17 January 2020
4 essential data sets for customer predictions
Hardly a day goes by without us hearing about how important data is. Everything must be data driven! But what does this mean? Here, Jesper Vagtel Johansen provides you with an overview of four data sets that you can use when working with customer loyalty.
Data is the new black, but all too often I find it just ends up being gibberish. If you feel the same way, this article was written for you. The goal is to provide you with an overview of what data can be beneficial for your company to use in building customer loyalty. And now to the point:
Being able to predict your customers’ future actions is the key to increased customer loyalty. The better you know your customers, the better you can offer them the right form of communication, the right product and the right service – in just the right quantities and precisely when the individual customer needs it. But in order to peer into the future, we must be able to understand the past, while keeping a keen eye on the present. And that’s precisely what we can use the data for.
But it isn’t just about the type of data we look at. It’s much more a case of how effectively we combine the data we have available to us. Because if we only look at the different data sets, our view of customers also becomes limited. Our demographic data set can, for example, tell us that our loyal customer is a 55-year-old man who drives a sports car, is a high earner and has children living away from home. But if we look more closely at his behaviour data, we discover that he has recently had his third child with his new wife, and perhaps this why he is looking to buy a new, and larger, car.
What data are we talking about?
Unfortunately, we can’t read our customers’ minds. However, we are close to being able to do this if we understand how to analyse the right data and how to use this data for the right purposes. Relevant data is, to some extent, defined by your product, but there is also data that always reoccurs, and that can be applied across all sectors.
Generally speaking, we capture demographic data, behavioural data and personal data.
When collecting data about your customers, it is important that you keep this in mind so that you are able to make your predictions. Depending on how you look at your data, it will tell you about when the customer will likely make their next purchase, or whether the customer is considering churning, that is, leaving the shop.
When we talk about data, there are different levels of measurement and it’s usually a good idea to use a combination of several data sets. In the following section I will go over the four types of measurement, one at a time, so that you can see how you can use each of them independently.
Demographic data tells us something about who the person is, based on gender, age, life stage, status, number of children, place of residence, income, etc.
For example, income can tell us about the customer’s purchasing power and give us an indication of things such as whether the customer is financially able to buy a house.
A classic example of this is when the first child arrives. It can trigger a domino effect that results in a large number of new purchases. This is why businesses such as car dealers, estate agents, baby supply stores, and local supermarkets selling nappies and baby food also find it very interesting.
Our behaviour probably says more about us than you might think. Behavioural data may include purchase history, behaviour on your website, interaction and communication with you, churn rate and behaviour on other digital channels. For example, by looking more closely at when customers make purchases, you can see patterns in when your communication should reach your target audience to be most effective. Looking at customer behaviour on your site can also be beneficial to you when you need to calculate how far down the purchase funnel the customer is. If you create content and email flows that gradually work the customer throughout the entire purchase funnel, then looking at customer behaviour will give you a hint about when they are ready to make a purchase.
Psychographic data consists of information about a person’s interests, values, opinions and attitudes, lifestyle and/or political conviction. If you identify what values or interests your customers have, then you will gain an understanding of what their next behavioural trait will be. You can collect data about these persons through questionnaires or deduce it from looking at behavioural data. For example, if a person buys a lot of organic products, they will likely have a set of values that favour animal welfare.
Modern archetypes are one form of data point that can be beneficial. The modern archetypes delve a bit deeper, as they reveal something about why we are who we are and how we look at the world from different perspectives. Therefore, this data point can tell you a bit about why customer behaviour differs, and not just how it differs.
It will provide you with a strong basis for how to formulate your communication and marketing to appeal to the various archetypes. If you combine the archetypes with data such as purchase history, then you can differentiate between the kinds of archetypes who tend to buy certain products. This tells you how to communicate about this product in the future, because different archetypes are attracted to or respond to different messages and arguments in different ways.
Better segmentation, better results
Once you know how to utilise the data collected about your customers, you will be able to segment your customer data much more effectively and be able to better adapt your communication to suit the individual segments. And if you think this sounds difficult or like a lot of effort, you are always welcome to contact us at here at InterMail, as we are highly experienced in this field. And when you do contact us, we will naturally have predicted it…