We use cookies. You have options. Cookies help us keep the site running smoothly and inform some of our advertising, but if you’d like to make adjustments, you can visit our Cookie Notice page for more information.
We’d like to use cookies on your device. Cookies help us keep the site running smoothly and inform some of our advertising, but how we use them is entirely up to you. Accept our recommended settings or customise them to your wishes.

Marketing's Primordial Soup

As an agency with deep roots in the technology of marketing, we’ve always specialized in bringing together disparate data elements to produce actionable, measurable results. Initially we provided data list management, then traditional marketing databases. Now we focus on industry-specific performance marketing databases that include technology-enabled analytics and overarching omni-channel, customer-centric solutions.

The rapid and almost frantic move to digital marketing is the main reason that marketers can't be complacent about their marketing database. The data and events that would result in actionable marketing information in the not-so-distant past were slow to arrive, relatively stable in variety and even predictable in nature. While implementing marketing solutions is never a trivial task, the challenge is now centered on integrating the most granular data across channel, media, and customer in a way that results in accurate, easily consumable insights. We need to drive omni-channel marketing strategies. In short, no matter how many grains of data elements from different media and channels it takes, we still want to know as much about that person or device as we can because it improves our marketing result.

The digital convergence brings a plethora of custom data elements, events, and artifact elements that need to be examined. For example, each website, social, gaming, loyalty, and user-interactive platform that consumers use on a daily basis collects different data points and has its own data event definitions. Search engines, tagging platforms, and data management platforms also generate and collect different data as well. Almost none of these events or data are uniform, and they need to be brought together in a meaningful way to allow marketers to fuel their customer-centric strategies and tactics. This marketing primordial soup of data requires that we look at these events in two broadly defined ways:

  • The first is events that by themselves and without any additional analysis can trigger a further engagement with a customer. Examples here might be an abandonment (shopping cart for example), proximity (physical presence inside a store), an email link click, and many others. Without any further information, marketers can pre-build an action trigger from these events without requiring any further analysis or even knowledge of exactly "who" that person is. The focus here is on reacting to a known important event within a reasonable time, where “reasonable” can be near real-time. Even a simple cookie can be the beginning of a series of “remarketing” actions that a marketer can undertake.
  • The second way is to examine events that are a series of disparate data items, where each one by itself may not represent an event that a marketer would tie to an action by itself. But intelligently recognizing omni-channel event patterns is exactly what marketers need now. These types of data points, for example, can include an organic website visit, a search engine referral, display ad impressions, inbound customer service calls, retail store visits, and customer email reactions. But these different data events must be associated correctly; this is no small task in itself. When connected properly together, these events provide insight into a customer journey storyline. This second way is the “middle” of the funnel on the journey and where the least amount of insight has typically been available to marketers.
Once the data is collected and brought together in an environment that can not only tie the events together, but can also tie event sequences to pre-defined actions, the focus becomes what that action should be. This requires foreknowledge of what past journeys may have worked to drive customer engagement. In turn, to answer this requires the collection of huge sets of unstructured events in a data repository to enable segmentation, analytics, and analysis. Using sophisticated analytic tools and approaches, the data is analyzed to pinpoint successful journey experiences. We can then apply “next step” actions to the known event chains where we have recognized them. This allows us to optimize customer engagement in mid-journey and, if necessary, in near real-time.

Even without knowing "who" a person is, we can market relevantly to them and help to drive their journey in a positive way for them and for the marketer. The added plus to this work, beyond helping prospects and customers in their engagement with the brand, is that the result of an interesting portion of this "anonymous" marketing yields a match of devices to eventual real, live prospects and customers.  This knowledge can be turned into higher quality actionable marketing offers to drive even further engagement.

Our understanding, as an industry, of the usefulness of this data soup and our ability to ethically execute against it is getting better and better. Putting the customer at the center, while sometimes a difficult transition, pays off in the best ways: Increased revenue, increased revenue velocity, and cost-effective marketing.