Most home and living brands look at their wholesale data through the rearview mirror — quarterly reports, end-of-season reviews, spreadsheet exports that are outdated before they are opened. FIRE Analytics changes the direction of the lens. Material velocity in real time. Room scene conversion per buyer segment. Cross-category attach patterns as they form. Walnut overtook oak in browse data 8 weeks before the order book confirmed it. Your competitors saw it in the quarterly report. You saw it live.
Bouclé overtakes linen in browse data six weeks before the order book shows it. A quarterly report catches the shift when production is already locked. Real-time material velocity dashboards catch it when you can still act.
Your ERP tells you the order value. It does not tell you that furniture buyers who see lighting in the room scene attach at 74%. Without room-level engagement data, cross-category strategy is guesswork.
Boutiques, department stores, interior designers, hospitality, and online platforms all browse differently. Without segment-level analytics, your marketing, pricing, and range decisions treat them as one. They are not.
Not end-of-season. Not quarterly. Live — because material shifts and buyer signals do not wait for reports.
Which atmospheres convert, which drive cross-category attach, which scenes buyers exit without ordering. Dwell time, attach rate, and conversion by room type — updated in real time throughout the collection cycle.
Filter selection share, product comparison rates, and material rejection patterns — indexed and trended. Bouclé overtaking velvet, walnut outperforming oak, travertine accelerating in dining. Visible 6–8 weeks before orders confirm the shift.
Session counts, average basket value, rooms per session, and reorder timing by segment — interior designers, boutiques, department stores, hospitality, and online. Filterable by market. Updated continuously, not end-of-quarter.
Which furniture attached to which textiles. Which lighting completed which room. Attach rate by room type, by buyer segment, and by material combination — so your team can curate room scenes and sales prompts around the combinations that convert.
Where buyers spent time, what they filtered, what they rejected. Session-level replay shows the intelligence behind every order. Which product comparison happened before the purchase. Which room they entered first. Which material combination triggered the basket.
Side-by-side comparison of this cycle versus the last two. Material velocity trends, segment growth rates, room scene performance, and attach rate changes — so your collection brief for the next Maison & Objet starts with structured intelligence, not seasonal memory.
The difference between a dashboard and an analytics platform is the data layer beneath it. Most B2B analytics show what was ordered — units, revenue, order frequency. FIRE Analytics shows why the order happened: which room scene the buyer entered, which materials they filtered and compared, which cross-category products they attached, and how long the session lasted before commitment. This is the intelligence layer that shapes collection decisions.
Material velocity is the clearest example. Your filter data shows walnut browsing frequency rising by 34% over six weeks while oak declines. That signal appears in FIRE Analytics as a real-time velocity curve — not as a data point in a quarterly report. The production team sees it while the allocation window is still open. The competitors who rely on order data see it four weeks after production is locked.
Room scene performance is equally actionable. FIRE Analytics scores every room scene by conversion rate, cross-category attach, dwell time, and basket value — segmented by buyer type. Warm Scandinavian outperforms Industrial Loft by 2.3× in boutiques but underperforms in hospitality. This shapes your showroom investment, your portal landing sequence, and your trade fair booth configuration. Without room-level analytics, these decisions are creative instinct. With them, they are evidence-based.
Most home and living brands run collection analytics from their ERP. They see what was ordered, what was returned, what sold through. This is rearview analytics. It tells you what happened after the decisions were made, after production was locked, after the collection was already in the market.
FIRE Analytics works differently. It captures every interaction with your collection — every room browse, every material filter, every product rejection, every cross-category session — and makes it visible in real time. Material shifts emerge in filter data six weeks before orders confirm them. Room scene performance is visible the day the collection launches, not at the end of the season. Buyer segment acceleration surfaces in session data before it appears in the order book.
After three collection cycles, this intelligence layer compounds. Your analytics no longer show you what happened this cycle. They show you what this cycle means for the next one — which materials to prioritise, which room scenes to develop, which buyer segments to build capacity for. That is not a dashboard. That is a planning engine.
Stop reading last season. Start seeing this cycle’s intelligence building.
See the Analytics DashboardTell us about your brand, your current B2B setup, and what you are looking to improve. We will show you exactly how FIRE works for your specific situation.
No generic demos. No slide decks. A real walkthrough with your products and your industry configuration.
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