What is Online-to-Offline Measurement?

Online-to-Offline Measurement or Store Footfall Attribution is a metric that checks the effectiveness of an online marketing campaign in driving footfalls to physical stores. Store Footfall Attribution is generally calculated by comparing visits from the exposed audience of a campaign to visits from the non-exposed audience. This helps marketers understand the campaign efficacy across media and better optimize channels that get them the best results for the marketing money spent.

With that in mind, let us dive deep into various aspects of Store Visit Intelligence.

Why is Offline Measurement Important for a Modern Digital Marketer?

As Online Measurement becomes a common marketing parameter, Offline Measurement is rapidly growing as an area of interest among marketers who want to stay ahead of the competition. Marketers can get a complete picture of how their campaigns are performing by measuring the impact in the offline world. Here are several ways they can put this tool to use:

  • Measure Marketing Impact on Store Footfalls
  • The success of online campaigns in the offline world is measured with respect to the lift in store visits. At Near, the lift in store visits is calculated as a ratio of the percentage of exposed audience seen at the relevant physical stores to the percentage of non-exposed audience seen at the same stores. This ratio is called the ALI (Attribution Lift Index). An ALI of more than 1 signifies an effective campaign. To calculate an ALI, brands usually consider an attribution window which is the time period from the launch of a campaign to the time a conversion is made. Usually, the attribution window ranges from 7 days to 14 days depending on the nature of the campaign.

  • Acquire Store Visit Intelligence on Attributed Audience
  • For modern brands, it is important to gather actionable intelligence about their audience’s engagement and store visit behavior. Store visit intelligence generally includes demographics, brand affinity, and interest-based information. These rich insights enable brands to have a more in-depth and multi-dimensional understanding of both attributed and control audiences.

  • Maximize Advertising Effectiveness and Increase ROAS
  • Store visit intelligence gathered on the audience will facilitate personalized audience curation for future campaigns. This can be achieved through fine-grained actionable insights. For example, a certain campaign can drive more millennials into the brand’s stores- that is a good way of understanding what works or does not work for different segments of the audience by comparing both attributed and control audience insights.

What makes Online-to-Offline Measurement a Challenge?

As per our recent survey, while a whopping 89% of marketers have an online attribution technology in place, only 33% of marketers measure success in the offline world. Though measuring the consumer journey is now a mature science in the online world, calculating an unbiased and accurate store footfall attribution still poses several challenges for brands and agencies at large:

  • Connecting online and offline data sets at scale
  • While data currency for online data sets includes cookie IDs and device IDs, offline data sets use latitude and longitude specifics. In order to create an online to offline attribution, unifying online and offline data sets is important and this requires access to large repositories of varied data types and a robust technical architecture to analyze these datasets at scale. Not many companies in the data space are equipped with capabilities such as linking online and offline consumer identifiers, location definition mechanisms, baseline definition for attribution lift calculation, and befitting reporting tools.

  • Availability of accurate real-world data
  • Availability of high volume and high-quality location data is sparse. Moreover, location pings need extensive cleansing and sophisticated data models to derive intelligence. For example, if a location is seen only as a pin and not as a polygon, the results might be inaccurate or inadequate. We spoke about the difficulties of processing real world data in our blog in detail.

  • Limited access to footfall data
  • Gathering complete and accurate information on the entire footfall in stores is not possible and offline attribution models have to rely on subsets of the total footfall as sample data sets.

  • Device/platform based vs user deduplication
  • Attribution models use device-based models to measure online to offline conversion. But since a consumer uses multiple devices in the real world, store visit attribution requires matching all devices of a single user to that individual. While this is a challenging task, it is crucial in order to deduplicate devices and get accurate results.

  • Consumer behavior is a complex subject
  • According to eMarketer, about 43% of millennials research online and buy in store, while 13% research in store and buy online- and then there are other categories- consumers who strictly stick to either online or offline channels. Consumers are not completely moving to single touchpoints-not anytime soon- and that makes the task of online to offline attribution even more complicated.

What are the Best Practices for executing a Store Footfall Attribution Campaign?

  • Find out ways to handle uncertainty
  • Unlike online customer journey, offline journey is complex and there are several sources of real-world data. It is measured by different individuals at different stages of the sales funnel and there is no common paradigm to refer to. Creating models that can not only link data coming from several sources across various phases of consumer journey but also account for real-world uncertainties is the key to accuracy of measurement.

    Detecting the campaign signal from different kinds of data sets and different kinds of location traffic have different kinds of sensitivities. Fine tuning this on a periodic basis is essential.

  • Internally baseline brand locations by spend
  • Before a brand uses a third-party vendor to run its campaigns, the first-party data has to be baselined. This means an index has to be created from the best locations with significant data in each market. The brand needs to know how each of these baseline locations performs when there is no campaign running- so that when the campaign is run, there is a clear baseline to measure the campaign effectiveness against. This baseline needs to be validated and updated regularly.

  • Choose a large attribution window
  • A larger attribution window results in a better sample size as it would include a larger number of users who were influenced by an advertisement. For offline attribution campaigns, an attribution window is a defined period of time in which a publisher can claim that a click or impression led to a store visit. Attribution windows are an essential tool for helping advertisers and publishers understand when a conversion takes place.

  • Try multiple vendors to detect the effect of the campaign in the most optimum way
  • In order to understand what is the most optimum performance of a campaign, marketers need to try several use cases across vendors to understand what works for their brand. They need to share the exposed IDs with these vendors and see if the outcomes match their expectations.

  • Don't always ask for footfall data
  • Depending upon the season, location of stores selected, and other offline factors, footfall data may vary and hence might not always be the best parameter of success. Marketers need to build internal indices to compare vendor performance.

  • Define success for your campaigns
  • Every campaign has different parameters of success- an attribution lift of 50% might mean a lot for one campaign while for another one, it could be a poor outcome. A simple example could be while people buy a car one in several years, fast-moving consumer goods are bought more frequently and repeatedly. Marketers need to define what success means for each campaign and what is the impact of positive vs negative impact for each brand.

How does Near Solve Online-to-Offline Measurement?

Near offers a platform-agnostic solution to measure online to offline conversion. It is built with capabilities to calculate Attribution Lift Index(ALI) for online campaigns in a privacy led environment. It helps marketers:

  • Leverage Offline Attribution to measure marketing impact in driving in-store visits to understand visit uplift and overall campaign performance.
  • Discover audience insights from exposed users who converted. Marketers can gather intelligence on footfall trends across the entire attribution window, the average time spent at a location, brand affinity, age, gender, home location, and more.
  • Leverage these insights for future campaign planning. They can use the insights to understand to what extent has the campaign driven in store visits and accordingly optimize future campaigns.