At least, marketing as we knew it. What formerly was known as research has now been baptized into insights, data science or even consumer science. All synonyms? Well, not quite. This new terminology induces the greater importance of this discipline and its expected business impact. From big data to fluid data, from predictive to prescriptive data. It is all about trying to better understand and influence the business drivers through data. The quest for ROI seems to be at hand. For marketers and their agencies, this implies a shift from media KPI’s (GRP) to real business KPI’s (sales, conversion and branding effects).
This elicits at the same time a huge challenge, as the past has often proved that research was considered as a dull and specialist matter often incomprehensible, somewhat business sterile and non-practical. In general most people do not even like figures. So why would this all of a sudden change, now that there is abundancy of data? Because data driven marketing has really been paying off for some pure digital players (Google, Facebook, Netflix, …); they have become a sort of role model for many others. More intuitive and performant technology as well as cloud computing have played a crucial role in the breakthrough of consumer science. The data scientist is now one of the most sought after jobs.
Data driven marketing has already been paying off for pure digital players.
But even if we entered the area of big data, contradictory enough, companies often lack these crucial data. In the traditional media sector, for example, identity management is a priority for many. However, these companies are often only at the beginning of the journey. It is crucial nowadays to have a real strategy in collecting the right data, data don’t come so easily. Certainly when you are not a pure digital player. Moreover is it no longer a silo activity of one or another department. Data are (potentially) everywhere in a company, across very different departments and in many forms. The challenge is to relate them based on clear hypothesis.
Remember the saying: “More data kills data?” It perfectly summarises the huge challenges ahead. Actionable insights need two conditions:
- A clear product ownership at the business side in order to define the right tooling (technology, data warehouse …) to translate big data into actionable insights and help drive the business.I do not dare to count the enormous investments made by companies in technology, with very disappointing result. It is not strictly a matter of tools, processes and working methods; it is more about the right competences and skills. And this is where the principle of a flexible organization comes in, this is using a network of specialists instead of hiring fix teams.
- Only progressive minds in data analytics can translate the observed reality into entrepreneurship.There is an urgent need for so-called data sponsors. These are business people who can first define the hypothesis to test by seeking relationships in the mass of data. Subsequently they need to be able to pilot data scientists as well as to define data collection needs and resources (CRM, contact centre, social, website, events …). Swift business decision making and taking must be the goal. Big data analysis begins with step by step learnings and automated scenario testing.
Data are not the new oil, but the new soil.
Who should be this data sponsor? I strongly believe that it is the CMO. He or she should marry art with science. There is a great saying with regard to this: “Data paints the picture, creative tells the story!” It’s not because there is marketing automation, that one can automate the art of marketing. Data will never replace creativity; they will lift creativity to a higher level. Data will enable new and better business models, since algorithms create more time for reflection and innovation. Data will empower the great minds. Data are not the new oil, but the new soil. This does imply that a good CMO will have to be a big psychologist, understanding the motivational drivers fuelling big data personalisation algorithms.
There’s no need to say that not all business KPI’s are that easily to measure or prove. The danger of the data mania is to take the obvious for granted by not being critical anymore or underestimating the real complexity of marketing. Some interesting examples showcasing this :
- The actual debate concerning the last click versus the net contribution to the conversion funnel, giving a different view on the ROI of used websites
- The use of clicks or impressions as an indicator for branding effects, in the absence of better indicators … for the moment
- Mobile advertising effectiveness is measured through CTRs, whilst it is probably time to reinvent the concept of mobile advertising instead
- Measuring the leverage effect of the off line media on the digital conversion pathway is a huge challenge for data scientists, due to the different granularity of the data
- The discussions on the correct KPI’s for measuring content marketing is still going on
- Debates concerning the short term effects versus the long term advertising effects, are still very actual
In the end, marketing will never be an exact science, since it is a human science. And data will never change this. Nevertheless, thanks to the raise of data driven marketing the art of marketing will be more and more estimated at is true value. It will even be CFO proof. So maybe the next generation of entrepreneurial CEO’s will be the great CMO’s of today?