"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Marc Grabowski, CEO at Iris Mobile.
It recently dawned on me that the entire world would be dramatically improved if more global leaders had a marketing tech or automation background.
Most notably, a broad range of industries would benefit from the ability to innovate and automate smarter processes.
My personal fitness goals have illuminated how a good technology execution could benefit from the overthinking of a marketing automation product leader. At the same, I learned a lesson on the importance of building technological partnerships to make interoperability the norm.
I’ve decided to run the Chicago Marathon on Oct. 11. I am now learning the nuances of marathon training.
In the process, I discovered that that the interoperability of disparate data sources is not just a marketing automation challenge. Nutrition, mileage trackers, heart rate zones, vertical oscillation, ground contact time, stride length and weight management are all metrics of distance run training that require specialized hardware for tracking and performance optimization.
For nutrition, I use Livestrong’s calorie and nutrient logging application on my iPhone. My Garmin watch measures things like mileage and stride length. Strava tracks cross training done on a road bike. A polar heart rate band keeps tabs on my heart rate zones and my Withings Scale uploads my weight to the cloud on a daily basis.
I recently stumbled on MyFitnessPal, which aggregates all data from these disparate sources into a single view. There are 50 different platforms that can integrate into MyFitnessPal’s view, giving the user flexibility of choice.
Now, through a single view, I see calories consumed, calories burned, weight change, running metrics and progress toward my goal of completing this race. The platform adjusts the suggested nutrition based a single day’s activity in relation to caloric intake and goal weight.
Organic to this app is a very light recommendation platform that sits on top of the existing devices tracking my activity. This is different than other nutrition-tracking platforms that require manual entry of weight, workouts and calories burned. I assume that at some point, MyFitnessPal determined it would get into the businesses of GPS watches, Wi-Fi scales and heart rate monitoring but it stuck with its specialty: interoperability, data aggregation and recommendations. It has resulted in a near-optimal end-to-end consumer experience.
Such a condition of siloed data is not unlike the countless specialty platforms marketers incorporate in their stack. Marketers have different partners for things like tag management, application analytics, display ad serving, data management, email distribution and content management, among others. As a result, a marketer’s data lives in silos, making it difficult to understand the impact of a single communication on the next in a sequence.
At industry conferences, we hear the rally cries to break down data silos. Enormous players in this space frantically buy point solutions to create their own end-to-end stack.
Companies, such as Adobe, Oracle and Salesforce, propose discarding one-off solution providers to centralize and replace with their own plumbing.
On paper, all of these businesses sound like fine solutions until you start to unravel the work previously completed by the marketing department. While a rip-and-replace strategy does have its place, the complexity of the modern-day marketing stack frequently renders such a strategy all but impossible.
Marketing departments have spent months or years qualifying technology solutions, negotiating contracts and overseeing custom integrations with their existing partners. They have trained their teams to become experts on those platforms and integrated reporting into their CRM, data warehouses or data lakes. Asking marketers to dump all of their partnerships in favor of an enterprise stack is a monumental request that will turn their workflows upside down.
That’s why the interoperability highlighted in the MyFitnessPal example should become the standard – not the exception – in our industry.
MyFitnessPal doesn’t have deep machine learning and real-time decisioning.
I would love to be able to set a few goals, such as to run a marathon and drop 10 pounds, which would prompt the application to change recommendations based on my needs and behavior.
For example, my training schedule requires a long run – 10 to 20 miles on Saturdays – so I need to eat more carbs the day before. In a perfect world, the app could calculate the mileage and paces of previous runs and draw connections between calories, food type, run time and outcome.
If my time was a little slower, the app may tell me to eat more carbs, drink more water or shift the timing of my meals. If the day’s run was a little faster than my normal pace, the recommendations may be different. Or, if my weight began to change differently than anticipated, the app should suggest more calories on specific days, building a reserve so I don’t starve myself during workouts.
In reality, this fitness app acts as a great example of interoperability but would benefit from real-time decision-making and machine learning so prevalent in modern marketing tech platforms. In many ways the marketing tech industry’s need to build complex algorithms optimizing on fractions of seconds has moved us ahead of other industries.
On the flip side, this same disposition of overinnovation may hurt the industry. When enterprise platforms develop subpar solutions in hopes of capturing 100% of a market’s budget without paving a path to interoperability, marketers will become frustrated. This frustration will lead to irreparable alienation of the people who really matter: our customers, the marketers.
Follow Marc Grabowski (@MarcTGrabowski), Iris Mobile (@Iris_Mobile) and AdExchanger (@adexchanger) on Twitter.