Ad targeting is essential to any successful ad business as choosing the right targeting strategy can determine any retail media network's sales, ad performance, and overall platform success. Many retail companies feel overwhelmed at the thought of integrating more and more targeting parameters, not knowing which ones will ultimately be right for their specific business.
Ad targeting can fall into three main categories: behavioral (first-party), contextual (zero party), and retargeting (third-party). Understanding each type of targeting, and how it works, can provide insights into which strategy best serves your business.
Behavioral targeting leverages behavioral elements of first-party data to target users, including things like their age, shopping habits, likes and dislikes, and other propensities. As third-party cookies are deprecating, the value behavioral targeting brings by leveraging first-party data is more essential now than ever. Knowing this, major retailers like Sonae, for example, have been collecting first-party data from their users for the past three years to create highly customized targeting segments to use on their onsite advertising campaigns.
First-party data behavioral targeting allows advertisers to pinpoint the exact customer type most likely to convert on their ads. Advertisers are willing to pay premiums for these user groups who fit their ideal customer profile, confident that these ads will perform better than generic, untargeted campaigns.
Behavioral targeting types and examples
There are a few behavioral targeting options that advertisers love. Let’s explore each.
Audience targeting
Also called crowd targeting or customer targeting, this approach divides customers into different groups based on their shared attributes in order to personalize the approach of each segment. Retailers can do this through collecting in-store loyalty card data or online first-party data to create segments like "gluten free shoppers" or "likely to buy snacks."
Pro: Customer targeting is specifically geared towards hitting your ICP (ideal customer profile).
Con: Retailers using this ad targeting method would need substantial first-party data.
Example: This method of targeting involves loads of first-party data, but that rich data can be used to create hyper-specific segments that can drive significant results. A retailer like Walmart sells thousands of products from thousands of different brands. A smaller brand like Purina doesn’t want to run a major homepage banner for the millions of Walmart shoppers who hit the site – they only want to serve impressions to customers who have previously bought dog food. Maybe to take it a step further to increase their market share, they want to target customers who previously bought dog food but not from Purina. This example of audience targeting is more likely to net new customers for Purina, but it’s also a win for Walmart, who can charge Purina more for that desirable segment.
Device type targeting
As it sounds, device type targeting targets the user based on which device they are using at the time. This is essential for breaking out ad campaigns based on their creatives, ensuring that each creative is correctly matched to the device that one is using at the time, thereby boosting a more positive user experience.
Pro: Device type targeting can help narrow the focus of campaigns based on the device your target customer is most likely to be using.
Con: Breaking out campaigns with this level of targeting might be more trouble than it's worth.
Example: A brand like Apple might want to specifically target its ads to Android users, or Windows might want to specifically target Mac users to grow incremental new-to-brand customers.
Demographic targeting
This is a way of targeting users by their demographic information, such as age, gender, income, education level, etc. If an advertiser knows which demographics their product appeals to, it can be especially beneficial for them to only target those demographics and not waste their spend on users they know won’t convert.
Pro: Can cut down on wasted ad spend by only promoting to ICPs.
Con: Could eliminate or reduce interactions with new or potential customers, especially those outside of the specific group being targeted.
Example: A major retailer like Target knows that their user base is 60% female, between the ages of 18-24, and generally is more affluent. Advertisers just by knowing these statistics know if their ICP falls within that category, any spending they do with Target is likely to be more effective than with someone like Walmart, whose general population is older and less affluent. But, to take things a step further, an advertiser like L’Oreal when promoting an anti-aging serum would want to exclusively target women in their 50s and above so as to not waste ad dollars on younger women. This benefits all parties: users are likely to enjoy the ads that are more tailored to their experience, advertisers are more likely to return to Target because of their improved performance, and Target makes more money on the increased anti-aging serum while being able to charge more for a desirable segment.
Geo targeting
This kind of targeting allows advertisers to target users based on the user’s location and break out campaigns by different areas. Oftentimes, geo targeting is used when advertising to local prospects.
