You’re running an E-commerce shop, or are in charge of advertising it, and you’re looking at a loooong list of products. You have cheaper products, and more expensive products. Popular products and less popular products. High-margin products and low-margin products. And there are a lot of them. Analysis paralysis is what comes to mind.
“I have over 8000 products on stock”, that’s what I heard from a shop owner. “I’ve tried to advertise some of them, which I knew were popular, but it did not work. In the end my cost per conversion exceeded my profit.”
Well, truth be told, you may not be able to advertise all of them without some serious automation in place. But what you can certainly do is to pick the lowest hanging fruits.
What you need for this is, of course, Google Analytics, your product database, and a bit of math. Nothing fancy, nothing scary.
And it all starts with profit. You want to spend less for advertising than you’re making by selling a certain product. You want a popular product, one that people search for, otherwise your ad will not show. And you want, of course, a high conversion rate.
Assuming that you’ve been running your website for a while before making the decision to advertise your products, you can filter, in the Content – Pages section of Google Analytics, all your product pages. Usually categories have some URLs, static pages have other URLs, and product pages have easily-recognizable URLs.
Let’s look at Interspire, a popular shopping cart software. A product URL is something like /products/some-words-here.html. Which means that if you filter for pages matching the ^/products/.+\.html regular expression, you’ll only have your product pages showing in your content report.
Export that URLs list, and go into the admin area of your E-Commerce solution and match those URLs to the corresponding products (SKUs).
You’ll end up with a list of products, and their respective pageviews, for a certain period of time.
Get the sales for that products, in the same period of time. Then get your margin, per product, from wherever you have it.
You should end up with a table showing you views, sales, margin and margin per view, for that list of products.
Now, a product is:
- popular, if it has many views
- high-converting, if the sales/view ratio (Bryan Eisenberg calls its inverse the “look to book” ratio) is high
- highly profitable, if the margin is high
When choosing your lowest hanging fruits, you’re looking for popular products with a high historical margin per view ratio. That’s the metric which combines conversion rate (sales per view), and margin.
And that’s it. Sort descending by margin per view and by views, and pick popular products with a high margin per view, and go advertise them.
Don’t know what the maximum CPC should be? You do know. It’s margin per view, which you may want to adjust a little to cover for the costs for running the business. That’s because the margin, i.e. the difference between the purchase price and the sales price, is not sheer profit.
So take your monthly costs for running the business, take your total margin and try to compute an adjustment factor for margin/view, then use it as your maximum CPC.
And if, through advertising and bidding a certain percentage of your margin per view you manage to attain the same conversion rate as your historical conversion rate, then your cost per acquisition -or CPA – should not exceed your profit per sale.
Once you’ve launched your campaign, all you need to watch for is the cost per conversion, which should stay under the profit per sale. The initial, target cost per conversion, is (max CPC) / (historical conversion rate). If you manage to get clicks for less, or if you manage to convert better, through more precise targeting, then your CPA will be lower than the expected one.
That’s it. No more analysis paralysis. No more “I think these products are profitable”. Your data knows which are profitable and which show potential.
Furthermore, if you analyze the performance of your products from this angle, you’ll quickly discover which products are totally uninteresting, so you can maybe decide to get rid of the stock at a lower price and replace them with something else.
Bonus: Don’t know which keywords to use when advertising those products you’ve just picked?
Oh, but you do.
Filter your product landing pages by the same regular expression as above (depending on your shopping cart solution), and add “Keywords” as the secondary dimension. Export that list and see which keywords are related to those products (which keywords were used in search by people who landed on those pages). Maybe use the keyword list splitter to separate more general keywords from the more targeted ones. The nice part about this technique is that if you’ve shown up in organic searches for that keyword, and got clicks, those keywords are relevant for that page. So the chances of seeing the dreaded “Keyword relevance: poor” in your keywords diagnostics are very, very low.