The global data monetization market size was valued at USD 1.30 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 24.1% from 2020 to 2027.
Unlike the most valuable resource of the 20th century — oil, — data is easily accessible. You don’t have to drill an oil well to get it. Literally, every online business has access to customer data. Many don’t use it properly, creating just another useless data swamp. However, using the right tools, it can always be turned into a crystal clear lake.
What is customer data?
Customer data includes various types of information about your customers. It could be divided into three main types:
Identity data. Anything that identifies a customer: name, email, phone number, locations, etc.
Descriptive data. Additional information beyond the identity that helps to understand a person better: demographic (age, gender, income, ethnicity) and psychographic data (lifestyle, personality, culture, attitude).
Behavioral data. How a person interacts with the digital world and your business particularly: browsing, purchase, and decision history.
Why customer data is important?
Customer data allows businesses to get much more information about their customers than ever before. When you know your customers well enough, you can:
- optimize marketing and target the right audience with the right products,
- understand what your customers want,
- personalize the customer experience and drastically improve the quality of service,
- predict customers’ behavior and anticipate their wishes.
How to gather customer data?
Basic customer data is relatively easy to get on a technical side, especially if you sell non-digital products — you would need at least a customer’s name and address to ship a product. Use additional fields in the registration form to get more identity data. But it’s more challenging now to explain to the user why do you need this data for (a.k.a GDPR)
To get descriptive data about your customers, you need to be creative. There are multiple ways to get it, from surveys to using social logins. But you need to incentivize customers to do it somehow: with special offers, discounts, direct payments, or just thank you — whatever works for your audience. Behavior data is trickier. Google Analytics may be enough for a start, but the extra effort and technical expertise are needed if you want to get more advanced behavior data.
There are solutions that cover all of these matters - reach me out I know a good one. You will get identity, descriptive, and behavior data with a single toolset. It also has a built-in incentivization mechanism to motivate your customers to share data with you in GDPR compliant way!
How to turn customer data into profits
Having data is the first step to a data-driven business. Now you can see and manually analyze the data of any customer. Is it helpful? Yes, to some extent. If you have a dozen customers, you could approach each of them personally — the human brain’s capacity is enough to store and analyze such amounts of data.
This is how small offline businesses work with loyal customers: they know them by sight and remember what their favorite coffee or haircut style is. However, it doesn’t give you much apart from good relations with a few loyal customers.
To get more out from data, you need to process it and apply in a decision making. The goal is to have knowledge about the whole audience of the business, not individual customers. This is when its real power unveils!
When you turn your data into knowledge, you can further turn it into profits — simply apply the knowledge to your business. For example, by reviewing your business strategy on a high level or by doing data-driven automated marketing with a personal touch.
What is data segmentation, and why you need it?
So, how process data properly and make knowledge out from it? Segment it! Segmentation is a process of categorizing data by specific parameters.
Segmentation can turn basic identity data into insights like:
— Where your best customers live?
— Which regions bring you more revenue?
— What products do customers from different places prefer?
That’s not much, but it’s already something. If you add up some behavioral data to it, you could get more insights. For example:
— When it makes sense to offer a custom discount to help a user to make a decision?
— How different pages affect customers’ chances to buy?
— What behavioral patterns identify your best customers?
Segmentation can be done in many ways. Even with simple Excel spreadsheets, you can manually group customers by specific parameters and see if there is any difference between them. Other tools can do all the job automatically.
But it is preferable if it is done by a machine learning engine that applies all possible combinations to categorize data and chooses statistically significant ones.
Those findings are then turned into actions with the predicted outcome, e.g., “offer product X to customers from segment A as they are highly likely to purchase it within next 30 days” or “target women 25-35 years old from Z region who have visited location Y within last year, as they usually bring 175% more revenue than your average customers.”
Although every online business sits on a gold mine of data, it’s quite understandable why many don’t use its full potential — gathering and processing data may sound easy, but requires effort and expertise.
At the same time, high-quality customer data is the greatest investment to your business as it can drastically improve your performance and boost revenue just because you precisely know who your customers are and what’s the best way to treat them.