Andre ChandraMost marketing is blind. Brands put out messages and hope they are found by enough people who want to be customers that it justifies the spend. Even with targeted marketing, most campaigns are sent to broad audiences defined by a few key attributes, but not enough to eliminate the massive waste inherent in customer acquisition marketing.
Customer acquisition is the most expensive part of marketing. It can cost five times more than retention, and the costs keep rising. Still, no company can afford to abandon marketing for new customers. Even the best retention strategies bleed customers at an alarming rate; prospecting is the only way to offset that loss and grow.
Acquisition is essential, but brands must find a way to do it more effectively, and that starts with tighter, more data-driven targeting.
Data-Driven Acquisition Marketing
Customer modeling is the key to better targeting your prospecting. If you dig into your existing customers, you can identify commonalities and buying signals that allow you to direct marketing spend more effectively and reduce the overall cost to acquire new customers.
The hard part is knowing which attributes correlate most closely to the likelihood of a prospect becoming a customer.
Demographics Aren’t Enough
Demographics are a mainstay of target marketing, but in 2020 they’re not enough.
While demographics do have power in targeting your marketing, they don’t reflect buying signals in their own right. They can still be useful for targeting messaging and creative around more impactful modeling methods, but it’s important to look deeper.
Ideally, you want to build a target list around buying signals, then segment that by demographic information and target your creative to those segments. This means optimizing the creative and/or offer by doing things like matching people in the imagery to the demographics of that segment.
Demographics are also useful in building look-a-like audiences to target new customers based on the customers you already have. Even though demographic data does not directly indicate buying behavior, it can reveal insights when analyzed as part of the wider customer picture with data modeling tools.
4 Data Points for Better Customer Acquisition Marketing
With the above qualifiers in mind, which information actually does line up with more successful acquisition marketing? There are four key data points we like to use for omnichannel targeting.
1. Buying Behavior
When the goal is to understand what type of offer motivates what type of people to buy, purchasing behavior is one of the most important data points to consider.
When you identify that certain list segments respond to deep discounts, you can hold them out from general mailings and bring them back in when you have deep discounts to talk about.
When you can identify audiences with a propensity to buy around certain price points, build offers around those price points. If it’s above your product price, bundle a strong package deal that will lift response and increase your average order value. If your price is above the target, present it as an installment option with payments in the target zone.
This is exactly the kind of actionable information you can get from deep-dive data that is missing from demographic information. You’re not just targeting an age group, area, etc. You’re making a surgical strike at the behavior you want to influence.
2. Personal Life Triggers
Timing is everything. Once you’ve narrowed your target market by interest and buying signals, life triggers become a powerful way to spur new action.
Life triggers can be tied to events ranging from birthdays and graduations to buying a home, getting a new job, retirement, and other once-in-a-lifetime moments. By targeting marketing to a specific time in a prospect’s life when they are most likely to be interested in your offer, you stand a much better chance of making the conversion.
3. Shared Interests
One of the most important indicators of customer potential is evidence of interest in the product category or the industry it serves. While you may not be able to read prospect’s minds directly, there are many data points brands can use to pinpoint interest.
One way is to target audiences and lists built around interests that are relevant to your target customer, such as subscriber files for related media.
Perhaps a more exciting option: Social media provides new opportunities to leverage interest data points. Facebook, for example, allows you to build custom audiences including specific interests.
4. Searcher Intent
“Search data captured across e-commerce, pricing comparison, and product review sites are one of the strongest signals of intent and best sources for new customer acquisition,” says James Green, CEO of Magnetic, and he’s right. Harnessing this data in your customer models is one of the best ways to more tightly target your acquisition efforts and cut down on wasted prospecting spend.
This is why Google now uses searcher intent as the main factor in targeting its search algorithm. The intent is the most reliable indicator of what searchers actually want, and that makes it a powerful marketing tool.
In practice, this means identifying visitor paths, either on your website or across the web, and matching them with desired outcomes. What product pages are they looking at? Did they come from a related external website? Did you catch them on a specific search ad that is relevant to what they may want? All of this data can be used to build a better, more efficient plan for your acquisition marketing.
Don’t Be Afraid to Ask for Help
All these data points are important for optimizing your acquisition marketing, but they’re not necessarily easily accessible. When you’re trying to do advanced customer lift modeling that includes things like buyer intent seen through visits to other websites, it really helps to have data scientists on your side. These experts can isolate those variables and build them into a view of the audience you’re trying to target.
These are essential tactics that businesses are using now, and more businesses will use them in the future. Make sure you get ahead of the curve by digging into the data points today.
