Like nearly all retailers, a large health and beauty organization is facing escalating competition and CPCs on search. The performance marketing team realizes it can’t keep paying heightening costs to acquire the same levels of revenue from repeat customers.
At the same time, the team recognizes it can better coordinate its strategy on other channels. Retargeting, email and direct can work together more cohesively to push customers to purchase once they’re in the door, or back in the door, from search.
They developed a new strategy for tackling Google Ads, one focused on identifying and treating new customers differently than returning customers. The ultimate goal is to achieve more granular return targets for new versus repeat customers, with repeat customers generating a much more efficient return than in the past.
This scenario is not an isolated case. Many performance marketing teams in retail are keen to understand how a new-versus-repeat customer model works for search. Some of the most common questions are: What should we know about this approach? What’s the process to implement it? How would we measure success?
Here are some best practices.
1. Realize the war for the wallet will be won at the top of the funnel
A new-versus-returning customer strategy can make a lot of sense in today’s competitive climate. Here’s why:
Both these realizations speak to the growing importance of the upper funnel. Similarly, acquiring new customers requires you to strengthen the top of your marketing funnel. And strengthening the top, in turn, requires you to shore up the middle and bottom of your funnel, so prospects move forward to conversion.
2. Define what ‘customer’ means to your business
Here’s one of the biggest pitfalls marketers face when developing an audience strategy: They overlook the step of defining what comprises a customer, and how that definition translates to their search campaigns.
That definition can vary greatly among marketing departments. Some define a customer as any visitor who has purchased in the last six months. Others define a customer as a visitor who has purchased at any point in time. Still, others consider a customer to be a returning visitor who is searching only using branded keywords.
Your definition of a customer should align with how you want to treat past purchasers. This thought goes back to the idea that “most retailers own their customers less and less.” If someone bought from you four years ago and hasn’t purchased since, would you still consider him a customer, and treat him the same as someone who bought from you a month ago?
Say two people bought from you yesterday. Theoretically, your brand is still fresh in their heads. But today, one shopper searches for the types of products you offer using a generic term. The other shopper uses a branded term. Would you consider both of them active customers? Or would you say you need to re-acquire the shopper who used the generic term?
Those are some philosophical considerations to help arrive at your definition of a customer. The other factor is data. Analyze your transaction data to identify trends in repurchase cadence. At what point in time does it become highly unlikely that the shopper will return? One month? Three months? A year? More? Those findings can help inform whether it makes sense to define a customer based on time, and what that timing threshold should be.
3. Understand your customers’ purchase path
Search is typically a new customer acquisition channel, and you can find new customers at varying levels of cost. As you move up the funnel within search marketing, it tends to cost more to acquire new customers.
However, if you have a strong understanding of your customers’ purchase path, you ideally know that a heightened cost is justified, because you can see your other channels—like email, affiliates, direct, etc.—are coming into play to nurture customers to purchase.
Gaining this understanding has a lot to do with your attribution model. Having a multi-channel attribution model is essential to viewing performance across your channels—and that also makes it a key best practice with a new-versus-returning customer strategy.
Most retailers’ audiences interact with the brand using multiple channels. A multi-channel attribution model lets you more accurately value the role of those channels. That knowledge can translate into critical information for determining the size of your investment and your ROI goal, channel by channel.
4. Create campaigns supporting each audience segment
Once you’ve defined what a customer means to your business, segment your ad campaigns based on new versus returning customers. This is where features like Remarketing Lists for Search Ads (RLSAs) and Customer Match can come into play.
Here’s an example setup involving these features and several similar ones. Keep in mind, this is just one way to slice it. You might find a version of this approach is better for your business and goals.
5. Set a unique return goal for each audience segment
Once you’ve developed your audience buckets, determine a unique return goal for each audience. A good return goal should align with the goals of your business and the campaign.
Also, it’s important to note the inherent relationship between return and revenue. Generally, a stricter return goal will limit revenue opportunities, and a more liberal return goal will open revenue opportunities.
For instance, you might be willing to target a less efficient goal for prospects (perhaps 30-45% cost/sale), a similar or slightly more efficient goal for the new and cookied audience (25-40% cost/sale), and a much more efficient goal for returning customers (about 5-10% cost/sale).
Generally, with a new-versus-returning customer model, you should be willing to spend more budget and operate to a less efficient return goal to attract new customers. By contrast, you should target a more efficient goal for returning customers because you’ve already invested in this audience and you’ve determined it is more likely to convert after having purchased in the past.
6. Segment each campaign further to align with your customers’ journey
Once you establish baseline campaigns for new and returning customers, analyze your data to determine if there’s enough volume to segment even further. For instance, do you still have enough data to split each campaign by device? If you know that more users are beginning their purchase journeys on smartphones compared to desktop or tablet, is there further value to be gained by targeting these mobile users differently?
Also consider whether you can segment by branded and non-branded terms, or trademarked and non-trademarked terms. That’s because search terms, naturally, reveal tremendous insight into purchase intent.
A new customer searching “laser printers” is probably at the top of the funnel, while a new customer searching “Brother HL-L2370DW printer” is further along in the funnel. If you have enough traffic hitting each of those two types of terms, consider segmenting by them in your new customer campaign.
The same concept applies to your returning customer campaign. For instance, If you see enough traffic going to generic terms versus branded or trademarked terms, consider creating campaigns for each type of query.
7. Watch for KPIs of success
Some of the most important questions to ask yourself as you evaluate performance are: Are you hitting your return goals? Are new customers aligning with your ideal customer profile? Are you increasing net new customers, while maintaining the same level of profit? Is cost per conversion down for returning customers?
Get in the habit of making incremental tweaks about every three months, depending on the trends arising in your data.
Your growth in search will naturally level off if you don’t innovate. Refresh your view of performance, and rethink the role of search in your performance marketing strategy. Consider whether your business and marketing goals are a fit for a model centered on targeting new versus returning customers.
This story first appeared on Search Engine Land. For more on search marketing and SEO, click here.
Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.
Steve Costanza is the Senior Analytics Consultant of Enterprise Customer Strategy at
This content was originally published here.