Since 2008 David has been executive director and partner at Marketdata, the data driven Centre of Excellence for the VMLY&R network. Marketdata has been awarded CRM Company of the Year nine times, working with around 70 blue chip clients over several geographies.
David has worked in well-known data driven companies, including Rapp (Brazil/Argentina/UK), Ogilvy (Brazil), and Wunderman (Europe) – and implemented his first data strategy whilst group marketing coordinator at Electrolux UK in 1990.
He is a founding member of the Global Advisory Board of the Data & Marketing Association (DMA) USA and Advisory Board of the Indian Data and Marketing Association (IDMA).
A member of the worldwide Echo Award jury for the last eight years, David is a regular speaker on data loyalty and marketing at events across five continents.
Just in time for Data Privacy Day - 28th January - David is the first person up in our brand new Magic Numbers interview series, which will be looking at what data-driven creativity really means, best practices and potential blindspots.
LBB> What’s the number one question that clients are coming to you with when it comes to how they can better use data to enhance the creativity of their content and experiences?
David> Obviously creativity is never an end objective, but a way to help ensure a favourable result. The main question clients ask is how they can enhance effectiveness through the use of data to bring a greater ROI from their marketing budget, whether that be relevance of content or enhancing the experience.
LBB> How can you make sure that data is elevating creative rather than forming a wind tunnel effect and knocking all the interesting or unique edges off that make something distinctive?
David> Perhaps data has a bad name, a sort of hangover from the old direct marketing days, where it was often thought you had to choose between data intelligence and creativity, and couldn’t ever have both. With the correct data approach, creatives have information and are freer to justify creative angles and support their ideas with facts based on the data rather than through subjectivity. When data and creative teams are integrated in the same environment the creative brief would normally include behavioural and segmentation data, helping to stimulate ideas.
Of course, it would be remiss of me not to mention that data elevates creative through the measurement and attribution of results. When we can tie the results from our omnichannel communications back to specific campaigns and see the lift generated by a particular concept, there is no discussion over whether it worked or not… the data shows the way.
LBB> Can you share with us any examples of projects you’ve worked on where the data really helped boost the creative output in a really exciting way?
LBB> More brands are working to create their own first party data practice - how can a brand figure out whether that’s something that is relevant or important for their business?
David> Indeed, whereas most companies receiving transactional data – finance, retail, telecom, etc. – are usually very advanced in the first party data field, with very few exceptions, the traditional FMCG companies are dashing to catch up. There has been an awareness over the last couple of years that knowing your customers’ needs and being able to speak directly to them is something of strategic importance that cannot be left to third parties, partners or media companies. With the increase in D2C we have seen new transactional customer data made available to brands that have not had access previously, which often triggers the first party initiative. This movement has also seen a shift from the product management culture in silos, to a truly customer focused approach with integrated CDP strategies employed across all brands and categories.
LBB> We talk about data driving creativity, but what are your thoughts about approaching the use of data in a creative way?
David> When I started out in the agency world the ‘rockstars’ were the creatives who managed to generate the most wows from us business types. This position seems to have passed on to the data scientists who are able to use systems, in order to ‘extract juice from the data’. (Yes, they even have their own jargon.) From a not too distant past where the data guys were more academically oriented and needed to have marketing/business partners to apply results to reality, we now have a breed of data scientists who understand customers’ issues and apply this know-how to their analysis to guide strategy, subsequently partnering with the creatives to deliver the results. As they say, the whole is greater than the sum of its parts!
LBB> "Lies, damned lies, and statistics" - how can brands and creative make sure that they’re really seeing what they think they’re seeing (or want to see) in the data, or that they’re not misusing data?
David> There is a plethora of data available to marketers today, but we have to be very careful to ensure harnessing the correct data for the correct use.
