Bearbottom Clothing is a sustainability-driven eCommerce brand that specializes in outdoor mens wear. They’ve recently been recognized as one of the fastest growing men’s apparel brands in the US!
We’ve been fortunate to get to know their CMO meaningfully over time – and his passion and hunger have never ceased to amaze. He is a detail oriented leader who spends time building, tweaking, running campaigns, and optimizing.
But underneath it all, he is laser focused on growth. He and his team aim to make today’s results better than yesterday’s. Every single day.
Given this strong desire for continuous improvement, he opted to start using an intelligence engine to ingest and make sense of all his data in new ways.
The analytical marketers and business leaders out there will likely resonate with this: we like to have control over defining our cohorts, understanding metrics, running our tests, and analyzing results. Frankly, it’s familiar and comfortable to navigate our data through a spreadsheet (…or perhaps your preference is python or SQL or a visualization tool, etc).
The reality of course is that “data-chaos” inherently will emerge in organizations – making a true understanding difficult to nail down, leading us to lean on heuristics and experience over the data. Beyond that, bandwidth concerns are also always a factor: it’s time intensive to micro-analyze and deploy truly actionable trends at scale.
Knowing all that, it is sometimes still hard to let automated solutions make data-based decisions on our behalf.
As a hands-on leader who was used to holding the reins, Bearbottom Clothing’s CMO relinquished some of that control for the sake of forecasted growth opportunities.
And we can appreciate the decision-making process.
He used our API and one-click apps to integrated his sales, marketing, website, and revenue data into the Aidaptive brain. This started with Shopify + Klaviyo + Hubspot.
The “brain” modeled predicted shopper scenarios and then deployed those micro-tests and optimizations along their buyer journey to drive better results.
To summarize what was going on:
- Aidaptive’s engine organized, calculated, predicted, and executed to coordinate the shopper journey more precisely.
- Software determined customer affinities, projected LTV and cart size, willingness to pay & purchasing power, and generated precise product recommendations and promotions.
- The intelligence was deployed across their online store and direct marketing touchpoints.
We call this predictive personalization.
And then what happened…
After testing the effectiveness of an ML predictive personalization engine against their already high-performing website experience, the leadership team for Bearbottom saw that the differences spoke for themselves. After all, certain types of activities and actions are performed better via data-processing automation.
(Here is the results section.)
- Recommended products across the web shopping experience had a 55% higher clickthrough rate.
- Overall conversion rate was 8% higher than their control group.
- The revenue per website visitor increased by 9%.*
*Historically this metric wasn’t used as frequently, but it helps to paint a picture that considers multiple aspects of the equation: A) high acquisition and ad costs → ROAS, B) pure conversion rate optimization, and C) cart size and AOV.
Machine learning can operate at a speed, scale, and accuracy that runs circles around legacy methods.
Further, one of the known value-adds of ML is that its recursive learning allows each day to be better than the last: which helps this team hit their ambitious goals. Every shopper who lands on their website or engages with a direct marketing campaign is a test whose results feed back into the machines predictive modeling to do better next time.
The Aidaptive team is building partnerships with other apparel brands like Bearbottom Clothing who are in a continuous-growth mindset, looking to drive incremental conversion volume and revenue per user.
We believe acquisition costs are too high to waste any shopper who lands on your website. The value of a loyal customer is too high not to treat every individual website visitor like gold.
If you’re interested in learning what our ML engine predicts for your store’s revenue lift, reach out to our team!