The spreadsheet had been running for three years. Every week, the owner of a mid-sized Australian online sporting goods store — 800 SKUs, Shopify, competing against rebel sport online, Anaconda, Running Warehouse AU, and a handful of smaller specialists — would sit down with a staff member and spend five to six hours manually checking competitor prices. They'd pull up each competitor's site, note the prices in a shared Google Sheet, flag the ones that needed updating, and then batch-upload the changes to Shopify.
It worked. Sort of. The problem was timing. By the time a price change was identified, logged, approved, and uploaded, two to three days had typically passed. On long weekends, the whole process stalled entirely. The store was always reacting — and always late.
Then came the moment that made the cost of that lag impossible to ignore. Their top-selling trail running shoe — a high-velocity SKU that accounted for nearly 8% of their monthly revenue — had been sitting at $14.90 above the cheapest in-stock competitor for three full weeks. Nobody had caught it. Their Google Shopping conversion rate on that product had dropped by roughly half. When they finally traced the drop back to the pricing gap, the maths were unambiguous: weeks of lost sales on a product they couldn't afford to be uncompetitive on.
That was the moment they decided to move from manual to automated — and set up with PriceSpy. What followed was a five-day process that changed how the business operated. This is exactly what happened, day by day. (Details shared with permission; some specifics combined across similar onboardings to protect the store's identity.)
Day 1 — Store Connection and Catalogue Handover
The first day required almost nothing from the store owner. Connecting the Shopify store via API took around five minutes — PriceSpy operates on read access to the product catalogue, so there are no permissions that allow the system to make changes without the owner's repricing rules in place first. The integration pulls the full product catalogue: every SKU, every variant, every current price and stock status.
With 800 products now visible to the PriceSpy team, the owner filled in a short onboarding questionnaire. It covered four things: which competitors mattered most (in this case, rebel sport online, Anaconda, Running Warehouse AU, and two specialist online stores); which product categories were margin-critical versus commodity; any products that should be excluded from repricing entirely (clearance lines, exclusive branded stock, gift cards); and a rough sense of their minimum acceptable margin threshold per category.
The questionnaire took about twenty minutes to fill in. It wasn't complicated — but it was the first time the store owner had been asked to articulate these parameters in writing. Most of it existed in their head. By the end of Day 1, the PriceSpy team had everything they needed to begin the matching process, and the owner had a written record of their own pricing logic for the first time.
Days 2–3 — Product Matching (The Part That Takes Time)
This is where most of the work happens — and it's work that happens entirely on PriceSpy's side, not the store's. For each of the 800 SKUs, the team needed to find the correct competitor listing on each of the five competitor sites, verify it was genuinely the same product, and flag any discrepancies that would make automated comparison unreliable.
Verification matters more than it sounds. A competitor might sell the same shoe model but only in a different colourway. They might bundle two items that this store sells separately. They might list a product under a slightly different name that returns in a search but isn't actually the same SKU. A wrong match doesn't just produce bad data — it produces confidently wrong data, which is worse than no data at all. Every match in the system is human-verified before it goes live.
Variants added a significant layer of complexity. Trail running shoes come in men's and women's cuts, with sizes from 6 through 14 and often three or four colour options per model. Each of those variants is tracked independently — because a competitor running out of stock in men's size 10 doesn't mean they're out of stock in size 8, and repricing rules need to respond to that granularity to be useful.
Of 800 products, 640 were successfully mapped to at least one competitor. The remaining 160 SKUs were either exclusive lines with no equivalent available anywhere else, or products where no verified competitor listing could be found — these were flagged as non-competitive and excluded from repricing rules. No guessing, no approximate matches.
Products where a competitor sold a bundle (say, a hydration vest paired with a bladder) against the store's individual listing were flagged separately, so any repricing decisions on those SKUs could be made with the context that the price comparison wasn't apples-to-apples. They weren't ignored — but they weren't treated as direct competitors either.
By the end of Day 3, the full competitor map was built: 640 products, across five competitors, with variant-level tracking where applicable. The system had everything it needed. What remained was the strategy layer.
Day 3 — Setting Floors and Repricing Rules
On Day 3, after the matching was complete, the PriceSpy team ran a 45-minute strategy session with the store owner. This is the call where the pricing logic gets codified — and where most store owners discover how much of their business strategy has been living entirely in their heads.
Working through each product category, they landed on a consistent framework. For the bulk of the catalogue — cycling accessories, fitness equipment, general outdoor gear — the floor price was set at landed cost plus an 18% minimum margin. The repricing target was to stay within 2% of the cheapest in-stock competitor, but never breach the floor. For margin-critical categories where the store had invested in depth of range, the floor was set a little higher and the repricing band narrowed.
The trail running shoes — the category that had prompted the whole exercise — got their own rule. Hero products in that range were set to beat the cheapest in-stock competitor by $2, with a floor at the minimum acceptable margin for each model. The system would never take them below floor regardless of what a competitor did.
