The Shop That Looks Active and Isn't
Walk into a kirana shop in Candolim on a Tuesday afternoon and it is open, the owner is present, and there is product on the shelf. Walk in on a Thursday and the owner is not there, the Rs.1 SKUs have not moved, and there is no cash in the till for a fresh order. Walk in the following Monday and the store is closed.
An FMCG field rep covering the Candolim beach belt who treats each visit as an independent contact is building a picture from single data points. The rep who has ten visits logged for the same shop — with visit type, INR value, and comments for each — is working with a longitudinal picture that shows whether this account is growing, stagnant, or at risk of de-listing.
Seven Visit Outcomes and What Each Signals
No Sale, Owner Not In, Rs.1 Products Only, Cash Sale, Product in Stock, Product Sold Out, Closed — the visit type classification is the shorthand that converts a field visit into an analyzable data point. No Sale on a single visit means nothing. No Sale across three consecutive visits means the account has stopped buying, and the question is why. Product Sold Out on two consecutive visits means your coverage frequency is below the store's consumption rate and you are losing sales to a competitor who arrived before you.
Rs.1 Products Only is the visit type that signals a specific market dynamic in the Indian FMCG trade: the store owner is willing to stock only the lowest-denomination SKUs — sachets, single-serve packs — which indicates low working capital, a high-frequency small-ticket customer base, or distrust in the product's sell-through rate. A shop consistently classified as Rs.1 Products Only across multiple visits is a different account development challenge than one that oscillates between Cash Sale and Owner Not In.
Owner Not In as a persistent pattern tells you the visit scheduling is wrong. If a shop owner runs a beach shack tourism business and is consistently absent from his kirana shop on weekday mornings, the visit cadence needs to shift to evenings or weekends. The pattern is only visible across the longitudinal record.
Total Sales and the Days-Between-Visit Calculation
The total sales calculated field sums all ten visit values — #{visit_01_value}+...+#{visit_10_value} — giving the cumulative INR account revenue across the logged history. For an individual shop in a dense tourist beach area where the trade cycle is seasonal and compressed, the cumulative total across ten visits over a two-month period tells you whether this outlet has been worth the coverage frequency you have invested in it.
The days visit01 to visit02 calculation — datediff(#{visit_01_date}, #{visit_02_date}) — is a single inter-visit interval calculation, but the pattern it enables is important. If the standard call cycle for the Candolim route is fourteen days, and visit01 to visit02 is twenty-two days for multiple accounts on the same route, the cadence problem is a routing or scheduling issue rather than an account-level issue. When the datediff is twelve for some accounts and thirty-five for others on the same route, the coverage is ad hoc rather than systematic.
GPS and Address as the Territory Mapping Layer
The client GPS location field maps every outlet precisely. In the dense lane network behind the Candolim beach road — where shops occupy the ground floors of guesthouses, share entrances with restaurants, and do not always have a visible street address — the GPS coordinate is often more useful than the address for navigation between consecutive stops on a route.
Client address as free text handles the informal addressing that characterizes Indian coastal tourist areas: "next to Hotel Seagull," "opposite D'Souza's bakery," "first lane past the petrol pump, left side." These addresses are genuinely useful local navigation instructions but not geocodable in any standard mapping system. GPS and address together give you both the machine-readable location and the human-readable directions.