How Predictive Analytics will Transform the CRM Experience

With the increasing number of firms to compete with, it’s essential to convince the customer why YOU are worth choosing over other firms.

To do this, you need to establish a relationship and an emotional connection with the customer. The best way to do this is through strategised Consumer Relationship Management (CRM).

Predictive Analytics

One of the most exciting fields with immense development when it comes to marketing technology is predictive analytics.

Predictive analytics are based on numerous factors and the analysis of data and patterns. They hence help predict phenomenon such as weather events – or in the case of consumers – behaviours.

They allow for predictive models to be made that help you to understand how to interact with the data source you analyse.

Potential Uses for Predictive Analysis in CRM

1. Understanding Consumers and Building Personas

Several companies and firms track user behaviour through cookies.

They analyse consumer behaviour to find out various things such as what approaches allow the largest rate of purchases or turnover rates.

This allows a consumer persona to be built, depicting the demographics of the target audience.

The producers can then adapt their marketing strategy in order to generate more sales, and hence, more profit. 

2. Segmentation

Predictive analysis can categorise customers into different groups and use separate marketing strategies based on their behaviour patterns. For example, the hours of peak activity could be recorded on the CRM platform. Hence, marketers can interact with them when responses are most likely.

Similarly, marketing can be customised for different consumers. Customisation shows customers that the firm cares about them, hence improving brand loyalty and conversions to regular clients. Alternatively, User Interface and Design could be edited to attract attention and clicks. 

3. Cross-Selling

Finally, based on previous behaviours, products can be cross-sold to consumers based on recent purchases.

For example, complementary goods such as recommending tennis balls to someone who purchased a tennis racket.

Responses can be recorded to improve future experiences, and predict those of other customers. This improves the CRM strategies of the firm. 

CRM is not limited to the software used by the company, and extends to every interaction with the consumer. Through predictive analysis, these can be turned into learning experiences for stronger bonds with customers, more word-of-mouth marketing, and a better understanding of the consumers’ desires.

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