In order to produce accurate attribution models, data must be combined, centralized, clean, valid, and recent. Brands that compile customer data from all channels and assemble the tech that produces multi-faceted views of customer journeys will have a competitive advantage. AI-driven modeling is possible with the right data tools in place.
Seeing a void in the marketplace, ad tech vendors are stepping in with tech solutions designed to help brands link in-store and online transactions to digital ads in real-time. Just this month, Verizon Media partnered with Catalina, a provider of consumer-driven marketing solutions, to help CPG brands more effectively connect digital campaigns with sales. The partnership matches Catalina’s sales data to Verizon Media’s identity graph, which means Verizon Media is now the first DSP to be integrated with a top CPG sales data provider.
In a loud election where social media is overrun with fake news and unsolicited user-created opinions, campaigns must communicate in a consistent and streamlined way with voters, serving only ads that they want to see in their preferred channels. Campaigns might not win a voter on one issue but could sway or motivate them on another if they know what resonates with them.
This election year, the power of email should not be underestimated.
The lines in the traditional funnel have blurred. Consumers may enter and pursue a non-linear route before making a purchase or moving on. The path from awareness to decision is no longer predictable in an omni-channel environment. A progression that works for one type of consumer may have no relevance for another. These changes necessite another look at attribution models.
Given that Apple’s Limit Ad Tracking feature already renders roughly one-third of iOS users totally anonymous, drive-to-store conversion measurement has been limited at the device-level for some time. The iOS 14 update from Apple simply adds another challenge on top of what was already a difficult endeavor. For marketers who haven’t done so yet, they should take this opportunity to pivot to measurement strategies that are less reliant on the ever-shifting policies of tech giants like Apple.
Techniques for measuring DOOH exposure and mapping to give cross-device measurement more meaning are being utilized by larger brand marketers, but smaller companies are also getting into the game and finding innovative ways to layer maps onto their local strategies.
Here are five ways that marketers can use mapping technology in their local campaigns.
Location intelligence is expanding beyond its well-known uses for advertising (ad targeting, attribution, etc.), supporting enterprises in a number of other ways. That includes supply chain management as well as decisions about where to open another store location.
All of the above applications of location intelligence are fueling UberMedia, our latest guest on Street Fight’s Heard on the Street podcast. UberMedia CEO Gladys Kong says that this expansion of location data’s utility was already underway but has accelerated in the Covid era.
After huddling with the editorial team about our July theme, we all agreed that it could be time to mix it up a bit. So we’re returning to a meat-and-potatoes theme in our lineup: Targeting Location. This will allow us to talk about something else while acknowledging Covid-19’s still rampant status.
What do we mean by “Targeting Location?” A central issue for location-based media and commerce, this is the moving target of how to pinpoint and optimize strategies around device location. It includes topics like location-targeted ads, building audience profiles, attribution, paid search, and location data strategies.
Location intelligence firm PlaceIQ bought fellow location data and measurement company Freckle IoT. The financial terms of the deal were not disclosed.
The move comes just a day after the bombshell announcement that location leader Foursquare was merging with location data firm Factual. Speculation that the Foursquare-Factual merger could portend additional consolidation in the location data-driven marketing and insights industry came to fruition quicker than analysts could have predicted.
What most ad platforms cannot tell you is how your ads drove foot traffic to stores and other physical locations you care about. If driving foot traffic to retail locations is your job, Google Ads and other digital ad dashboards can’t help you. When in-store foot traffic attribution is crucial, how do you solve for it?
In this article, we cover three ways to solve for attribution, ranging in difficulty from easy to hard. We look into easy options that are inexpensive but tend to be unreliable. We evaluate a medium option that has a moderate cost but is highly reliable and bypasses human error. And lastly, we look at a hard option that incorporates several tools and, while highly reliable, comes at a high cost and is difficult to scale.
By facing the harsh truth that we need to lean into disruption – instead of patching up past approaches or creating inadequate work-arounds – our industry will build something better that helps us increase value in our marketing spend. Shifting to CMM would provide a framework to address the full business (not just marketing) needs, and help us all be ready to adapt through data-driven decision making. And when you can adapt, you can build competitive advantage, evolve, and thrive.
Shim now faces the challenge of steering a fast-growing tech business through uncertain times for data-driven companies. While location tech is a lucrative business that provides crucial insights for brick-and-mortar companies and has yet to hit peak productivity, the industry is also facing concerns of an unprecedented scale about how much it knows about the people who power its insights.
Without pixels, marketing in the digital world would be a guessing game. However, with 90% of all commerce still taking place in the physical world, oftentimes marketers find themselves in the dark, not knowing how their customers are interacting with their brands offline. Enter location intelligence, or as we like to call it, pixels for the real world.
Take a moment to reflect on the past few weeks. Did you stop at a coffee shop on the way to work? Did you work out on specific days of the week at a nearby gym? Are there restaurants you frequent when you are too lazy to cook at home? In a study, published in Nature Human Behaviour, researchers found that people frequent up to 25 places at any given time period. Similar to marketing pixels placed on websites, the ability to understand physical, real-world behavior such as path-to-purchase, visitation patterns, day-of-week preferences, and daily activities fuels more strategic decision making.
Responsible location intelligence involves practices like “stop data,” to measure users’ location dwell times, and the scale Foursquare achieves in its network of app publishers. Placed is one of the first location data players and a leader in attribution since 2011.
Now that the two companies have come together via acquisition, how does that position Foursquare for interstellar domination of the location intelligence market? It’s about greater capability and scale, say Foursquare’s Josh Cohen and David Shim, our guests on the latest episode of Heard on the Street.
Automotive OEMs have bulk data plans with cellular carriers primarily for collecting vehicle diagnostic data (e.g. mileage, engine warnings, etc.). As a result, it is now possible to capture data from millions of vehicles. This presents an opportunity to capture exponentially larger audio data sample sizes, especially for AM/FM radio, which will fundamentally change audience measurement, ad attribution, and program insights. While data today is primarily audio listening, the introduction of autonomous vehicles will result in significant consumption of video that can be measured in a similar way to audio.
The result of this data deluge? Organizations lack the insight into their customers they desperately need to deliver meaningful experiences, secure sales, and retain customers. New research estimates 48% of them struggle to gain these insights due to the data silos and more than half admit they don’t have a full picture of their marketing data and their customer journey.
Given the many challenges marketers are up against, it’s no wonder they struggle to define their customer journeys and optimize customer interactions. Below I offer some advice for those in this data struggle.
The blurring lines among search, social, and e-commerce only muddy the water when it comes to determining the customer’s journey to conversion. So, how can advertisers accurately attribute their marketing dollars to customer wins? Increasingly, marketers are turning to a multi-touch attribution strategy that includes both online and offline conversions, thereby moving away from simplistic last-touch attribution models.