Personalized Product Recommendations at Scale
Engage with your customers through conversational channels and instantly offer personalized product recommendationsbased on their preferences and chat history. Drive more prospects to the closing stage of your sales funnel. With VocalLabs' bots, users can get answers to their general queries without spending time scouring webpages—boosting conversions and satisfaction in one go.
- Plays
- Cross · Up · Replenish
- Reads
- Live catalog
- Asks
- 2 questions
- Suggests
- 1 + 1 alternative
01 — 05
Five cards, the way the engine files them: what it reads, what it ranks on, and the sentence the customer actually hears. Drag the rail sideways.
What the recommendation is built from
Nothing here is a black box. Every suggestion decomposes into six weighted signals, and the bot can say which one it used when a customer asks why. Stated preference always outranks anything inferred.
One call, six moves
- 01
Resolve the customer
The number is matched to a commerce profile before the greeting finishes — orders, returns, loyalty tier, open cart. An unknown number starts cold and is treated as a first-time caller, not a blank one.
Under 900 ms · fails open to a cold flow
- 02
Ask two questions, not ten
The bot asks only what the signals cannot answer: usually occasion and budget band. Everything else it already knows, and asking again is how a recommendation call starts sounding like a form.
Median 2 clarifying turns
- 03
Rank against the live catalog
Candidates are scored on fit, deliverability and attach potential, then filtered to what is actually in stock for that pincode. Out-of-stock SKUs never reach the caller’s ear.
Read-through to catalog, not a nightly copy
- 04
Recommend with the reason attached
One primary suggestion and one alternative — never a list of five. Each carries the reason in plain language, because “because you bought X” converts and “our system suggests” does not.
1 primary + 1 alternative
- 05
Handle the objection
Price, timing and “let me think” are the three that matter. The bot trades down a tier, offers the smaller pack or books a callback — it does not repeat the same pitch louder.
3 scripted ladders, each one turn deep
- 06
Close or hand back
Payment link on WhatsApp, order placed on the call, or a warm transfer to a human with the full context attached. The recommendation, the reason and the objection all land in the CRM note.
Write-back within 5 s of hangup
Three plays, three sets of rules
They are not the same conversation and they should not share a script. The guardrail column is the part most recommendation engines skip — and the reason customers stop picking up.
Two questions, one recommendation, one objection
A real shape of call: the bot already knows the machine, asks only what it cannot infer, names the reason with the price, and when the price lands badly it trades down instead of pushing.
- 00:00SYSTEM
Inbound · +91 98•••• 4417 · matched to profile #C-40912 · 6 orders · loyalty: silver
- 00:02BOT
Hi Meera, Vocallabs here for Aurel. I can see the espresso machine you picked up in March — is this about beans, or something else today?
- 00:09CUSTOMER
Beans, yes. I am almost out and I want to try something different this time.
- 00:14BOT
Got it. Two quick things so I do not send you the wrong thing — are you after something darker than the last one, or lighter? And is this still just for the espresso machine, or filter too?
- 00:22CUSTOMER
Lighter. And only espresso.
- 00:24SYSTEM
Signals: PH (3× Kenyan medium) · SP (lighter, espresso-only) · ST (in stock, 560001) · RT (none)
- 00:26BOT
Then the Yirgacheffe single-origin — it is two notches lighter than your usual Kenyan, and it is ground for espresso so you will not need to change anything on the machine. 250 grams is ₹740.
- 00:37CUSTOMER
That is a lot more than I usually pay.
- 00:40BOT
It is about ₹180 more than your regular bag, yes. Two options — the house light roast is ₹520 and close in profile, or you take the Yirgacheffe in the 125 gram trial at ₹390 and see if it is worth it before committing. Either is fine by me.
- 00:53CUSTOMER
Do the trial one. And add the descaling tablets, I think I am due.
- 00:58BOT
Done — trial bag and the descaler, ₹738 together. You are actually eleven weeks since the last descale, so that timing is right. Payment link is on WhatsApp now, and it ships tomorrow.
- 01:06SYSTEM
Outcome: converted · AOV ₹738 · attach 1 · objection: price → traded down to trial SKU · CRM note written
What moves, and by how much
- 18.4%
- Attach rate on recommended calls
- 11%
- Lift in average order value
- 27%
- Repeat purchase on replenishment calls
- 2.1
- Clarifying questions before a suggestion
- 640 ms
- Catalog read-through latency
- 92%
- Recommendations that were in stock
One accessory per call, never two
Measured against the same cohort, no bot
Outbound, 5 days before predicted run-out
Median across 40k calls
Live SKU, price and stock, mid-sentence
Filtered on pincode deliverability
Aggregated across retail and D2C deployments. Your catalog, margins and cohort will move these.
It only works if it knows the catalog
The bot is only as good as the feed behind it. These are read-through connections — the recommendation is scored against what the store has right now, not against last night's export.
- 01Commerce
Shopify
Products, variants, inventory, orders, carts
- 02Commerce
WooCommerce
Catalog, stock levels, customer orders
- 03Commerce
Magento / Adobe Commerce
Catalog, price rules, bundles
- 04OMS / WMS
Unicommerce · Increff
Pincode serviceability, dispatch windows
- 05CRM
Salesforce · HubSpot · Zoho
Profile, tier, consent, prior calls
- 06Engagement
CleverTap · MoEngage
Segments, journey state, suppression lists
- 07Catalog feed
Google Merchant feed
Any catalog with a standard product feed
- 08Bring your own
Custom REST / GraphQL
A product lookup and a stock check is enough
