What's eating your conversion and support margins
Pre-purchase product questions go unanswered
"Is this in size M?", "will this fit my car model?", "is it dishwasher safe?" — the answer is on your PDP but the customer doesn't find it. They bounce, they don't come back.
WISMO calls drown your support team
"Where Is My Order" tickets are 30-50% of support volume in retail. Almost all of them can be answered by reading the tracking event from your courier API.
Abandoned carts never convert without a nudge
70%+ of retail carts are abandoned. WhatsApp recovery flows that explain why the customer should come back (price drop, low stock, free shipping threshold) outperform generic email by 5-10x.
Solutions
What Truvenix builds for retail
Product Q&A chatbot trained on your catalog
Knows your product range, sizes, materials, compatibility, care instructions, return policy. Resolves pre-purchase questions in the customer's own language without sending them to a static FAQ page.
- Ingests your Shopify/WooCommerce/Magento catalog automatically
- Multilingual — handles Hindi/Gujarati/Tamil for India, German/French/Italian/Dutch for EU
- Cites the source PDP in its response for trust
- Captures pre-purchase intent into your CRM for retargeting
Order status + return automation
Reads from your courier API (Shiprocket, Delhivery, DPD, GLS, Bluedart, custom) and answers WISMO queries with the current tracking event. Initiates returns by collecting the reason, validating eligibility, and triggering your existing return workflow.
- Hooks into Shopify Orders, WooCommerce, Magento, custom OMS
- Configurable return policy per SKU / category / region
- Hands off to a human for damaged-in-transit and warranty cases
- Reduces WISMO support volume by 60-80% in pilots we've run
Cart recovery via WhatsApp + multilingual outbound
Triggers a WhatsApp message 1, 4, and 24 hours after cart abandonment with the actual reason a customer should reconsider — low stock, an offer expiring, an answer to the question they didn't ask. Patterned by cart value and customer history.
- Personalized recovery copy in the customer's language
- Smart timing — fires only when the customer is likely to engage
- Coupon distribution rules to avoid margin leakage
- Attribution back to your analytics so you see what works
Related case study
Production-grade retail ML in our portfolio:
Demand forecasting for e-commerce — 31% reduction in carrying costs