It may be an eCommerce staple for many, but audience targeting has passed its sell-by date. Want an edge in online retail? Here’s why you should prioritize data-driven, customer-centricity instead.
The Mad Men era kickstarted lots of best practices in advertising. Many have since faded away, but one remains inexplicably top-of mind. I’m talking about audience targeting…and want to let you in on a secret.
Everything you thought you knew about it is wrong.
Fortunately, you have everything to gain from this revelation. Because turning instead to Customer Lifetime Value and financial accountability as your North Star offers a far better route to long-term health.
Audience targeting used to make sense
Audience targeting once made sense. Yesteryear’s Brylcreemed execs had little to go on besides aggregate sales and basic demographics, so they made the most of what they had.
Plenty swear by a more advanced version of it today – and at face value, this makes sense. For one thing, intent-based targeting can feel slicker than relying on keywords alone. After all, we’ve been taught since Marketing 101 that customers that look alike tend to, well, act alike.
It was common knowledge that segmenting consumers by personas – whether it be interests, age or otherwise – gets you closer to your customer base.
From Ladies’ Accessories to Sports Apparel, personas do a great job of boosting the practice’s appeal. Whether it’s “Working Wanda” or “Baseball Bobby” – people can relate to them. Especially retailers’ decision makers, to whom agencies propagate such templates on a regular basis.
Audience targeting makes things neat and tidy. Mr. X belongs in one bucket; Mrs. Y another. It smooths the path, too. You can formulate campaigns that dovetail with “their” lifestyles.
Don’t believe the hype
When something’s too neat and tidy to be true, it probably is. Personas may well be appealing. But within the context of modern-day approaches to eCommerce, they’re also insulting.
Buckets and personas hide an awkward truth – they’re usually not borne out by data, but by intuition. And even if they are, the range of differences they support across a given customer base remains too narrow to be creditable.
I’m not rejecting personas completely. People who drive a Lexus, enjoy buying steaks and go shopping on Tuesdays likely share other traits, too. But demographics should support the process of determining long-term value, not lead it.
Start with financial accountability
In 2018, startup Blue Apron drew a stinging assessment of its financial health despite an imminent IPO. Most commentators praised its impressive growth. But this analysis ruffled feathers.
According to Daniel McCarthy, an expert in Customer Lifetime Value (CLV) who analyzed Blue Apron’s accounts, it had built its rapid growth on shaky ground.
Blue Apron’s success boiled down to aggressive marketing spend. Around 70 percent of its resulting customers churned after six months.
This trapped the start-up in a cycle of ever-increasing acquisition costs. Other analysts picked up on McCarthy’s customer-based corporate valuation. Blue Apron’s initial valuation sank.
In fact, it sparked a new way of assessing a company’s value – away from the traditional, context-laden way of doing things to a firm focus on numbers.
Why leave the onus on customer caliber to the realm of academia? Retailers that scrutinize the quality of their customers build much firmer foundations for the long term.
McCarthy’s colleague and fellow CLV guru Professor Peter Fader revealed as much at Crealytics’ latest retail advertising salon.
Real insights occur when you start digging beneath the surface. Don’t just tell people what your top-line revenue is, or how you grow it. Explore the underlying factors instead:
- How many customers are you going to acquire?
- How long will they continue to maintain a relationship with you?
- How many transactions will you make over a given horizon?
- How much margin will you make on each transaction?
A customer-centric mindset exposes audience targeting’s flaws. You stop looking for shared traits in the easiest places to find them (known as the “streetlight effect”). Instead, you cherish the unique differences between customers and recognize the vast heterogeneity across different cohorts.
Ultimately, you can better allocate your resources to maximizing Customer Lifetime Value (CLV).
The importance of CLV for customer acquisition
In terms of acquisition, Customer Lifetime Value forms a fundamental part of modern eCommerce. At its most basic level, it moves you from a focus on measuring transactions in favor of customer quality (as defined by key metrics such as propensity to return and net margin contribution.)
Leading with CLV also provokes two related questions that come via a cohort analysis of your CRM: How much is my average customer worth? How much are my best customers worth?
But audience targeting’s pitfalls require you to go beyond cohort averages. Remember, marketers who underscore their targeting with pre-packaged demographics invite assumptions rather than true-to-fit datapoints.
Each group of customers you acquire will express a huge amount of heterogeneity. And to top it off, the next group you acquire will be a different mix again. If you work with a static persona, though, it won’t reflect how the CLVs of these cohorts change over time.
Remember, marketers who underscore their targeting with pre-packaged demographics invite assumptions rather than true-to-fit data points.
Calculating individual CLV helps you build personas from the bottom up as opposed to top-down. You can thus focus your acquisition efforts on a small, select group of loyal shoppers – even clustering them into different tiers of buying propensity (e.g. high, medium or low).
It then becomes an issue of how you market to them.
Do personas have any role to play?
If you’ve already made the move to customer lifetime value – great. But while it should be the main tool for mastering a customer-centric approach, that’s not to say standard demographics should be discarded completely.
Cohort analysis and, ultimately, individual predictions should prove influential in understanding the differences in value between your customers.
However, layering certain demographics on top of your own analyses can often reveal further clues. Following them might just leave you better equipped to distinguish the differences between your higher and lower value customers.
Want to learn more about CLV-centric advertising? Check out our explainer here.
