A paid click has already brought someone to your Shopify store. They like a bag, but they cannot tell whether their laptop fits. If they leave to find the answer elsewhere, another ad will not fix the missing detail. An AI agent can help at this point: answer the question using your catalog, show a suitable product and offer the next purchase action. Whether that increases conversion needs to be measured in your store.
Start with the question that blocks the purchase
Look at the products that attract visits but leave customers asking the same things. Will it fit? What comes in the box? Which version should I buy? Can this material be washed? These questions reveal a gap between the information on the page and the decision the customer is trying to make.
Pick one collection for your first test. Review recent support messages and search terms, then write down the questions that require someone to connect two or three catalog details. A clear size chart may be enough for a simple lookup. A shopper choosing between two bags may benefit from a conversation that asks about laptop size and commuting needs.
What Obsedia changes in that visit
Obsedia’s Ecommerce Agent uses merchant-approved product and store information to explain options. It can ask a useful follow-up, show product cards with price and variants, and offer an explicit Add to cart action for an eligible variant. The customer decides whether to use that action.
Give the agent enough information to make the answer specific. “Fits most laptops” is a weak source. Internal compartment dimensions and a documented compatibility list are more useful. If a measurement is missing, the agent should identify the gap instead of promising that a particular device fits. The conversation improves only as far as the source information supports it.
A worked example: one visit, one resolved doubt
Consider an illustrative store selling two backpacks. A visitor asks, “Which one fits a 15-inch laptop and a gym kit?” The agent asks for the device dimensions if needed, compares the documented compartments and explains which bag has more room. It presents the relevant product with its price and available variant.
There is no need to insert a discount or recommend a second bag. The useful change is that the customer can make a decision without opening several pages or waiting for an email. This is a demonstration of the mechanism, not a claim that every conversation produces an order.
Calculate the opportunity before calling it a win
Suppose a store has 10,000 comparable sessions and 200 purchasing sessions. That is a 2% session conversion rate. At 220 purchasing sessions from the same number of sessions, the rate would be 2.2%: a rise of 0.2 percentage points, or 10% relative. These numbers are hypothetical and do not describe an Obsedia customer.
Twenty extra orders would not automatically justify the app. Calculate their contribution after product cost, fulfillment, returns and any discount. Compare that contribution with the software cost and the time spent maintaining product information. Keeping ad spend unchanged is a useful constraint, but traffic quality, stock and promotions still need to be comparable.
Measure all eligible visitors, not just people who chat
Shoppers who open chat may already be more interested in buying. Comparing their purchase rate with everyone else can overstate what the agent caused. Where your experimentation setup allows it, compare comparable visitor groups with and without the agent, keeping the rest of the experience consistent. This is a measurement recommendation, not a claim that Obsedia includes a built-in experiment platform.
Use Shopify’s conversion reporting to follow the store funnel and record the test conditions. Keep cart additions separate from completed purchases. Do not end a test after the first good afternoon; decide the minimum evidence and review period in advance. If you lack enough traffic for a reliable result, report the uncertainty and use conversation quality to decide what to improve next.
Let the result tell you what to fix
If customers get clearer answers but still stop at payment, investigate checkout, delivery costs or payment availability. If the agent repeatedly needs a product detail, add that detail to the source. If conversations grow longer without helping the decision, shorten the reply style and remove unnecessary follow-up questions.
Start with five real buying questions and run them through the Ecommerce Agent demo or your configured store setup. Check accuracy, product relevance, variant selection and the path to cart. That review gives you a practical starting point for a conversion test without buying more traffic.
Choose one collection and one purchase-blocking question. Fix the source information, test the agent’s answer and measure completed purchases across comparable visits.
