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CLTV: How to predict what business value your users will bring

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CLTV: How to predict what business value your users will bring

It is no secret that loyal customers are almost always more valuable to a business than unique buyers, so having a tool that helps us predict which of those potential buyers will be brand lovers and loyal consumers will be a powerful weapon to boost sales. return on investment of our actions and marketing campaigns magic? No, it’s data intelligence!
A strong customer retention strategy allows you to maximize customer LTV by inviting them to buy from our business again and again. Retention is one of the major drivers of the growth of a business, as evidenced by the study of CUSTOMER RETENTION STATISTICS – The Ultimate Collection for Small Business , where indicates that 65% of the business of a Panama Phone Number List comes from existing customers.Under this premise, it is logical to say that we must Invest in existing customers with paid media actions both off and on, loyalty programs or marketing automation will be a success to generate recurring income and make the business profitable. Naturally, these actions drive customer value to grow throughout the business relationship, but there are always some users that reduce profitability.If we can identify these patterns of behavior that compute negatively in the business, segment customers and act accordingly, we will improve the ROI of all our actions.To get to this point, the Customer Lifetime Value (CL TV) is one of the most relevant indicators for this.What is Customer Lifetime Value (CL TV)?If we google the term, several results will appear with different ways of calculating it, leaving us with the feeling of having generated more questions than answers. For us, broadly speaking, the definition that best fits with what the CL TV is is:The revenue value that a customer generates while maintaining a satisfactory business relationship with the brand, in a period of time of analysis.For many digital (and more traditional) businesses acquiring new customers is a fundamental activity, new customers help grow and increase sales. If, in addition, we can point to these new clients being potentially prone to creating a long-term relationship, the profitability terms skyrocket.Although, it is not convenient to do it at any cost , it is necessary to know where the fine line is between the return on attracting qualified customers in the medium / long term and the investment to reach them.Why is it important to calculate the CL TV for my business?If a brand invests more in a potential customer than he will spend in the future, it is to ensure that the investment will generate losses for the business in the near future. “Pure mathematics” From our perspective we can say that working based on the CL TV is especially useful for the following reasons:

The CLTV is a metric that helps to better plan our resources and budgets of acquisition and marketing showing the current value of each buyer and the future of the relationship with this client.
Because strategic decisions based on business data are highly effective and with minimal margins of error.Customer retention strategies are more profitable than investing in the acquisition of new customers . The probability of selling to an existing customer is 60-70%. The probability of selling to a new potential customer is 5-20% ( According to Small Business Trends ).
It helps to focus retention or engagement strategies, to generate personalized content, and also to design a loyalty program according to your profile with the aim of increasing CLTV. Maintain the relationship with customers who are interested in the brand.Taking these aspects into account, it is logical to say that our main objective when we work based on metrics like this will be to increase it.The higher the CLTV, the more economic effort we can make in attracting , since it will be more likely that the return on the investment will be amortized and profitable.According to Econcultancy reports, “64% of companies rate the customer experience as the best tactic to improve customer lifetime value (CLV), followed by better use of data and personalization.” So customers give us many clues of what to do to have a growth in the business. We are going to use all this information !!And if you’ve made it this far, you’re probably asking yourself this question, why am I not calculating this metric yet? or defining strategies based on it? . Don’t worry … here we are to help you do it;)How to calculate CLTV efficiently?As for how to calculate this metric, the same thing happens with the definition, there are several, and it depends a lot on what you really want to analyze, what parameters make up the profitability of your campaigns, and in what period of time we are talking.

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The machine learning algorithms used for Customer Lifetime Value analysis use historical data to make predictions and infer customer behavior, in order to estimate the number of purchases or average value of purchases of the segments we analyze. Using this equation we can observe that clients historically have a very high negative value, and although for them it might be too late to take an action, if we take them as a reference and generate a pattern of behavior we can anticipate what type of potential client will convert from the same way, and thus avoid capturing it, or defining a specific investment cost at the time of capturing it. This is one of the reasons why it is important to predict future CL TV values ​​with machine learning.And this is where the fun part begins, to classify customers into segments we will use K-means clustering. Once the clustering of CL TV is finished we will group them into 3 segments (the number of segments really depends on the dynamics and objectives of your  Aol Email List . At Elogia we have developed a method to predict CL TV , based on statistical tools, which customers will have in the next analysis period. The key is in the preliminary analysis, not in the subsequent analysis.In a summarized and simplified way, we can say that we follow the following steps to build a machine learning model that predicts the LTV of our clients:
We use clustering with the K-means algorithm to assign each customer to a cluster. (The K-means algorithm identifies the patterns that we are going to use to predict based on the RFM variables.)We select the variables that we will use to calculate the CL TV
We define an appropriate time frame for the calculation of the CL TV. We calculate the value of LTV. We separate the data into two groups, training and model testing, to validate it.We train the K-means algorithms to classify customers.We predict how the clusters have changed for the next 3 months for example.And we have it! A machine learning model that predicts the future CL TV segments of our clients, thanks to this we can easily adapt marketing actions based on that. In a next Post we will tell you an example of how we do it, but if you can’t wait any longer contact us and we will apply it to your data.

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