Sena Decision AI shows furniture teams which pieces stay in room settings, what ticket shoppers see, and why they order a rival piece. Every figure traces to a showroom photo, a shopper answer, or the team's own files.
Furniture industry analysis usually starts from order books, retailer sell-through, and website prices. A sofa sells from a room setting, and a missing floor model, a sharper rival ticket, or a tired vignette sends the shopper to the next store.
Retail plans set which pieces each partner shows. The floor changes as retailers rotate stock and rival pieces take the space.
Sena:Geo-verified photos from the showrooms a question names show each floor model, its room setting, and its ticket.
A sofa carries a planned ticket. Holiday sales, clearance tags, and rival markdowns rewrite it from showroom to showroom.
Sena:Sena reads each ticket from a named showroom photo and lines up promoted, planned, and rival prices.
Order files show the final piece. Shoppers weigh comfort, delivery dates, and financing across showrooms and websites to get there.
Sena:Sena captures the pieces shoppers tried, the one they ordered, and what would change their mind, by segment and market.
Sena turns furniture industry analysis into four decisions: the floor, the ticket, the shopper, and the collection.
Photos from the showrooms a question names set each floor model and room setting beside the partner's plan.
Decides:Which partner's floor gets reset first, and with which pieces.
See sales decisions →Sena reads tickets from named showroom photos and marks each piece priced above the plan or above the rival sofa beside it.
Decides:Where to correct price, fund a promotion, or match the rival.
See revenue and pricing decisions →Shoppers name the sofa they almost bought and the comfort, delivery date, or financing term that tipped the order, by segment and market.
Decides:Which comfort feature, delivery promise, or financing offer wins the next order.
See marketing decisions →Shoppers in the target market react to each style, fabric, and ticket, and name the piece at home it would replace.
Decides:Which styles and fabrics make the line, at which ticket, and where they launch.
See product decisions →Each figure links to the photo or answer behind it, so a team sets it beside its own sell-through files.
A floor model counts as shown once a dated, located photo confirms it, and the ticket in that photo is the price the reading uses.
Each order reason comes from a furniture shopper who chose to share it. Every capture clears a multi-level QA flow. Sena declines a capture that misses the check, and the decline stays visible.
Furniture sales analytics shows what sold. The floor gap compares the floor models partners agreed to show with the ones a photo confirms, by partner, showroom format, and room category.
The floor gap calculator values the missing floor models at the team's own monthly sales.
The walkthrough follows one room category in one market, photo by photo, to the floor reset.
Pick the closest fit, and the walkthrough starts from that team's question.
Floor models and room settings checked against each partner's plan, in each named showroom.
Book a sales walkthrough →Tickets, promotions, and rival prices read from photos of the showrooms the team picks.
Book a pricing walkthrough →Furniture industry analysis by segment and market: why shoppers order one piece and pass on another.
Book a marketing walkthrough →New collections for the home furnishings industry, tested with shoppers in the target market.
Book a product walkthrough →Sena captures geo-verified photos and consumer activity in the markets a question names, when the question needs it.
Sena reads those captures beside the files and systems already in use, across 250+ integrations.
Sena compares the market against the plan and shows each gap, with every figure traced to its source.
| Area | Common method | Sena |
|---|---|---|
| Floor models | Partner floor reports | Named-showroom photos of each floor model and room setting |
| Tickets | The price plan and website prices | Tickets read from named showroom photos, beside the rival |
| Shopper choice | Furniture sales analytics by SKU | The piece shoppers almost bought, and why they switched |
| New collections | Furniture market trends reports and buyer instinct | Shopper reactions to each style, fabric, and ticket |
| Auditing | Floor reports and price checks reconciled by hand | Every figure opens its dated photo or consented answer |
Talk to the Rwazi team about one category, one market, and the decision on the table this quarter.