Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconWhat is Customer Analytics and Why it matters?

What is Customer Analytics and Why it matters?

Last updated:
11th Jul, 2018
Read Time
7 Mins
share image icon
In this article
Chevron in toc
View All
What is Customer Analytics and Why it matters?

Today, customers are more hooked up to the Internet than ever. They are always active on social media, online shopping portals, online platforms with informative content, and so on. As a result, consumers are now always updated about the latest trends in the market, be it regarding technology, products, and services, fashion, or education – they know it all. This has made it all the more important for business firms and organizations to gather an all-comprehensive knowledge about their customer base, their likes and dislikes, and their tastes and preferences.
In the cut-throat competitive business world, if you fail to ‘learn’ about your customers’ behavior and preference, you might fall behind in the race. If customers see that you’re failing to understand their ‘pain points,’ they know they have other options to look up to. Thus, to retain a loyal customer base, you need to invest in Customer Analytics.

Explore our Popular Data Science Courses

What is Customer Analytics?

Customer Analytics essentially refers to the techniques that are employed by business organizations to extract extensive information about their customer base while also keeping a close watch on customer behavior. It allows companies to identify potential customers and devise strategies to attract new customers as well as retain the old ones.
Customer analytics comprises the backbone of a business’ marketing strategies and integrates advanced techniques like data visualization, predictive modeling, information management and segmentation.
Converting Business Problems to Data Science Problems

Check out our data science training to upskill yourself

Why Customer Analytics Matters?

There’s an impeccable and straightforward logic behind investing in customer analytics – the better you understand and know your customers (their buying habits, their preferred choices, and the offers that they respond to), the more accurately will you be able to draw predictions regarding their future buying behavior patterns.
According to a McKinsey & Company survey, businesses that heavily invest in customer analytics are more likely to outperform their competitors, be it on the grounds of sales, or revenue, or ROI.  
Extensive use of customer analytics drives corporate performance heavily.
Every single interaction with your customers is sure to leave a trail of data (information) which when combined helps paint a clearer picture of what your customers expect from you.  If utilized wisely, customer analytics can be one of the greatest strengths of your business – it will allow you to transform data (social media posts, comments, and mentions; customer interactions with your channels and media pages; customer behavior to your products/services, etc.) into resourceful insights that can scale up your profits considerably.
Customer analytics can help you –

  • Reduce attrition rates significantly be accurately forecasting about the time periods when customers are most likely to leave, thereby allowing you to chalk out proactive plans and campaigns to retain them.
  • Boost the response rates, customer loyalty, and your ROI by allowing you to target the right audience with attractive and befitting offers.
  • Reduce campaign costs by streamlining campaigns to target only the customer base that is most likely to respond.
  • Optimize the overall customer experience by creating personalized selling and marketing strategies for the different customer segments.
  • Identify the current trends in Big Data to boost sales.

Top Essential Data Science Skills to Learn

Converting Business Problems to Data Science Problems

Factors That Ensure You Reap Optimal Benefits From Customer Analytics

McKinsey & Company maintains that every company should strengthen three aspects to harness the potential of customer analytics fully. They are:

  1. Do Not Get Stuck Up On Mediocrity. Aim For Excellence.

Over 85% businesses state that only when they start using customer analytics (IT and predictive analytics) extensively that they begin to see an impressive and notable boost in value contribution from customer analytics, as compared to 20% and 30% of companies where the utilization of customer analytics is relatively low and moderate respectively. These figures establish the fact that businesses can only reap the benefit of customer analytics when their operations achieve a level of excellence.
Excellence in IT, analytics, and execution drives value contribution of customer analytics.
Thus, it is essential for an organization’s managerial wing to understand what customer analytics tools they need to invest in to bridge the gap between their current customer analytics infrastructure and state-of-the-art customer analytics frameworks that are being used by the magnates in the industry.

Read our popular Data Science Articles

  1. Encourage Fact-Driven Decision Making

Although IT and predictive analytics are pivotal to customer analytics, these alone aren’t enough to pull customer analytics to the optimum level. The key to success lies in taking a holistic approach to customer analytics. The guidance of a leader who stresses on fact-based decision making is also crucial to customer analytics. Insights offered by customer analytics can only be translated into real and actionable decisions by an organizational framework having a logical and rational culture at its core. For instance, instead of focusing on the speed at which the insights are being put into action, you should first focus on the smooth integration of all the tenets required for customer analytics including IT, analytics, and the organizational framework.
5 Reasons Why Marketers should Invest in Developing Data Skills

upGrad’s Exclusive Data Science Webinar for you –

Transformation & Opportunities in Analytics & Insights

  1. Encourage The Involvement Of Senior Management In Customer Analytics

According to McKinsey & Company’s report, businesses where the senior management isn’t involved with customer analytics of the organization display as low as 28% value contribution through customer analytics. Contrary to this,  almost 69% of the companies where the senior management is actively involved in customer analytics report of having attained great value from customer analytics.
Given the fact that all the vital decisions concerning a company’s sales and marketing strategies, product/service improvement strategies, campaigns, and so on, are taken by the top management wing, it is only wise to get them involved in reviewing the customers’ side of things. This way they’ll be able to understand better what the customers are looking for and make decisions accordingly.

