Just about everyone who researches the effectiveness of returning customers vs new customers comes to the same conclusion:
Repeat customers are better in almost every way.
So why is so much more attention given to acquiring new customers?
I can understand the volume argument, the "my repeat customers aren't enough to keep my business going by themselves" but isn't that a symptom of a different root problem? That you're not getting enough customers to come back because your repeat customer process is performing so poorly?
It feels like the grass is greener joke.
"What I see everyone else is doing must be better, right?"
New customer acquisition is flashy. New tech. New tactics. New case studies of how someone did something different and made a bazillion. All stuff you wish you could be doing.
Repeat customer retention are boring in comparison. It's not an entertaining. It's consistency. Trust. Loyalty. Long-term work where even a 1% lift can shift profit graphs.
But repeat customer retention is also hidden.
If you were keeping 90% of your customers from year to year and making sale after sale from them, you'd consider keeping that private so your competitors don't "borrow" your tactics.
I think the really great companies know this and they downplay or keep their repeat customer systems hidden away. Based on what I've seen with some metric reviews I've performed for Repeat Customer Insights customers, there are some sleeper companies out there doing crazy amounts of repeat business.
You might want to consider a "boring" old repeat customer retention system before venturing into the next flashy traffic idea that bloggers pitch.
Eric Davis
Segment your customers to find the diamonds in the rough
Not all customers are equal but it is difficult to dig through all of your data to find the best customers.
Repeat Customer Insights will automatically analyze your Shopify customers to find the best ones. With over 150 segments applied automatically, it gives your store the analytics power of the big stores but without requiring a data scientist on staff.