Pro: By combining geo targeting with IP exclusion, it’s possible to hide your ads from competitors, while still reaching customers within your competitor’s area.
Con: Companies with many locations close together may experience self-made competition, with locations impeding upon the success of each other’s campaigns.
Example:Though retailers can ship anywhere, especially for grocery delivery, geo targeting is paramount. If a customer is placing a grocery order to pickup that day, they are looking for items available in the store closest to them. Certain brands in grocery stores are regional, so those regional brands want to limit their campaigns to local stores where customers are likely to be familiar with the brand.
Ad targeting type #2: Contextual targeting
Contextual targeting is a type of targeting that requires no data at all– a “zero party data approach.” Contextual targeting uses the surrounding page context to determine what ad should run. This is one of the most common types of targeting in retail media today.
Contextual targeting types and examples
Keyword targeting
Keyword targeting is based on the surrounding keywords on the page or based on the keywords in the search query.
Pro: Keyword targeting is easy to sell. Advertisers are familiar with paying for keyword targeting, and likely have strategies they’d like to quickly implement.
Con: Though it seems simple to integrate, keyword targeting requires sophisticated keyword matching to account for user mistakes like typing “spda” instead of “soda.” While your search engine likely can accommodate for these mistakes, it's important to make sure your ad server can as well.
Example: An advertiser like coke would like to be the first sponsored listing for the keyword “soda” to have the most impact as the customer visits the page. Or, maybe Coke would add the keyword “chips” and run a banner ad next to the chips listings as a way to cross-promote their product.
Category targeting
Category targeting matches ads to the category a page falls under–it’s essentially broader than keyword targeting.
Pro: Category targeting allows brands to increase visibility across the same category their target audience would be shopping in. Allowing a chair company to promote their product on a “furniture” page would promote incrementality.
Con: The vagueness of category targeting might not be as appealing to advertisers.
Example: It’s possible to also group site pages by categories, for example, Walmart could group pages into grocery, lifestyle, and clothing. Then, a brand like Tropicana could focus their targeting to just grocery pages rather than lifestyle pages to reach their target customer.
For retailers, retargeting on and offsite can be a way to reinforce a product that a buyer may have been considering, but hadn’t yet purchased. If a customer added a product to their cart, but didn’t purchase it, that brand would want to keep targeting that customer until the conversion was made.
However, because retargeting is mainly done through the use of third-party cookie tracking, fewer retail media companies are investing in this strategy. Third-party cookie tracking is less reliable and more privacy invasive than leveraging first-party data, with companies like Google, Criteo, and Facebook facing lawsuits and fines for misusing user data.
Retail media, commerce media, and financial media companies all have various levels of experience and expertise. Some might have hundreds of ad ops employees and large ad sales teams, some might just be starting out. This is our recommendation for starting out no matter what stage you’re in:
Start collecting first-party data: Using a CDP (customer data platform), start gathering information about your users. The key to this is making sure your data stays in house, otherwise, with a shared platform, competitors will be using your data for their own profit. The more data you have, the better segments you can build in the future.
Ditch your retargeting partners: They may have taken you to first base, but they won’t bring you the home runs you need. With first-party behavioral targeting or zero party contextual targeting, you’ll be able to optimize for conversions and hone in on your ideal customer profile.
Start with keyword targeting: To launch, it’s simple to integrate keyword targeting into your retail media platform. It gives advertisers an easy way to bid on targeting segments, and gives you a chance to understand how valuable your targeting segments are so you can charge accordingly.
Integrate audience targeting: Once you know more about your customers, you can start integrating and selling audience segments to your advertisers. As you realize what drive the most ROAS for those advertisers, you have reason to increase your pricing.
Taking ad targeting to the next level
Managing your data in a privacy-safe way while scaling your ad serving is no small feat. That’s why companies like Edmunds, Slickdeals, El Corte Ingles, and other major retail media companies chose to work with Kevel. We have built in targeting that makes it easy for you to scale your ad business quickly. And, because we know you know your customers best, we allow you to integrate your own AI/ML models to target customers with.
