5 DATA MARKETING TRENDS FOR 2017

In 2016 (as in 2015), data remained at the centre of attention for marketing decision makers as much as DMP (Data Management Platform) which has seduced numerous marketing services.

1- Removing organisations and data silos

For 20 years, each time a major evolution arises between brands and consumers, organisations tend to add a new layer to the organisation before diluting it globally.

This is true for marketing transformation which went from a product centric approach to a customer centric one: a CRM department with its own processes and tools has been implemented and added to the existing organisation and required several years before companies realised the client relationship was the concern of the entire company and not only the concern of the CRM department.

This happened to digital: companies created web departments, then digital departments with the rise of mobility. Today, we’re talking about digital transformation where all company departments are concerned.

Big Data and Data Science have not escaped that rule: companies have setup DMP for digital data, web analytics department adding up to client knowledge and research department who usually work independently with their own set of data analytic and output tools.

In 2017, data driven companies will unify data within a unique client repository (the famous 360° vision) accessible by all. As for the CRM and digital, data exploitation will become a stale for each department according to their own problematics.

2. DMP/marketing automation coupling

DMP implementation has enabled brands to improve their client knowledge and to build segment based essentially on targeting business rules. Afterwards, DMP users went further by integrating predictive models based on behavioural browsing or purchase patterns. But a DMP on its own is useless and moreover offers no Return on Investment.

In 2017, the most advanced companies will worry about increasing the level of automation of their campaigns, from the collect of data through on the fly scoring to cross-channel activation and performance monitoring.

Once these scenarios automated, the campaign manager’s craftsmanship will evolve. Presently, a campaign manager spends most of its time setting up the campaign where in the future it will lean towards performance analysis and fine tuning existing campaigns.

3- Taking into account the client’s life cycle to become customer-centric

Scripting the client relationship will come from a deep understanding of the client’s life cycle or it will face perverse effect. A campaign’s ROI will individually be well improved but could have negative effects on the value of the client base in the midterm.

A global approach to ROI based on client value is therefore crucial to preserve its potential, notably by working on indicators like Customer Lifetime Value (CLV).

Right now, most companies work on a campaign strategy, then look for the most reactive targets for each campaign. To counteract the fact that most reactive customers are often solicited, they set marketing pressure rules which prevent an individual from being solicited more than x time within a specific period.

This operation isn’t optimal because it does not take into account the client’s life cycle: for instance, a customer cannot be solicited on a campaign where his reactivity is high because his marketing pressure limit has been reached.

A paradigm changes for 2017 must occur in order for each client to have a personalised campaign strategy plan according to its appetency, its value and life cycle.

4. Market research and Big Data convergence

With the explosion of the amount of data available for companies to refine their client knowledge and better target them with their marketing campaigns, one can wonder if traditional market research can converge with Big Data.

In 2017, more and more bridges will appear between the two. Some are already existing. From a simple online survey, it is now possible to track down the corresponding visits on one or more websites. Within audience research, we can now compare what the respondents say and what they really do. It is also possible to geo-locate IP addresses and therefore enrich profiles with socio-demographic open data.

Client activation, does not limit itself to pushing products, couponing or re-targeting on abandoned basket. We can utilise web tracking to probe internet users according to their browsing behaviours and triggering pop-ups or emails containing a link towards a customised questionnaire according to a certain number of times the user re-visit the site, time spent on the site or reached a specific page. This is very useful when you wish to obtain feedback on your site’s ergonomic or feedback on parts or all content of your website. Where A/B testing answers the What? Survey answers the Why?

5. B to B Open Data

2017 is the big revolution for B2B companies: institutional Open Data becomes more and more available and it becomes easier to obtain businesses data to better target customers according to their activity, their size… Or to better optimise expansion strategies on geographic areas by focusing their actions where potential clients are the most present.

But this data is not only useful to B2B: retailers will be able to use it to optimise their network deployment strategy. Combined with Open Data, it will be possible to map active areas according to their market potential and competition presence.

Finally, let’s go back to our 2016 predictions and see how far from reality we were:

Data visualisation expansion: from our point of view, the interest for data visualisation is growing but it remains more of a gadget (impressive magnificent 3D graphs) than a real tool enabling faster decision making process.

In store customer journey’s digitalisation: numerous tests have been realised by the biggest chains but few have revealed themselves as conclusive. For the consumer to accept to be tracked in store, a real incentive is required. The real solution is yet to be discovered.

Artificial Intelligence advances: we talked a lot about it in 2016 but essentially in the field of chatbots which have appeared on numerous platforms. The use of Artificial Intelligence is the decision making process is still to be confirmed.

Big Data at the service of Human Resources: if predictive analysis has invaded new generation recruiting websites to operate candidate/company matching, HR departments remain cautious about legal issues that raise probabilistic methods to determine the potential of an employee. The first proofs of concept should see the light in 2017.

Data monetisation: the implementation of partnership to manage the second party data has been developed in 2016 and new actors of third party data have emerged allowing to provide more precise and revealing data.

About the author: Thibaut Lagorce has dual competencies in computing and marketing. Thibaut has accompanied companies from all industries with the implementation of their data driven strategies and customer centric for 20 years.