The Media Data Maturity Timeline

Data is complex. And it is easy to focus on the data itself rather than the outcome data should help create. Going on 20 years in media and digital marketing I have personally seen how data is adopted within media organizations, and there is a common timeline that everyone goes through.

In Sweden we have a saying that you can get “home blind”, that is a bias that means that you don’t appreciate what you got or that you assume things based on your own experience. I am guilty of this as much as the next person, and it has led me to make some significant mistakes. I was “home blind” to the reality of my customers, in the beginning of my entrepreneurship.

When I talked about first-party data, about a decade ago, no one really understood what I was talking about, or didn’t care. The most common comment was “Well, there is so much buy-side data that we don’t need to work with our own data”. And this was in most part true, but I didn’t want to admit it to myself, because I looked further into the future and saw something else. And at that time, 10 years ago, our potential customers had more pressing fires to put out; the decline of print, profitability, loss of media budgets to Google and Facebook. So naturally, getting ready for the next technical shift that was 3-5 years down the road, wasn’t really a top priority. And as a consequence, neither was my data services. 

The Do-It-Yourself Era

The most common approach was that media houses built their own data platforms. And most often it led to a 18-24 month project that then got cancelled as costs skyrocketed, or there was a shift in the strategy or there was a merger. Very few of those data platforms exist today.

But what is also very clear and what I have seen during this past decade is that the business comes last in a data project. It is mostly about laying the data pipes, not about the business use of the data. 

A great example is when we bought the Krux DMP (that later became the salesforce DMP), we installed it on over 150 websites, classified the collected data and finally got it all in the platform. Then it was time to build the audiences for the ad sales to sell. I was a technical project manager and not at all sales oriented. So naturally I contacted Krux and asked “Ok, so how do I build a good audience” (the question was a bit naive, I know). The response was very fleeting… “Well, it depends on what you want… you could do this.. Or you could do that…” There was no interest in saying what to do as this would mean that they would influence the business decisions and ultimately be responsible for the results. 

That was the moment I realized: A DMP is just a storage locker. It doesn’t sell anything. The tech vendors were happy to take our money for the infrastructure, but they took zero responsibility for our revenue.

The Most Common Data Timeline

1. Doing nothing. Many avoid taking on data as it is outside the “comfort zone” and perceived as both complex and expensive. It might sound a bit harsh but in my experience change is more fear-driven than opportunity-driven in many traditional media houses.
2. Free or integrated tools. This is usually where most media houses start, using the data capabilities of the ad server or SSP, or maybe a free tool like Google Analytics. The issue here is that those tools are built to optimize bids, deliver on campaigns or in the case of GA to validate Google’s services.
3. In-house data project. When you realize that the free tools aren’t working comes the next phase: Talking to IT and getting the “We can build that, it’s not that complex” response. Which in turn leads to a technical project that takes longer than expected and when all the data pipes are laid gets delivered to the business-side, who in turn say “Ok, what are we going to do with this?”. The data is hard to realize into the business, doesn’t get broad adaptation and ultimately becomes a “nice to have” that is draining technical resources and budgets.
4. The Franken-stack. This is often an extension of the in-house data project, whereupon realising that what you built isn’t enough and you need to integrate and add on some other external systems like contextual analysis or a CDP. What you end up with is still a very technical focus with several systems stitched together that increases the integration overhead. At this stage the business side is often more involved which increases the demand for development resources.

The Data Pipelines You Need

This is the complete first-party data foundation, because there is only so much data available to manage: 

• Online data – collected from the browser and the websites you control, such as: language, cookies, ID’s, URL’s visited, categories visited, etc.
• Content data – contextual analysis and behaviour from users interacting with your content and classifications of this content.
• Relational data – subscription data, contact information, payment information – information that let you talk directly to an individual.
• External data – 3rd party data that can be added to your online visitors and logged in users. Such as: income, accommodation, car ownership, family, etc.
• Ad serving data – Information that is generated from programmatic bids, ad serving and user interactions with your ads.
• ID Management – Information on who has what data connected to them, to ensure that the data can actually be used in the business.

