Retention begins Pre-Acquisition
“My acquisition numbers are looking great. It’s just my one & done customers that are killing me.” Then your acquisition numbers aren’t really looking great…
Acquisition isn’t about transactions
We all know this truth to be self-evident…but sometimes a reminder is good. When we are lofting our media out on Meta – our goal isn’t to pick up transactions…our goal is to pick up customers. Sure – we like a healthy ROAS (Return on Ad Spend)…even better…some NCCD (net customer contribution dollars) to fill the coffers and secure some accolades…unfortunatelybut that really isn’t the measure of customer acquisition…and why your one and done rates are still a problem.
Look aLike Seeds (LaLs)
For those unaware – Look aLikes are a “sample” audience (commonly called “seed” audiences) that you send to your advertising partners and say “find me people who look like these.” I built my first Look aLike program over a decade ago with my friends from at The FinishLine – and the 4x-12x increase in ROAS over traditional ad targeting pretty much signaled the end of an era in “old school media targeting.”
Look aLike Advertising: Not all wine and ROASes
The problem with Look ALikes (prt1): Off Target
WARNING: REAL WORLD EXAMPLE. Let’s say that you sell smoking jackets – cool smoking jackets…the kind with leather on the elbows. Your core customers base is an aging collection of college professors from Ivy League schools. However, you want to expand into an emerging market of younger, handlebar-moustached, craft-cocktail downing hipsters (cause that is the future). You cut a seed audience of recent purchasers (aged college professors) but your ad targeting criteria (and creative) is for your moustached hipster. Yeah – you see the issue here…
Conflicting criteria between seeds and targeting will cost
between 2x to 5x incremental ROAS
Aside from making Meta (even more) schizophrenic, you just tanked your program. Conflicting criteria is where most “vendor” based Look aLike LAL automation programs fall flat…to the point of (at times) driving negative lift…and it happens frequently
The problem with Look ALikes (prt2): Bad Seeds (grow bad customers)
Let’s talk about the Look aLikes themselves. What criteria are you using? Are you pulling people who purchased in the last 30 days? People who browsed? What about factoring in customer LTV (Life Time Value). Well…recent purchasers are reflective of the past…and a lot can happen in 30 days. Browsers….yeah – it’s intent…but not $$$. And LTV – love the idea…but you are pulling a Look aLike LAL of people close to the END of their purchasing journey.
The quality of your Look aLikeLAL seeds are the
single biggest contributor to your 1 & done rate.
Stop Living in the Past: AI Assisted Predictive Seeds
What if you could produce a list of Look aLike seeds that will be purchasing smoking jackets in the next 30 – 60 days. What if those customers were also selected by their persona (twitchy moustaches and all). What if those customers all had a high predicted future value? What if that seed wasn’t just reflective of the predictive purchase of the new felt smoking jack – ohhh, but also the matching slippers?
Predictive seeds acquire customers on a journey, not simply transacting.
My Retail family – this isn’t hyperbole. I’m not an analyst painting pristine pictures of some rose colored nirvana-esk view of what could be…this is real. In fact – it’s been proven by brands that you know. The simple reality is that –
- Predictive Seeds will increase ROAS an incremental 7X over existing Look aLikesLALs
- Predictive seeds will more than halve your one1 & done rates
- Predictive seeds 3x strategic acquisition effectiveness (acquiring your coveted “who”)
Oh…and as a little bonus…predictive seeds work for new customer acquisition for your website…wait for it…waaaaaaait for it….stores and marketplaces! Yeah…and it’s damn effective (especially when coupled with DMP in-market signals).
