Has your audience strategy kept pace with recent automation enhancements?
With Black Friday only a few days away, we’re on the cusp of a period of heightened e-commerce activity that’s all the more critical given the challenges retailers face in 2020. During the crucial upcoming weeks, it is imperative that your audience strategy is tailored to and cohesive with the active bid strategies in your paid search accounts. And it’s especially important this year given advertisers’ increased adoption of bid automation. By applying the five pillars of audience strategy below, you can put your account in a better position for success as we close out 2020 and head into the new year.
1. Apply User Behavior-Based Remarketing Audiences Across Campaigns
One of the most basic aspects of audience strategy involves the application of remarketing lists across relevant campaigns. For advertisers who already use the correct tagging implementation, the first step is to create thoughtful remarketing lists for application to paid search accounts. Scale is crucial to the optimal functioning of bid automation, so unless you have highly segmented campaigns with unique associated user behaviors, the most effective lists will focus on past visitors, cart abandoners, and users who spend significant time on-site.
Once these lists are created, it is best to apply them across campaigns that are opted into an auction-time bidding strategy, either via Google Ads, Search Ads 360, or Bing eCPCs. These tools incorporate auction time information at the audience level and adjust bids accordingly. This means that advertisers no longer have to periodically review audience-level performance and manually adjust bid modifiers.
2. Use Customer Data to Implement Customer Match Lists, With Similar Audiences for Scale
To complement a healthy RLSA foundation, advertisers should also utilize first-party data to create Customer Match lists. These lists can be tailored to unique audience segments and take into account factors such as lifetime value (LTV). Customer match audiences are highly targeted, and they tend to be smaller scale, so advertisers should consider pairing their implementation of Customer Match lists with associated Similar Audiences. This will better provide algorithmic bidding with sufficient data for optimizing toward return targets.
It’s important to note that because CRM lists are based on first-party data, they must be consistently refreshed by the advertiser. Determine the appropriate cadence for refreshing your Customer Match lists and coordinate with your paid search team to ensure that the engines aren’t working with stale data.
3. Implement Google Analytics Smart Lists to Take Advantage of Machine Learning
Smart Lists are generated by Google Analytics to help maximize conversions. They apply machine learning to sets of conversion data to target users likely to convert based on a variety of intent signals. These lists automatically update, eliminating the need for continuous manual refreshing. Smart Lists pair very well with automated bidding, as the machine learning works across two fronts to best take advantage of user intent at the time of the auction.
4. Adjust Messaging for Key Audiences to Improve Engagement
Once you’ve built out a robust list of audience segments, advertisers can begin testing more advanced approaches to improve engagement, CTR, and retention by targeting these different audience segments with unique ad creative. By using Ad Customizers, advertisers can adjust copy at the audience level even during promotion-heavy periods. This could be particularly important during the holiday season.
Ad Customizers allow advertisers to modulate their approach depending on where a user sits in the conversion funnel, and provide the right message to drive different audiences to take action. Some examples include:
- Providing cart abandoners an extra incentive to convert
- Featuring more granular promotions for category-specific frequent purchasers
- Highlighting loyalty program benefits for those who haven’t yet signed up
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5. Inform Reporting by Applying In-Market and Affinity Audiences
Finally, advertisers can better understand user behavior by implementing in-market and affinity audiences. The users in these audiences haven’t necessarily interacted with your business (yet!), but they are actively searching for similar products. The data associated with these audiences informs Smart Bidding in standard Google Shopping campaigns. They do not inform automated bidding for text or Smart Shopping campaigns.
Additionally, in-market and affinity audiences contribute reporting insights that can inform the creation of targeted, audience-specific campaigns. While the above recommendations focus primarily on audience strategy best practices at the account level, advertisers shouldn’t discount targeting particular audiences exclusively with a unique, dedicated campaign. Following the above guidelines will help advertisers ensure that their high-level audience strategy is robust and aligned with their use of bid automation. A cohesive set of RLSAs, CRM lists, Similar Audiences, In-Market and Affinity segments work in tandem with bid automation to help drive the right message to the right users at exactly the right time, giving prospective and existing customers the best experience possible. And because these audience lists now work in conjunction with bid automation technology, audience management is both more powerful and less resource-intensive.