This is all the data you need to manage. But in order to do so you need the following platforms at a bare minimum: 

• Data Management Platform (DMP): Manages the online data collection, exports and audience building. This DMP should not only be integrated with your ad tech but also your editorial and CMS-stack.
• Customer Data Platform (CDP): Manages your logged in users and the data connected to them. Needs to be integrated with the DMP in order to be used in the ad and content targeting.
• Contextual analysis: Structures and classifies your content so that users can be segmented based on behaviour as well as for contextual targeting.
• Data Enrichment Services: The content data is not a great match for how advertisers describe their customers, they often describe them based on age, gender and socio-demographic information. To deliver those types of audiences you need to enrich your users with this information.
• ID Management: To ensure addressability across platforms you need to leverage one or more ID-solutions and maintain the addressability.

The Business Challenges

Having seen many media houses go through these phases I decided to create a platform that already has the data pipelines connected, integrate all the systems needed to support a modern data setup and instead focus on the business side of media. The data tech should just exist. And be as cost-efficient as possible. 

The Sales Confidence

This is probably one of the biggest challenges for media houses today. They get challenged by agencies that put the ad spend with Meta and Google rather than with the local or national media. It is hard to argue over data. And with the marketing currency set in clicks, likes and conversions, media sales often come up short. 

The key here is to reclaim the conversation, set your own marketing currency and be able to prove your point with data, data you own and control and that is easy to understand and communicate.

This requires hands on tools that you can put in the hands of your sales reps that support them in their daily work, talking to advertisers.

The Marketing currency

I touched on it above but the marketing currency is key to changing the conversation. And having done media sales myself I know how to change the conversation. And it needs to start in the first meeting: 

1. Use data that you know you can deliver on.
2. Define the market.
3. Define the customers.
4. Define the target audience.
5. Deliver reports that prove the delivery.
6. Integrate and track all the way to conversion.

This requires tools that don’t come out of the box from any DMP. These are tools that need to be built specifically for media. Tools that integrate with your own first-party data and can match your targeting data to the reporting, to the market and to the advertisers’ customers.

The Advertiser Integration

A key to the success of Google and Meta is in my opinion the analytics and integration layer that they provide. For Google it is Google Analytics and for Meta it is the “facebook-pixel”. These tools help them validate the delivery and measure all the way to conversion. 

If you can create this connection, you will get much closer to the advertisers, understand their business better and be the advisor rather than the sales rep.

But in order for this to be effective it needs to be based on the same data as you have on your own visitors and subscribers.

The Subscription Sales

A huge challenge for most media houses is still converting print to digital subscriptions, and to rejuvenate their audience. It is after all the reason advertisers spend budgets with you – to get in contact with your audience. 

What I have seen is that often the editorial and commercial sides are divided, not only organisationally but also technically. They describe the same audience in different ways, and the data is often also separated. 

In order to cater to your audience you need to use the same data across the whole business and leverage investments in commercial data to also support editorial with data.

Closing comment

Looking back over the last decade, the biggest mistake media houses made wasn’t ignoring data, it wasn’t not acting; it was believing they had to build everything themselves. They spent millions laying the “data pipes” while Google and Meta spent millions perfecting the “sales currency.”

Independent media cannot afford to play catch-up on infrastructure anymore. The survival of media depends on shifting focus away from tech debt and putting it entirely on commercial outcomes: giving your sales reps confidence, providing advertisers with transparent ROI, and converting casual readers into loyal subscribers. 

In the end you need to fund your journalism, a democratic non-negotiable.

That is why I built Samhub. I took the DMP, the CDP, Contextual Analysis, Data Enrichment, and ID Management and combined them into a single, cost-efficient platform that lets you bypass the painful maturity timeline and jump straight to profitable outcomes. What is 4 years of data projects worth? And what is a 4-year better time-to-market worth?

But more importantly, I built the business tools that sit on top of that data; our market analysis, our customer matching, the automated reporting & campaign analytics, our Editorial API and integrating with advertisers using the same data that you have as a media, so your team can stop selling impressions and start selling results.

Our data pipes are ready to use.

The question is: are you ready to connect them to your business outcomes?

Martin Bergqvist
CEO & Founder, Samhub