40% Repeat Purchase Rate: How over&back Used Similarweb to Launch on Amazon with Data-Validated Product Strategy
over&back's Challenge
over&back — a premium tableware brand that had built its business through wholesale distribution — was approaching the limits of its existing channel. Wholesale relationships limited margin, constrained brand expression, and made it impossible to access direct consumer data on purchasing behaviour or category trends. The brand wanted to launch on Amazon and build a direct-to-consumer presence, but had no independent visibility into which product categories were growing, which price points were winning, which competitors held the most share, or which trends were driving consumer search. Launching without that data meant betting inventory and marketing spend on guesswork in a highly competitive marketplace.
How Similarweb Solved It
over&back used Similarweb's Retail Intelligence and Shopper Intelligence platforms to analyse Amazon category dynamics before committing to product selection and inventory planning. The team benchmarked competing tableware brands on traffic share, product page performance, and consumer search behaviour — identifying which categories were growing, which were saturated, and which trends (colour, material, style) were driving purchase intent. Similarweb's search trend data flagged sage green as an emerging consumer preference in home goods before it was widely reflected in competitor product ranges. The brand used this intelligence to inform its initial product lineup, brand store design, and keyword strategy — entering Amazon with data-validated positioning rather than assumption.
The Outcome for over&back
over&back achieved a repeat purchase rate of approximately 40% on Amazon — significantly above the industry average for new marketplace sellers, which typically run below 20% in the first year. The brand's data-informed product selection proved accurate: sage green products, identified through Similarweb trend analysis before the colour reached peak market visibility, contributed 15–20% of total Amazon sales. Average order values were also above category benchmarks, attributed to data-informed brand store design that surfaced complementary products and communicated premium positioning effectively. The channel expansion successfully reduced reliance on wholesale intermediaries and established a direct consumer data feedback loop the brand had previously lacked.
What over&back Learned
over&back's case makes a simple point with precision: the cost of wrong inventory decisions in consumer goods is not just the write-down — it is the opportunity cost of not having the right product when consumer demand peaks. Sage green is a specific example of trend intelligence translating directly into sales share. The broader lesson is about what market intelligence changes in the channel transition decision: without Similarweb, over&back would have been guessing which products to launch and which categories were growing. With it, the brand entered Amazon with the same analytical advantage that larger competitors pay enterprise data teams to generate. For SMB manufacturers navigating wholesale-to-DTC transitions — where the cost of a wrong product bet is magnified by limited inventory capital — digital market intelligence functions as a risk management tool, not just a marketing input.
- Consumer trend data available in search and retail analytics is a leading indicator of demand — brands that act on it before it peaks in competitor catalogues capture higher-margin early-mover share.
- For SMB manufacturers transitioning from wholesale to DTC, digital intelligence compresses the information disadvantage versus larger competitors who have internal data teams and category data partnerships.
- Repeat purchase rate is a more durable success metric than first-year GMV: it signals that product selection and positioning are resonating with the right audience, not just converting traffic.
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