For the past few years there has been an urgency for organizations to transform into digitally-driven businesses. Because more than 80% of customer interactions happen through the web or a mobile app, marketing organizations have been center stage in most of these early digital transformation projects. Enter massive initiatives to revamp user experience design, website speed and performance, mobile app development, and so on. To support these initiatives, a myriad of platforms and tools were also introduced to the market to manage the automation and analytics of it all.
Digital transformation efforts have yet to reach their peak in many businesses, and yet already marketing teams are hitting roadblocks when it comes to data collection and analytics. All it takes is one look at the platform diagrams from the 2016 MarTech Stackie Awards to see that there’s a specialized marketing tool for almost every tactic in the funnel. A majority of the tools also have proprietary methods for collecting information to review performance. This hyper-specialization often means that marketers have a siloed and shallow view of their efforts. While “shallow” is great for speed in making guided day-to-day decisions, it is counterproductive when trying to determine what the true system of record is for their sustained measures of trends and improvement for the business.
The need for the CMO and CIO organizations to be purposeful collaborators has intensified and is showing no signs of letting up. Big data initiatives call on tactics such as machine learning, artificial intelligence, and augmented reality to name a few. Marketing data holds the keys to providing the human-centered aspects of unleashing the potential of these tactics.
Consider SnapChat’s recent efforts to use machine learning tactics to predict who will swipe up on certain ads. That’s one company whose sole purpose is siloed to communication and social interaction. Now imagine your own organization trying to be a disruptor by using its own business data. Internal marketing data, being rich in proprietary audience behavior and interaction data, will be critical for aggregation with third-party and operational business data to predict marketplace trends for your messaging and products.
Explore New Data Products
“Data as a product” is another phrase that yields buzz and promise for new opportunity in a digital-centric world. Consider workout clothing with embedded sensors. By creating clothing that collects human performance data like body dimensions, heart rate, hydration levels and other physical information about a person during a workout, that manufacturer obviously has massive amounts of data. Although the primary purpose of this data is to make it so incredibly useful to their customers that said customers will buy more gear so as never to have a sensor-less workout.
The beauty of this data is in what the manufacturer hadn’t intended to collect through these sensors. In this mass of human performance data are the body measurements of every customer who wears the clothing. This manufacturer now has the potential to remove personal data and package the balance of it into averages of ages, genders, chest size, waist size, and other body dimensions.
What if fashion houses and clothing manufacturers could use this data to make informed decisions about estimating size charts for “real” body types?
Well, are there frustrated consumers who feel like they can never find clothing that fits? Yes. Is it likely that the marketing or product teams of this manufacturer had any intention of addressing this problem? Probably not.
The unintentional act is the perfect example of how a clothing manufacturer's own data can work together as both a disruptor and a catalyst for a new data product that this manufacturer might never have intended.
Collaborate Across Organizations Through Data
When an internal big data effort is designed to collect data for all departments into one main repository, internal business disruption is possible. Consider a strategy such as predictive subscription models or account-based product pricing. By aggregating collective client interactions with your brand, current revenue and profit projections, and third-party data, it is possible to transform and mine this data to provide right-time pricing estimates that match marketplace demand with the profitability trends of your business.
This scenario isn’t possible unless there is a way to bring your marketing data (think web logs, automation statistics on certain messaging, etc.), client success data, sales and accounting data together behind the scenes.
Each of these scenarios makes one thing clear: Big data initiatives are a “whole business” initiative and the CMO organization must dive in head first. The CIO organization might own the tools and methods for production, but they are doing so with the objective of being able to take data from customer-centered organizations, align it with internal operations data to provide an efficient and useful data repository. This is why marketers must make it their business to align with their IT team to know what’s possible for making marketing data a key collaborator in their data-powered business.
About the Author
Susan Peich is the Digital Marketing Manager for Zaloni. In addition to her daily marketing activities, you can find her researching most topics related to big data, marketing technology and how people use tech to connect with the world around them. Her enthusiasm for technology extends beyond the workplace into her hobbies: Running, rowing, general fitness, and travel. What does all of this mean? Follow her on Twitter, Instagram or LinkedIn.Follow on Twitter More Content by Susan Peich