In a world of cross-channel advertising, first-party data is the most valuable and scarce resource available. First-party data is the data that aggregates the most accurate visitor behavior: content consumption, website visit patterns, as well as product and service interests. However, the reality is that most advertisers will only have access to a few sources that can accumulate and deliver this type of data.
With this in mind, what outcomes would we have if a group of companies that shared mutual interests came together and organized a first-party data cooperative? A joint effort is driven by the common interest to run smarter audience data and signal powered marketing. Theoretically, and for the sake of this article, this group would function as a micro API economy. All members would benefit from access to first-party data which, if agreed upon, could be exchanged through appropriate audience-based or transactional means.
Let us consider two different scenarios to see how such a situation could play out:
Scenario #1: Private Club, First-Party Data Cooperative & Building a Marketing Signal Network
Consider a situation where a loyalty company exchanges data with different industry partners: finance, retail, travel, insurance; the possibilities are endless. These partners could then share data as well as run campaigns through the loyalty company’s website to reach an audience of customers. As a loyalty company delivering ads within their primary online property, pressure for better results, in this case, conversions, would be ongoing. What if this same organization would alter its business model of solely serving impressions, and instead pivot towards becoming a data cooperative that powers campaigns for its customers with purchase and behavioral signals?
If you’re using software such as Adobe marketing stack, IBM Campaign, and Interact, or UBX solutions, you can then technically qualify as a loyalty customer, whom we’ll now call a conductor. A conductor would be capable of defining a value chain of companies that would benefit from either sharing particular types of first-party data or related audience signals.
For example, a banking partner just indicated that Mr.X get approved for a mortgage (let us also assume there are a few hundred similar instances identified over the same week). There is immediately an audience signal for a chain of partners that a high-value audience segment is available for advertising opportunities. Partners such as insurance companies, security systems, and internet providers (just to name a few), can then automatically prepare to promote their relevant products to this audience segment. At this point, the companies that received a signal gain access to a shared audience segment where the frequency and apparent risks of customer ad fatigue are controlled by the conductor group using specific campaign logic automatically powered by one of the software mentioned above.
A banking partner company that sends the initial audience signal can in return receive additional first-party data or revenue sharing from the partners involved in the value chain. Interesting enough, the software used for the initial audience signal indirectly becomes a big data campaign enabler for the entire chain. This process would involve millions of profiles and real-time executions while powering the whole advertising initiative.
Scenario #2: Using Purchasing Trend Behavior Change as a Marketing Signal
Let us consider another example where changes in purchasing behavior functions as the audience signal. In this case, the retail partner sends a message that somebody just started buying baby products (diapers, baby food, and other baby care products). This information works as a direct signal for complementary cross-selling across other retail partners.
Some might suggest that similar results are obtained by using second or third party data providers. However, can these parties provide real-time responses, how far back is this data dated, and can the data sources be trusted to provide accurate information?
If the conductor company is technologically savvy, they can take this approach to the next level by taking ownership of both a demand-side platform (DSP) and an audience data platform (DMP). Once the conductor has acquired the audience data, they may set up a specific logic where audience segments pass through a DSP and DMP solution via IBM Universal Behaviour Exchange. In this case, our conductor company can deliver cross-channel, real-time marketing opportunities to its partners while running additional revenue streams by managing campaigns that have a larger reach for the same valuable segments.
Another opportunity lies in expanding into look-alike modeling (finding similar people) that match specific first-party segments enriched with first-party data from partner networks. Now, whether to run this at scale on an impression-based model or charge big dollars for each conversion depends on the company that runs both the IBM side of the stack and DSP / DMP combo via Universal Behaviour Exchange (UBX).
IBM Becomes a Power Engine for a First-Party Data Cooperative, Signal Synchronization & Campaign Execution
Here are the six main points to keep in mind when accessing these potential scenarios:
- Controlled audience / first-party data and signal exchange via IBM marketing tech. stack
- Increase in revenues by gaining access to audiences ready to buy
- Gaining access to marketing opportunities that would otherwise not be available
- Increasing reach through shared audience data
- Having a conductor company take control of managing marketing platform complexities
- In the case of no leader / conductor – running UBX enabled and controlled audience data exchange
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