In my last post, I talked about how the Marketing Singularity has arrived. How the number of options marketers have to reach their target audience has gotten to the point (and will continue to grow) that they cannot continue to do things the way they did before. They have to change. And this is particularly true when it comes to the management of all of the data that exists — and will be generated in the future — in the digital marketing ecosystem.
Here’s an overview of different types of marketing data, how they are currently being managed, and what improvements are needed to address the Marketing Singularity…
Audience data is the data that advertisers use to target users. The data consists of trackable user data, such as cookie IDs and first and third party data, that is used to create audience segments. The initial application of such data was display ads. Display ads have been around for a while and there are modern tools for managing audience data; in particular, Data Management Platforms, or DMPs. As described in a Digiday article…
“With the rise of ad tech, advertisers now buy media across a huge range of different sites and through various middlemen, including DSPs, ad networks and exchanges. DMPs can help tie all that activity and resulting campaign and audience data together in one, centralized location and use it to help optimize future media buys and ad creative. It’s all about better understanding customer information.”
DMPs track users across different media and websites by stitching together cookies via a centralized ID database. However, a couple of trends are occurring that are making it difficult to obtain a complete view of user behavior:
- More and more content is being consumed within mobile apps, which don’t support cookies. In the future, it is very likely that there will be other media and devices that don’t support cookies as well.
- Popular marketing platforms, like Facebook, are not allowing third parties to track what is happening within their platform.
The providers of DMPs will need to figure out ways to extrapolate what users are doing as the customer journey becomes more fragmented. There are some startup companies that are taking an algorithmic approach to this problem; what is called cross-device tracking. This approach combines user behavior data on smartphones, tablets, and desktops, and then uses probabilistic models to link cookies across multiple devices.
Digital (or Marketing) Asset Data
The visual nature of digital marketing has created an explosion of digital assets — videos, graphics, etc. The volume of digital assets will continue to increase at a rapid pace as new marketing platforms are introduced into the market.
Many organizations are storing their digital assets centrally in what is called a Digital Asset Management (DAM) — or a Marketing Asset Management — system. The problem with such systems today is that many of them are not integrated with the systems marketers use to execute their marketing activities, requiring manual work to transfer assets from one platform to another. The integration between DAMs and execution systems is just starting but will soon become critical, as the diversity of customer touch points and the volume of activities increase.
In addition, due to the fragmentation of media, there needs to be a way to evaluate the performance of assets across marketing channels; for example, a particular image may be used for multiple pieces of content such as a Facebook post, a landing page, and a display ad. This could be accomplished by ingesting performance data from execution systems into DAMs, or by having a centralized data store that contains both the creative assets and its performance with respect to each use (see Performance Data section below).
It comes as no surprise that a lot of data is generated by the systems that execute activities, whether it is a display ad executed in DoubleClick, a search ad executed in Marin Software, or social messages generated in Sprinklr. Execution data is siloed within the execution systems and from an execution perspective, there is no strong need to bring data together from disparate systems. However, as I will explain later, it is important to bring together certain aspects of execution data to understand overall marketing performance.
Corporations have stored important business data — like sales transactions — centrally in large, internal databases, known as data warehouses, well before the rise of digital marketing. The Marketing Singularity won’t necessarily have a big impact on the volume of business data generated, but it will become important for organizations to develop the ability to integrate their business data with their marketing data (performance data, in particular), in order to understand the true impact marketing is having on their business.
The data onslaught that comes with the Marketing Singularity affects no area more than performance data. The ability for an organization to effectively absorb all marketing results will be critical to manage the complexity of digital marketing and to drive marketing impact and efficiency. Performance data will become the compass for marketers in the fast-changing marketing landscape.
Unfortunately, this is the one area that lags behind the others in the marketing ecosystem, in terms of how data is being managed. Marketing performance data is all over the place; it is generally not managed centrally. There are compelling reasons why performance data should be brought together, as I have discussed before. By having a centralized store, marketing organizations can:
- Have a single source of truth that can be used to make timely decisions and discover actionable insights.
- Have a one-stop shop to understand the performance of their marketing activities that span channels, campaigns, brands, products and regions.
- Make in-flight optimization decisions on cross-channel campaigns based on the latest results.
- Have the foundation for an enterprise-wide, standardized marketing measurement system.
- Increase transparency and collaboration in their interactions with agency partners.
But bringing together performance data is not easy because:
- There is so much volume and diversity of data from different marketing channels and systems, each system representing data in a different manner.
- New data is being added at a fast pace and metrics need to be updated often. The best example of this is social media metrics, which are constantly changing.
- The nature of the underlying data changes as marketing platforms evolve. For example, data for Facebook, Twitter or DoubleClick continuously changes as new ad types are introduced.
- Global organizations have many instances of marketing accounts and properties (e.g., hundreds of websites, Facebook pages, DoubleClick accounts, etc.), making it difficult to organize their results.
- Marketers need to make decisions that span both the left (quantitative) and right (creative) sides of the brain and as such, they need both highly structured performance data and highly unstructured marketing asset/creative data at their fingertips.
- The questions marketers ask of their data and the way they want to organize it constantly change.
In summary as I discussed in the previous post, one of the challenges of the Marketing Singularity is effectively “absorbing” all of the marketing data that is generated; not an easy challenge considering the volume, velocity, and diversity of marketing data today. Moving forward, it will be imperative for the data platforms in each area — audience, digital asset, etc. — to continue to scale and address new marketing channels and devices as they become available. Then, in order for marketing organizations to understand what is happening and to make timely decisions to improve their effectiveness, it is critical to bring everything together with a performance data layer that delivers to marketers the information they need, when they need it.