available to small businesses hoping to gain a competitive advantage by mining their data and generating actionable knowledge.

Predictive analytics may help your firm with everything from mapping customer purchase habits to optimizing product promotions.

While this isn’t a new notion, the broad availability of predictive technologies and the rapidly growing field of digital marketing have given these insights a new lease on life. As a result, predictive analytics provides your company with the competitive edge it requires, allowing you to focus on future growth.

These tools enable the company to apply predictive analytics to any use case. For example, using forecasting, regression, clustering, and other methods to analyze many use cases. Use cases such as:

  • Customer acquisition, planning, targeting
  • Cross-sales opportunities, pricing, and promotional targets.
  • Analyzing and predicting customer preferences and buying behaviors

Some Important Predictive Analytics Tools

So, how can you get the most out of predictive analytics for your company? Let’s check some of the essential prediction tools and how they may be used to aid your company:

Customer Behavior Prediction Modeling

You can create predictive models to draw correlations between past behavior and demographics using data points gained from prior campaigns. This model tries to assess each consumer based on their likelihood of buying specific products, as well as project when and how to approach them.

You may have noticed strategies like suggested products being provided to you during your online purchase checkout in the wild. This is an illustration of how the model works in practice.

Lead Qualification and Prioritization

It can be costly to pursue a lead that is unlikely to convert. Predictive analytics can help you get more bang for your money when it comes to lead modeling. It scores leads based on known interest, authority to buy, need, urgency, and accessible cash using an algorithm.

The system examines, compares, and contrasts consumers who converted with those who did not and then found “similar stuff” among the incoming leads, using public and unshared data.

The lead is more qualified the higher the score. Therefore, high-scoring prospects should be led to sales or offered early incentives to convert; medium-scoring prospects should be given a drip campaign, and low-scoring prospects should be ignored.

Customer Segmentation and Targeting

Customer targeting and segmentation are among the most common uses of predictive analytics. There are three basic types of customer targeting and segmentation:

Affinity analysis is the practice of clustering/segmenting a customer base based on shared characteristics, allowing for more precise targeting. Response modeling checks previous stimuli offered to customers and the responses generated to estimate the likelihood of an approach receiving a positive response.

The attrition rate checks the percentage of customers who leave over time and the potential revenue lost from their departure.

A company can anticipate the Customer Lifetime Value by using predictive analytics technologies (and others) (CLV). This metric checks different factors of past behavior to determine:

  • The most lucrative customers over time and,
  • The purchase spending trends around which activities produce the best ROI.

This model estimates expected retention in the equation to evaluate the future value. Once you’ve gotten the CLV, you may adjust your acquisition costs and marketing budget to attain your desired ROI.

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