Historically, the metrics of measurement in Out-of-Home (OOH) and Digital Out-of-Home (DOOH) advertising have always focused more on quantity than quality. But today, this has changed; there is much greater focus on quality. Data Science and Location Intelligence (LI) are playing an important role in quality. In retail, while brands may not know how many people are coming into a store every hour, they need to know the profile of the visitors to understand the conversion rate. Similarly, it’s no longer about how many people could see a billboard ad. Rather, we focus on which customer segments saw the ad and what are the outcomes of those segments’ engagement with the ad. The approach to datasets also changes with different brands. A mass-market brand could be looking at one set of audiences, while a brand like Tesla could have its focus on a completely different audience.
“Data Science and Location Intelligence are playing an important role in data quality.”
Segmented datasets
We segment data in three ways— planning dataset, real-time programmatic dataset and result dataset. First, we look at past data to plan for the future, considering variable factors like seasonal changes, changing trends, and more. Real-time programmatic datasets, on the other hand, are about the present —actionable data that can help drive decisions. The results dataset tracks the effectiveness of a campaign. To evaluate that effectiveness, it compares the result with the first two sets. This is how they are interconnected. When it comes to location data, it’s never about just one location. We look at 20-40 locations, frequency and deeper aspects of the data to design our campaigns. Location data also helps us amplify data processing. For instance, a person who watches a lot of cricket or football is not necessarily a target for a sporting gear brand. By analyzing the person’s location behavior, we can determine whether he plays those sports in real life or is just a spectator. Hence, location data can exponentially increase the insights that we can obtain from a dataset. Today, most brands are building their own LI stacks. Companies that operate using a locational aspect, such as Zomato and FoodPanda, aggregate a lot of location data. They are also obtaining additional end-user data based on the usage — eating habits, personal preferences, budgets, and more. In combination with the LI stack, this data helps them drive marketing and business decisions in a much more informed manner.
Evolving data trends
During the pandemic, we saw individual data being collected through the track-and-trace apps. On top of this, mobile Bluetooth data was collected to determine the proximity of people for safe distancing measures in small environments. All of this is first-party data, which is what brands seek today. We are in a flux where more first-party data streams are coming in but, at the same time, more privacy norms and regulations are also coming in. Eventually, anonymous data usage, not at an individual level but at a cluster level, will be the way forward.
