https://www.datatobiz.com/blog/customer-analytics/ Corporations across the globe are trying their best to look at the business from a customer-centric view. This exercise opens for them a window to peek into the interests of their clientele and create policies accordingly. But in today’s volatile business environment judgments built simply from past experience or intuition is increasingly unreliable. Customers today are more connected and empowered. Access to the internet all the time has allowed them to become more specific about their needs. They are aware of everything that is trending in the market. In such a scenario it becomes important for a business owner to predict a customer’s response with respect to his organization. The deeper businesses understand their customers’ preferences and lifestyle habits, the more they are able to attract potential buyers. However, it is not as simple as it seems. It is a big challenge for organizations to understand customer feedback, behavior and needs, well enough so as to make data-driven decisions about what customers are likely to respond to or what they are likely to purchase. Customer analytics or customer data analytics is that significant insight gained with the help of data science, that allows businesses to use customer data in order to make key business decisions. The information obtained from the process is used for designing effective marketing campaigns, site selection, customer relationship management, and secure decisions for the future. Insights pertaining to the customer’s feedbacks and responses drive the organizations to directions that help them outperform their competitors. Strategizing everything beginning from their production to their supply far before the demand arises, helps them improve their key performing metrics.

1. Customer Analytics
WIN YOUR CUSTOMERS AND INCREASE REVENUE

2. Why Do We Need Customer
Analysis?

3. Around 82% of businesses said that retaining customers is cheaper than acquiring new
ones. Moreover, 65% of sales come from existing customers. Having repeat customers is
good for the business. But, for this to happen, providing the customers with what they
want is important.
You have to understand their product preferences, their expectations from your business
and find ways to prevent them from going to your competitors. Customer analysis helps
answer these questions and increase the retention rate. The more you know your
customers, the better you can meet their expectations.
Increase Customer Retention Rate

4. Geographic segmentation: based on location
Demographic segmentation: based on age, gender, occupation, income, etc.
Behavioral segmentation: based on purchase history, search history, spending
habits, etc.
Technographic segmentation: based on the technology they prefer for marketing-
mobile, web, traditional, etc.
Psychographic segmentation: personal values, personality traits, etc.
Segmenting customers and grouping them into different categories helps in targeted
campaigning. There are different ways to segment customers-
Segmenting customers into neat categories is possible only when you know enough
about them and their preferences. Consumer data points are vital metrics that provide
insights into customer preferences and behavior.
The data points give enterprises a picture of the products preferred by customers, the
frequency of purchase/ usage, and most used features/ functionalities, and so on.
Customer analysis gives you the insights needed to know your customers.
Better Customer Segmentation

5. Once you segment customers and prospective leads, you can plan a comprehensive
marketing campaign for each segment. For example, sending emails to a customer
who is old-fashioned and doesn’t check emails every day is not an effective marketing
strategy.
From choosing the marketing channel to determining the type of approach, customer
analysis can help your sales and marketing teams fine-tune promotional tactics to
increase market reach, sales, and returns. It also helps in understanding the market
trends in relation to customer preferences.
Develop Personalized Marketing
Strategies

6. Limited Decision Making: customers deciding based on limited data available,
mostly comparing two or three products
Extended Decision Making: customers spend a lot of time doing research and
knowing more about the product before they decide if they want to buy it or not
Habitual Decision Making: things customers buy more like a habit or a routine part
of their lives, such as groceries, accessories, etc.
Variety-Seeking Decision Making: customers who like to try similar products by
different brands, either out of curiosity or for fun
Stages in Customer Buyer Journey: it is the steps a customer takes to buy a product.
It is broadly classified into- Unaware stage, problem aware stage, and solution
aware stage
Customer behavior is hard to predict without using historical and real-time data.
Customers decide whether or not to buy a product based on several factors.
Customer behavior is broadly classified into the following-
Accurately Predict Customer Behavior

7. Limited Decision Making: customers deciding based on limited data available,
mostly comparing two or three products
Extended Decision Making: customers spend a lot of time doing research and
knowing more about the product before they decide if they want to buy it or not
Habitual Decision Making: things customers buy more like a habit or a routine part
of their lives, such as groceries, accessories, etc.
Variety-Seeking Decision Making: customers who like to try similar products by
different brands, either out of curiosity or for fun
Stages in Customer Buyer Journey: it is the steps a customer takes to buy a product.
It is broadly classified into- Unaware stage, problem aware stage, and solution
aware stage
Customer behavior is hard to predict without using historical and real-time data.
Customers decide whether or not to buy a product based on several factors.
Customer behavior is broadly classified into the following-
Accurately Predict Customer Behavior

