Uplift
Conversational cross-sell & recommendations
Your highest-intent moment is a customer already talking to you. Uplift reads the live session — what they browsed, what is in the cart, what is in stock — and makes the one suggestion worth making.
- Intent-aware upsell and bundle prompts that fire mid-conversation
- Recommendations ranked on real-time behavior, not last month's segment
- Native measurement for AOV, attach rate, and incremental conversion
Illustrative interface. Not real customer data.
The recommendation carousel is not where the money is.
Static "you may also like" strips run on rules someone wrote a year ago and segments recomputed last month. They sit below the fold, they ignore what the customer said thirty seconds ago, and everyone has learned to scroll past them.
Meanwhile the moment of real intent — a live conversation, a filled cart, a question about sizing — passes with nothing attached to it. Uplift puts the suggestion where the intent already is, and only when it holds up.
What Uplift actually does.
Intent-aware placement
Prompts fire when the signals line up — a resolved question, a confirmed size, an item added — not on a timer and not on every message.
Live behavioral ranking
Candidates are ranked on this session: pages viewed, cart contents, dwell, past orders. Not a segment computed last month.
Bundle and compatibility logic
Catalog rules decide what can legitimately go together, so you never suggest a cable that does not fit the device.
Margin and stock aware
Rank on contribution, not just propensity. Out-of-stock and low-margin items are suppressed before they are ever offered.
Restraint controls
Caps per conversation, cool-down windows, and suppression rules — no upsell attempts in the middle of a complaint or a refund request.
Native attribution
Holdout groups and per-suggestion tracking, so incremental revenue is measured rather than assumed.
From connected to live.
Four stages. The first two are where the real work is; the rest is calibration.
Read the session
Browsing events, cart state, conversation context, and order history are assembled into a live picture of what this person is actually trying to do.
Rank the candidates
The catalog is filtered by compatibility and availability, then ranked on likelihood and contribution margin together.
Place the prompt
A single suggestion is phrased in context and delivered inside the conversation — with your caps and tone rules applied.
Attribute and learn
Acceptance, rejection, and holdout performance feed back in, so ranking improves against your catalog rather than a generic benchmark.
Where teams point it first.
Attach during support
A customer messages about delivery, gets an answer, and is offered the consumable that pairs with what they bought.
Cart completion
A cart with a device and no accessories gets one relevant addition before checkout, not five.
Replenishment timing
Consumables are suggested when the previous order is running out, based on purchase cadence rather than a fixed cron.
Post-purchase attach
The order confirmation conversation is a second chance to add a compatible item before dispatch.
What you will be able to measure
Uplift reports on its own effect. These are the metrics it exposes out of the box — the numbers will be yours.
Average order value
Overall and split by whether a suggestion was shown.
Attach rate
Share of conversations where an additional item was accepted.
Incremental conversion
Measured against a holdout group, not inferred from a lift chart.
Suggestion acceptance
How often a shown prompt is taken — the honest read on relevance.
Revenue per conversation
The number that tells you whether the channel pays for itself.
Margin contribution
Because a high attach rate on low-margin items is not a win.
Sits on top of the stack you run.
Prebuilt connectors for the common systems, and an integration path for the ones that are yours. Nothing here asks you to migrate.
Do not see yours? Ask us — most integrations are a connector, not a project.
Uplift, answered plainly.
Will this make our brand feel pushy?
That is the main risk with conversational selling, and it is why restraint is a first-class control rather than an afterthought. You set the cap per conversation, the cool-down between prompts, and the situations where suggestions are suppressed entirely — complaints, refunds, delivery failures. The default is conservative: one well-placed suggestion beats four ignored ones.
How do you prove the revenue is incremental?
With a holdout. A configurable share of eligible conversations get no suggestion at all, and the difference between the groups is the incremental number. It is the only honest way to answer this, and it means the reported figure will be lower than a naive "revenue from suggested items" total — deliberately.
Does Uplift need Resolve to work?
No. Uplift runs inside whichever conversational surface you use, including your existing chat tool. It gets better with Resolve because the two share the same session context and the same customer record, but it does not require it.
Does it respect stock, pricing, and promotions?
Yes — availability, price, and active promotions are read live from your catalog and inventory systems. Anything out of stock or excluded by a merchandising rule is filtered out before ranking, so a customer is never offered something you cannot ship.
Bring a real workflow. We will show you Uplift running on it.
Thirty minutes, your data, no slideware. If it is not the right module for your bottleneck we will tell you which one is.