The fight against customer attrition is a real problem for companies faced with customers who are still in their database but inactive. Inactive customers: customer attrition When you work on your animation strategy, you often find that you have to deal with a major problem: the inactivity of some of your customers. Indeed, as soon as an activity has a certain age, we quickly notice that the inactive segment will take a preponderant place in the customer file. However, these inactive customers, apart from no longer being of any use to the brand, will often have a cost (hosting the data, promotional actions to reactivate them, etc.). As a result, fighting against customer attrition will become an issue that should not be neglected. Treating attrition: curative or preventive When it comes to dealing with customer attrition, there are two ways to look at it: curative and preventive. 1. The curative method Traditionally, retailers deal with attrition in a curative way. Two main devices are then implemented: Unfortunately, working on attrition in a curative way is sometimes already too late and the efforts to reactivate a customer can be as important as recruiting a new one. This is where preventive attrition treatment is of great interest. 2. The preventive method Indeed, the idea of the preventive fight against attrition is to anticipate the fall into inactivity by setting up a predictive model. This attrition score will allow us to estimate the probability that a customer will become inactive in a given time frame. It is then necessary to determine the period during which we want to measure the activity or not of the customer. Thus, based on past data (customer profile, consumption data, actions and reactions to animation actions, etc.), it will be possible to calculate the adaptation model and thus anticipate the fall into inactivity. Once this probability has been calculated, we can then define a specific target in the animation plan on which we will implement retention actions. These actions will be either through specific offers designed to encourage customer consumption, or relational actions to encourage customer commitment. The general idea is to reduce the cost of retention actions (volume of messages sent, generosity rate…) compared to reactivation actions while improving performance. How does datacadabra deal with attrition? datacadabra allows you to deal with issues related to attrition. The Segment module allows you to build easily active segments on which to define triggers. The proposed transition matrices will also allow you to measure the different issues of the animation strategy. At the same time, the Predict module will allow you to work on different scores, including the attrition score. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra
Characterization data plays a key role in refining marketing strategy. However, this data is not always available, which is where Open Data comes in. Build a lasting relationship with a good knowledge of the customer thanks to the customer profile Knowing the customer profile is a key element to define your marketing and CRM strategy. Indeed, the more finely we know our customers, the more we are able to build a lasting relationship with them. This will allow you to adjust your offers according to the different customers, to personalize your approach and thus to meet the needs and expectations of the individuals concerned. As a general rule, profiling is based primarily on the brand’s own data. We can thus use customer characterization data (date of birth, place of residence…), data related to his consumption (products consumed or not, frequency of purchase…) or relational data (email opening, visit to the site…). Role of characterization data Characterization data has a key role in customer profiling, as it allows to better understand who the customer is, what he consumes and how he interacts with the brand. A good use of this data will allow a brand to better communicate with the customer, to be closer to his interests and therefore to increase the engagement of the latter because it is chosen according to the customer profile. This data will also improve the feeling of belonging to a brand community for the customer. Customer profile data often incorrectly entered Nevertheless, many databases are poor in characterization data. There are many reasons for this. In the banking/insurance sector, the level of collection of this type of data is often more important than in other sectors, but with a freshness of information that often depends on the first account opened. Companies that have developed a loyalty program have often collected a greater amount of data… when the customer decides to give it to them. Finally, many pure players, who want to improve their conversion tunnel, have limited the collection of characterization data to a strict minimum. The richness of Open Data However, there are solutions to get around these problems of quality and/or missing data. Indeed, beyond the solutions of data enrichment via megabases, open data offers a real opportunity to improve customer knowledge. In France, the systems put in place by the government (INSEE, data.gouv.fr) have made it possible to collect a large amount of data at a fairly fine level of granularity. In particular, the creation of the IRIS concept (Ilôt Regroupé pour l’Information Statistique) at the end of the 1990s made it possible to group data on a geographical level equivalent to the neighborhood. We thus find a large amount of information on different themes: population structure, household composition, distribution by age, level of education, employment, travel, equipment, income level, etc. The strength of the IRIS is that they have been constituted while respecting administrative and geographic boundaries and ensuring that the types of housing in the neighborhood are homogeneous. As a result, there is a homogeneity of consumer profiles within the same IRIS. How does datacadabra support you on the subject? In datacadabra, the Describe module allows you to create profiles of your customers using Open Data. Indeed, Géotypo, our segmentation of French neighborhoods, groups all IRIS into 6 families and 23 segments. It allows you to characterize your customers by comparing them to the overall structure of the population. This will allow you to understand the over- and under-representation of your customers’ sociodemographic profiles and therefore to improve your communication. In addition, datacadabra also allows you to compare different groups in your customer file. Geotypo offers many benefits. Improve your communication, identify the profiles to be recruited in priority, enrich your database… In short, many subjects that will allow you to improve your performance. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.