Why martech data should drive ad tech
Customer data, the engine that drives your ad campaigns, is often divorced from the ad tech stack. Columnist Chuck Moran discusses how integrating martech and ad tech stacks can enhance programs, optimize delivery and maximize ROI on media spend.
It’s not a shock to say that chief marketing officers today are likely spending significant time on tech solutions management — often taking them away from their core marketing focus.
A big portion of a CMO’s role today is to drive the marriage of martech and ad tech into a unified stack that provides a 360° view of their consumer. This more complete consumer view is what powers a CMO’s efforts to develop and deploy rich, integrated marketing programs designed to engage and nurture customer relationships, as well as drive consumer acquisition and advocacy. But the processes of integrating martech and ad tech can be all-consuming, especially because platforms that comprise the technology stack do not always speak harmoniously to one another.
To avoid drowning in the complexities of marrying platforms, I suggest an iterative approach to integration that focuses on data and its complete journey through the martech and adtech platforms. In an ideal world, data is the engine that gives marketers the 360° consumer view. It enables a marketer to identify and enhance all the trigger points in their relationship with consumers, powers their ability to build macro- and micro- consumer target profiles and provides them specific direction for media placement, messaging and media types.
By focusing on how data flows through the stack and impacts marketing programs, a CMO gets a much clearer path forward for their integration efforts.
Customer data is the engine
Generally speaking, direct and indirect customer contact data is collected, combined and enhanced within the martech stack and then leveraged within the ad tech stack. Data can come from CRM, a CMS and transactional account platforms and include triggers such as visits to a corporate website, online or brick-and-mortar store transactions, interactions with a company’s social platforms or signups to an email list.
The next step is data modeling with both first- and third-party data sources. This involves looking at profiles and patterns within the data set to draw conclusions not only about who your target consumer is (demographics) but also how the consumer behaves (psychographics) — this includes shopping habits, typical lead time to purchase, triggers for appropriate cross-sell and upsell and so forth.
The insights gained from data modeling within the martech stack can then be brought into the ad tech stack and ultimately made addressable, alone or in combination, through physical address, email address, IP address and mobile device ID (UDID), ensuring you reach the right consumer at the right time with the right message.
It sounds very simple and straightforward: customer data should be the engine of the ad tech stack. However, the reality is that customer data is very often divorced from the ad tech stack. I’ve pointed to one of the culprits for this messy relationship: silos. Those silos are a byproduct of a marketing discipline that is still shedding its “traditional media” (a horrible legacy term for non-digital media) roots.
We may be 20 years into the digital revolution, but simply looking at the disparity between media budget allocated to platforms and the amount of time consumers spend with them shows we are still in an era of catch-up. And we are catching up quickly — progress is driven by the targeting capabilities digital platforms afford, as well as the emergence of programmatic buying as a predominant means to access ad inventory.
Digital media, when paired with consumer data, is addressable media. That fact was quickly recognized in the early internet days with basic remarketing — a strategy embraced by nearly all marketers. Those early efforts ushered in an era of high-volume, high-repetition campaigns that for the most part were memorable for their annoyance.
Today, with greater access to first- and third-party data, and the explosion of connected devices (mobile, wearables, smart homes and so on), remarketing and targeting strategies can be much more sophisticated and customized. That customization is what is needed for a marketer’s message to actually cut through the glut of advertising that is bombarding consumers — and ensure that marketing dollars have a chance to deliver the ROI a CMO expects.
The methods of media buying have also rapidly evolved over the past several years. The defining characteristic of digital media buying was its immediacy — a campaign could be implemented relatively quickly as compared to other media types. The rise of programmatic buying methods has put that immediacy on steroids and has brought media buying much closer to the marketer.
The beauty of bringing the buying modality closer to the marketer is that it allows for much simpler deployment of data to target a campaign. Data from the martech stack can very easily be on-boarded into programmatic platforms (ad tech) and deployed to target very specific audiences across digital ad inventory.
Moreover, the lack of distance between the ad tech and martech stacks enables a more real-time bidirectional flow of data. That flow provides the martech stack with data to refine customer profiles and targeting schemas, which can be brought quickly back to the ad tech stack and deployed to enhance programs, optimize delivery and maximize ROI on media spend.
Fine-tuning the engine
Everyday marketing decision-makers at brands live the adage: Data drives marketing success. What makes it so difficult to build off these simple words is the complexity of the stack that marketers must use to manage that data. Managing the complexity, as pointed out, can consume one’s day — and budget.
To successfully navigate the data waters to a 360° view of the consumer, a CMO needs to commit to finding the right human resources to shepherd the integration of the martech and ad tech stacks. Having that resource available allows the marketing leader to focus on the data and its impact in understanding the consumer, creating meaningful and targetable segments, defining advertising strategies, programs and message, and helping identify optimization opportunities that provide maximum return on marketing dollars.