Recommendations

Recommendations are the prompts that help customers discover a better fit for their order — a larger combo, an add-on side, or a simpler option when they are about to leave. They sit on top of your synced menu and respect the same channel, store, fulfillment, and schedule rules as everything else you sell. For marketing and growth teams, recommendations are how you merchandise in the flow of ordering, not only on a static category page.

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The three recommendation types

Fire spark supports three types. Each answers a different commercial moment.

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Upsell — sell the more complete version

Use upsell when the customer already wants your category, but you can expand the order with a higher-value configuration:

• Upgrade from a single item to a combo or bundle
• Surface a premium modifier preset (pre-selected quantities on premium toppings)
• Move from a standard size to a larger or featured version

Upsell is not “random extras.” It is the next step up in the same purchase intent.

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Cross-sell — sell something extra

Use cross-sell when the main item is right, but the basket can grow with add-ons that make sense together:

• Side dishes, drinks, desserts
• Accessories or companion items
• Popular pairings your CRM or analytics already know

Cross-sell adds parallel lines to the order; it does not replace what the customer already selected.

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Downsell — save the sale

Use downsell when you would rather convert at a lower ticket than lose the customer:

• Offer a single item when the combo feels too much
• Suggest a simpler configuration when behavior shows drop-off risk
• Present a smaller alternative before the customer exits

Downsell is a recovery lever, not a discount engine. You still sell real catalog products at real prices.

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Two ways to power recommendations

You can run recommendations in two complementary ways.

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1. Send recommendations in menu sync (your rules)

During menu sync, your integration can include a recommendations catalog and link it to products. When you send them, Fire spark uses exactly what you define for that product and placement. This is the right model when:

• You already calculate suggestions in an external service (CRM, personalization, data warehouse, aggregator logic)
• You run A/B tests or campaigns in your own stack and want Fire spark to reflect them immediately
• You need full control over copy, targets, and timing per sync

Your team owns the logic; Fire spark owns delivery across channels with operational consistency.

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2. Fire spark recommendation engine (backoffice)

From the backoffice, you can turn on Fire spark’s recommendation engine per merchant. When the engine is on and you did not attach a recommendation to a product in menu sync, Fire spark generates and surfaces suggestions for you — upsell, cross-sell, or downsell as appropriate for that context. When the engine is off, or when you did send recommendations in sync for that placement, the engine does not override your catalog rules (see precedence below). This helps especially when:

• The merchant is new on Fire spark and you have not built a sync payload for every campaign yet
• Marketing wants to start fast while operations keeps syncing the menu from the POS
• You prefer the platform to merchandise gaps you have not modeled in your integration

You can enable the engine per merchant and combine it with your own sync rules over time.

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What takes precedence

In mixed setups:

• Keep strategic campaigns in your sync (limited-time upsell, partner promos, CRM-driven cross-sell)
• Leave unconfigured placements to the engine when backoffice is on (new products, long-tail cross-sell, stores without a personalization stack)

You never fight your own integration: your sync wins whenever you send a rule.

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How Fire spark trains the recommendation engine

Fire spark does not guess in a vacuum. The engine learns from signals you already send and from order behavior on the platform: The more you sync — especially explicit recommendations and bundle definitions — the better the engine understands your catalog even before order volume is large. Sync recommendations also teach the platform your strategy; they are not only rules the engine must skip when present.

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How teams typically work

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What customers experience

Recommendations appear in the ordering flow where your channel UX supports them — as suggested upgrades, add-ons, or simpler alternatives tied to the product they are configuring. They respect:

• Menu composition per channel, store, and fulfillment
• Availability — out-of-stock targets are not promoted
• Reachability — only recommendations whose target product exists in that composed menu are kept

Operations still updates the POS once. Marketing decides what to suggest; Fire spark ensures those suggestions stay aligned with what is actually sellable.

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