“Tengo problemas para retener a mis clientes”: ¿Le suena conocido? - DAnalytics

I have problems retaining my clients. Does it sound familiar?

Today we are going to talk about data-driven decision makingdata driven decision makingwith an example, or better with a problem we live or at least we have heard: how I keep my clients. One would think that the most obvious thing is to try to retain the most profitable clients, but nowadays the task seems titanic because the clients come and go as there are many offers, it is challenging to differentiate and the retention programs lost their usefulness since many companies they handle them, among other factors.

Even so, that does not mean that everything is lost. On the web there are many tips on what to do to overcome these obstacles. For example, “7 tips to retain clients” (Betech, 2013) gives 7 tips that consist of:

Faced with these tips, we can do two things. On the one hand, we can only be guided by our intuition to set goals and strategies to implement one or all of the tips. Maybe we get good results, or maybe not, the truth is that the uncertainty, in this case, is quite large. On the other hand, we can analyze our data (internal and external sources), and make decisions based on the results we extract (that is decision-making based on data) to set goals and design strategies 1 Here I want to make two parentheses. First, data analysis do not guarantee that what you design and implement will work successfully, but it increases the chances of it being so. Second, analytics and decision-making based on data (DDD in English) within your organization is not going to replace your intuition, you are the one with the knowledge of the business sector, data analytics is a tool that will complement your analysis, will validate your hypothesis or on the contrary will reject them, so use it and get a differentiating advantage. [/ note]

Yes, for each of these tips you can apply some data analysis technique. For example, for tip one, you need to study your current and historical retention rate to define a goal. For type 2, you could analyze how your customers have reacted to different promotions in the past to predict how they will respond to different promotions you have in mind and choose the one that is most likely to succeed. Alternatively, for type 4 you can study your clients to identify those who are more sensitive to reacting before new developments and focus your marketing strategies on just them. Imagine with thousands of customers, a marketing strategy to cover them all is costly in financial and human terms, and today there are better options to address this problem, but about what we will talk in another blog post. Alternatively, for tip six you could use a clustering technique to group your clients based on their purchasing behavior and the sociodemographic characteristics that you have available.

Moreover, several studies show the benefits of making decisions based on the data. Today I bring you a study from the Massachusetts Institute of Technology (MIT), one of the most important universities in the United States, which found that the more decisions are made based on data, the more productive the company is. A high standard deviation in the data-based decision index (DDD) is associated with an increase in productivity from 4 to 6%. Besides, the DDD has a positive and statistically significant relationship with indicators of return on capital (ROE) 2 Indicator measures the ability of the business to generate profitability with the resource invested by the partners. It is measured by dividing the net profit after taxes (Income Statement) by own capital or equity contributed by the partners (Balance Sheet). [/ note] return on assets (ROA) 3 Indicator measures how profitable a company is in relation to the size of its assets, that is, it gives an idea of ​​how efficiently assets are being managed to generate income. It is measured by dividing the net profit after taxes (Income Statement) by Total Assets (Balance Sheet). [/ note], the market value of the company, among others (Provost & Fawcett, 2013).

In conclusion, the taking of DDD is highly recommended !!! If you are already implementing it, in future posts we will give you advice on how to make a primary diagnosis of DDD in your organization and some general recommendations. If you are not yet doing it, do not worry the critical thing is to recognize its importance and start to make the strategic planning of it so that you can implement and align the structure and culture of your organization. Although I have mentioned many times the word data as an essential asset for the taking of DDD, it is important to remember that there must be a human talent that has the capacities to extract knowledge from the data. This talent can be internal to your organization, or it can be external. That is, data + human talent = successful DDD.


  1. Betech, E. (2013, junio 25) 7 tips para retener clientes. Disponible en https://www.entrepreneur.com/article/266062
  2. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.

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