Catalyst Insights & Resources
Expert perspectives on AI-driven sales performance, prioritisation, and retail execution data
Catalyst is 1Channel's predictive AI engine. Every week it reviews sales, visit, and stock data across your field team and turns it into a plain-language coaching report for managers, plus individual nudges for the reps who need them.
Every walk-in, sale, and stock update logged by the field team feeds a predictive analytics model that spots patterns a manager would otherwise have to dig for.
Catalyst compares this week's numbers against months of history, using AI forecasting to flag when a rep or territory is trending away from its normal range.
Managers receive a written summary in plain language: what improved, what's at risk, and which individuals need a conversation this week.
Each report closes with specific AI recommendations, from stock reallocation to which reps to coach first, so managers act instead of just reading numbers.
Expert perspectives on AI-driven sales performance, prioritisation, and retail execution data
From a week of field data to a manager's coaching report and rep-level nudges, screen by screen.
Today's walk-ins logged: 42. Today's sales logged: ₹18,600. Invoice uploaded ✓
Each store visit, walk-in count, and sale is captured directly on the field app as it happens, giving Catalyst clean, consistent data to work with from day one.
Stock updated: 12 SKUs. 2 items flagged at 0% demo availability.
Reps also record stock and demo unit counts at every store, so gaps that quietly hurt sales, like a product with nothing left to show a customer, get caught early.
Weekly analysis running. Comparing this week against the last 3 months of team data.
Once a week, Catalyst's predictive AI looks past any single day and compares recent performance to each rep and store's own history, so a bad week reads differently from a real trend.
Team performance is up 6% this month. 3 reps show a sharp decline. 2 categories have a stock gap.
Instead of another dashboard to interpret, the manager receives a short written summary in plain language covering what's improving, what's slipping, and where the biggest risk sits this week.
Suggested: coaching session for 3 reps this week. Reassign demo stock to 4 low-availability stores. Review 2 unusually high orders.
The report closes with named reps, named stores, and a clear next step for each, including a nudge to double-check any order that looks unusually large before treating it as a genuine win.
The same AI capabilities are built into every core 1Channel module.
It predicts which reps, stores, or product categories are heading toward a problem, such as a sales decline or a stock gap, before that problem shows up in a month-end review. The predictive analytics engine learns each team's normal range from months of field data, so it can tell a real shift from ordinary week-to-week noise.
AI forecasting compares each rep and store against their own recent history rather than a single fixed target. A store that quietly drops from a strong run gets flagged the same way a store that misses its target outright does, so managers see the full picture, not just the obvious misses.
Specific, named ones. Instead of a generic "improve performance" alert, Catalyst's AI recommendations name the reps who need a coaching conversation, the stores that need stock reallocated, and the categories that need a refresher on product training, so managers know exactly where to start.
Yes. Sales forecasting AI looks at trends across months of data rather than any single reporting period, so a rep who has one slow week isn't treated the same as a rep whose numbers have been sliding for a month. That distinction is what keeps coaching conversations focused on the right people.
Catalyst is built around AI decision intelligence, meaning every flagged number comes with the reasoning behind it and a suggested next step. It also prompts managers to double-check unusually large orders before crediting them as genuine wins, so decisions get made on verified data, not raw totals.
It combines what reps log every day, walk-ins, sales, demo stock, and visit frequency, into a single view of retail intelligence per store and per category. That combined view is what lets Catalyst connect a sales dip to its actual cause, such as a stock-out, instead of just reporting that sales dropped.