Clearance items and exclusive lines were simply excluded. They already had the prices they needed; adding them to a dynamic repricing loop would have created noise without value.
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The store owner reflected afterwards that this had been the most useful 45 minutes of the whole process. The pricing strategy had always existed — it was just distributed across years of instinct, historical decisions, and informal rules. Getting it into a structured system forced clarity. For the first time, a staff member could look at the repricing rules and understand exactly how the business approached pricing, without having to ask.
Days 4–5 — Go Live and the First Repricing Cycle
The system went live on the afternoon of Day 4. Within hours, the first automated repricing run had completed — and it produced a few surprises.
43 products were immediately repriced. Some had drifted above their target range during the weeks the matching process had been running; others had never been priced competitively in the first place. The repricing was automatic: rules triggered, floors were checked, new prices were pushed to the Shopify store. No spreadsheet. No batch upload. No waiting until the following Monday.
Twelve of those 43 products were actually raised in price. The owner had assumed that automated repricing would mean prices going down. What it actually meant was that prices moved to the correct position — and in several cases, the correct position was higher than where the store had drifted to. A handful of products had been sitting below their floor without anyone noticing, the result of old batch uploads that had never been corrected. The system caught all of them on the first pass.
Eight products had competitors who were currently out of stock. With no active in-stock competition, the repricing rule had no lower benchmark to match against, so those products held at their current price — or in some cases, the system applied a modest upward adjustment to capture the available margin while the competitor was absent. This is one of the less obvious benefits of real-time stock monitoring: it's not just about matching lower prices, it's about recognising when you don't need to.
The product that had started the whole exercise: the store had been $14.90 above the cheapest in-stock competitor — rebel sport online. The repricing rule (beat cheapest by $2) dropped the price to $2 below rebel sport's current listing. The price was updated in Shopify automatically, the Google Shopping feed reflected the change within hours, and the product was now competitive for the first time in weeks.
On Day 5, the first daily digest arrived. A clean summary of every competitor price move from the previous 24 hours — which products had changed, which direction they'd moved, and how the store's prices had responded. Two competitors had run overnight flash sales across several product lines; the repricing system had already responded and reverted when the sales ended. The owner reviewed the digest over breakfast. It took eight minutes.
What Changed at Week 2
After the first full week of live operation, the numbers told a clear story.
The owner had spent zero hours on manual price checking. The five to six hours per week that had previously been locked up in spreadsheet work was simply gone. Three alerts had arrived during the week — flagging significant competitor moves that fell outside normal parameters and warranted a human decision. Reviewing those three alerts and approving the suggested responses took ten minutes in total. That was the entire time investment for the week.
Google Shopping performance on the trail running shoe category had recovered. Impression share on the hero product was up roughly 40% compared to the previous week — a direct consequence of returning to a competitive price point after three weeks of being priced out of the visible range. The click-through rate had lifted too. Conversions were following.
Average order value moved up across the catalogue. This was counterintuitive at first glance — how does automated repricing raise AOV? — but the explanation was straightforward. Several products that had been manually discounted in previous months, in an attempt to stay competitive without proper visibility, had been sitting below where they needed to be. The system raised them to the correct position. Customers were still buying; they had simply been paying less than they needed to.
The most unexpected result came from competitor out-of-stock events. During that first full week, 22 products had a period where their primary competitor went out of stock. On each of those products, the system captured a margin uplift — small per item, but meaningful in aggregate across 22 SKUs over seven days. Before automation, the store would have had no visibility of those windows at all. They'd have kept selling at the same discounted price into a gap they didn't know existed.
No single one of these changes — the trail shoe fix, the floor corrections, the OOS margin capture — was transformative on its own. Together, running continuously across an 800-SKU catalogue seven days a week, they compound into a meaningful shift in both revenue and margin. That's the value of monitoring that never stops.
The store owner also gained something that doesn't show up in a weekly report: a clear picture of the competitive landscape across the full catalogue. After one week of monitoring, they could see which product categories were genuinely price-competitive (trail running, cycling computers) and which categories had very little competitor activity at all. That visibility changed how they thought about where to focus their buying decisions, not just their pricing.
Five Days to a Different Business
Five days from manual spreadsheet to fully automated repricing. The owner could see every rule, every floor, every match driving the pricing decisions — nothing was a black box. What they gained was visibility they had never had before: a clear record of their own pricing strategy, a complete map of the competitive landscape across every SKU, and a system that responds to the market in real time while they focus on everything else. The spreadsheet worked well enough for years. It just couldn't keep up.
If your pricing process still relies on manual checks and batch uploads, explore the PriceSpy demo to see what automated competitor monitoring looks like across a real product catalogue — or get in touch to talk through what onboarding would look like for your store.