Imagine owning a retail store that has hundreds of customers and not being able to tell which products your customers prefer or if your customers pay mostly via credit or cash, or not being able to get feedback from your customers. What a disaster, right? The lack of customer data and customer analytics solutions can have equally disastrous effects if you are in the banking, telecoms or marketing sectors.
Keeping records started way before the use of the computer became popular. People would have manual ways of keeping records about the customers albeit in a more simplistic way. Now through the computer, one can conduct a more detailed analysis of thousands of customers’ likes and dislikes, their payment preferences, their age, sex and gender among other beneficial information.
Using the collected information, companies can identify customer trends and patterns through customer analytics. Business owners are now able to paint a picture of customer journeys and behaviours, which in turn leads to strategic decision making, better communication and most importantly, the meeting of customer needs.
A study by McKinsey Global Institute found that data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result -a testament of the importance of data and in this case, customer analytics.
Before we delve into the three types of customer analytics, let’s look at the types of customer analytics data.
Types of customer analytics data
Understanding the type of customer data to collect is the prerequisite of analysing it. With the help of our data experts and our customer analytics solutions, you can easily learn how to capture relevant customer data for your business.
Below is an overview of the different types of customer data:
This is information about your customers. It consists of the user profile with information such as age, income, gender, date-of-birth, telephone, email address, etc.
This will give more insight into the behaviour pattern of the customer. It includes information like previous purchases, subscription details, customer loyalty programs, website visits, product views, product abandonments, product returns, etc.
This shows the level of interaction a customer has with a brand on marketing databases like the number of likes, bounce rate, email forwards, query details, feedback, etc.
This shows the preferences, opinions, and perception a customer has for your brand. You can collect this type of data via surveys, polls, feedback, and reviews. This data is useful in descriptive analytics.
Types of Customer Analytics
Once the above data is collected, the next step is to analyse it. Analysed data will tell you more about your business by informing you how to bring positive change to customer service.
Customer analytics is using historical and current customer data to explain the context, make predictions and recommendations for your business. it’s the use of customer analytics solutions to analyse customer data and make sense of the present and future.
Below are three ways customer data is analysed:
Descriptive Analytics (History)
It is advised that one should not forget where they came from because history helps to give more insight into the present. This is exactly what descriptive analytics does. It describes what happened in the past thus giving context to the present state. It also helps in drawing comparisons like month-on-month sales growth. Experts use raw data like income, age, gender, total inventory stock among others to describe a past phenomenon. This helps marketers or customer service experts get historical insight.
Through descriptive data experts are able to pick up trends, for example, a surge in ticket sales at a time in the past, because of a product launch or promotion. Another example is when a bank has historical data showing an increase in the number of people opening bank accounts right after the launch of a new service.
Predictive Analytics (Projections)
Catching a glimpse of the future seems neat and this is what predictive statistics help businesses do. It gives experts the ability to know what could happen in the future.
It works by feeding historical data into a machine-learning tool, which comes up with patterns and trends that are used to predict the future. Through big data and algorithms, machines learn what has happened in the past combine this information with current data to forecast a certain phenomenon for your business.
For example, if there is historical data showing an increase in the number of children being admitted to a children’s hospital during the holiday period, then this data can be used to predict what will happen during the next holiday. Similarly, in the case of a bank, predictive analytics gives information on what is likely to happen if a loan is given out to a customer.
Prescriptive Analytics (Recommendations)
Prescriptive analytics goes beyond predicting what the future holds by advising on the outcome. It shows what one should do after understanding the future. It uses a variety of statistical methods to come up with possible decisions and their effects on the business.
For example, if the children’s hospital mentioned above expects more patients then the analytics tool will recommend that the hospital should prepare by increasing the number of staff or beds before the next holiday. And in the case of a bank, prescriptive analytics may give recommendations on the types of loans customers prefer. Also, it’s very useful in the manufacturing sector as it advises production, equipment management among other aspects.
So there you have it, the type of customer data and the different types of analytics that can be applied to provide meaningful information on how you can serve your customers better.
At Sunesis Consulting, we understand the importance of customer analytics for your business. That’s why we use a set of Google and Microsoft platforms to help create trends and patterns about your customer behaviour. We believe there’s a need to measure the whole customer experience to meet customer needs hence, our customer analytics solutions. We would be happy to work with you. Contact us here for more.
Upcoming Training March 18th to 20th March
In this training, delegates will be taught how to analyse and visualize data using a tool called Microsoft Power BI. This is a business analytics solution that lets you visualize your data and share insights across your organization or embed them in your app or website.
It also allows you to connect to hundreds of data sources and bring your data to life with live dashboards and reports.
Key concepts you will learn include:
– Connecting & Shaping Data
– Data Modeling
– DAX functions
– Visualizing Data
Click the link below for more details on what Microsoft Power BI can help you do.
To book your spot, reach us on: 0736568899, 0716568899 /firstname.lastname@example.org /https://sunesiskenya.com/contact/