Big Data. Au-delà des discours marketing, des positionnements et des rêves du début, de plus en plus d’entreprises savent maintenant qu’elles doivent d’une manière ou d’une autre passer à l’action.
En 2013, les idées clés autour du Big Data se clarifient et s’organisent. Les tous premiers retours d’expérience de projets réels se dévoilent et viennent enrichir la connaissance du sujet.
Lire la suite sur le JdN : Vade-mecum du Big Data en 2013 – JDN Web & Tech.
Un article très intéressant paru dans le nouvel économiste :
Big data : comment passer de la donnée à l’information | Le nouvel Economiste.
Vision 2013 sur le Data Mining de l’éditeur du logiciel Coheris SPAD
Synthèse vidéo de la conférence de Jeudi dernier sur MD-Fair
Vidéo intégrale de la conférence de ce matin sur MD-Fair
Vision 2013 sur le Data Mining de l’éditeur du logiciel Coheris SPAD, utilisé par plus de 900 Clients dans le monde.
Predictive marketing
By Didier Gaultier, Head of Coheris Datamining Business Unit
Even though it is largely used, the expression “predictive marketing” is quite restrictive because the best practices in B2C or B2B marketing leverage what we can call “descriptive data mining”. Descriptive data mining includes the fact of doing surveys, polls, descriptive analysis of the marketing databases, segmentations and clustering, with all the subsequent interpretations about the client’s behavior that this can imply.
Nowadays, people’s life has become much denser, much more active, and therefore much more complex. Many opportunities of service have appeared in about every domain. For example, the needs in terms of “personal services” have literally exploded over the last few years. Similarly, in B2B, needs that are specific to each trade have also appeared.
Purchasing behaviors have also diversified in consequence. We used to refer to the “household under 50 years old”. That was a cliché. But nowadays, this cliché doesn’t belong to any reality. For example, in many areas the man/woman factor is rarely a strong enough criterion to differentiate alone the marketing messages while it used to be in the past, except for cosmetic or specialized products.
This evolution has not come without consequences in the world of data mining. The reality has become more complex and we had to adapt ourselves to it. Many criteria which seemed strong, such as the socio-professional category, the age or the gender, which were the main criteria used to define marketing targets in the past, can no longer be used as such today. You can no longer do it, not only for ethical reasons, sometimes, but above all because it doesn’t make anymore sense! For example, a couple with a somehow low range income can come to realize important purchases, even if it means high debts, because it may correspond to a strong need at some time. These punctual behaviors depending on the stage in life do not correspond to the “cliché” type of behavior traditionally expected.
It becomes therefore harder and harder to just use intuition in marketing. A marketing analyst needs landmarks in the middle of all the data available. The role of descriptive data mining is precisely to build a real “map” of the data available.
To make an analogy, would you sail in the middle of the ocean without a good map to get directions? Nowadays, the same question applies with all the marketing databases that are available to the advertiser. We just need to cite a few to be convinced: CRM databases, transactional databases, e-commerce platforms, web analytics, call center databases, email database, SMS, etc. each of these databases (whether they are interconnected or not) can represent a different – but mainly complementary – view of the same customers.
Your map is precisely what descriptive data mining – and only it – gives you. In this respect, you won’t be able to do predictive analysis if you didn’t complete a good descriptive analysis before. It is one of the rules of data mining that is sometimes hard to admit for advertisers: in other words, data must be “understood” before trying to create a model. Going through an often very advanced descriptive stage becomes necessary. This stage itself shall include data management stages that are more or less complex depending on the multiplicity of the sources one wants to process.
Descriptive data mining answers the question “why did it happen?”.
Raw data are unfortunately impossible to understand most of the time. Not only are they very large and composed of numerous different formats, but above all, the information that is really useful can be diluted or completely hidden. You can change this data into information, which give scales, dashboards, ideas … This is all good, but you still cannot understand “why” and it’s usually not enough to act efficiently.
Only descriptive data mining can bring you the tools, sometimes using specific technical knowledge, like in finance or marketing, which will really help you to reveal the critical and actionable knowledge that lies in your databases. This is why, in data mining, we often have to wear a hat of technical expert along with our hat of statistician. The broadcasting and sharing of the results of predictive marketing happens mainly at that level nowadays. That descriptive analysis truly enables the creation of models and all the subsequent predictive computing. Let’s forget right now the wrong, and usually too common idea that only predictive marketing adds value. The opposite is usually true, and in fact, the length of the descriptive data mining stage is sometimes gigantic compared to the length of the predictive stage. It is more or less a Pareto principle: 80% of the time is used doing descriptive data mining and only 20% doing predictive data mining. In the end, the predictive data mining is a bit like the cherry on top of the cake, the last result issued from data mining, or the visible part of the iceberg that is published. But very often, a great share of the important things we learn about our clients, markets, products and campaigns mainly comes from the descriptive part.
