Yesterday I was talking to a friend about his plans for 2018.
He runs a one-person ecommerce/coaching business with a very personal oriented marketing.
He wanted to increase his sales this year and had a bunch of plans around attracting new customers.
But one thing he said caught my attention.
He said he didn't have "enough reach" for the level of sales he wanted.
I hear this from a lot of businesses so my first question to him was, how does he know he doesn't have enough? What would "enough" be?
After a few minutes of discussion it was clear to both of us that he wasn't sure.
He had a "grow revenue" goal and just reached for one strategy: more traffic.
Getting more traffic can be an important strategy but it might not be the most important strategy right now.
The best way to know is to look at how your existing traffic and customers are behaving. Then create a few forecasts of what would happen if that behavior changed.
What would happen if you add 20% more traffic? 50%?
Or what if 1% more of your existing traffic to bought?
Or what if 10% more of your one-time customers made a repeat purchase?
The advice I gave him was to take a look at his overall funnel. Look at his traffic, list growth, purchase rates, and repeat purchase rates. Figure out how the entire funnel is performing, and only then decide which area to focus on.
For a couple of hours of effort, he might see a much more valuable problem to solve than more traffic.
If he was using Shopify I'd have him signup for my Repeat Customer Insights app. It won't answer all of his questions (no single tool will), but it'd give him a much better idea about the later stages of his funnel (purchase, repeat purchase, churn).
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.