Since its founding in 2016, SNOCKS has taken the German eCommerce retail market by storm. With a combination of performance marketing, a personalized social media strategy and organic traffic this blooming company now generates over 30 million euros in sales per year.
Most of their products – from sneaker socks to boxer shorts – are sold at the company’s own online store and on Amazon at a bundle discount (the higher the number of products you buy, the lower the price per item). When buying a 12-pack, for example, customers save 10% on the retail unit price and when buying an 18-pack, SNOCKS gives an impressive 20% discount.
This kind of tactic is common across the industry but quite haphazard in its application whilst the steadily growing online retail business is leading to an increasingly dynamic market. Setting competitive prices without reducing the profit margin plays an essential role for SNOCKS. As the CEO of a young, digitally-driven company, Johannes Kliesch was enthusiastic about the possibilities of AI-based decision-making right from the start.
"One of our success factors at SNOCKS is that we are always testing new things, so dynamic pricing was at the top of my list of essentials.”
Co-Founder and Managing Director SNOCKS
SNOCKS set its discount rates just once and it did not feel necessary for them to make any adjustments after that. This discount level was mostly determined randomly, so instead of relying on validated data the team was guided by personal intuition and perceived experience.
What’s more, additional factors such as advertising spend on Facebook played no basic role in setting prices manually. In other words, SNOCKS didn’t have a well-structured overview of which factors were influencing business growth and profitability.
So the key questions for implementing paretos were:
How can discount pricing be optimized to achieve both a higher average shopping cart size plus a higher profit margin? And which additional factors influence the purchasing decisions of potential customers?
With the help of our paretos AI, SNOCKS established an intelligent pricing management system within a very short period of time that allows the company to control its price adjustments in a data-driven and demand-oriented approach.
The paretos algorithm takes a variety of factors into account: The first step was to connect marketing and historical sales data to the platform and enrich it with external information such as weather data and the days of the week. In the subsequent data analysis SNOCKS discovered, for example, that it was not only ad spend and discount values that had an important influence on margin optimization and shopping cart size, but also the outside temperature and what days demand is at its highest. Because of this, the marketing team was able to identify a relationship between ad spend and total profit for the first time.
Furthermore, the paretos algorithm visualizes specific trade-offs between shopping cart size on the one hand and profit margin on the other. This allows SNOCKS to identify a variety of possible solutions for optimizing discount values and advertising spend, while at the same time incorporating relevant external factors such as the day of the week or the weather into discount optimization.
Thanks to the visualized trade-offs created by the paretos algorithm, depending on the discount size SNOCKS could increase its margin by 30% within just five months.
In addition to this, thanks to the dynamic discounting algorithm that automatically selects the optimal discount for different bundles, SNOCKS was able to maximize both KPIs (margin optimization and basket size) that resulted in an overall higher total profit margin.
profit margin increase
average order size increase
paretos has shown us how to customize our discounts to optimize margins for the entire store. What I really like is that it gives us deep insight into how factors effect sales and margins of our product bundles and which perform the best. We’ve not only learned an insane amount of information from our own data, but also how much potential AI-based decision scenarios actually have for Dynamic Pricing or Ad Spend Optimization.
Co-Founder and Managing Director
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