Kohler Distributing · Customer Reset Tracking
Did sales go up or down because a store got a reset? We look at two groups separately — 2026 resets (the main focus) and 2025 resets — comparing each store's own sales right before and after its reset, in two different ways so a normal busy season doesn't get mistaken for the reset's own effect. The 2025 group, since that year is already over, also gets a full year-to-date check.
Picture every reset store lined up together, like one big store. This box is that store's report card.
Accounts Evaluated — how many stores this report card counts.
3-Mo Lift (vs. Last Year) — add up what all those stores sold in the 3 months right after their reset. Compare that to what they sold in that very same 3 months last year, before this year's resets happened. Bigger this year = green plus. Smaller = red minus.
YTD Lift (vs. Last Year) — the same "vs. last year" idea as above, just stretched out to cover the whole year so far instead of only 3 months.
Click Constellation Brands at the bottom to expand a spotlight on one supplier (Corona, Modelo, Pacifico, Victoria, and a few smaller labels) — same math as the two numbers above, just filtered to only that supplier's brands, with a 3-Mo and a YTD column for each individual brand. It's its own separate total, not a slice of the numbers above it — Constellation's resets happen to be running ahead of the full roster (roughly +3–4% here vs. the +0.6% blended across everyone).
Same "3-Mo Lift (vs. Last Year)" math as above, but grouped by the month each store's reset happened in (all the March resets together, all the April resets together, and so on). Each bar shows how that group of stores did, so you can see whether resets earlier or later in the year tend to work better.
Segmentation (A/B/C) comes from the reset roster as-is; its exact definition hasn't been confirmed (assumed to be a volume tier) — shown here as a grouping, not an endorsed metric.
Same idea as "By Reset Month," but grouped by each store's Segment letter (A, B, or C) instead of by month. It's basically a size/tier label Kohler assigns to stores — think of it like grouping students by grade level before comparing test scores. We're not 100% sure what A/B/C officially stands for, so treat this as "here's how the lift looks broken out by that label," not a confirmed ranking.
Click a column header to sort. Click the ▸ next to an account to see its Brand Family breakdown.
This is the full list — one row per store — that everything above is built from. Use the two buttons to change what it's comparing:
Click the ▸ arrow next to any store's name to see its sales broken out by Brand Family, for whichever comparison is currently selected. ("Misc" is never in this list — it's removed from the data completely, see the notice near the top of the page.)
3-month window (year-over-year): the reset's own calendar month plus the following two months (e.g. a March reset pairs March+April+May), compared against that same 3-month window one year earlier at the same account. Sales inputs here are monthly account totals, not a dated transaction ledger, so windows are whole calendar months, not a rolling 90-day span from the exact reset date.
YTD window: January through the latest fully-elapsed month in that cohort's data (),
vs. the same January–that-month range one year earlier — computed directly from each cohort's own
monthly sales file (sales_2026.csv / sales_2025.csv), same as the 3-Month Reset
Window numbers and the Brand Family breakdown, so everything on this page traces to one consistent source
per cohort.
Lift %: (post total − pre total) / pre total, on Cases — the only metric in these exports (no $ Volume or Gross Profit here, unlike an earlier build of this dashboard).