Particular products for particular people

Just a few minutes ago, Ilana Davis asked me if I wanted a cookbook someone was offering for free.

My reply was full of snark but got me thinking:

No thanks. That's for people who like cooking, not eating.

Having already read that book, I was familiar with it enough to know there wasn't anything wrong with the recipes in it. Just its approach was different than what I valued in my cooking.

I look for:

90% of what I cook these days I do from memory, the rest are either experiments or new recipes I'm still learning.

The recipes in that book didn't match up to my goals:

If someone made them for me, I'd eat them (tasty, healthy) but I wouldn't want to cook them myself (complex/rare ingredients, rigid steps).

I'm a different customer segment than who the book is targeting. There's some overlap (tasty, healthy) but not enough for me to be a customer.

Within the cookbook industry there are 100s of attributes that create 1,000s of customer segments just based on the product attributes alone. Compare that to something like toilet paper where are probably only a dozen or two different segments and attributes.

Vastly different markets. Vastly different buyer behavior.

Figuring out what attributes your products have and the attributes your audience look for is a key part of merchandising and marketing. Done right and you have a hot product that won't stay on the shelves.

Customer behavior segments, like what Repeat Customer Insights measures, are different as they measure what the customer actually does. When combined with product data, like in the Nth product analysis, they can tease out some hidden product attributes on your best sellers.

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.

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Topics: Product analysis

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