According to research conducted by Gartner,Customer Experience (CX) is the top priority for companies who have invested in analytics software. The goal for any company is to have an ‘always on’ view of how their operational performance that impacts on the way that customersexperience their brand across all touch-points. This is now possible by using untapped machine data in combination with more traditional measures of customer satisfaction such as Net Promoter Score (NPS).

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Monitoring Analytics To Create Customer
Value And Experience
Hello friends! Welcome to our 5th part of Customer Experience Series study. Coming back from
our previous article based on measuring customer experience, today we are going to learn about
Analytics and how it is leveraged to give a phenomenal customer experience.
According to research conducted by Gartner,Customer Experience (CX) is the top priority for
companies who have invested in analytics software. The goal for any company is to have an
‘always on’ view of how their operational performance that impacts on the way that
customersexperience their brand across all touch-points. This is now possible by using untapped
machine data in combination with more traditional measures of customer satisfaction such as Net
Promoter Score (NPS).
According to analyst firm IDC, big data and business analytics applications and services will
increase from $122 billion in 2015 to more than $187 billion in 2019.Gartner predicts that by
2018 search based and visual based data discovery will converge in a single form of next-
generation data discovery that will include self-service data preparation and natural language
generation.
In the age of digital, aMcKinsey report identifies six models for leveraging digital disruption for
competitive advantage. From the point of enhancing customer experience, we will go through
three of the analytical models and its data implications when monitored and managed.
 Hyper-scale, Real Time Matching

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What: This is about creating a digital marketplace that matches up the participants in real time so
that they can conduct a transaction.
Example: Ride-share services like Ola and Uber are matching people looking for a ride with
drivers who are available to give them a ridesaving customers’ time and money or; matching any
type of buyers and sellers.
Data Management Implications: The most effective marketplaces are usually those that are the
biggest, with strong competition and many choices available. The data size for a given
transaction may not be very big but the overall transaction volume is likely to be enormous. It
will be important to support real time data management of identities and matching criteria of the
parties usedthat it is most critical to business success.
 Personalization With Predictive Analysis
What: This is about delivering a highly personalized experience regardless of the type of product
or service being used. This is an area where we see a large number of organizations investing to
separate themselves from their competitors.
Example: A shopping portal that knows and understands all of your preferences, proactively
make suggestions and take actions based on their previous experience with you.Online fashion
retail marketplace, Myntra is using customer data to curate lines based on current fashion trends
and make a one-of-a-kind personalized store experience.
Data Management Implications: For this type of use case you are most likely using a great deal
of data from widely disparate data sources. The key to success here is to be able to relate all of
this data quickly to the right person, customer, etc. so that it can be used in human real time to
provide a better customer experience. This could involve knowing the customer’s needs,
preferences, history, and past experiences with this company, for example.
 Data-Driven Discovery
What: The use case is about using analytics, in all forms, to gather new insights and to ask new
questions that were never technically and/or economically feasible to ask previously. By
combining many sources of data, data scientist will have the environment they need to
experiment, fail fast, and iterate until they find a new and useful insight that can be used to
deliver better outcomes and services. Increasingly, we are seeing customers using machine
learning to deliver the results.
Example: Training systems to deliver algorithms that see patterns in user behavior that would
suggest a buying preference in the future.”Algorithms are the base for everything online —
shopping, shipping, packaging, payments, price points etc.,” says SandeepAggarwal, founder,
Shopclues.
Data Management Implications: This typically involves many large data sets to create a useful
algorithm. “Good enough” data may be sufficient in the early stages to test a hypothesis. But,

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when it is time to put this into production, trustworthy data for critical data items, will be a
requirement.
Using Analytics To Create Customer Value
Robust analytics and insights have given marketing teams insight into how customers interact
with brands, highlighting product preferences, purchase sequences, and so forth. And they reveal
how top of the funnel marketing activities—such as an online display ad or TV commercial—tie
in to in-store sales or an online website conversion. Measurement and analytics allow brand
marketing and performance marketing to complement each other for the customers’ benefit.
“The most successful companies use analytics to understand how well they generate demand and
the quality of the customer experience they provide,” says JoergNiessing, a marketing professor
at INSEAD.
A Harvard Business Review finds:
 In some cases, companies that have captured the full customer journey by integrating
multiple sources of data are generating up to 8.5X higher shareholder value.
 While any company can use data to optimize costs or sell more products, real
differentiation comes from understanding new information about customers and orienting
that business around those insights.
 In 2015, 60% of companies said that organizational silos were the greatest barrier to
improving customer experience. Successful companies are finding ways to organize
around customer needs, creating nimble teams with the customer experience at the center.
Conclusion
In the last couple of years, etailers are focusing on how to come out of deep discounting and
show profits. CX has become the most favored metric for all organizations.They are finding
ways to identify and supply their organization with useful insights from data to improve
customer experience.