“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 Chris O’Hara, vice president of global data strategy and agency lead at Krux.
Understanding the customer journey in a world of fragmented consumer attention and multiple devices is a hoary old chestnut.
Attribution is a big problem, f
or which marketers pay dearly. Getting away from last-touch models is hard to begin with. Add in the fact that many of the largest marketers have no actual relationship with the customer – such as CPG, where the customer is actually a wholesaler or retailer – and it gets even harder.
Big companies are selling big-money solutions to marketers for multitouch attribution (MTA) and media-mix modeling (MMM), but some marketers feel light years away from a true understanding of what actually moves the sales needle.
As marketers take more direct ownership of their own customer relationships via data management platforms (DMPs), “customer data platforms” and the like, they are starting to obtain the missing pieces of the measurement puzzle: highly granular, user-level data.
Now marketers are starting to pull in more than just media exposure data, but also offline data, such as beacon pings and point-of-sale data where they can get it, modeled purchase data from Datalogix, IRI and other vendors and weather data to draw a true picture. When that data can be associated with a person through a cross-device graph, it’s like going from a blunt eight-pack of Crayolas to a full set of Faber-Castells.
Piercing The Retail Veil
Some companies that make single-serve coffee machines make their money on the coffee they sell rather than the machine, but they have no idea what their consumers like to drink. They sell coffee but don’t have a complete picture of who buys it or why. Same problem for the beer or soda company, where the sale and customer data relationship resides with the retailer.
The default is to use panel-based solutions that sample a tiny percentage of consumers for insights or wait for complicated and expensive media mix models to reveal what drove sales lift. But a company could instead partner with a retailer and a beacon company to understand how in-store visitation or an offline visit to a store shelf compared with online media exposure.
The marketer could use geofencing to understand where else consumers shopped, offer a mobile coupon so the user could authenticate upon redemption, get access to POS data from the retailer to confirm purchase and understand basket contents – and ultimately tie that data back to media exposure. That sounds a lot like closed-loop attribution to me.
Overcoming Walled Gardens
Why do specialty health sites charge so much for media? Like any other walled garden, they are taking advantage of a unique set of data – and their own data science capabilities – to better understand user intent. If I’m an allergy medicine maker, the most common trigger for purchase is probably the onset of an allergy attack, but how am I supposed to know when someone is about to sneeze?
It’s an incredibly tough problem, but one that the large health site can solve, largely thanks to people who have searched for “hay fever” online. Combine that with a seven-day weather forecast, pollen indices and past search intent behavior and you have a pretty good model for finding allergy sufferers.
However, the allergy medicine manufacturer already has access to all that data, including past purchase data. Users can be segmented and located via overlap analysis on sites with $5 CPMs, rather than $20 CPMs. That’s the power of user modeling. Why don’t sites like Facebook give marketers user-level media exposure data? The question answers itself.
Understanding The Full Journey
Building journeys always stalls due to one missing puzzle piece or another. Panel-based models continually overemphasize the power of print and linear television. CRM-based models always look at the journey from the email perspective and value declared user data above all else. Digital journeys can get pretty granular with media exposure data, but they miss big pieces of data from social networks, website interactions and things that are hard to measure, such as location data from beacon exposure.
Today marketers can combine granular attribute data to complete the picture. The potential data sources are vast: addressable media exposure (ad logs), mobile app data (SDKs), location data (beacon or third-party), modeled sales data (IRI or Datalogix), actual sale data (POS systems), website visitation data (JavaScript on the site), media performance data (via click and impression trackers), real people data through a CRM (hashed and anonymized), survey data mapped to a user (pixel-enabled online survey) and even addressable TV exposure (such as comScore’s Rentrak data set). Wow.
Data science is the new measurement because when a marketer has all of that data at their fingertips, they see customer journeys in their entirety. They not only see all events in every journey, they can now see what drove that rare conversion event that really matters. By optimizing across the entire set of journeys, they can formulate their top-of-the-funnel strategies for budget allocations, as well the tactical real-time decision to show personalized messages to the person on the site. In other words, something close to true attribution becomes possible. Now that marketers have the right tools to draw with, the winners are going to be the ones with the most artists (data scientists).
It’s a really interesting space to watch. More and more data is becoming available to marketers, who are increasingly owning the data and technology to manage it, and the models are growing more powerful and accurate with every byte of data that enters their systems.
It’s a great time to be a data-driven marketer.
Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter.
