According to eMarketer[i], almost 80% of marketers across the globe have invested in audience targeting techniques over the past 12 months to improve campaign effectiveness. However, only 34% of marketers believe they are able to deliver their message to the right audience.
Traditional audience targeting options typically rely on socio-demographics, broad interest groups, keywords, or historical behaviors to build audiences. But these signals don’t show true purchase intent and often lead to wasted impressions and inefficient or irrelevant ads.
The truth is, real consumers don’t fit into the neat little targeting boxes we’ve created for them. Here’s an example:
An automotive marketer wants to target young men ages 20-35 who are interested in cars and are sports enthusiasts. Their ad is shown to Marco, who is 20, enjoys rock climbing and hiking, and sees the ad while he’s researching eco-friendly mobility alternatives. Marco technically checks their targeting boxes, but his behavior shows that he’s not interested at all in buying a sports car. In fact, he buys a bike instead of a car.
Intent data is one of the strongest ways to overcome these targeting challenges. It allows us to reach audiences based on their observed behaviors and actions, rather than making assumptions about who they are and what interests they have. With high-quality intent data, it’s possible to target high-value audiences for more efficient advertising, and ultimately, a better customer experience. We’ll explain how.
What is shopping intent data?
Shopping intent data is comprised of all the signals that show someone’s level of interest in purchasing something in the near future. It includes real-time and historical shopping behavior, such as browsing, adding to cart, and buying products. Many different intent signals can be observed, including but not limited to:
- Interactions with online media
- Website visits and product searches
- App installs
- Online and offline transactions
Ideally, shopping intent data is multichannel, stitching together interactions across the web, mobile, and in-store for a comprehensive view of the consumer journey.
But, not all intent data is created equal. Here’s what to look for.
There are a lot of places to find shopper intent data, including walled gardens like Facebook and Google, trade desks, demand-side platforms (DSPs), and data management platforms (DMPs). Your ad performance is only as good as the data behind it and not all intent data is built the same. Here’s the checklist you should have for your shopping intent data:
An understanding of shopping intent beyond walled gardens and search.
Walled gardens have a lot of data, but they only understand the consumer journey within the confines of their walls. And keyword search data isn’t the only, or the best, indicator of current intent. You want intent data that looks at the whole picture, including global interactions across websites, apps, and stores. This will give you a clear picture of what people are actively shopping for.
A granular list of product categories.
The more granular you can get with product category interest, the more precise your advertising can be. Look for intent data that goes beyond primary and secondary categories, so you can, for example, target people with an interest in expensive yoga mats and blocks, rather than just an interest in fitness or yoga.
Additional layers on top of product categories.
Don’t settle for just product information. Shopping intent data can also tell you the brands someone likes, their purchasing power (do they tend to purchase more or less expensive items?), and if they buy more women’s vs. men’s items. Using the same yoga example, this allows you to focus on women with high purchasing power who are interested in brands like Adidas and Puma and are in the market for yoga mats and blocks.
A foundation relying on deterministic matching.
Accepted as the gold standard and the most accurate way of matching consumers across devices, deterministic matching limits false positives.
A massive identity graph.
The more shoppers in the graph, the more patterns and behaviors that can be identified, for better targeting.
How does intent data translate into audiences?
The best audience for any digital ad campaign is an in-market audience built from all of these intent signals. In-market audiences are groups of consumers actively shopping for products or services similar to those you offer. Intent signals show that they will make a purchase in the near future, which makes them a highly valuable and profitable audience.
In order to turn intent data into audiences, you need artificial intelligence (AI). Sophisticated AI can analyze billions of consumer events and identify correlations and patterns of shopping behaviors. Those correlations and patterns are then used to created different audience segments.
The segments available vary by technology provider, but the more granular, the better, so you can zero in on exactly the right customers for your business. Here are some examples of in-market audiences that shopping intent data can create:
Home exerciser: Buys from major activewear brands and has recently browsed or purchased exercise and fitness gear.
High-end home improver: Has visited several home décor websites and has recently browsed or purchased high-end furniture.
Work-from-home warrior: Is outfitting their home office and has recently browsed or purchased office furniture and/or electronics such as webcams and monitors.
Criteo can help you reach the right audience
Criteo observes browsing and shopping behaviors from over 2.5B+ anonymized online users who are actively navigating the web. Billions of events are captured, so you can target your ideal audiences based on their product interests, brand affinity, and purchasing power.
With our self-service platform, you can easily build your audience and launch your digital advertising campaigns. Choose from thousands of in-market segments and see your estimated potential reach change in real-time as you make selections.
[i] Source: eMarketer: Customer Experience 2019, Jun 6, 2019