5 Guidelines for Data-driven Marketing: Saegus’s Point of View

Opinion| data science| business | | Romain Doutriaux

Ever wondered how to achieve your marketing data-driven momentum? We asked Stephane Marcovitch-Bruneau, Manager at Saegus.

Stephane Marcovitch-Bruneau, Manager at Saegus
“As classical segmentation is being replaced by predictive analysis and machine learning, feedback is becoming the marketer’s most precious asset. Pre-established rules can only benefit from being continuously challenged and refined by looking at data. That’s the virtuous circle of data science: the more assumptions we make, and the faster we test them, the more we learn – and again it brings more assumptions, and the circle goes on. But in order to achieve this kind of data-driven momentum, teams and organizations need more agility."


Why Agility Matters

Your Data Science projet is all about feedback. In every team, there are people who know one subject-matter better than others, others who understand one customer category better, others who know one data source better… Whatever the question one is asking, it’s almost certain that another knows how and where to look for the answer.

5 Rules to Generate Agility for Data-driven Marketing Projects

  • Test and Learn Iteratively. Don’t get trapped in long debates and huge specifications of what an algorithm or model should look like. For example, if you want to build a product recommender system, don’t think ahead all the features you could add to improve its relevance. It will only make the test and learn more complex. Take one step at a time.
  • Make Daily Scrum Meetings. Organize daily 30 minutes meetings where each member of your team can tell others which assumption or question she/he is working on, and which data she/he is going to use to test it. If someone else has had findings on a related issue the day before, or has encountered a bug on the data source, it’s better to let her/him know.
  • Give your Data Scientists the Big Picture. Because they need the freedom to formulate and test new assumptions as they learn from previous ones, data scientists therefore need to know the big picture: not only that you want to predict churn from customer online behaviour, but also why. In addition, they need a sandbox environment where they can experiment freely without being blocked by performance or security constraints.
  • Cherish your Data Sources. Data sources are often viewed as letdowns: if only that field was always filled correctly, or refreshed in real-time... But they can also be underestimated gold mines. Keep occasional dayparts for unsupervised data discovery, without any question to answer. Also, always keep raw data archives in case you missed something, and be close to your data source guy – the one who knows every field and every bias of the providing system.
  • Challenge your View of the Customers. Make your data-driven use cases the reflection of your customers’ journey. Take each step of your customer experience, and think about how each data source – whether it’s in your data lake or not – might increase knowledge about what happens at this point, and which indicator you could optimize. You can use advanced tools for use case design (ex: Foreseeds), or the good old white board.

How to get real?

All of these guidelines aim for the same result: any team should put together people with business knowledge, customer knowledge and data knowledge, and make them have regular conversations about what they do. This, along with good tools and individual skills, is the third key to successful data-driven marketing.

Take Action!

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