Disposable customers. Segmentation for repeat purchases
- Transfer
Each client has its own repeated habits and traditions. Whether it is a favorite day for shopping, preferences for the average cost of goods or for additional options to the product. All this information will help us encourage the customer to make a second purchase and transfer from a new user to a regular customer.
Moving from one-time purchases to regular customers
Well, you managed to convert the client. It's cool, but what's next? According to statistics, from 30% to 80% of buyers in the ecommerce industry make an order only once in their entire life cycle. In the field of games, the second order is made by 60% of clients. How do we get regular customers with such disappointing numerical data?
This question concerns marketers around the world. They work, exerting all their efforts on transferring clients from one-time to permanent. Whether it is primary orders in retail or deposits in online games. Why is the second order so important? If a customer makes his second order or makes a second deposit, the probability of making a third order increases tenfold compared with customers who have completed only one order.
The table below summarizes the data from the top ten ecommerce companies in Europe and the USA. As can be seen from the graph, the probability of the next transaction increases with the number of current transactions.
The likelihood of the next transaction depending on the number of current transactions
Many companies combine one-time customers into one group and use different methods and messages to encourage a second purchase. Sounds like a good plan, doesn't it? Anyway, all customers in the group are one-time customers. But we are here to discuss a different approach. This second method is to divide a group of one-time customers into different segments, depending on the characteristics of their first transaction. We dive deeper and consider the reasons for such segmentation.
We will begin by analyzing the behavior of multiple buyers of 10 leading companies and try to understand whether there is a relationship between the first order day and the second order day. Let's start with the analysis of sports betting. In this area, the client makes a deposit on the day when the most matches of his favorite team occur, usually on Saturday or Sunday. The likelihood of such customers returning and making bets these days is high enough.
We collected data and tested, the results were the same as we expected. Basically, the second deposit was left on the same day of the week as the first. This is evident from the maximum probability value located on the diagonal of the square.
The likelihood of making a transaction of a second transaction depending on the day of the week of the first transaction in sports betting
The relationship between these two transactions can help better target advertising activities to different cohorts of one-time customers (in this case, we have 7 groups based on the bottom of the first transaction) and remind about making the second transaction on the appropriate days. A more interesting step is testing this hypothesis in retail.
The likelihood of making a transaction of a second transaction, depending on the day of the week of the first transaction in retail.
We can see a similar situation. Customers make a second purchase on the same day of the week as the first. It is important to note that the dispersion in the retail sector was higher than in the field of sports betting. But the dependence itself manifested itself for each company. The most popular day for shopping is Monday, the most unpopular is Sunday. If we consider the dependencies between orders for only one brand, we get this.
The likelihood of making a transaction of the second transaction depending on the day of the week of the first transaction in retail for one brand
. We have a simple explanation of this behavior for sports betting, but why do we see such a result in retail? The reason may be that buyers have certain patterns in life. You go to the gym on Thursdays and Fridays, walk with your family on weekends, stay up late at work on Mondays and meet with friends on Fridays. The shopping pattern does not look strange, considering all the others.
As you might have guessed, we tested similar hypotheses for the time of day. Is there a correlation between the time of day of the first order and the second, if the second order was made at least seven days later? We divided the day into 4 periods: night, morning, evening and noon - and checked the distribution of the second orders for each time period for 6 brands.
The likelihood of a second order depending on the time of day of the first order.
The relationship between the first and second orders by the time of day looks obvious. Customers who order late at night for the first time are likely to make a second order at the same time.
As marketers, we strive to increase the number of products in the order. Pre-sales in the world of marketing is a way of life, and if not, then it should be. But should we always try to sell the goods? Is this solution the best for all our customers? In our analysis, we investigated whether the cost of goods increases in the second order compared to the first.
Different brands are used as data sources, therefore, for each brand, individual segments were identified with the value of the goods. It turned out 6 price groups.
Probability of the cost of the second order, depending on the value of the first order
Most of the customers whose orders were made in a low price range, remained in the same range and in the second order.
Our analysis above shows that we can learn from the first orders. The main thing that we need to remember is that we should not put all one-time clients in one group. It should be segmented customers depending on their day of the week and time of purchase, the cost of the order.
