Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketingdepartment’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 socialchannels 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 agethings have changed drastically. Customer is supermannow. 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 churnand retention but more focus is going on how to improve the customer experienceand 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 360degree 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 dataplatforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.
Then came Cloudbased platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.
Predictivemodels of customer churn, Retention, Cross-Selldo 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-personalizationis the 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 computingand Artificial Intelligenceusing IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learningneural network algorithms provide a game changing aspect.
Tomorrow there may not be just plain simple customer sentiment analyticsbased 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.
