Deliver a result shaped by your judgment, your values, and your feedback — across assortment, pricing, demand planning, and growth in your 9 BC stores.
Prepared for James Budd & Kelly Abbott - 9 stores, BC - 2026-07-08
Your VP team
Three named VPs, working from one data foundation.
Vervana gives you a small team of named VPs - Competition, Buying, and Pricing. You are the president; you direct the VPs, and each VP has a job, a clear result, and a success metric you can trust. The team keeps testing: try new bets to learn, then back what works.
You talk to and steer a few VPs, not a crowd. Each VP owns a clear job and delivers one answer you can act on.
Each VP learns from you: your goals, your values, your judgment, and what success or failure looks like in your stores.
Behind each VP sits a workforce of roughly 1,000 specialized employees doing the detailed work: matching products, scoring every order, watching every SKU, store, and competitor, and forecasting demand.
A decision you make once - keep this product, cut that one, hold a price - becomes a standing way of working.
Everyone on your team gets their own VP. Each person stays president of their patch: you make the calls, you own the result.
This is what justifies a VP price. You are not buying a tool. You are getting a VP backed by a thousand specialists, so the granular work turns into one clear answer.
Your VPs coordinate. They share one canonical data foundation and pass context to each other: a pricing move informs the buy; a competitor shift informs the assortment; assortment depth sets the buy list.
That is what lets you scale with control - 9 to 20 to 30 stores and beyond.
1VP of CompetitionKnow your local competitors — and tell you only what actually matters.
James: "I don't need to know everything they carry, because I don't care. But I do need to assess what are the most important things to them, because if I can attain what's most important to them in their business, then I can compete with them on those."
What this VP does
Track every competitor within 1–2 km: assortment, SKU counts, and price changes.
Find the handful of SKUs driving their business.
Compare apples-to-apples on THC, size, terpenes, and trim.
Recommend match, hold, or go aggressive per store.
Success: Per store, you can name the handful of competitor SKUs that actually drive their business, compared apples-to-apples on THC, size, terpenes, and trim, then decide match, hold, or go aggressive in minutes.
Failure: A dashboard of everything they carry that still takes three hours to study, with smalls and craft treated as the same product, so you end up reacting to noise.
Tooling: identity + attribute schema, canonical product resolver, provenance on every competitor estimate, and market-fit scoring. "Blu Dream" vs "Blue Dream" collapses to one product before anything is compared.
2VP of BuyingCarry the right products, at the right depth — then turn demand into a shopping list you can trust, and get it ordered.
James: "What I want is a consistent assortment, unless the revenue warrants an expansion or contraction."
James: "If you want to introduce something, tell me what's coming out of the assortment."
James: "What I'm trying to do is get the demand planning process to a point where I can actually just give a shopping list to somebody."
James: "If we can't sell a case of product within four weeks, it doesn't get replenished. We plan to hold three weeks of stock."
What this VP does
Set depth per consumer role per store, e.g. the $25 sativa eighth.
Hold the 300–350 SKU sweet spot; flag over-skew.
Enforce replacement-in / replacement-out.
Right-size stock from live demand, including bulk-buying regulars.
Protect loyalty-anchoring favorites from stockout.
Honor case-as-MOQ, the four-week case sell-through rule, days-of-supply gates, and ~3 weeks of stock.
Produce the weekly per-store shopping list: what to buy, how much.
Watch the ~20 LP relationships and data-programming economics: roughly 6–8% of COGS (the cost of goods, not revenue) on supported products; keep any LP under ~9% share.
Stretch: cleanly turn the list into a cart in LDB Wholesale Operations, the login-gated BCLDB last mile; honest it's out-of-core.
Assortment reality.
Average carried is about 300 SKUs per store. Smallest is about 230, largest about 600. Sweet spot is about 300-350, and you've said the highest-volume store is over-skewed.
Supply reality.
