Openers or non-openers? That is the question. One thing is certain: being able to better manage your commercial pressure is an undeniable asset for optimizing your results. From the consumer’s point of view, commercial pressure corresponds to the pressure felt by the customer due to the multiple solicitations he faces (emails, SMS, advertising banners, retargeting, outgoing calls…). This is expressed by the number of solicitations received by the customer over a given period. The impact of commercial pressure on your campaigns Generally speaking, commercial pressure is negatively connoted because we observe a decrease in the performance of direct marketing campaigns and customer engagement beyond a certain threshold. Customers will tend to get annoyed with too frequent solicitations and potentially unsubscribe. Poorly managing CRM pressure by over-soliciting customers can have catastrophic consequences on the customer file and the company’s commercial performance.In order to optimize the commercial pressure, you may have to implement different analytical tools. 1. Analyse the existing situation to identify the appropriate pressure thresholds In order to optimize commercial pressure management, different types of analysis can be carried out. Firstly, an assessment of your animation plan will allow you to define the commercial pressure threshold that should not be exceeded by segment. This will allow you to adjust your strategy so as not to exceed the level of contact that could have a negative effect on commercial performance. It should be noted that as a general rule, the more committed customers are to a brand, the more likely they are to accept a high level of commercial pressure. 2. Send the right message at the right time with predictive models This first step will provide a general framework for managing marketing pressure within the animation plan. However, if you want to have a more reactive management of marketing pressure, it can be interesting to associate predictive models to make sure you send the right message at the right time. In particular, the implementation of a repellent score will allow to anticipate the non-opening of emails on a given animation sequence. Let’s take the example of a company that would build its animation plan on 6-week sequences. Implementing a repulsor scoring on this type of mechanics would allow to estimate the probability that a customer will not open any of the emails sent during these 6 weeks. As a result, we can exclude customers with a high probability of not opening any emails. Optimized results for performance The benefits of this mechanism are multiple. Firstly, reducing the volume of emails sent to customers with a high probability of not opening will have a mathematical impact: increasing the opening and click rates and therefore the perception of ISPs on the messages sent. Secondly, we will succeed in improving the performance of customers with a high probability of not opening at a given time. Indeed, these non-opening customers often have a strong aversion to direct marketing. Consequently, they tend to be more active when they are not solicited than when they are. Reducing the commercial pressure will thus allow two things: to encourage their natural activity and to make the smallest messages received more attractive because they are less perceived as intrusive messages. How does datacadabra help you manage your commercial pressure? Within datacadabra, you will find a set of methods allowing you to apprehend this problem. The Follow module will allow you to implement different dashboards to monitor your activity. The Describe module will help you define useful segmentations to build your strategy. Finally, the Segment module will allow you to set up a scoring system and optimize your mailings. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.
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
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.
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.
Since customers have change their purchasing behavior, the coronavirus has forced us to review their consumption habits and has led to the digitalization of brands in order to become even closer to their customers. The development of new digital channels The coronavirus crisis has considerably reduced physical interactions between individuals. Physical stores have been directly impacted by the different measures taken in response to the health crisis. As a consequence, the brands have been thinking hard about the digitalization of their activity (acceleration of click and collect, delivery, drive,…).In this context, it has also been necessary to rethink customer relations. How can you stay close to your customers when you can’t see them anymore with digitalization? Here again, digital is the answer. On the condition that the relationship is individualized as much as possible. So no, we don’t believe in a real one to one marketing where each individual would benefit from a unique communication tool (just to follow the global performance of the actions, it would be complicated). On the other hand, a well thought-out one to few marketing can considerably increase your performance. Sharpen your action plans and activate the right drivers. The key is to activate the right customer knowledge drivers. Indeed, by crossing a customer segmentation with various scores, you can easily build your global strategy (see the diagram below). From this framework, we can adjust the action plans to have the most adapted communication to the different targets and thus be as close as possible to their concerns and needs. With datacadabra and our Segment module, building your segments and adapting your animation strategy is disconcertingly simple. Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.