Didier Gaultier, Head of Coheris Datamining Business Unit, and Professor of predictive marketing at EPF
Coheris is a leading French Software Vendor for Customer Relations Management, Analytical Management and Predictive Analysis.
Cyrille Aguinet, Analytics Director EMEA chez Epsilon et directeur du master CRM/Stratégies Client à l’INSEEC, expose le décalage entre les évolutions du marketing et la formation à ses métiers.
Comment l’évolution du marketing impacte-t-elle les métiers ?
J’interviens en école de commerce et ce le lien entre l’évolution de l’analytique et la mutation des métiers est flagrante. Pour mes étudiants, j’ai une comparaison : les philosophes au siècle des lumières. A l’époque, ils étaient également mathématiciens et dans des logiques de découvertes, de grandes généralités. Au contraire, aujourd’hui, la recherche fondamentale de ces deux domaines est distincte et, rien que pour l’univers des mathématiques, un chercheur a forcément son domaine d’expertise précis. C’est ce qui est en train de s’opérer au niveau du marketing.
Le marketing généraliste laisse place à de nombreuses approches où chacun doit témoigner de son expertise
Cependant les écoles de commerce continuent de former des marketeurs sur les grandes généralités du marketing. Or beaucoup de spécificités sont en train de s’imposer, notamment autour du marketing digital (univers e-commerce, email, réseaux sociaux, etc.), qui est une vraie révolution avec des modes de pensées complètement différents. On a donc à faire à des marketeurs qui sont en manque d’expertise dans un environnement plus complexe, surtout lorsqu’il est de plus en plus dicté par l’approche quantitative.
En quoi l’approche analytique est-elle liée à ces changements ?
En restant très pragmatique Le marketing est conduit par une équation qu’on pourrait simplifier par « combien cela coute et combien cela rapporte = performance ? ». Pour y répondre, un directeur marketing a besoin de s’appuyer sur l’analytique. Et pour comprendre cette démarche et être à l’aise avec, il faut très souvent avoir une formation spécifique et le goût pour le pilotage des KPI (Key Performance Indicators).
Quand on est analyste de formation, il est assez simple d’aller vers le marketing. A contrario, pour les personnes qui sortent des écoles de commerce, se mettre à l’analytique est un peu plus compliqué. Ce n’est pas très accessible et c’est très souvent une façon nouvelle d’aborder les connaissances.
Ces mutations qui s’opèrent au niveau du marketing et au niveau des profils des personnes, passent par une nécessité d’expertise de plus en plus pointue. Le marketing online est un bon exemple.
Il y a donc un décalage entre les offres de formations et les besoins réels des métiers ?
Au sein du groupe INSEEC, nous avons des masters spécialisés en stratégie de la marque, en marketing international des secteurs, etc. Nous avons de nombreux étudiants qui s’orientent sur ces formations. Par contre, sur les deux masters « e-Business » et « CRM et Stratégie client », nous peinons à recruter. Non pas par manque d’intérêt des futurs étudiants mais tout simplement une méconnaissance de ces nouveaux métiers en amont. Les étudiants se demandent ce que CRM et stratégie client font avec le marketing. De plus, le CRM a été galvaudé par une restriction à la technologie , qui donnait l’impression que tout fonctionnait tout seul en appuyant sur un bouton.
Des étudiants commencent progressivement à venir vers ces formations, mais nous constatons une appréhension.
Pour arriver en école de commerce, il est possible d’avoir une formation littéraire, assez ouverte. Or, dans des masters qui vont aborder le digital, la stratégie client, il est fait appel à des technologies, des bases de données, et notamment beaucoup plus d’analytique. Les étudiants devraient avoir un profil beaucoup plus quantitatif, voir scientifique.
C’est le challenge actuel des écoles : arriver à recruter des étudiants et offrir des formations qui sont liées aux besoins réels du marketing. Ces derniers ont évolués beaucoup plus vite que ce que les écoles et le contenu des enseignements associés ont été capable de mettre en place. Sachant que le défi est aussi de pouvoir recruter des enseignants en mesure de délivrer l’expertise nécessaire.
Au-delà de la génération qui est en train d’arriver, les personnes déjà en place doivent se construire une nouvelle expertise malgré leurs habitudes…
Heureusement dans le monde du marketing, il y a beaucoup de gens qui sont curieux et qui ont cette volonté d’en apprendre plus. Plusieurs challenges s’offrent alors à eux. Dans beaucoup d’entreprises avec lesquelles je travaille, c’est souvent des personnes qui viennent du marketing offline qui ont été amenées sur le online. La première étape est donc déjà de comprendre les métiers du web, la complexité qui existe au sein du e-commerce, de l’emailing ou à présent des réseaux sociaux.
