When developing your marketing strategy, it is essential to define your different segments in order to build a solid analytical base. When you want to optimise your marketing and CRM strategy, you often start your customer knowledge work by implementing your analytical base. The first step is to characterize your segments by setting up a customer segmentation. This will enable you to identify the main groups to be managed and to define the main actions to be carried out on each of them. In order to better understand the characteristics of each group, it is often necessary to characterize your segments. Indeed, whatever the segmentation carried out, it is interesting to understand the profiles of the different segments in order to improve the customization of the segmented animation plan. Example 1: Differences in consumption per channel In terms of implementation, characterizing segments is based on an analysis of customer profiles and their consumption according to the segment to which they belong. It will thus be possible, for example, to analyse the distribution of segments by consumption channel. The table above shows that Very Important Customers and Very Good Customers are over-represented among mixed customers. While New Customers are over-represented among exclusive web consumers and Occasional Customers are more likely to buy in-store. This information will allow either to direct customer communications towards the preferred channels of each segment, or to favor omnichannel by proposing offers in favor of the complementary channel. Example 2: Differences in product consumption In the same way, it will also be possible to analyse the consumption of the different segments by product family. The table above shows that Very Important Customers are over-represented in the Accessories family and especially in the Apparel family. The Very Good customers are over-represented in Accessories. We will therefore personalize the product offer according to the segments. In parallel, we can deduce that diversification in terms of products is also a vector of loyalty. It will therefore be appropriate to highlight certain products to the soft core segments to increase their knowledge of the brand, their consumption and therefore their loyalty. Example 3: Differences in sociodemographic profiles Another element that will be important in understanding the different segments is the analysis of their sociodemographic profile. This will enable us to understand the differences between the different segments in terms of age, sex, socioprofessional category, standard of living, etc. The graph above gives an example of the characterization of loyal segments versus the population through GeoTypo. We can see here that the active segments are over-represented in rather urban and SPC- areas, whereas they will be under-represented in SPC+ districts. This socio-demographic information will also make it possible to improve the digital acquisition process by focusing on the characteristics of the most loyal segments. How does datacadabra help you characterize your segments? As we can see, the characterization of segments will make it possible to find numerous drivers for improving communication. Within datacadabra, the Describe module will allow you to work on different types of profiles, on the brand’s own data or on Open Data. Want to know more? Do not hesitate to contact us or to ask for a datacadabra demo.
Suspect, prospect, customer, it is now essential to master the customer life cycle or sales funnel to make your sales strategy profitable. What is the customer life cycle? The customer life cycle can be defined in different ways in marketing. Either a literal definition corresponding to the events that the customer will experience in his life (marriage, birth of a child, etc.), or a definition relating to the relationship between the customer and the brand. In this second case, which is of interest to us here, the customer life cycle will designate the different stages in the evolution of the relationship between the customer and the brand. A relationship that starts long before the purchase: the suspect and the prospect The relationship between an individual and a brand starts long before the first transaction. Even before the first interaction between the contact and the brand, if the individual corresponds to the target established by the brand, he will be considered a suspect. Subsequently, as soon as the first exchanges start and the brand is able to identify the individual, he will have the status of a prospect. The brand’s first challenge will then be to succeed in optimizing the conquest phase to transform this prospect into a customer. These first three phases already require numerous actions on the part of the brand to increase the performance of its recruitment processes. In particular, test and learn phases and the creation of recruitment scores will make it possible to improve the relevance of the first stages of the customer life cycle. The first transaction: the customer Because once the contact has carried out his first transaction and acquired the status of customer, he will then be able to follow a path that will take him from new customer to loyal customer to departing customer to inactive customer and finally to reactivated customer. When you build your customer segmentation, and you study the segmentation flows over time, you can identify the main paths followed by customers. A direct impact on business investment The interest of knowing the customer life cycle well and being able to measure and optimize the commercial investments to be made at each stage. Obviously, from one sector to another, the customer life cycle lasts more or less time. For example, on the Internet, customers are quite volatile, whereas in the insurance sector customers are much more loyal. The role of data science in the customer life cycle At the various key moments in the life cycle, we will be able to use data science techniques to help the business in its actions. For example, for acquisition, we can work on recruitment scores or identify the priority profile to recruit. In order to correctly develop its customer file, it will be possible to segment it. We will set up targeted actions on the core target to increase loyalty. Another important point: we will anticipate attrition through dedicated scores. Finally, we can prioritize certain targets to be reactivated by using models specific to this problem. Data Science will therefore be able to interfere at different moments in the customer life cycle, either through descriptive analyses or predictive analyses. The most important thing is to prioritize the actions to be taken so that the analysis system is as effective as possible. In the end, it’s a bit like building a house. Within datacadabra, the Describe and Predict modules will allow you to work on these issues in order to optimize your action plans. 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.