90-95% of product comes from BCLDB: about 1,300-1,400 central products, plus about 500-600 direct-delivery products. The historical BCLDB catalog is about 15,000 products.
Right-size the stock — the Goldilocks problem
Too little: you stock out, lose the sale, and some customers walk for good.
Too much: cash is frozen on the shelf, product ages out, and dead stock needs markdowns.
Just right: the answer moves by product and by store. The VP reads the live demand pattern, including regulars who buy in bulk, instead of flattening everything into the last 3-week average.
Scoreboard tie-in: getting this right is where faster turns + fewer stockouts + less dead stock turns into gross profit.
A stockout on a favorite costs more than one sale
Which stockouts cost you loyalty is different at every store. The VP measures it from your actual visit, spend, and customer-retention data - not from a generic "flower is high-risk" table.
Across anonymized clients, one edibles-driven store saw visits fall ~46% and spend fall ~47% when a customer's edible was out. Many stopped coming entirely. At that store, edibles were the loyalty anchor and flower barely moved.
At a connoisseur / flower store, flower and concentrate stockouts crushed spend by about ~43%. We have also seen the pattern invert: when a customer's usual cartridge was out at one store, they came in more and spent more because they traded up. Pre-rolls are the one steadier pattern: people tend to know what they want.
The assortment decision: this tells the VP how many brands to stock per store, the right brand depth, and which favorites should never be allowed to run out. Keeping loyal customers shows up as protected gross profit.
Spend drop when a favorite is out of stock
Anonymized cross-client evidence. Directional, not a guarantee for 137.
Store A: edibles anchor
Edibles~47% spend drop
Flowerbarely moved
Cartridgesinverted: traded up
Store B: connoisseur / flower
Flower + concentrate~43% spend drop
Ediblesmild
Pre-rollsmore consistent
Which stockouts cost you loyalty is different at every store — measured from your data.
Success: Each store carries ~300–350 SKUs that earn their place by role and velocity; new listings name what they replace; the weekly per-store shopping list is ready in minutes, with right depth per role, case treated as MOQ, honoring "sell a case in four weeks or it isn't replenished."
Failure: Assortment balloons past what the store can turn; dead stock and over-skew tie up cash; the assortment grows without the revenue to justify it; the shopping list is not trusted, so you rebuild it by hand.
Tooling: assortment role + depth, order-scoring engine using inventory fit, economic quality, consumer fit, brand fit, and market fit; velocity = units / days; days-of-supply gates; case size enforced as MOQ; daily views and proposal output.
Core VP work: produce the trusted list. Stretch work: see whether that list can cleanly become a cart without brittle browser automation.
Automating order placement in LDB Wholesale Operations, the manual BC Liquor Distribution Branch (BCLDB) portal - roughly 250 SKUs, one by one, behind a login - sits outside our core. You were one step from a working cart-builder. We will be straight with you about whether there is a clean fix, and we will not pretend the login-gated checkout is already solved.
3VP of PricingTell you where price should sit, store by store — and whether a change actually worked.
James: "If price reductions do zero to drive more business in our stores, then why the hell are we doing this?"
There are five pricing models across the 9 stores. Cannabis has been deflationary. Your VP needs to show when a price move changes behavior, and when it only gives away margin.
James: "All we've seen in cannabis is deflation. Through all the major inflationary pressure over the last couple of years, cannabis has just been constant deflation."
James: "A consumer comes in and says, I want an eighth that is 25 bucks, that is sativa, that has certain terpenes. You've got four of them. I'll pick the one that suits my needs."
James: "It's like wine. Your taste in wine doesn't change that dramatically from one month to the next."
What this VP does
Separate real elasticity from macro deflation.
Track same-consumer migration: trade up, hold, or erode.
Hold margin where consumers aren't voting on price; react where a local move shifts behavior.
Attach a defensible gross-profit case to every price recommendation.
Success: For each store and pricing model, real price elasticity is separated from macro deflation, you can see whether the same consumer traded up, held, or eroded, and every price recommendation carries a defensible gross-profit case.