Customer Analytics Tools

Customer analytics tools are applications that have been designed to help businesses gain useful insights into customer behavior and customize marketing campaigns and strategies to suit the specific needs and demands of the various customer segments. These tools can perform a host of tasks ranging from data sourcing to data analysis and visualization.
Here are five top-rated customer analytics tools:

  • Adobe Analytics
  • Google Analytics 360
  • IBM Watson Customer Experience Analytics
  • SAP Hybris Marketing Cloud
  • SAS Customer Intelligence 360

Customer analytics tools from IBM, SAS, Adobe, Google and SAP
Data Science Summarized In One Picture

So, now you have the complete guide to leveraging customer analytics to scale up your business to new heights of success!


Abhinav Rai

Blog Author
Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.

Frequently Asked Questions (FAQs)

1What is Customer Analytics?

Customer Analytics also known as Customer Data Analytics is the process of acquiring and analysing consumer data and behaviour which the companies undertake to identify, attract and retain its customers. Customer analytics helps a company know more and understand better about:

1. Customers needs, preferences
2. Customers spending habits
3. Payment method preferences
4. Product/service utilization patterns of a customer
5. Reveals customer problems and complaints

2Why Customer Analytics matters?

In today’s marketplace, the customer is the king and the better the understanding of customer behaviour and preferences, the more accurate is the marketing and sales strategy. Consumers are always updated about the latest market trends, may it be fashion, technology, education, products and services, etc. They get all the information from social media, online shopping portals, etc.

Therefore, investing in customer analytics helps businesses improve customer acquisition, retention and engagement. This will not only help you to know and understand your customers’ buying habits, their preferences, etc but also help anticipating consumer behaviour by using predictive analytics. In short Customer Analytics helps companies gain a 360 degree view of their consumers for creating the best strategies for

1. Customer acquisition
2. Customer retention
3. Proactive engagement with customers

Companies use this Customer Analytics in their sales, marketing and product development.

3How does Customer Analytics work?

Basically, it’s a three step process:

1. Data collection : Data collection is the process of aggregating data from all the customer interaction channels, including website analytics, customer service calls, social media and customer feedback.
2. Data Validation : A proper data validation is necessary for all the data you have collected to ensure its accuracy.
3. Data analysis : After data collection and validation, last step is to analyse your data. For this you must have your customer personas in place. With data analysis you can create a predictive model for your business. This will help you identify customers choices that directly impact your business. Data analysis is mostly done through AI and Machine learning tools like Google Cloud ML Engine, Big ML, TensorFlow, Knime, PyTorch, IBM Watson Studio, Apache Mahout, and many more.

Explore Free Courses

Suggested Blogs

Most Common PySpark Interview Questions & Answers [For Freshers & Experienced]
Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s
Read More

by Rohit Sharma

05 Mar 2024

Data Science for Beginners: A Comprehensive Guide
Data science is an important part of many industries today. Having worked as a data scientist for several years, I have witnessed the massive amounts
Read More

by Harish K

28 Feb 2024

6 Best Data Science Institutes in 2024 (Detailed Guide)
Data science training is one of the most hyped skills in today’s world. Based on my experience as a data scientist, it’s evident that we are in
Read More

by Harish K

28 Feb 2024

Data Science Course Fees: The Roadmap to Your Analytics Career
A data science course syllabus covers several basic and advanced concepts of statistics, data analytics, machine learning, and programming languages.
Read More

by Harish K

28 Feb 2024

Inheritance in Python | Python Inheritance [With Example]
Python is one of the most popular programming languages. Despite a transition full of ups and downs from the Python 2 version to Python 3, the Object-
Read More

by Rohan Vats

27 Feb 2024

Data Mining Architecture: Components, Types & Techniques
Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a
Read More

by Rohit Sharma

27 Feb 2024

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About
What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes
Read More

by Rohit Sharma

19 Feb 2024

Sorting in Data Structure: Categories & Types [With Examples]
The arrangement of data in a preferred order is called sorting in the data structure. By sorting data, it is easier to search through it quickly and e
Read More

by Rohit Sharma

19 Feb 2024

Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics
Summary: In this article, you will learn, Difference between Data Science and Data Analytics Job roles Skills Career perspectives Which one is right
Read More

by Rohit Sharma

19 Feb 2024

Want to build a career in Data Science?Download Career Growth Report
footer sticky close icon