Aterian has rolled out the Genesys Cloud platform across its key e‑commerce marketplaces—Amazon, Walmart, eBay, Temu and Shopify—to streamline support operations and deliver a more consistent, AI‑powered customer experience.
The new system is expected to cut average handle time by 25 % and lift agent satisfaction by 33 %, while the company projects a 65 % reduction in the cost of ownership for its support function. These savings are intended to offset the company’s recent revenue decline and widening net loss, which fell to $19.0 million in Q3 2025 from $26.2 million a year earlier and widened to a $2.3 million loss from a $1.8 million loss in the same quarter last year.
Management explained that the cost‑cutting drive is a response to macro‑economic headwinds and tariff‑related price increases that have pressured margins. CEO Arturo Rodriguez said the company is “focused on expense control, margin enhancement, sales channel expansion, new product introductions and sourcing diversification,” while CFO Josh Feldman highlighted a 180‑basis‑point improvement in gross margin and a doubling of contribution margin in Q3 2025, underscoring the impact of disciplined spending and a shift toward higher‑margin consumables such as Squatty Potty Flushable Wipes and Healing Solutions skincare.
Investors have reacted cautiously, noting that Aterian’s consistent revenue misses and widening losses have tempered enthusiasm for the technology upgrade. The company’s guidance for the second half of 2025 remains flat, with net revenue projected between $36 million and $38 million and adjusted EBITDA ranging from breakeven to a $1.0 million loss, reflecting management’s focus on stabilizing cash flow while pursuing growth in the consumables segment.
Aterian is also diversifying its sourcing away from China, moving a significant portion of its dehumidifier production to Indonesia to mitigate tariff exposure. The company’s strategy to combine AI‑driven support with a higher‑margin product mix is intended to create a more resilient business model in a challenging macro environment.
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