How Customer Analytics has evolved…


Simplified Analytics: How Customer Analytics has evolved…

How Customer Analytics has evolved…

Customer analytics has been
one of hottest buzzwords for years. Few years back it was only marketing
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.
In the 90’s came web
analytics, it was more popular for page hits, time on sessions, use of cookies for
visitors and then using that for customer analytics.
By the late 2000s,
Facebook, Twitter and all the other social 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.
With the digital age
things have changed drastically. Customer is superman 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 churn and retention but 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 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.
From the technology
perspective, the biggest change is the introduction of big data platforms which
can do the analytics very fast on all the data organization has, instead of
sampling and segmentation.
Then came Cloud based platforms,
which can scale up and down as per the need of analysis, so companies didn’t
have to invest upfront on infrastructure.
Predictive models of
customer churn, Retention, Cross-Sell do exist today as well, but they run
against 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
the center stage now which gives your customer the right message, on the right
platform, using the right channel, at the right time
1501424573_334_how-customer-analytics-has-evolved How Customer Analytics has evolved...
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.




see-this-simple-introduction-to-natural-language-processing-nlp How Customer Analytics has evolved...



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