Pour continuer d’évoluer, soit il faut accepter de se replonger dans les études, et/ou faire des formations complémentaires. Soit, il faut motiver sa direction d’engager de nouvelles compétences ad hoc. Et cela veut dire, accepter qu’à un moment donné, il y ait besoin de profils de type « ingénieur » dans le marketing. Il faut aussi avoir les possibilités d’embaucher, car le marketing a tendance à être rythmé par les résultats court terme, l’opérationnel du quotidien, ce qui ne laisse pas toujours la place pour de la stratégie à haut niveau.
Le challenge, c’est donc aussi de pouvoir prendre du recul.
Il faut convaincre les directions générales que l’investissement dans un tel poste va permettre de générer de la satisfaction client, du chiffre, et de prendre en charge des projets plus techniques. La tendance actuelle des entreprises reste de faire appel à des sociétés d’expertise externe dont le savoir-faire est ré-internalisé une fois la performance établie.
Le monde du marketing est en train de s’auto-éduquer face à toutes ces modifications, mais pour bouger, il a aussi besoin du soutien des directions générales.
Predictive marketing
By Didier Gaultier, Head of Coheris Datamining Business Unit
Even though it is largely used, the expression “predictive marketing” is quite restrictive because the best practices in B2C or B2B marketing leverage what we can call “descriptive data mining”. Descriptive data mining includes the fact of doing surveys, polls, descriptive analysis of the marketing databases, segmentations and clustering, with all the subsequent interpretations about the client’s behavior that this can imply.
Nowadays, people’s life has become much denser, much more active, and therefore much more complex. Many opportunities of service have appeared in about every domain. For example, the needs in terms of “personal services” have literally exploded over the last few years. Similarly, in B2B, needs that are specific to each trade have also appeared.
Purchasing behaviors have also diversified in consequence. We used to refer to the “household under 50 years old”. That was a cliché. But nowadays, this cliché doesn’t belong to any reality. For example, in many areas the man/woman factor is rarely a strong enough criterion to differentiate alone the marketing messages while it used to be in the past, except for cosmetic or specialized products.
This evolution has not come without consequences in the world of data mining. The reality has become more complex and we had to adapt ourselves to it. Many criteria which seemed strong, such as the socio-professional category, the age or the gender, which were the main criteria used to define marketing targets in the past, can no longer be used as such today. You can no longer do it, not only for ethical reasons, sometimes, but above all because it doesn’t make anymore sense! For example, a couple with a somehow low range income can come to realize important purchases, even if it means high debts, because it may correspond to a strong need at some time. These punctual behaviors depending on the stage in life do not correspond to the “cliché” type of behavior traditionally expected.
It becomes therefore harder and harder to just use intuition in marketing. A marketing analyst needs landmarks in the middle of all the data available. The role of descriptive data mining is precisely to build a real “map” of the data available.
To make an analogy, would you sail in the middle of the ocean without a good map to get directions? Nowadays, the same question applies with all the marketing databases that are available to the advertiser. We just need to cite a few to be convinced: CRM databases, transactional databases, e-commerce platforms, web analytics, call center databases, email database, SMS, etc. each of these databases (whether they are interconnected or not) can represent a different – but mainly complementary – view of the same customers.
Your map is precisely what descriptive data mining – and only it – gives you. In this respect, you won’t be able to do predictive analysis if you didn’t complete a good descriptive analysis before. It is one of the rules of data mining that is sometimes hard to admit for advertisers: in other words, data must be “understood” before trying to create a model. Going through an often very advanced descriptive stage becomes necessary. This stage itself shall include data management stages that are more or less complex depending on the multiplicity of the sources one wants to process.
Descriptive data mining answers the question “why did it happen?”.
Raw data are unfortunately impossible to understand most of the time. Not only are they very large and composed of numerous different formats, but above all, the information that is really useful can be diluted or completely hidden. You can change this data into information, which give scales, dashboards, ideas … This is all good, but you still cannot understand “why” and it’s usually not enough to act efficiently.
Only descriptive data mining can bring you the tools, sometimes using specific technical knowledge, like in finance or marketing, which will really help you to reveal the critical and actionable knowledge that lies in your databases. This is why, in data mining, we often have to wear a hat of technical expert along with our hat of statistician. The broadcasting and sharing of the results of predictive marketing happens mainly at that level nowadays. That descriptive analysis truly enables the creation of models and all the subsequent predictive computing. Let’s forget right now the wrong, and usually too common idea that only predictive marketing adds value. The opposite is usually true, and in fact, the length of the descriptive data mining stage is sometimes gigantic compared to the length of the predictive stage. It is more or less a Pareto principle: 80% of the time is used doing descriptive data mining and only 20% doing predictive data mining. In the end, the predictive data mining is a bit like the cherry on top of the cake, the last result issued from data mining, or the visible part of the iceberg that is published. But very often, a great share of the important things we learn about our clients, markets, products and campaigns mainly comes from the descriptive part.
Didier Gaultier, Head of Coheris Datamining Business Unit, and Professor of predictive marketing at EPF
Coheris is a leading French Software Vendor for Customer Relations Management, Analytical Management and Predictive Analysis.
Didier Gaultier 