Customer analytics has been one of hottest buzzwords for years. Few years back it was only
department’s monopoly carried out with 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 restricted to conducting segmentation of customers who are likely to buy your products or services.
By the late 2000s, Facebook, Twitter and all the other
channels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.
things have changed drastically. Customer is
now. Their mobile interactions have increased substantially and they leave 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 the customer analytics forever.
Today customer analytics is not only restricted to marketing for
and retention but more focus is going on how to improve the
and is done by every department of the organization.
A lot of companies had problems integrating 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
degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.
From the technology perspective, the biggest change is the introduction of
platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.
based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.
models of customer churn, Retention,
do exist today as well, but they run against more data than ever before.
Even analytics has further evolved from descriptive to predictive to
. 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
is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time
Tomorrow there may not be just plain simple customer
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