8. What are the Types of
Customer Data?

9. This is the data that’s used to identify a customer. It is the primary information
collected when the brand and customer have their first interaction. Details such as
name, gender, location, contact address, email id, phone number, social media profiles,
etc., come under this type.
Basic data is used to send promotional material, personalize emails and messages,
and cross-check if you are the same customer who tagged them on social media. This
data is collected through surveys, subscriptions, and feedback from first-time
customers. For eCommerce businesses, basic data is collected when users create
customer accounts on the website to place an order.
Customer identity data is normally stored in the CRM systems and is occasionally
updated to ensure that the promotional messages are reaching the customers.
Customer Identity Data or Basic Data

10. Descriptive data describes the customer and gives more insights into who they are
beyond their identification details. Information such as their occupation, age, marital
status, education, lifestyle details, etc., is termed descriptive data. These details assist in
developing the customer profile.
For example, the purchase history of a student is going to be different than that of a
homemaker or an elderly person. Their preferred channels of communication will also not
be the same.
Descriptive data cannot be collected as easily as basic data. You’ll need to conduct in-
depth surveys, interviews, etc. so that customers voluntarily share the information with
you.
Descriptive data helps in accurately segmenting customers and predicting their purchase
habits. Let’s take another example of a chef and a hairstylist. Their primary product
requirements are different, and hence their purchases will also be different.
Customer Descriptive Data

11. Behavioral data is useful in understanding and assessing the purchase patterns of
customers. This is complicated data and is collected through techniques like website
scraping, cookies, and more. Customer’s wish list, items in the cart, cart abandonment
frequency, repeat purchases, and searches, etc., help get insights into customer’s
purchase behavior.
When you know how many times a customer has opened the newsletter and clicked on
the CTA button, used the discount code, or made a purchase will give you a better idea
about what makes them buy a product.
This data helps in optimizing the customer’s journey with the business and maintaining
communication through their preferred channel. Online tracking tools are used to
collect this information. Visual analytics is used to process the datasets to visually
represent large datasets in various formats such as graphs, charts, maps, etc.
Customer Behavioral Data

12. Qualitative data is complex, ever-evolving, and hard to collect. This data will show you
exactly why a customer picked a certain product or changed their mind and went to
your competitor instead.
What motivates a customer to buy something? We are talking about their opinions, the
emotional factors that determine the final act of purchase, and other such information.
Why did a customer choose the yellow top over the pink one? Why did a regular
customer not buy anything in that month? How did the customer react to your latest
business policy or a political statement?
The answers to questions like these come under attitudinal data and help have a
complete picture of the customer. This data will change over time as customers’
opinions also change.
Qualitative or Attitudinal Data

13. Big Win Of Customer Analytics

14. The e-commerce industry is the one that has been exploring customer analytics to its full
potential. Customer data is being utilized at every step of business decision making and
that has surely been fruitful. For instance, the data like what a customer has purchased,
watched, or stored on its wish list on an online selling portal offers the analytics to derive
various conclusions. Some may include the demand pattern of a certain product,
interests of the customer, product reviews, and much more. Based on these assumptions
the machine learning algorithms and anticipatory models are able to offer
recommendations to the customers. This not only enhances the experience of the user
but also improves the chances of retaining customers. Some of the most successful e-
commerce businesses that use customer analytics include Amazon, Flipkart, and Netflix,
etc.
E-Commerce

15. When we talk about applying customer analytics to the health care system, what is the
first thing that pops up in mind? Who are the customers here? Anyone who is seeking
medical treatment can be considered as a customer here. To understand the role of
analytics in healthcare let us take an example. A lot of patients are undergoing a
diagnosis of cancer every day some tests are positive and some negative. These results
can be used for creating a prediction algorithm for checking the early onset of the
disease. Every customer analytics approach that is used in the health industry is for the
benefit of humankind.
Healthcare

16. There is no market without an efficient logistics industry. If a customer has ordered a
product it must be delivered on time and in good quality. The logistics industry uses
customer data to ensure that their vehicles reach out in time and offer good services.
The customer reviews are utilized to understand the problems that arise during the
delivery of products and find means to resolve the same.
Logistics Industry

17. Customer analytics incorporated into the banks’ working has made handling finances
easier for the client as well as the bank itself. The customer data analytics allows the
bank to predict whether a person has the credibility to repay the loan based on its
credit score and other parameters. It also allows creating effective marketing and sales
strategies for their financial services. Rather than trying to sell any product the banks
can now create campaigns as per their customer insight and attract potential clients.
Risk management is one of the other advantages offered by customer analytics which
allows the banks to predict probable fraudsters.
Banking Institutions

18. DID YOU FIND IT HELPFUL?
https://www.datatobiz.com/blog/customer-analytics/
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