Does data define the retail winners and losers in 2026?
Rarely, if ever, have the challenges facing Australian retailers been fiercer. Retail insolvencies are up 31% year-on-year, according to data from ASICs. Amazon, Temu, and Shein are projected to control 36% of Australian eCommerce by the end of 2026 and 50% by 2030. And two in three Australians say they won't return to a retailer after a poor delivery experience, according to Shippit research. Acquiring a new customer is 5-7x more expensive than retaining one, which means a bad delivery erodes a valuable asset.
Against that backdrop, you'd expect every retailer to be interrogating their operations data with fresh urgency. Most aren't. Only one in three retailers actively use their delivery data to optimise operations, reduce costs, or improve customer experience. The other two in three are making decisions on gut feel, outdated reports, and reactive firefighting; leaving them dangerously exposed at exactly the wrong moment.
The gap between the data haves and have-nots is widening faster than most retailers realise.
The problem isn't a shortage of data
Picture two retailers reviewing the same 12 months of delivery data. The first sees a challenging year: messy peak season, costs that crept up, a couple of underperforming carriers. They review the data, then hope the same doesn't happen this year. The second sees opportunity. Two preventable margin leaks: a carrier lane bleeding costs, and a fulfilment routing decision adding 200km to orders that could have been dispatched from a store 8km away. Combined, they're worth hundreds of thousands of dollars.
Both retailers have the same data, but only one is doing anything with it. The disconnect isn't about data access. Most retailers are overflowing in data. The problem is fragmentation. Information sits across six to eight siloed systems with no single view and no framework for action. And even when retailers do look at their data, many are looking at it the wrong way.
A monthly carrier performance report was considered progressive two years ago. Today, by the time it lands, the moment to act has passed. Retrospective reporting is like looking in your rear-view mirror while you're driving. It might tell you what went wrong last month, but it's not a decision-making tool.
The retailers pulling ahead have shifted from descriptive intelligence ('what happened?') to diagnostic and predictive ('why did it happen, and what's about to?'). For example, rather than simply knowing that your DIFOT (delivered on-time, in-full) rate was 87% in 2025, retailers should be using their data to determine which carriers, which lanes, which freight types, and which dispatch times are preventing it from reaching 90-95%. Moving from 87% to 93% can reduce customer service contacts by up to 20% and materially lift NPS.
What good looks like
Shippit works with retailers to connect delivery performance, freight costs, and customer outcomes into a single view that makes this kind of forward-looking analysis practical. Take Petbarn, for example, which generates 3.5x higher spend and 35% more orders from customers that select on-demand delivery.
It didn't guess that its on-demand delivery customers were more valuable, its data showed it. The analysis is replicable for any retailer with the right mindset and software. Segment your delivery option data by customer behaviour and ask whether customers choosing faster options have higher lifetime value and repeat purchase rates. Most retailers already have this data, it's just not connected to delivery choices yet.
Freedom Furniture is another fantastic example. It used freight dispersion data to map order origins against store locations, shaping a ship-from-store rollout that scaled from five to 62 fulfilment centres. Routing by proximity rather than assumption delivered approximately a 20% reduction in freight costs. For other retailers wanting to do likewise, find the postcodes generating the most orders, overlay your store and distribution centre (DC) locations, and identify the gaps. A store five kilometres from a high-demand cluster being fulfilled from a DC 500 kilometres away is a pilot location hiding in plain sight.
Three things to do this week
Retailers who look at their data often find 10-15% in freight savings almost immediately. Not through complex modelling, but through three questions most haven't asked:
- What is your cost per delivery by carrier, lane, and freight type? If you can't answer it in 10 minutes, that's where you start.
- Do fast delivery customers spend more and buy more often? The data is almost certainly already there, so run the segmentation.
- Where are your orders going versus where your stock sits? A freight dispersion analysis typically surfaces both cost savings and ship-from-store opportunities at once.
None of this requires a data scientist. It requires visibility and the discipline to ask before the moment to act has passed.
The window is closing
You cannot out-price or out-invest Amazon or Temu, but you can out-know them when it comes to your own customers: who they are, where they live, what delivery experience they had, and whether they came back. That's the advantage data gives you.
With iconic brands like Catch, Jeanswest, and Rivers all closing in the past year, no retailer is immune to today's headwinds. The retailers who act in the next 12 months will define the competitive landscape. The window is still open, but not for much longer.