RFM, relational, behavioral, there are many types of segmentation, many complementary methods at the service of marketing performance. Types of segmentation for differentiated marketing  When you want to implement differentiated marketing, the first question is how to determine which customer groups you will be able to animate, as there are many types of segmentation. For this purpose, the basic concept of “market segments” will be used. This concept is used in market research, but also in relationship marketing in order to structure your actions and communication. In the context of differentiated marketing, market segments are groups of customers that are homogeneously characterized by a combination of factors, such as their needs, preferences, actions or size. They can be identified by a multitude of different criteria. It is these criteria that are used to identify the type of segmentation used. Transactional segmentation: the RFM method The first type of segmentation that comes to mind is transactional segmentation. Indeed, this type of segmentation, which is based on the purchases made by customers, is often the basis for any differentiated business strategy. Based on customer value, RFM segmentation is the most common method of customer segmentation. It takes into account the Recency (date of last purchase), the Frequency of purchases over a given period and the Amount (the turnover over the period studied) to establish homogeneous customer segments. The purpose of this segmentation is to help you approach the 20/80 law to identify the customers who contribute most to your company’s results.When you have a more limited transaction history or when the frequency of purchases is very low, you can also use the PMG segmentation. This is essentially based on the notion of the cumulative amount of purchases over a given period of time to constitute the groups of customers. Relationship segmentation: measuring the level of commitment. When you animate a population of customers, it is often interesting, in parallel with identifying the transactional value of individuals, to measure their level of commitment.Indeed, we often ask ourselves the question of the commercial pressure we apply to our customers. Are we asking too much of them? Or not enough? Do some customers systematically open our communications? Do some of them show a significant lack of interest in my messages despite the solicitations we send them? While there are different methods for calculating relational segmentation based on available data, the purpose is always the same: to better understand customer engagement with the brand. The results will show how many customers are most loyal, most supportive, most critical or most disengaged. By organizing customers into these groups of segments, marketers can more easily target their actions. Product segmentation: how your customers consume   Another interesting area of analysis is the products consumed. A product segmentation will thus make it possible to identify how customers consume your different products, which ones are the most loyal or which ones should be highlighted in communications. This type of segmentation will also allow you to better understand product mixes. Behavioral segmentation: reconciling data typologies The last example of segmentation is behavioral segmentation. This will make it possible to reconcile different types of data (sociodemographic, transactional, relational, etc.) in order to create groups of customers with homogeneous behavior. Combining the different types of segmentation There are a multitude of segmentation techniques. By combining these different areas of analysis, you can determine, for example, which customers are the most profitable, which are the most loyal, or which customers should receive more attention in communications. How does datacadabra deal with this subject? datacadabra offers a large number of segmentation methods natively within the Segment module. Want to know more? Do not hesitate to contact us or to ask for a datacadabra demo.

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

From its creation to today, Artificial Intelligence has become a powerful tool for marketing and CRM through the use of predictive models. The origins of Artificial Intelligence  When we think about Artificial Intelligence (AI), we can naturally think about great Science Fiction movies (the famous Terminator), innovations related to our daily life like the developments related to the autonomous car for example, or other similar topics. In the context of marketing actions, AI also has a great role to play. But before going into the uses of AI, we should first recall the concepts associated with this term. The notion of Artificial Intelligence was born from the work of mathematician Alan Turing in the 1950s. It is a very vast and rather vague concept that groups together a wide variety of treatments that all have the same goal: to allow a machine to reproduce human behavior.  Thus, simple logic programming IF… THEN… ELSE… is a form of AI. Research today tends to try to find ways to create Artificial Intelligence capable of learning almost by itself, like AlphaGo for example, for the game of Go. There are two types of artificial intelligence: weak artificial intelligence, capable of reproducing human behavior but without consciousness, and strong artificial intelligence, which does not yet exist, and which could allow machines to be endowed with consciousness and sensitivity.  Machine Learning, Deep Learning, Artificial Neural Network, AI vocabulary Within this large group of techniques related to Artificial Intelligence, we find Machine Learning. Machine Learning, or automatic learning, will be able to take the decision to adopt and create the most relevant model possible given the available data. A large number of tasks will thus be automated depending on the situation. The term Machine Learning is not new either, it appeared in the 80’s when statistics allowed to improve computer algorithms to make them intelligent. The general idea was then to find a model that was as close as possible to the reality of the data to be analyzed. The first regression methods were born. Machine Learning is very efficient in a situation where, from a very large data set, the algorithm must discover an atypical behavior (fraud, purchase of a product by a minority of individuals…) We are finally talking about Deep Learning. When we think of Deep Learning, we automatically think of Neural Networks which aim to reproduce the functioning of the human brain to make decisions in certain situations. In reality, Machine Learning and Deep Learning are forms of Artificial Intelligence, but the opposite is not true: not all forms of Artificial Intelligence are based on Machine Learning or Deep Learning techniques. AI in the service of marketing  When using Artificial Intelligence techniques in marketing and CRM, we will mainly work on predictive models. The idea is to anticipate customer behavior on different issues (appetence, attrition, purchase intention, interest for a product…) in order to improve action plans. The benefits are numerous, both in terms of performance improvement (increase in sales and/or average basket, reactivation of customers, limitation of inactivity) and in terms of cost reduction (reduction of commercial pressure, optimization of channel choices according to targets). We can thus increase our performance by several points thanks to Artificial Intelligence. How does datacadabra support you on the subject?  The models within the Predict module will naturally allow you to work on all the business problems encountered by marketers while relying on proven Artificial Intelligence models thanks to predictive methods. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.

