How Predictive Analytics Will Transform Crm Experience

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Andreena Sung
May 19, 2019   •  12 views

Today the market is expanding at an unprecedented rate. The number of customers has increased multi-folds, accompanied an increase in the number of firms. This staggering increase in the number of firms, means stiffer competition for a market share. In the simplest sense, it means convincing more customers to choose your products, services and goods over other firms.

In order to achieve this, businesses need to establish a bond with the society in which it thrives. The relationship and emotional connect that businesses build with their customers is what attracts customers to them and keeps them there (like loyalty to a brand). This process doesn’t have be intuition dependent. This process can be data driven and strategized through Customer Relationship Management (CRM). One marketing technology that helps you ace CRM is predictive analytics.

Predictive analytics is a recent field of market technology that holds immense developmental potential. Predictive analytics as the name suggests predicts consumer behavior, after analysis of patterns and data. These predictive models can help understand how to interact with consumers through strategies and policy.

We can find the simplest example of this when we are purchasing something online and see a list of products tagged ‘products you would like’ or ‘customer who bought this, also bought’. Through your online shopping data, businesses offer better services and products.

But what are some other uses of predictive analytics for CRM. We have listed down some right here:
1.Understanding target audience to build persona
One of the easiest ways firms and organizations track user behaviors is by using cookies. This helps in finding out what approaches and strategies work and what don’t. This can further help in building a customer persona (general characteristics of your target audience). Based on this information, targeted marketing strategies can be deployed to generate sales and finally, more profit.

2.Segmentation of market
Categorizing consumers into different groups based on their behavior patterns is another aspect predictive analysis can assist with. For instance, peek hours of activity on social media can be recorded, which can guide marketer’s decision to strategies for online marketing. User Interface and online design can be modified to hold attention and promote clicks.

In the same way, customized marketing is also possible for consumers. Relevant customization shows that businesses care about them, which in turn can improve brand loyalty and conversions.

3.Cross-selling
Based on a customers’ purchase history, products and services can be cross-sold to customers. For instance, suggesting a customer to buy pens, when the customer has added a notebook to the cart. This information enhances the CRM strategies of the business.

Predictive analytics changes the process of interacting with consumers into a learning experience. It directly informs a company’s knowledge of the consumers desires, which can lead to stronger bonds with consumers.

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