In 2019, brands are spending unprecedented amounts of money on data, and that’s because the promise of true people-based Marketing has never felt so near at hand as it does today. It is possible to envision a world in which data informs every Marketing decision, from creative on through to activation and measurement. Unfortunately, there are still a number of areas where the promise of people-based Marketing outstrips reality, and this is due to the fact that brands and their partners in large part must continue to rely on legacy data businesses. These firms are quickly becoming obsolete as they have failed to transform their technology, core business models and fee structures for the digital and mobile media age.

Over the past decade, as data sources have proliferated and as marketing has moved into the real-time realm, the staid data Goliaths of the industry such as Acxiom, Experian, and Epsilon have been challenged with some tough pivots. I experienced those challenges firsthand at Acxiom and Experian at a time when shifting marketplace realities pointed to a need to tear the business down to the studs and rebuild it to address the need for real-time decisioning. The quarter-to-quarter mentality of public companies makes that nearly impossible.

Rather than observing market trends and listening to their clients, industry Goliaths chose to make minor renovations preferring to minimize costs rather than invest. The flaws of this strategy, or lack thereof, is now clearly apparent. If you look behind the curtain, you’ll find the following three barriers are preventing every brand that relies on these legacy data players from effectively using data.

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Economic Barriers

Every particle of consumer data has some amount of value in making better targeting and measurement decisions, and in gaining greater insight about customers. If brands are to maximize their use of consumer data, it must make economic sense to do so in every possible situation. This requires the cost model of data to be radically changed to make it cost-effective to deploy it in every potential application and decision across the enterprise.

The legacy data companies don’t want to play that way. First, they sit atop very large existing revenue streams that depend on maintaining historical “oligopoly” price levels. Were they to price their data to match the value it creates in each client’s business, they would be reducing prices for many clients, leading to pressure from other clients for price reductions.

Further, their pricing is based upon use case. Want to use this data for CRM customer acquisition? Fine, here is the price for that. Want to also use it for measurement? Want to use it at your DSP of choice? That’s going to cost more, too. These are separate use cases and you need to pay an additional price.

With these 20th-century pricing practices dominating the third-party data industry, it’s no wonder that brands and their partners have found it challenging to make an economic sense of using this data for better business results. A new model is long overdue.

Technical Barriers

Legacy businesses tend to have legacy technology, and this is particularly true in decades-old, large data companies. As these businesses have acquired other companies and grown incrementally over many years, they have needed to stitch together islands of technology, as well as the occasional new stack here and there, to meet evolving client needs. Complete re-architecting on the newest, fastest tech is seldom a viable option. The result? Very slow turnaround times, something that is very much at odds with today’s real-time decisioning needs. Low ability to meet SLA’s. Proposals that say “no” to key client needs.  Inflated costs associated with old tech. It’s not the legacy data companies’ fault, it is a condition of their size and age. In any case, a new model is long overdue.

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Service Barriers

One tenet of data-driven marketing is “data never does anything on its own.” Put another way, consumer data needs to be cleaned, normalized, combined with other data, scored, segmented and distributed in the right way to create business value. While it would be wonderful if this could be done safely, accurately and effectively through automation, these operations are far too complex to make that possible today. Automation is perfectly suited to perform many repetitive data manipulation tasks, but knowledgeable humans must provide advice, curation and quality control to ensure brands and their partners get the help they need in using data.

Many brands today lament the lack of this service from their data providers and are stranded in the underserved zone between two extremes. One end of the spectrum is the legacy data companies that offer dedicated customer service reps to their clients. These well-meaning individuals can carry out client requests for help but often lack the expertise, particularly in digital media and cross-device data, to show a client how to accomplish their goals. On the other end of the spectrum are data providers with SaaS business models. These companies are usually digitally native and knowledgeable for their clients but are very careful to not provide a high level of human service, favoring automation and self-service technology to preserve their investors’ margin requirements. Brands and their partners need to know their data provider is both digitally savvy and fully supportive of human service to ensure the success of their data projects each and every time. Once again, a new model for our industry is long overdue.

The Time for Retrofitting is Over 

Legacy data businesses have made an awkward transition to the digital world, and today’s brands are still underwriting the failed aspects of their transformations (or rather – retrofitting). This isn’t a scenario that can hold for much longer. Today’s brand marketers need to shed their old notions of what a relationship with a data provider entails and ensure that their relationships—and fee structures—are relevant for today’s fast-moving digital world.

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