Failure: Giving away margin with no lift, the last eighteen months, chasing "cheaper" everywhere, with no way to tell if a change worked or the market just moved.
Tooling: consumer segmentation over 3-4 years of unified sales, brand-erosion modeling, local competitor sets, local market-fit scoring, pricing experiments measured on gross profit and same-consumer migration, and experiment measurement against retained gross profit and consumer migration.
How your VPs know
Every claim carries its source.
Every VP works from the same provenance layer, so a SKU, competitor estimate, cost, attribute, and consumer behavior can be trusted in context. The "Blu Dream" vs "Blue Dream" point gets resolved before any VP compares products or makes a recommendation.
Gross profit is the scoreboard. Weekly gross profit across the 9 stores is about $110-120k. Total revenue is much higher; the largest store does about $80k/week in revenue and the smallest about $12k/week in revenue.
$110-120k
weekly gross profit across 9 stores
30%-47%
gross margin range today
10-20%
Target: a gross-profit increase, James's ambition range
James: "20% growth in gross profit would be very successful. We need a minimum 10% improvement on where we are."
James: "The revenue is less of a concern than gross profit is. We only pay people based on gross profit, not revenue."
Target: a 10-20% gross-profit increase. That is the ambition range; the first commitment is the diagnosis. We find where gross profit actually lives and leaks, store by store, across the four families below, then size the store-specific lever mix to work toward it.
The 30% to 36% and 47% to 56% figures are an illustrative hypothesis to validate, not a promise. We will not pick a number in advance and chase it; that is how price-cutting burned 18 months without proving the volume came back.
Where does the gross profit come from?
Gross profit is the scoreboard; these are the levers that move it. In Month 1, we diagnose and size them per store before committing to a target.
Buy better
Cheaper supply / smarter costLift: better cost and supplier mix. Ceiling: BCLDB price is the same for every retailer.
Rebate / data-programming captureLift: capture roughly 6–8% of COGS (the cost of goods, not revenue) through program support; it is effectively margin. Ceiling: only supported products qualify.
Forward-buy on cost-downsLift: lock lower cost ahead of price drops. Ceiling: cash and case minimums.
Price & protect margin
Price where it paysLift: hold or raise where consumers aren't voting on price. Ceiling: local competition, deflation.
Cut markdowns & wasteLift: move product before it ages or gets discounted. Ceiling: freshness, demand.
Promo disciplineLift: make every discount pay, or make it supplier-funded. Ceiling: competitive promo pressure.
Sell more & sell richer
Bigger basketsLift: more per visit. Ceiling: single-item shopping missions.
Better mixLift: steer to higher-margin categories and tiers. Ceiling: what your consumers actually want.
Trade-up & retentionLift: move customers up the value ladder and keep them from defecting. Ceiling: preference, competition.
Turn faster & fill smarter
Faster turnsLift: more profit per dollar of cash each week; less stockout loss and dead stock. Ceiling: case minimums, especially small stores.
Slot productivity & lifecycleLift: make each of your ~300-350 slots earn; pick winners early, cull losers fast. Ceiling: the SKU cap itself.
Substitution captureLift: when the first choice is out, keep the sale on an in-stock, ideally higher-margin alternative. Ceiling: availability, shopper acceptance.
Per-store localization: every store has a different ceiling. The 30%-vs-47% margin gap proves it, so we apply these per store, not chain-wide.
How we prove it: observe the local signal, test one move, measure gross profit and consumer migration, then keep it or kill it.
Tooling: margin views, order proposals, controlled pricing tests, and scale-ready daily outputs.
Trust
How you will know it is working and how to trust it.
Your VP earns trust the same way the business does: by making recommendations you can inspect, test, and judge on gross profit.
Proof by results.
We run the work in selected stores, hold comparable stores as a control, and measure the lift on gross profit. That is how fraud systems are trusted: by measured results against holdouts, not by asking you to take every call on faith.
