Granular geographic data can deliver near-time, privacy-compliant insights

On the one hand, privacy regulation and changes to how tech companies manage data have rendered solutions such as multi-touch attribution (MTA) obsolete. On the other, marketers are still under pressure to make sure every ad dollar works as hard as possible amid macroeconomic uncertainty and consumers moving into an expanding universe of digital platforms.

The good news? There’s an approach that can square this circle and deliver granular, privacy-compliant insights when marketers need them: granular geographic data.

An uneven global playing field

Although Google has again pushed back the phasing out of third-party cookies in Chrome, the direction of travel remains clear. Moves by the world’s largest technology companies to limit the extent to which online consumer behavior can be tracked on their platforms show one destination: a privacy-first world.

Meanwhile, regulators worldwide continue to bear down on companies that do not comply with a smorgasbord of privacy legislation. The UK’s Information Commissioner’s Office fined a catalog retailer £1.48m ($1.7m) for breaking data protection and electronic marketing laws in October.

The implications for MTA, which has promised to determine the value of each digital touchpoint a consumer makes on the way to a sale, are clear. MTA is no longer fit for purpose in a world where access to user-level data is not guaranteed.

Marketers remain unsure about the best way forward. While 92% of CMOs at multinational companies are prioritizing an ethical approach to data use, half do not know what this means when it comes to the processes and practices they need to apply both internally and across their marketing supply chains, the World Federation of Advertisers reports.

The power of granular geographic data

Access to privacy-compliant data that enables all media to be measured and optimized on a timescale that gives marketers the insights to demonstrate the effectiveness of their campaigns is possible.

Geography is the glue that underpins this. Think about the delivery information that accompanies every ad placement. Whether it’s a 15-second TV commercial that runs on a particular network during a specific time slot, a digital display ad that features on a dedicated section of a website, or a paid social media campaign, each contains information about where the individual media impressions were served.

In the UK or US, for example, delivery metrics for every ad placement can be accessed at the postcode or Zip Code level. Crucially, this data does not contain personally identifiable information (PII). So, unlike MTA, it’s compliant with data protection regulation by default.

When you layer KPIs (like sales metrics) and other factors (such as local economic conditions, promotions, and weather) on top, you can start to build up an accurate picture of how your campaigns are performing across a large, statistically significant number of micro-geographic areas.

What not everyone realizes is that there is relative homogeneity at a postcode or ZIP code level. In other words, there are striking similarities between people who live close to one another. So marketers can compare how ads resonate in micro-geographies based on key characteristics, such as age, gender, income, occupation, and more.

Want to compare how successful your paid social campaign was with high-income males in one city versus another? Geographic media delivery data combined with other privacy-compliant information enables you to analyze impact at this granular level.

Introducing foresight into your marketing strategy

As well as providing you with hindsight about the past, these insights can also help you with present and future campaigns. Getting information in near-time (via data automation and advanced modeling methods) allows us to understand performance across geographies. This means you can optimize campaigns on the fly. If an advert is working in well in one area but not another, or with one audience but not another, you can change the underperforming creative unit, or pull the campaign entirely and reallocate the budget.

By understanding audience performance across micro-geographies, you can improve who you target in the future and maximize performance. It’s important to focus on audience attributes that are buyable within programmatic media platforms. These attributes ensure that your measurement-to-insight-to-activation loop is a complete circle.

Crucially, all this can be done using data that is compliant with a privacy-first approach to build trust with consumers.

At Gain Theory we have our own multichannel attribution solution, Sensor. A global automotive manufacturer used it to measure and optimize across omnichannel campaigns that aimed to increase test drive numbers and, ultimately, sales of a specific vehicle. By identifying demographic attributes to target and household income filters to apply, we could explain car-buying behavior and media responsiveness. Test drives grew 18% and $56 million in incremental sales were generated.

Measuring the effectiveness of media and advertising may feel like a complex and daunting prospect in the current data and privacy environment, but a tried and test approach, based on granular geographic data, can deliver improved results for advertisers and peace of mind to consumers.

For more on how the world of data-driven advertising and marketing is evolving, check out our latest Deep Dive.