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How Customer Analytics Has Evolved

A brief history of data analytics

29Aug

Customer analytics has been one of the most enduring buzzwords. A few years back marketing departments' had limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics, but it was restrictive in conducting segmentation of customers who are likely to buy your products or services.

In the 90’s came web analytics. It focused on page hits, time on sessions, use of cookies for visitors, which it then used for customer analytics.

By the late 2000s, Facebook, Twitter, and all the other social channels had changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.

The customer is Superman now. Their mobile interactions have increased substantially and they leave a digital footprint everywhere they go. They are more informed, more connected, always on, and looking for exceptionally simple and easy experience.

This tsunami of data has changed customer analytics forever.

Today, customer analytics is not just something marketing use to look at churn and retention - more focus is going on how to improve the customer experience and is done by every department of the organization.

A lot of companies had problems integrating the large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360-degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.

The introduction of big data platforms has enabled organizations to conduct analytics very fast on all the data they collect, while Cloud based platforms can scale up and down as per the need of analysis, so companies don't have to invest upfront in infrastructure. Predictive models of customer churn, Retention, and Cross-Sell do still exist today as well, but they run with more data than ever before.

Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore, but what actions you need to take is becoming more critical.

There are various ways customer analytics is carried out:

  • Acquiring all the customer data
  • Understanding the customer journey
  • Applying big data concepts to customer relationships
  • Finding high propensity prospects
  • Upselling by identifying related products and interests
  • Generating customer loyalty by discovering response patterns
  • Predicting customer lifetime value (CLV)
  • Identifying dissatisfied customers & churn patterns
  • Applying predictive analytics
  • Implementing continuous improvement

Hyper-personalization is center stage now, which gives your customer the right message, on the right platform, using the right channel, at the right time

Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect.

Tomorrow there may not be just plain simple customer sentiment analytics based on feedbacks or surveys or social media, but with help of cognitive, it may be what customer’s facial expressions show in real time.

There’s no doubt that customer analytics is absolutely essential for brand survival.

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