Let AI analyze your session data and optimize your conversion strategy.
Setting up dynamic discounts can sometimes feel like a guessing game. Is a 10% discount enough to convert Mid-Range device users? Are you giving away too much to Low-Cost users when a 15% discount would have sufficed? Traditional analytics tools provide raw data, but they rarely tell you exactly what you should do with it.
Devicely's Smart Sales Assistant was built to bridge this gap. Instead of leaving you to decipher complex spreadsheets, the assistant actively interprets the data for you.
The assistant works by continuously monitoring the relationship between device tiers, the discounts offered, and the resulting conversion rates. It tracks thousands of sessions to establish a baseline performance metric for your specific store.
For example, it might notice that users on Low-Cost devices are abandoning their carts at an unusually high rate compared to industry benchmarks, despite receiving a 10% discount. Conversely, it might notice that Mid-Range users are converting at an exceptional rate with a 20% discount, suggesting that you could lower the discount slightly and retain the same volume.
When the Smart Sales Assistant identifies an inefficiency in your pricing strategy, it generates a clear, actionable recommendation directly in your Devicely dashboard.
You might log in and see a prompt stating: "Increase your Obsolete tier discount from 15% to 20%. Historical data suggests this demographic requires a stronger push, and this adjustment could yield a +6% Order Volume Growth with negligible overall margin impact."
The recommendations are strictly data-driven, ensuring you make business decisions based on statistical probability rather than gut feeling.
Our engine generates specific alerts based on your store's performance. Here is exactly what they mean and how they help you optimize:
This alert triggers when the assistant detects significant traffic from a specific device tier (e.g., Low-Cost devices) but notices you haven't set up an active discount rule for them. It essentially tells you that you are leaving money on the table by treating these visitors like premium buyers.
If you offer a 5% discount to Obsolete devices and the conversion rate remains stagnant, the assistant will flag this rule. A discount that doesn't increase conversions is simply throwing away margins. The assistant will recommend either increasing the discount to find the "sweet spot" or removing it entirely.
This alert fires when your discount structure is too flat (e.g., offering 10% off to Middle, Low-Cost, and Obsolete tiers simultaneously). The AI recognizes that different demographics require different incentives and will suggest staggering the discounts to protect margins while maximizing OVG.
A counter-intuitive but crucial alert. If a 25% discount to Mid-Range users causes an absolute explosion in conversions, the assistant might suggest testing a 20% discount instead. You might be giving away more margin than strictly necessary to secure those sales.
If a historically stable device tier suddenly stops converting, the assistant alerts you immediately. This could indicate a market shift, a broken checkout flow on specific devices, or a competitor launching an aggressive campaign, allowing you to react instantly.
This is the alert every merchant wants to see. It triggers when a specific rule achieves perfect equilibrium: high conversion volume with minimal margin sacrifice. The assistant will recommend locking this rule in and potentially scaling ad spend targeting that specific demographic.
Sometimes a $10 fixed discount works wonders on low-ticket items but fails on high-ticket items for the same device tier. The assistant detects this discrepancy and will suggest switching to a percentage-based rule (or vice versa) to align with consumer psychology.
If conversions are high but the AOV drops dangerously low due to heavy discounts, the AI intervenes. It will recommend adjusting the rules to encourage larger cart sizes—for instance, changing the discount to only trigger when the user adds a second item.
When you apply a recommendation from the Sales Assistant, the engine doesn't just forget about it. It actively tracks the performance of the new rule against the historical baseline to validate its hypothesis.
If the change successfully boosts conversions as predicted, the assistant solidifies the rule. If market conditions shift, the assistant will promptly advise you to revert or further adjust the discount structure.