The top drivers, on every call.
Each recommendation shows the main reasons behind it, such as low weeks of stock, strong margin, and strong local position. It is honest attribution: the biggest factors behind the call, not a false claim of perfect explanation.
You stay in control.
You review the list, override anything, and teach your VP with every override. Trust builds as the results come in; you are teaching it, not obeying it.
Always testing: explore and exploit
Explore: try new products, prices, and placements in a controlled way, so you find the next winner before competitors do.
Exploit: back what is already working with more depth, better placement, and cleaner ordering.
That balance keeps the recommendations improving every week: small bets to learn, bigger backing when the store data proves it.
The levers interact, so no one can give a perfect single-cause explanation for every call. What you get instead: proof by results, the top drivers behind each call, and you in the loop.
Constraints
Constraints, and how we deal with them.
Case-size MOQ
Small stores cannot hold deep inventory.
James: "In my smallest stores it will take me two and a half months to sell that case. Unless I want to increase my inventory level, I can't afford to list it."
Approach: treat case as MOQ in the order engine, favor 6-packs, and apply days-of-supply gates before listing.
BCLDB channel
Costs and prices move for everyone.
BCLDB is the main channel and prices are uniform across retailers. When a cost or price drops, your VP detects it from the extract and tells you whether to match, hold, or go aggressive.
Data programming
Protect supported economics.
LPs pay roughly 6–8% of COGS (the cost of goods, not revenue) for sales data. About 78% of assortment is supported. The low-20s LPs drive about 80% of revenue, and no single LP exceeds about 9% share in-store.
Approach: protect the supported 78%, still spot unsupported products worth carrying, and keep LP share under about 9%.
Deflation
Do not chase volume into the floor.
Your VP's job is to defend gross profit per visit, not reward every market price cut. Brand-erosion modeling shows whether a move retained profit or trained consumers down.
Last mile
LDB Wholesale Operations is login-gated.
The trusted list is core. Automating checkout behind the login is the out-of-core last mile. We will test for a clean path and say plainly if it is brittle.
Competitor scrapes
Useful, but directional.
Kelly, your scrapes are daily cart-fill inventory estimates, SKU counts, and price changes. They may run against site rules, so Vervana corroborates with the BCLDB extract and never presents an estimate as fact.
Built to scale
HQ stays small while the store base grows.
James: "The plan for this company is to grow exponentially in terms of store base."
Kelly: "Without HQ, if we just had the stores, it would be extremely profitable, but it's just not possible."
Your VP does the work a larger supply-chain team would normally do: resolve the data, score the order, flag the price move, and produce the daily views. That lets 137 Brands move from 9 to 20 to 30 stores and beyond without a proportionally bigger HQ. As the store base grows, every new manager gets their own VP team, not a bigger HQ.
Next steps
Next steps & timeline.
We recommend an initial target of about 3 months to achieve the goals. Early results will tell us what's actually happening - and give us a sharper read on timeframes as we go.
Month 1 is the diagnosis gate: find where gross profit lives and leaks across the four gross-profit families - per store, using 3-4 years of history. At that gate, we set the gross-profit target and the lever mix to reach it.
The initial ~3-month target is delivery against the target we set together after the diagnosis, not a margin number or fixed timeline promised on day one.
Weekly check-ins run throughout — we drive the agenda. Every check-in follows the same structure:
Vision & core values of the collaboration
KPIs
Lightning round on open tasks
Roadblocks
Open topics
Who, what, when
Did we miss anything?
Kelly ⇄ Justin open the data connections: POS live + converted history, BCLDB extract, competitor scrapes.
Vervana ingests and resolves the data to canonical identities.
First views stand up for the VP team.
Kelly joins a working session to inspect the ingested data and confirm the operating read.
Sign-off
The plain ask.
Tick the box, add any notes, and send it back. If something is off, write it here so we can fix it before we go further.