Adopting a Data-Driven Mindset as the Third-Party Data Era Draws to a Close

The industry’s 25-year reliance on third-party data is coming to an end. Government regulations in the US and EU, along with major cookie restrictions on Apple’s Safari and Google’s Chrome browsers, pose an existential threat to some providers. But advertising will go on. What everyone wants to know is, ‘what are the brands going to do?’

As these changes were long-time anticipated, many medium to large-sized brands are duly looking into ways of better controlling and obtaining more value from its own data. “First-party data first,” says Sir Martin Sorrell and nearly everyone agrees. But some brands are not seeing the results they want after investing heavily in the people and the technology. Why?

Truly data-driven companies embrace organizational and procedural changes…not just throw money at the problem. To be a data-driven, privacy-complaint, nimble operation with full transparency into your digital advertising campaigns, you need a plan. The plan must be built around an overarching analytics program, where your tools, teams and their roles and responsibilities are orchestrated towards achieving the organization’s particular business goals. Attention to detail is crucial here are you’re going to deal with a plenitude of challenges, as the complexity of the ecosystem – both technical and legislative – is only increasing.

Yet, companies that have institutionalized a data-driven mentality are demonstrating quantifiable success. Google, in partnership with BCG, built the Digital Maturity framework which shows how marketers further along in the maturity process are seeing improvement from data-driven marketing: “Marketers successful at data-driven marketing are seeing revenue increases of as much as 20%, and cost savings of up to 30%”.

Google/BCG define four levels of business maturity:

• Nascent – Campaign-based execution mainly using external data and direct buys with limited link to sales
• Emerging – Some use of owned data in automated buying with single-channel optimization and testing
• Connected – Data integrated and activated across channels with demonstrated link to ROI or sales proxies
• Multi-moment – Dynamic execution optimized towards a single customer view across channels”

This framework is currently utilized by Deloitte and Google in the publishers’ space -, digital transformation for news and media companies across the globe is driven through the implementation of 9 use cases, which built around the same maturity stages and pillars as BCG: data usage practices, personalized experience for users, the need for cross-functional teams.

Many companies in SEA are currently at nascent or emerging stages. It’s here where a company pays for a number of tools and some expectations are built on how these tools should pay off.

When recently speaking to a brand prospect, he said, “Ok, we have invested in a [particular solution/tech stack] but why doesn’t it tell us how exactly we should be improving our bounce rate?” The reality is it’s not enough to just own a spaceship, there is a craft and a journey. And to make things even more complicated, expertise, processes and well-oiled cross-team collaboration – or, in other words, the actual cost of operating the spaceship.

At the point, when a business feels like its analytics tech is not delivering up to the expectations, many tend to hire more professionals, which (surprisingly?) doesn’t automatically solve the problem either. Another prospect recently said, “We hired all the best talent, including people from Silicon Valley, but we didn’t actually achieve much”. 

Currently, the market offers specialists with a deep, but a quite narrow scope of expertise – it’s hard to find professionals who connect the dots between business objectives and technical challenges. It’s even harder to establish cross-functional teams rather than relying on great solo minds.

So how can a company navigate toward digital maturity? Here are some steps to take in preparation for becoming a data-driven company.

1. Define your goals (or problems) and adhere to a time frame. Analytics framework should be built upon and tied to the resolution of specific business goals/problems (best communicated in $$ value). These goals or problems should have the time frame (expiration period), which also dictates the lifetime of your analytics solution.
2. Identify what sort of skill sets would your organization need to deliver against these goals or to resolve the found issues, and map these requirements against your existing talent pool and structure. The biggest challenge at this stage will be to achieve synergy between the brilliant solo minds that you employ by building strong collaborative teams.
3. Design use cases for bigger and smaller victories and place it along your timeline. As you will move your company towards digital maturity, progress from easier to more complex cases – for example, from simple media optimization to sophisticated multichannel predictive models, personalization engines, etc.
4. Design your data model and make sure you have the required tools. Now, once you have a well-defined analytics strategy built around concrete business problems and use cases serving to resolve it, you are able to establish what sort of data and tools are required to make your plan work.

The catch here is that the tools come last, while many think (and act) the opposite.

So, your spaceship is ready to take the organization to the moon. It may not be a smooth ride, but it is going to be exciting!

In conclusion, don’t forget to relay the successes of benefiting of your first-party data, the toolset, teams and processes you’re invested in – using language that is understood by C-level management, of course (revenue or cost savings work best). Establishing value realization will not only bring credit to your team but also secure buy-in from the management. This will result in establishing a data-driven culture across the whole organization.