We had an example with a telecom company who had measured which clusters of customers had the lowest CAC or customer acquisition costs. They then put together a strategy to focus on these clusters to generate optimal returns on the marketing budget, only to discover that these ‘easy to win’ customers were the least profitable in terms of value added services, average ticket value and payment of bills. A credit company discovered which customers had the highest engagement levels with their social media posts and aided by their agency decided to invest/target this audience, only to discover that they were often desperate for credit with low credit scores and very rarely approved when they wanted to sign up.
Generally, if you look at the lifetime value of groups of customers over time it is difficult to go far wrong when choosing your audience.
If we were to run predictive analysis to foresee who might buy/cancel/upgrade/etc., we would analyse historic data to build a model. This model can be back tested to gauge its accuracy and then tested to live audiences before adjusting or rolling out. If a test and learn strategy is used, based on solid data, it is very difficult to err too much.
LBB> What are your thoughts about trust in data - to what extent is uncertainty and a lack of trust in data (or data sources) an issue and what are your thoughts on that?
David> It is totally unnecessary to look at data and ‘bet the farm’ on what you see. As mentioned, when the intelligence from the data demonstrates a certain path, that path would be challenged and then tested to ensure satisfactory results. An important aspect is that as results can change over time, so algorithms are adjusted regularly to ensure optimal results, often using machine learning techniques to finely tune. Normally, demonstrating solid results tends to create trust and confidence.
We have been invited to work on projects where the data is old, incomplete, unreliable, etc. and expectations have not been realistic. It can be very counterproductive for a brand to use incorrect data, but can be difficult for them to admit this.
LBB> With so many different regulatory systems in different markets regarding data and privacy around the world - as well as different cultural views about privacy - what’s the key to creating a joined up data strategy at a global level that’s also adaptable to local nuances?
David> Data privacy can easily be an entire chapter, however from a practical viewpoint there are a few things that guide us.
GDPR and other privacy legislation, such as LGPD in Brazil, have greatly improved the responsible use of data and share many principles between them – as GDPR was the benchmark for several other countries. We apply the guidelines for data management, including opt-in, data consulting, eradication etc. etc. when designing a data strategy, but make sure that legal departments in each country are satisfied with the result or can help us adapt to any local nuance. Our standard CDP is configured to conform with the legislation so we do not have to reinvent the wheel each time a strategy is implemented. We talk a lot about the importance of data privacy and it is very important, but equally if not more important is data security.
LBB> What does a responsible data practice look like?
David> I would say a responsible data practice is one where there is an understanding that corners cannot be cut. There is a certain investment necessary to go about things in the correct way and if correct process is not followed, the fallout from a problem occurring can be huge for all.
Credibility is key in this area and there needs to be mutual trust with the correctly qualified people involved from a technical, legal and data protection standpoint.
LBB> In your view, what’s the biggest misconception people have around the use of data in marketing?
David> There seems to be a good understanding of first party data, with most companies now engaged in improving their strategies in this area. There is also a great understanding of anonymous data and its use for approaching clusters of their audience without needing to know who they are, if they have recently purchased through other channels in marketing departments. Perhaps the confusion is still around the meeting point between these two types of data and in fact if there should be a meeting point at all.
From a consumer point of view there seems to be a growing mistrust of companies learning information and using this to approach them without them fully understanding how ‘x’ company knows so much! There is possibly scope for companies to perform a little better when leveraging information to direct communications to consumers, setting them more at ease.
LBB> In terms of live issues in the field, what are the debates or developments that we should be paying attention to right now?
David> From a data and creative angle, one of the major challenges will be martech content delivery systems. In the past creatives would create ‘the entire piece’ which whether an ad or an email would be sent to the selected audience. There would often be a story told, with a logical sequence of information leading to a call to action once the consumer has passed through the AIDA (or AIDCA if you were a direct marketer) stages. Similar, if you like, to a bands’ album where the sequence of tracks has been carefully created to produce a cumulative emotional experience.
With the use of martech content delivery systems, we now write 50 headlines, 50 intro paragraphs, tied to 50 images together with variable data fields to ensure a personalised experience. The mix of ‘assets’ is put together based on the data to ensure maximum response from each consumer – a bit like a Greatest Hits album.