The right data management platform for an omni-channel view of your customers

Big data has been getting a lot of attention for the past few years, but companies also need good ways to solve “smaller” data problems.

One small-data problem is “data fragmentation,” where you collect data on your customer on various different devices but have no way to merge all of that data into one, single view. This blocks you from delivering an omni-channel customer experience — or even getting anywhere close to it.

[aditude-amp id="flyingcarpet" targeting='{"env":"staging","page_type":"article","post_id":1758694,"post_type":"guest","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,marketing,","session":"B"}']

Simply put, data is the wiring that connects and stitches together the consumer journey; it is the only piece of the puzzle that allows brands and marketers to pick up engagement from where they left off with the customer, especially as she hops across multiple channels and touch points. So fragmentation across data, marketing technology, and organizational structures is one of marketing’s biggest challenges.

Enter the Data Management Platforms

Data management platforms (DMPs) present us with a big opportunity to address the data fragmentation issue. They allow brands for the first time to have a universal consumer profile that is truly “360.” If implemented right, these platforms can pull together into one view:
* Online and offline consumer data — a huge focus area especially for multi-channel retail players
* First-party, second-party, and third-party data sources — a huge priority for packaged goods manufacturers who for the most part don’t have visibility into the last mile — especially what goes on in the offline world
* Behaviors across online search, social, and shopping — imperative for anyone trying to drive participation through immersive seamless experiences.

Everything is a DMP Now?

While we are just beginning to understand and rationalize the power and opportunity these DMPs bring to the table, there is already quite a bit of confusion around the different “flavors” of DMPs. And the landscape has become even more confusing due to the fact that every marketing technology or media or analytics and insights platform has begun pitching itself as a DMP. Technically of course, these are all data management platforms because they help manage data, but from a brand’s perspective there is a need for a data management layer that is truly channel agnostic and consumer focused — that can literally span the entire digital ecosystem.

Here is a high level overview of just a few flavors:

1. General DMP – This is a DMP, nothing more and nothing less, with a caveat that it is focused on managing consumer data ideally across all the fragmented and isolated data sources. It provides data storage with potential segmentation, insight, and reporting capabilities and a potential service layer to drive data integration with internal and external systems. It’s a glorified CRM database and may even include a decisioning engine, an API layer used to syndicate data out to other systems and touch points, and of course reporting, insights, and analytics.

2. Media or Trading Desk DMP — Primarily originated from within the broader media ecosystem as a result of the “programmatic” evolution — real time bidding (RTBs), media and portfolio optimization, behavioral targeting. This type of DMP is tightly coupled with a demand-side platform, which means that it can only be leveraged as a DMP in combination with the DSP and not as a stand-alone. Some of the leading DSP/DMPs will also provide predictive and attribution modeling and cross channel attribution capabilities, especially within the world of paid media.

3. Channel-Agnostic DMP — An ideal DMP would be an independent layer that is agnostic of any channel or touch point in terms of collecting and connecting data, harmonizing it from across all channels, across paid owned and earned, across first-party, second-party, and third-party. This flavor, of course, includes the usual components of a DMP – the decisioning engine, reporting, analytics, and insights, and predictive and attribution modeling. But most importantly it should be able to communicate the “decisions and recommendations” around the right content, the right creative, the right online advertising and validation based on who the consumer is, regardless of channel or touch point. It uses external data services like Datalogix, LiveRamp, or others to connect offline and online data and to connect first-party and third-party data through cookie matching. The result is a true universal profile and identity of your consumer base. The true DMP works as a backbone to make the omni-channel vision a reality by allowing brands to deliver the right content to the right consumer at the right time within her journey.

There are a very few independent or standalone DMPs left right now, and I won’t be surprised if the species becomes extinct soon, simply because they will be acquired by a DSP or a bigger fish. Krux, Nielsen (especially with the acquisition of Exelate), and a couple others could qualify as standalone DMPs, while RocketFuel, MediaMath, TradeDesk, and AOL provide more connected and coupled offerings. There is no one platform that will work for every single organization; they all have a lot of similarities as well as some unique capabilities. What may have worked for Marriott and the effort that their Global Marketing Officer, Karin Timpone, is driving could be different from what we are doing at Kimberly Clark, where we started our journey as one of the earliest CPG adopters of programmatic with our own trading desk and are now moving on to a more holistic channel-agnostic connected data management layer.

[aditude-amp id="medium1" targeting='{"env":"staging","page_type":"article","post_id":1758694,"post_type":"guest","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,marketing,","session":"B"}']

Likewise, there are a number of key principles that you may want to think through as you define your DMP strategy. Covering them would take a detailed post of its own, but at a high level, here are the areas you’ll want to think through:

* Assess your consumer data landscape – all data sources, from first-party, second-party, and third-party
* Tagging Strategy – from across channels
* Right KPIs and Measurement Strategy – ultimately this is only useful once you figure out how to best use the data and don’t end up being in an analysis paralysis situation
* Personalization and Testing Models – using agile methodology
* Content strategy that will feed off all consumer insights – how does the data influence your content. The goal: show the right content to the right consumer at the right time and context.

Whether it is Oracle’s acquisition of Bluekai or now Nielsen’s acquisition of Exelate, this space will continue to disrupt and grow and in many ways put the ball back in the marketer’s court. The data can provide all the insight and hypothesis, but ultimately it still requires a holistic “content strategy” to deliver immersive, seamless consumer experiences that will drive and change consumer behaviors. The consumer has always been at the center of our ecosystem and our strategies, even five decades back. However, as an industry we have lacked the ability to execute to that promise. The new world of DMPs now gives us that unique opportunity — assuming we know how to apply it.

Mayur Gupta is Global Head of Marketing Technology and Innovation at personal-care product manufacturer Kimberly-Clark.

[aditude-amp id="medium2" targeting='{"env":"staging","page_type":"article","post_id":1758694,"post_type":"guest","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,marketing,","session":"B"}']