It is true that, to some extent, your data is used to provide you with better products and services. There are various pros. For example, you can find the products you are interested in more easily now. You can also get in touch with the customer services whenever you have a problem without having to get up and pay a visit to the store. On the other hand, the fact that stores and brands are able to analyze your personal data to such an extent means that they know you better than you know yourself. Their ability to encourage you to buy more increases every day, because they use the data they process to come up with the best services and marketing strategies to convince you to buy more. 

This is where “customer lifetime value” comes into play. This term is used to define the overall revenue a customer will bring to a brand or store in their lifetime. And, it plays a crucial role in helping stores determine their marketing strategies and business models.

How is “Customer Lifetime Value” Calculated?

Customer lifetime value can be calculated in various ways via analyzing data from the past or via making projections for the future. If you are using data from past experience, customer lifetime value is equal to the total amount spent by a customer multiplied by the store’s average gross margin. This formula also takes into account the amount that the store has spent so far for customer services, advertisements, etc.

All sorts of variants can be included in this calculation. A set of variants like average number of transactions in a month, average amount spent per transaction, average gross margin and how long the customer has been involved with the brand, etc. can be multiplied to project a customer lifetime value for the future.

Why is Customer Lifetime Value Important for Stores?

Stores analyze their relationship with their current customers and try to put a numeric value to this relationship by calculating the customer lifetime value for various reasons. First of all, it is expensive for stores to try to attract new customers. It requires managing several sets of marketing strategies addressing different demographics, which costs a lot. However, it is much easier for stores to develop the necessary marketing strategies for their current customers whom they have already know a lot about. Studies show that trying to attract new customers cost 5-25% more compared to trying to maintain current customers. According to a study conducted by Business Strategist Frederick Reichheld, when stores increase their customer retention rates by 5%, they increase their profits by 25-95%.

Based on the customer data on their database, stores calculate their customers’ lifetime values to detect the customers that they have a risk of losing. Then, they start working on how to retain these customers by coming up with new strategies. Calculating a customer’s lifetime value helps a store detect the customers it runs the risk of losing, take the necessary steps, and determine its strategies and business models to increase its customers’ overall lifetime value.

In short, by better analyzing their customers’ shopping habits and trying to better understand them in general, stores can offer more customized services to their customers. So, instead of trying to attract new customers, they try to encourage their current customers to spend more. This explains why we keep seeing the same products over and over again when we are browsing through different websites on our computers or smart phones. We keep receiving emails from stores, including promo codes for our birthdays or other special occasions, which are part of their endeavor to create customer loyalty. Creating customer loyalty, in other words, increasing a customer’s lifetime value, depends on encouraging us the customers to shop more (without realizing, of course).

Artificial Intelligence will Take Customer Data Analysis to Another Level

Artificial Intelligence is used to calculate customer lifetime value and implement marketing strategies that are customized to each customer. AI helps stores analyze different demographics in a more efficient way by providing personal analysis technologies. AI is used to analyze our personal data which includes our activities, transactions, the products we view, the product categories we might be interested in, how often we shop, what shopping incentives work on us the best. The analysis of our personal data leads us to see a reflection of it when we are shopping online. Because the data concerning our shopping habits are processed and used to determine everything from the website interface to what products and advertisements we see online. In short, we see a customized version of the online shopping platform that is catered to “us”.

AI technologies keep evolving every day. And, as customers, we know that our data is being processed with each step recorded. So, it is difficult to guess what the future of AI will bring. Currently, many stores are in partnership with IT companies offering AI-driven customer tracking tools. However, it is certain that the customers will be the ones affected by the use of these technologies, for better or worse. Even we ourselves aren’t aware of our online shopping habits, but stores are on top it thanks to AI-driven technologies. This, of course, will lead to us being encouraged to spend more and more. Therefore, we must always remember that what we are seeing in online shopping platforms or in the online ads we come across, are an array of products that are selected based on our past behavior and personal data processed. Even though it is getting harder and harder to remind ourselves of this fact, keeping up with the latest marketing strategies and being able to recognize them is the only way of becoming conscious consumers.