Decoding Data for Customer Intelligence and Business Gains

by August 14, 2020

Customer Analytics

Customer data is a goldmine for enterprises and marketers to channelise massive gains

The new-age customers intentionally and unintentionally share massive information about themselves. Credit to social media, they openly voice their emotions like anger, happiness or irritation on the social media channels over a product or a service experience. Customer data can provide enterprise leaders with powerful insights about individual customers their age, their income patterns, and so on to offer customised products and services. Ultimately it is only through the use of analytics tools that decision-makers can decode customer data to convert data into intelligence. The popularity of data has surged, which explains why 81 per cent of enterprises are using analytics to improve their understanding of customers, according to a KPMG study.


Gains from Customer Intelligence

The integration of Analytics with Customer Data lets the marketers know what is happening with customers instead of assuming with what is perceived. Machine learning and analytics can lead to profound insights, helping customers to analyse the right data, breaking down silos and understand how the information fits into the business.

Customers Insights covers valid pointers like-

• The percentage of discount offered, tailored to each customer with the data collected from them intentionally and unintentionally.

• The IoT devices, the WiFi and the store’s wireless internet collect massive amounts of data to let the store and marketers know about a customer’s online and offline location and physical activity.

• The responsibility of data governors about data collected should be used/ shared with third-party vendors.


Breaking Data Silos

Customers are not one-dimensional. They deploy multiple channels to showcase their behaviours and their attributes, which are constantly shifting. Data silos are huge, and breaking these data points assist marketers from having a consistent view assuring the best practices of Predictive Analytics across the customer lifecycle giving insights into-

• Customer Acquisition

• Customer Retention

• Customer Engagement

• Customer Recovery

• Customer On-Boarding

It’s still early days for many companies in their use of customer analytics and there are tremendous opportunities for moving from customer data to intelligent insights to tangible actions. The marketers of today and the future will only use customer data and analytics to improve their understanding of customers, to create tailored offers to prospective customers.

Going forward, customer data will be dynamic and it will go forward channelising poor data quality, poor integration of customer data between functions and channels, and a lack of resources and tools to let the data speak for itself weaving intelligent and successful data stories.