Customization has become a must in marketing strategy. One of the first steps is to identify your personas in order to adapt your communication. Customization at the core of the marketing strategy When implementing customization actions in your marketing strategy, you must first improve your understanding of your different targets. In this case, the creation of personas can be of great interest. From a statistical point of view, typology will play an important role in this process. What is a persona? The main interest of the typology is that it will allow you to create personas representative of your customer groups. Indeed, in marketing, a persona is often defined as a fictitious person representing the group to which he belongs. The persona is endowed with characteristics specific to its group, whether it be socio-demographic, relational or transactional. To this we can often add qualitative data, from surveys or round tables, in order to improve our knowledge of each persona’s profile. The typology will make it possible to synthesize the information from the different types of data available in order to group individuals according to their proximity, measured in relation to a set of criteria that they have in common. We will thus be able to define a certain number of groups of individuals with their own characteristics. Tailor your offer to your persona The final objective is to facilitate the understanding of the different customer profiles that constitute your file. And this in a transverse way in all the company. A good tool to allow this information to be disseminated is the creation of summary sheets presenting the main characteristics of each group. This will also allow you to identify the specific needs and expectations of each group and ultimately to build action plans and a product offer adapted to each group. How does datacadabra support you on the subject? Within datacadabra, the Segment module allows you, thanks to its Typology method, to build your customer typology by associating different statistical techniques allowing you to create homogeneous groups and to obtain the assignment rules allowing you to assign this typology to your entire database. The report associated with this method will provide you with a set of group characterization elements that will allow you to define your personas. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.
The objective of the ominichannel analysis is to be able to follow the consumer throughout customer experience via different consumption channels. Omnichannel: a change in behavior linked to COVID The year 2020 was affected by the COVID epidemic that we all experienced. In this context, many customers have changed their consumption behaviors: change of brands, change of purchase frequency, change of consumed products and average basket amount… Many are the evolutions and adjustments that have marked consumers during the year. Among these changes, we have also seen a shift in consumption channels, in particular in favor of digital channels and omnichannel. If in the context, this evolution has been more undergone than provoked by the brands, it turns out that a large majority of brands are looking to increase the omnichannel consumption of their customers. Numerous customer knowledge tools allow them to find drivers to encourage multi-channel consumption. Identify the consumer typology First of all, setting up a comparative profile of the different types of consumers (exclusive to stores, exclusive to e-commerce, mixed for example) will allow us to understand the particularities of each group and to identify the drivers to act on. For example, let’s imagine that a product is over-consumed by “store-exclusive” customers, customers that we would like to see become “mixed” customers and therefore also buy on the e-commerce site, we could then make a specific offer on the product in question for any purchase made on the site. On the same mechanism, we could also imagine a special “click and collect” offer for e-commerce customers that we would like to bring to the store (provided, of course, that a store is located near their place of life). Sharpen your strategy with predictive models To go further, predictive models, and in particular the channel score, will allow you to anticipate natural changes in behavior and thus sharpen your strategy to influence consumer behavior. For example, if we set up a purchase intention score on the e-commerce site among “store-only” customers, we will be able to predict the probability that a given customer will buy on the e-commerce site in the coming weeks. Based on this information, we can then differentiate the animation strategy into different groups. For example: How to promote omnichannel with datacadabra? Within datacadabra, many methods are available to work on these issues. In particular, the Describe module will allow you to work on customer profiles and compare the consumption behaviors of different groups. Within the Predict module, the different scoring models will allow us to anticipate future consumer behavior. Our different analysis methods are very simple to implement and allow you to build your action plans with ease. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.