Dividing your customer file into homogeneous groups has many advantages in marketing efforts, segmenting your customers is therefore a key performance factor.  Why segment your customers? What is segmentation?  Very often, the knowledge you have of your database corresponds to the image you have of your average customer. The objective of segmenting your customers is precisely to “break” this relationship with the average and to identify different groups of customers through segmentation. Segmentation is defined as the action of dividing a population (customers, prospects) into homogeneous sub-groups according to different criteria (sociodemographic data, purchasing behavior, etc.). The segmentation criteria chosen must make it possible to obtain segments of homogeneous populations of sufficient size and operational. The main objective of segmentation is to understand the similarities between customers in the same segment and the differences in behavior between the different groups. Segmenting your customers: a key performance factor Marketing segmentation is an essential strategic step that will allow you to optimize your marketing efforts, to better satisfy your customers and therefore to increase your profitability. In particular, segmentation will bring key benefits that will increase performance. Segmentation to help customization of the customer relationship Firstly, segmentation will allow a better customization of the offer and messages to existing customers. Indeed, a well-constructed segmentation will allow to identify distinct groups of individuals in terms of consumption and profiles. We can therefore move from mass marketing to segmented marketing. This first point will have a direct consequence: the improvement of the global performance. Indeed, by having more targeted speeches, closer to the expectations and needs of customers, we will be able to increase the overall performance of our animation plan. As a general rule, without any other optimization tool (scores for example), segmentation can lead to an increase in sales of around 2 to 5%. This obviously requires a reflection on the animation strategy to be adapted to each group according to the problems to be solved: increase loyalty, fight against attrition, improve customer reception and the commitment of new customers, boost reactivation… Adapt and reduce your commercial pressure At the same time, customer segmentation will allow you to better adapt your commercial pressure to the different groups. By analyzing the needs and expectations of the different customer groups, it will be possible to define the optimal commercial pressure and send only the necessary messages. Beyond the economic (and environmental) impact of a reduction in the number of messages sent, this will also have a positive impact on customer satisfaction, as they will perceive the brand as more attentive to their needs. Optimize available resources More generally, segmentation will also allow the company to better allocate resources. By having a more precise vision of the profitability of each group, it is indeed easier to define the right rate of generosity, to adapt the types of promotion, to manage the time to be spent by the sales representatives on such or such target… In short to define the means adapted to each group of customers. Define your target audience to increase your brand awareness  A better control of your customer knowledge resulting from the segmentation will allow you to better know your core target. As a result, institutional and general public communication will be better adapted to its target. It will therefore be more effective and will increase the brand’s awareness among your target audience.  Strengthen your acquisition strategy  Finally, the segmentation of your customers will allow you to optimize your acquisition strategy! For example, when you set up a digital acquisition strategy, if you base it on the average profile of your customer file, you will recruit very loyal customers, occasional customers and future inactives without distinction. By focusing your strategy on the best customers of the segmentation, you will probably recruit a little less but in a more targeted way and therefore more profitable in the long term! How can datacadabra help you?  Within datacadabra, the Segment module allows you to build different types of segmentations based on your available data. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.