The balance of power has shifted from brands to consumers. The modern consumer wants contextually relevant experiences with brands, regardless of time, channel, and lifecycle stage from anonymous to known. If they don’t get the experiences they want, then the customer is more likely to switch to a competitor.
Achieving this goal of understanding the customer and providing relevant experiences was the focus of my recent webinar with Forrester’s Joe Stanhope, “From Mad Men to Mad Tech: Bridging Martech and Ad Tech to Optimize Customer Engagement.” In the webinar, we discussed the convergence of marketing technology (martech) and advertising technology (ad tech), along with the role identity resolution plays in closing the gap between customer experience and expectations.
Identity Resolution and Customer Engagement
Resolving customer identities is vital for providing contextually relevant interactions. If you’re able to identify a customer across their various states and channels, you can more effectively analyze, personalize, and deliver messaging in the moment of need. Mastering identity resolution, Stanhope said, means you can develop coordinated customer experiences and achieve deep customer insights to understand more today and provide better information tomorrow.
Identity resolution also helps with harnessing customer context, which is similarly crucial for creating relevant customer experiences. If you can identify the customer, you’re able to better understand who they are and what they bought, as well as the customer’s history with your company, and the situational context that will determine whether a particular interaction is appropriate.
Data Management Platforms and Customer Engagement Hubs
Systems like data management platforms (DMPs) often talk about handling first-, second-, and third-party data for cross-device engagement. They were all the rage a couple of years ago, and still offer a viable option in specific use cases. For example: DMPs focus on anonymous data for targeting and segmentation – but this is at the ID or cookie level. The use case is really to help target and aggregate attributes around specific requests, such as “Starbucks lovers from California,” for ad display targeting.
These systems often require batch updates, which can take three to four days to complete, and use look-alike modeling tactics to acquire additional contacts that have similar attributes and characteristics. DMPs are used predominately for ad tech, ensuring more targeted audiences can be pushed to demand side platforms (DSPs) for display and acquisition.
On the other hand, there is an emerging class of technologies that focuses on a slightly different set of challenges, yet also offers very complementary capabilities. Known as (CEHs), they are really an evolution from solutions that previously were suites and platforms. Hubs are open in nature and can serve as the connective tissue in the enterprise to refresh the use of many in-house and standardized technologies.
Customer engagement hubs focus on first-, second-, and third-party data, and can handle anonymous information, but are more powerful for data on known customers. CEHs are often used for direct customer engagement or marketing. Targeting known customers in online and offline channels might include email, direct mail, SMS, mobile apps, IoT, POS, or any channel imaginable. The speed that they operate in tends to be a bit more fluid, which might include ingesting data and acting in seconds, minutes, or hours, at the speed of the customer. CEHs can have comprehensive analytics capabilities, including building and training models, A/B testing, optimization, and machine learning. The orchestration piece focuses on ensuring that all touch points are aware of one another and working together in tandem.
There are solid synergies between both systems, ensuring the anonymous insights that are captured by DMPs can be shared (where privacy is appropriate) with direct engagement systems and vice versa. Being able to add known profiles to ad targeting for lookalikes can be powerful, and bringing first-party insights from DMPs back to append to customer profiles is now possible.
The Martech/Ad Tech Convergence
Advertising and marketing are increasingly becoming part of the same ecosystem, which is reflected in a coming convergence between ad tech and martech. Called “madtech,” this convergence will blend the anonymous audience acquisition capabilities of ad tech with the relationship-management functionality of martech into a single ecosystem that allows a central point of control for both.
Stanhope said that the convergence between martech and ad tech comes as marketers experience a shift toward continuous engagement and more fully understand the need to turn business insights into action. As marketers increasingly take ownership over the customer experience, it is this understanding of customers throughout the lifecycle that will drive results.
The martech/ad tech convergence is fast approaching, and the need for marketers to provide consistent brand experiences across channels only hastens the arrival. The long and short of it is simple: You need to understand your customer throughout their history of interacting with your brand. Blending martech and ad tech helps achieve this goal so you can more effectively orchestrate contextually relevant messages across channels and improve customer engagement.