Cuebiq’s mission, as an offline intelligence and measurement company, is to deliver the most accurate and reliable insights on how digital marketing efforts impact offline consumer behavior. This case study shows how Cuebiq, despite signal loss, partnered with Experian to continue delivering in-store lift analyses. To achieve this, Cuebiq used Experian’s Activity Feed to resolve digital ad exposures to in-store purchases, so that marketers could know the effectiveness of their clients’ media campaigns.
Activity Feed helped Cuebiq increase its match rates by using all the identifiers supported in Experian’s signal-agnostic Digital Graph, reducing its reliance on third-party cookies. By partnering with Experian, Cuebiq could help their clients, marketers, more accurately measure their campaigns and optimize their media.
What is Activity Feed?
Experian’s Activity Feed pulls together fragmented digital event data from all digital channels, including browsers like Safari and Firefox that restrict traditional tracking methods. Activity Feed ingests and ties this digital ad exposure data to household or individual profiles hourly, helping clients associate that data to offline purchase activity made by that household or individual. Activity Feed plays a crucial role in overcoming fragmented data and helping marketers accurately measure their cross-channel marketing efforts.
Challenge: Increasing match rates across digital platforms
Cuebiq wanted to enhance how well they connect digital ad exposures, across web, mobile and connected TV (CTV) to specific mobile ad IDs (MAIDs), of those who visited clients’ stores. They needed a single technology partner who could collect data across these environments and improve these connections, especially as iOS updates, like iOS 14.5, posed potential challenges.
With the ability to resolve exposures to households, individuals, and MAIDs to then facilitate attribution of digital exposures to offline store visitation, Cuebiq could continue to provide accurate reports on how online ads impact offline consumer behavior. This clarity in data enables their clients to fine-tune their marketing strategies.
Cuebiq’s key objectives included:
- Resolving digital exposures to MAIDs
- Increasing overlap of offline and online data
- Improving the effectiveness of offline measurement offerings
Activity Feed: The solution to increase match rates
Cuebiq used Activity Feed to resolve data from cookieless environments like Safari to a single household or individual and saw significantly higher match rates. Cuebiq was able to track cross-channel media exposures, resolve them to MAIDs, and then use the Activity Feed output to correlate in-store visitation and sales to their clients’ media campaigns. Cuebiq also implemented the Experian pixel, which they placed to track all their marketers’ impressions (mobile, CTV, web traffic, etc.). The Experian pixel collects information in real-time, such as:
- Timestamp
- Cookies
- Device ID (MAID/CTV) when available
- IP address
- User-Agent
- Impression ID
“Before we started working with Experian, we couldn’t fully maximize ad views across the complex digital landscape. In just a few weeks, they were able to maximize the match rate across the fragmented digital inventory, solving a huge problem when it comes to cross-channel attribution.”
Luca Bocchiardi, Director of Product, Cuebiq
Results
Activity Feed combines separate data streams and matches them back to a household. This enables Cuebiq to expand household IDs and accurately identify MAIDs that are seen in-store for cross-channel measurement. Over a 21-day period, Cuebiq passed ~1 billion events to Experian. Activity Feed resolved 85% of total events to a household, 91% of which were tied to MAIDs.
By implementing Activity Feed, Cuebiq was successfully able to:
- Gain clearer insights into the success of their client‘s campaigns
- Match consumer engagements in a privacy-compliant manner
- Tell the story of the key performance indicators (KPIs) related to their marketing efforts
Prepare for a cookieless future with higher match rates
Activity Feed is prepared for a cookieless future and uses alternative IDs, like ID5 IDs, hashed emails, and IPs for identity resolution, ensuring no reliance on third-party cookies. Experian remains fully committed to exploring a suite of next-generation solutions and prioritizing continued testing of different industry solutions, including the Google Privacy Sandbox, to help customers prepare for a future without cookies. We’ve identified six viable alternatives to third-party cookies, how these alternatives fall short, and how Experian can help you navigate these alternatives.
“Experian’s customer service is extremely efficient and collaborative. We trust them to keep putting our business first long-term.”
Luca Bocchiardi, Director of Product, Cuebiq
Download the full case study to discover how Cuebiq used Activity Feed to overcome their challenges. Your path to maximizing match rates and resolving data from cookieless environments starts here.
About Cuebiq
Cuebiq is transforming the way businesses interact with mobility data to providing a high-quality and transparent currency to map and measure offline behavior. They are at the forefront of all industry privacy standards, establishing an industry-leading data collection framework, and making it safe and easy for businesses to use location data for innovation and growth.
To learn more, visit their website at www.cuebiq.com
Latest posts
Experian Simmons presents a new list of the top 20 television programs for reaching political party loyals as well as three key swing voter segments.
Marketers have always struggled to target the right consumer with the right offer. And with more than 313 million people live in the United States according to the U.S. Census, the challenge is more difficult than ever. With the proliferation of the Internet and mobile technology, today’s consumer operates differently and expects more from their favorite brands. To adapt to that new American consumer, marketers are using highly targeted strategies to drive interest. These can be messages that are relevant to a few hundred consumers or detailed one-on-one communications that target individuals at the point of sale or online. But some marketers struggle to execute these tactics effectively. Most segmentation is currently done prior to a campaign, meaning that marketers determine which message a consumer will receive before ever interacting with that individual. Unfortunately, with the rapid nature of purchasing decisions and buying transactions, businesses often miss opportunities because it takes too long to get the right message to the right consumer. To keep up, marketers need to collect intelligence at the point of contact so they can understand each individual consumer’s habits and preferences during that connection. This intelligence can then feed modeling algorithms that enable automatic offers based on an individual’s preferences. To develop a strategy around real-time marketing intelligence, marketers should take the following steps: Clean existing data – at the root of any intelligence strategy is data. Information determines a company’s ability to reach target individuals – and understand who they are and what they’re interested in. Unfortunately, if the data that feeds intelligence efforts is inaccurate, marketers are simply unable to communicate with or understand consumers. Ensuring the validity of contact information, internal records and third-party data elements helps organizations target consumers and ensures that sophisticated analysis is as precise as possible. Identify strategies – organizations should analyze their target markets and determine which communication channels could benefit from a more personalized customer experience. Marketers should decide how they want to change each communication to help drive the desired action from each consumer. Consider personalizing website displays based on geographic regions, customizing an introductory message or revamping loyalty campaigns based on purchase history and consumer interests. Real-time intelligence – marketers should build models to help predict the best offers for each target audience. These models can be designed to take into account demographic and behavioral information, as well as purchase history and internal data. Marketers can feed these models with intelligence gained at the point of contact to prompt consumers in real time with specific, relevant offers. As marketers continue to enhance and refine targeting efforts, it’s important to gain customer insight. Those who leverage these advanced technologies and strategies will create stronger customer engagement. Segmenting customers and taking measurable action in real time are advanced techniques that appeal to many marketers today. Achieving this level of interaction allows organizations to optimize marketing efforts and provide the right offer at the right time to the right consumer.
In today’s multi-channel shopping world online behavior can’t be overlooked. Marketers need to enhance their offline marketing efforts with online data to take advantage of incorporating online behavior into targeting efforts.