The volume and speed of data means that advertisers need to reassess how they make decisions. The sheer amount of available data means that the problem facing marketing, media and customer experience managers is no longer where to source raw material but where to focus their attention. An approach which is far from simple, whether you want to harness the potential of data to remain a market leader or simply stay in the game.
While professionals are increasingly aware of its importance, analytical approaches are still highly disorganised – even chaotic – and applied in isolation. Knowing which projects to prioritise tends to be the weakest link e.g. structuring, data historisation, choosing which data topics to industrialise, etc. By creating links between business and data, data science can not only further the long-term vision but provide answers in terms of implementation.
According to Forbes, the most innovative marketing directors i.e. those who invested in data-driven approaches have seen their marketing ROI rise by at least 5%, and their performance by over 7%. The majority of these marketing professionals rely on a statistical approach to their marketing mix to identify factors of success and failure, and the impacts of online and offline to create predictive scenarios.
Data science is the in-depth analysis of data to optimise what already exists and create new growth drivers. Often described as analytical expertise, it only really comes into its own when the business side of things is fully understood. The difficulty lies in finding talents who have excellent analytical skills coupled with a sound business approach. This is very rare among data scientists, if we are to believe the head of marketing at an international brand selling food processors. In addition to their roles of statistician, translator and storyteller, they need to be able to share their recommendations with marketing execs who often have limited experience in this area.
In old-school organisations, experience, seniority and hierarchical status are often overvalued. Yet relying on one person’s opinion or the usual expert’s view is risky. By providing tangible and measurable data, data science can lead to new ways of doing things i.e. evidence-driven approaches. It is important not to fall into the trap of solely relying on data or intuition, the key is to combine experience and data to make real inroads. The luxury industry has perfectly grasped this winning combo by combining team experience, the vision of a renowned branding designer, and taking an analytical approach to optimise merchandising and in-store sales.
By asking the right questions, data scientists can deconstruct the decision-making process in order to identify flaws, bad habits and business conventions that may be toxic in terms of the ROI. Data science challenges established ideas, expert opinions and ways of working. While this can be initially unsettling for marketing executives and their teams, it is ultimately beneficial as a data-driven approach significantly improves performance.
The sheer volume of data coupled with powerful technological tools makes it possible to ‘dig deeper and dig faster’ resulting in greater clarity and pragmatism in terms of the decision-making process i.e. understanding ‘what we do’ and ‘how we do it’. For example, statistically modelling the marketing mix can shed light on a brand’s sales volume, turnover and product range in terms of different levers (TV, digital, price, promotion, product launch, trade marketing, etc.). It also makes it possible for marketing executives to isolate ROIs and growth levers, and adjust the budget accordingly i.e. to where it will have the greatest impact. Data gives information about the past to better understand the future. As it is totally neutral, it can confirm or reject intuitions, create scenarios, analyse weak signals and anticipate risks.
Hitherto scattered around the company, data has the power to unite people around a mutual project. Creating a data lake and rationalising media investments makes it necessary to get teams around the same table to collect the necessary data, and identify shared KPIs and specifications that are understood by all. Like it or not, working on a data science project puts an end to turf wars and encourages collaboration where silos once reigned (i.e. between IT and marketing, between HQ and country subsidiaries, etc.). Although to difficult to set up, this approach is highly promising as it allows companies to introduce collective intelligence mechanisms, which – in the long term – create positive dynamics throughout the entire organisation.
When coupled with a clear vision, data science is much more than a simple analysis method. It becomes a truly powerful transformation tool, and one which is still underused in France. To reap its full benefits, advertising professionals must avoid falling into two traps: that of relying on technical tools with no long-term objectives, and that of having a data vision but no roadmap.
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