Most marketing and optimization tools on the market today talk about “improving sales”, “optimizing for AOV” and “improving customer retention”, and rightfully so. Providing a superior customer experience that drives shoppers to spend more money is the goal of many online stores and tools like Nosto help facilitate this.
There is however, one aspect that we seldom talk about, which is optimizing for increased profitability. This is equally as important as driving sales on a larger scale, because selling 40 T-shirts with a margin of 1% might provide less butter on the bread than selling 1 T-shirt with a margin of 20%.
Why Margins Matter
Back in the day, I used to work as a salesperson at a respectable electronics store. Because if you’re a tech geek and get discounts on fancy consumer hardware, why not?
I remember when I encountered my first B2B customer who wanted to buy new computers in bulk and I needed to calculate an offer for the entire order. This was the first time when I started paying attention to the margins of both the laptops and also the third party accessories. Let’s take a hypothetical scenario: If the company buys 50 laptops with a margin of around 8%, there is only so much I can do – however if we also throw in 50 laptop bags with a margin of roughly 70%, we have some moving room with the end price of the total order. In context of e-commerce, this allows more marketing dollars and euros to be spent.
Introducing Rational and Absolute Margin Filters to Retailers
For many online stores, the difference between margin percentages across categories and brands are very different and moving the shopper between different products based on profitability is hard, I mean, very hard. We launched a simple filter for margin that can be used either with absolute or relative values to solve different use-cases, and I’ll walk through how these can be utilized in any online store using Nosto today.
Note: as you might expect, the examples provided in this article are fictional, since sharing any store specific information on margins publicly would not be acceptable. We achieved the examples below by importing fictional margin data into a real store catalogue and then filtered the output in preview mode to achieve a working preview of how this works in practice.
Use Case 1: Absolute Margin Filtering for Store Best Sellers
Let’s look at a fictional example of one of our demo stores called Costo, which is very much a real store that we frequently use as a go-to-example. In this case, completely simulated since neither we – nor they – want to disclose the real margin of their products.
Let’s start with a Best Sellers element where none of the fictional margin data has been used yet. The “Wau” beanie collection is clearly trending here. Let’s see what happens when we add some margin rules into the mix.
Margin rules are applied to the Onsite Product Recommendations level within a certain campaign under Include/Exclude rules. For an absolute margin rule on our Best Sellers, we can set a minimum of 15% to example upsell our shoppers on the homepage.
The output in the example doesn’t change dramatically since the “Wau” beanies are still considered the Best Sellers but four out of five products changed due to the slight adjustment in margin, meaning our example works and we are now making a minimum of 15% profit on every item sold. Hooray!
Use Case 2 : Relational Margin Filtering on Individual Product Pages
When looking at the product pages, playing with margin filters become a bit more interesting because there might be a big variation between profitability within a category or a certain brand. On product pages, you can also use the relational margin filter where a base value is set from the currently viewed item that can then be used to look for other items.
Again let’s start with a completely unfiltered cross-seller element on a product page. I’m viewing a “Wau” beanie so the relationship with the other similar styles is obviously the most related products here. Let’s apply some fictional filtering to see how we can change this view.
The relational margin filter accepts a value with a + or – prefix to distinguish between higher or lower values. In this example, I want to expose products with a 10% higher margin than the item I’m currently viewing. Let’s see how it goes.
The output is now very different than the unfiltered version, since the fictional margin was spread out across different categories assuming that the “Wau” beanies all have the same margin percentage, which is also quite probable. The example output has a bit less relevance. However, we are still sticking to mostly beanies with 2 Kombai caps sticking out in between. You can also note how the average price has gone up from all 42 € to allow for a bit more variation.
Main Takeaways When Using Absolute and Relative Margin Filters
That it’s possible to use both absolute and relational margin filtering when using Nosto Onsite Product Recommendations to optimize for increased profitability with a slight cost to relevance. However, this is very much industry and store-specific but you are the best judge on how this should and could be used in your specific context. If I can leave you with some advice, this would be it:
- Configure Nosto on your online store so that it behaves, looks, and feels like you expect it to. This is the crucial first-step so you have a baseline to compare to.
- Gradually start experimenting with absolute and relational margin filters and validate this both visually as well as through an ongoing A/B test if possible.
- Compare if there is a noticeable loss in sales (because of gradual loss in relevance) vs. the increase in profitability and calculate if you are improving or decreasing on your bottom line because of it.
- Tweak the setup and reap the gains.