What does a data-driven ad model actually look like?
A view from Rupert Staines

What does a data-driven ad model actually look like?

It is often said that data-driven marketing is the future of marketing. Rupert Staines of RadiumOne explains how to turn theory into reality.

Straight off, so as not to lose anyone who thinks this isn’t relevant, this piece is NOT about "online advertising", nor is it about digital.

That’s because digital technology has manifested itself in two key ways that have implications not just for marketers but for the entire business around them.

Consumer consumption is much more sophisticated and the increasing number of touchpoints has made them more elusive to reach. In turn, this means the "interface" (an abstract term) marketers use has also become more complex.

It’s like putting a Ford Model T driver in a 747 cockpit and asking them to fly the damn thing.

We’ve gone from the four Ps of marketing (product, price, place and promotion) to a cockpit interface extremely fast, and that impacts the whole industry not just the pilot.

Thus, a data-driven ad (or marketing) model is about recognising all the available data sources on the customer journey and then transforming and organising your entire business so you can harvest them in a way that moves from A (consumer) to B (seller) in the most efficient way.

The process

This approach to overall business transformation should start in the marketing department – the one most equipped to understand the consumer.

Step 1, data awareness. This requires a clear understanding of where your business wants to go and all the data sources that can potentially get you there.

Essentially, you’re asking yourself, do I have all the pieces that I need to complete a jigsaw puzzle that is a picture of a customer wanting to do X, and do I have the tools to put my brand in front of them to help them achieve their goal?

Step 2, data management. This is about having the software, analysis tools and data partnerships in place to exploit this ecosystem to its full advantage – your digital plumbing.

The most obvious manifestation of this step is big enterprise and consultancy companies, such as Accenture, IBM and Adobe, now having a seat at the marketing table.

They’ve gone from back-end IT to the front of the business where the "rubber hits the road" but it is new territory for marketing personnel.

Step 3, data science. This is about applying science to organise these disparate data points as close to real time as possible which then creates predictive solutions for your strategy – be it any piece of communication (such as an ad) or content (such as a programme).

This step is the geeky stuff but it constitutes the codification and relevance of the burgeoning AI sector to the marketing industry. 

Netflix’s recommendation engine is a simple and pure example of this. As a consumer I don’t have to program any preferences into the system to get recommendations. All I have to do is use the service and it does the learning for me. 

Essentially, that’s what you’re trying to achieve across your entire business, making it artificially intelligent to a point where you are far better equipped to benefit from the inherent, adaptive learning that will drive greater success.

Alongside Netflix, Booking.com and Amazon are great examples of applying the data-driven ad model theory, in that they’ve organised their business around the idea of providing a service to customers who get what they want in the most timely and efficient way.

What you need in place

Creativity: If you don’t have the creative process baked into the data-driven ad model, you negate its effect from an advertising viewpoint. The best creative minds must be bought in quickly to this new world system so the creative spokes become part of this wheel.

For example, easyJet applied AI to years of customer data and combined this with real-time data to create smart communications based on predictive analytics. The company's CMO stated, "It’s improving the airline’s efficiencies, bringing down costs and driving customer satisfaction levels".

Without intelligence, creativity is sucked of all its oxygen. Conversely, data intelligence on its own means nothing. It’s when AI and creativity are meshed together, that the best strategies and plans can be proven.

Speed and efficiency: Today’s competitive and technology-driven world mean you need to put your message in front of the consumer fast or you lose the moment.

Consequently, you need to use programmatic technologies to provide the efficiency to transact in real time so you can buy that user, at the right time, in the right media. Think of a programmatic media transaction as "Martini Media" – anytime, anyplace, anywhere.

Creative agility: This fuses the above two points. You need multiple creative options so the right one can be picked to address a particular scenario. Think of it like a pack of cards and a game of bridge (or gin rummy if that’s easier!)

Systems not silos: Transforming a business so that marketing is most likely to succeed not only requires merging data sources but also joining the dots between all aspects of the business. It’s about working as a holistic system, not in silos. 

The best talent: Sounds simple but those most likely to succeed in the future will have the very best data scientists as well as the best storytellers to put your brand front and centre at that moment the consumer(s) most want it.

Rupert Staines is the European managing director at RadiumOne.

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