Using these methods and steps will help you better understand how to increase LTV and get more loyal customers.
Moving from one-time purchases to regular customers
Well, you managed to convert the client. It's cool, but what's next? According to statistics, from 30% to 80% of buyers in the ecommerce industry make an order only once in their entire life cycle. In the field of games, the second order is made by 60% of clients. How do we get regular customers with such disappointing numerical data?
This question concerns marketers around the world. They work, exerting all their efforts on transferring clients from one-time to permanent. Whether it is primary orders in retail or deposits in online games. Why is the second order so important? If a customer makes his second order or makes a second deposit, the probability of making a third order increases tenfold compared with customers who have completed only one order.
The table below summarizes the data from the top ten ecommerce companies in Europe and the USA. As can be seen from the graph, the probability of the next transaction increases with the number of current transactions.
The likelihood of the next transaction depending on the number of current transactions
Many companies combine one-time customers into one group and use different methods and messages to encourage a second purchase. Sounds like a good plan, doesn't it? Anyway, all customers in the group are one-time customers. But we are here to discuss a different approach. This second method is to divide a group of one-time customers into different segments, depending on the characteristics of their first transaction. We dive deeper and consider the reasons for such segmentation.
Day of the week
We will begin by analyzing the behavior of multiple buyers of 10 leading companies and try to understand whether there is a relationship between the first order day and the second order day. Let's start with the analysis of sports betting. In this area, the client makes a deposit on the day when the most matches of his favorite team occur, usually on Saturday or Sunday. The likelihood of such customers returning and making bets these days is high enough.
We collected data and tested, the results were the same as we expected. Basically, the second deposit was left on the same day of the week as the first. This is evident from the maximum probability value located on the diagonal of the square.
The likelihood of making a transaction of a second transaction depending on the day of the week of the first transaction in sports betting
The relationship between these two transactions can help better target advertising activities to different cohorts of one-time customers (in this case, we have 7 groups based on the bottom of the first transaction) and remind about making the second transaction on the appropriate days. A more interesting step is testing this hypothesis in retail.
The likelihood of making a transaction of a second transaction, depending on the day of the week of the first transaction in retail.
We can see a similar situation. Customers make a second purchase on the same day of the week as the first. It is important to note that the dispersion in the retail sector was higher than in the field of sports betting. But the dependence itself manifested itself for each company. The most popular day for shopping is Monday, the most unpopular is Sunday. If we consider the dependencies between orders for only one brand, we get this.
The likelihood of making a transaction of the second transaction depending on the day of the week of the first transaction in retail for one brand
. We have a simple explanation of this behavior for sports betting, but why do we see such a result in retail? The reason may be that buyers have certain patterns in life. You go to the gym on Thursdays and Fridays, walk with your family on weekends, stay up late at work on Mondays and meet with friends on Fridays. The shopping pattern does not look strange, considering all the others.
Times of Day
As you might have guessed, we tested similar hypotheses for the time of day. Is there a correlation between the time of day of the first order and the second, if the second order was made at least seven days later? We divided the day into 4 periods: night, morning, evening and noon - and checked the distribution of the second orders for each time period for 6 brands.
The likelihood of a second order depending on the time of day of the first order.
The relationship between the first and second orders by the time of day looks obvious. Customers who order late at night for the first time are likely to make a second order at the same time.
The cost of goods in the order
As marketers, we strive to increase the number of products in the order. Pre-sales in the world of marketing is a way of life, and if not, then it should be. But should we always try to sell the goods? Is this solution the best for all our customers? In our analysis, we investigated whether the cost of goods increases in the second order compared to the first.
Different brands are used as data sources, therefore, for each brand, individual segments were identified with the value of the goods. It turned out 6 price groups.
Probability of the cost of the second order, depending on the value of the first order
Most of the customers whose orders were made in a low price range, remained in the same range and in the second order.
Conclusion
Our analysis above shows that we can learn from the first orders. The main thing that we need to remember is that we should not put all one-time clients in one group. It should be segmented customers depending on their day of the week and time of purchase, the cost of the order.
Using these methods and steps will help you better understand how to increase LTV and get more loyal customers.