Predictive analytics can estimate the future value of a customer by comparing a customer to the millions of others that have come before them.
Predicted customer value is the projected value, revenues, and costs, adjusted for the time-value of money, of a customer looking forward several years. Your average retention rate will tell you how many years in the future, on average, you will retain a customer and how many years of future revenues to take into account. We typically look one to three years ahead when calculating predicted customer value.
Predicted customer value is very useful, especially when deciding how much money to invest in acquiring or retaining a specific customer. If you were to only look at historical lifetime value, you would significantly underestimate the potential of a customer and likely under invest in the acquisition or retention of certain customers. It can also be used to identify high-value customers very early in the life cycle. After her first purchase, a future high-value customer looks just like everybody else. If you could recognize the high-potential customer early in the life cycle, you could start differentiated treatment right then and there and increase the odds this high-value customer will stick with you.
One person might have just bought this expensive jacket but he might have only been a customer for two months, but another customer might have been a customer for five years and bought the same jacket. If you were to look at historical lifetime value, you might draw the conclusion that one customer is more valuable than the other. However, these two might very well become equal value customers and should probably be treated in much the same way. If you look at historical lifetime value you look too much at old customers and will miss the opportunity to acquire or retain more recent, high-potential, customers.
With predictive analytics, you can estimate the future value of a customer by comparing a customer to the thousands or millions of others that have come before them. You can predict future lifetime value by finding customers that look just like them. From the example we used earlier, buying a certain type of jacket may very well be an early indicator of a well-known pattern of behavior for a high-value customer. Even if predicted customer value is not accurate in absolute rupees terms, the rank order it provides gives the marketer focus on the right segment and trends.
Here are some examples of factors that can signal future lifetime value. Predictive marketing software typically looks at hundreds of factors like these but will only use those that actually correlate with future lifetime value in your particular company or situation:
Recency of engagement: The recency of purchases, web visits, reviews, and email clicks may all be important predictors of future purchases and thus future customer value.
Size of the first order: Customers who make a large first order is more likely to end up being valuable shoppers.
Discount on the first order: Customers who buy full price are more likely to become valuable over their lifetime.
Multiple types of products in the first order: Buying from different categories, such as shoes and electronics, in your first order is a signal of future customer value.
The time between orders: Most valuable shoppers make frequent purchases and thus a shopper who places a second order quickly is more likely to become a high-value customer.
Time spent on the website: The more time prospects or customers spend on your website, the higher their likelihood to buy and the higher their predicted customer value.
Social and email engagement: Customer engagement of any kind, including email opens and clicks or social engagement, are great predictors for likelihood to buy and predicted customer value. Often it is not the amount of engagement that matters, but the consistency or frequency of engagement. Spending a little time every day is a more reliable indicator than spending hours sporadically.
Acquisition source: It turns out that certain channels drive higher value customers than others. The customer who came from a fashion blog may have a higher predictive value than the customer acquired through a banner ad.
Geography: Customers in certain zip codes have a greater predicted customer value than others. Rural populations tend to be more stable, move less frequently, and therefore have more loyal purchase behavior. Zip codes can sometimes predict what type of products people buy.
Seasonality: Retail customers who are acquired during the holidays tend to be about 14 per cent less valuable than those acquired during other times of the year.
Personal referrals: People who came to your brand through a personal referral tend to be more loyal than those who buy because of an advertisement.
Predictions about lifetime value are not destiny. Marketers can do much to change the course of history here. Take, for example, the fact that shoppers acquired during the holidays tend to be less valuable and less loyal than shoppers acquired at other times during the year. One skin care company decided to focus its retention efforts on this holiday cohort specifically. It set up an email marketing campaign to increase brand loyalty among new cyber monday customers, sending regular reminders for refills and recommending other products of interest.
They were able to reverse the trend, and lifetime value of these new holiday customers is now five per cent higher than the company average. By focusing on specific outreach to undeserved customer segments, the company was able to offer personalized promotions that ultimately drove greater brand loyalty. The important lesson is that once customers are acquired, the best strategy is to focus on engaging them to grow and retain them, ignoring the cost of acquisition.
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