Australian Supermarket Behaviour Study
What shoppers say versus what their permissioned transaction and location data actually shows. Every chart below is interactive — hover for detail, click a bar, bubble or state to filter the page.
Where our shoppers are
Fill = average claimed weekly grocery spend by state · bubble size = number of respondents. Click a state to filter.
Lower spendHigher spend
At a glance
Key Findings
Auto-generated from the current segment — the headline gap between claimed and observed behaviour
Primary Supermarket — Market Share
Which retailer each respondent shops at most often (Q5) · click a bar to filter
Sample Composition
Demographic profile of the responding segment · click a bar to filter
State / territory
Age group
Household income
Household composition
Shopping Behaviour
Frequency, spend bands and category mix (Q7–Q9)
Claimed weekly grocery spend
How households shop
Category importance in the weekly shop
Basket Economics
Median implied spend per shopping trip (actual weekly spend ÷ visits per month), by how households shop — based on respondents with both Open Banking and location connected
Average basket value per trip
Average shopping trips per month
Cost-of-Living Pressure, by Income
Claimed change in grocery spend over 12 months (Q10) and share who took a cost-cutting action in the past 6 months (Q11), by household income
Spend trend vs. 12 months ago
Diverging from "about the same" — blue = spending less, red = spending more
Much lower
Slightly lower
About the same
Slightly higher
Much higher
Took ≥1 cost-cutting action in the past 6 months
Say vs. Do — Claimed vs. Observed Behaviour
The core pilot question: how far does self-reported behaviour drift from permissioned transaction data? Based on respondents who connected Open Banking (n shown per chart).
Estimated vs. actual weekly spend, by income band
Light dot = claimed (survey band midpoint) · dark dot = actual (Open Banking transactions)
Claimed (survey)
Actual (transactions)
Share of grocery spend at claimed favourite retailer
Decision Drivers
Average importance when choosing where to shop, 1 (not important) – 5 (extremely important) · Q12
Brand Perception Map
Which retailer respondents most associate with each attribute (Q13) — share of votes by supermarket
Loyalty & Switching Risk
Attachment (Q16) plotted against likelihood to switch in the next six months (Q17)
Loyalty risk quadrant
Respondent count per cell — darker means more respondents. Hover a cell for detail.
ChampionsHigh attachment, unlikely to switch
ConflictedHigh attachment, yet open to switching
IndifferentLow attachment, unlikely to switch anyway
Flight riskLow attachment, actively likely to switch
Claimed vs. observed switching (12mo)
Loyalty programs used
Shopper Personas
Each respondent assigned to the single decision driver they rate highest — a simple, explainable segmentation for targeting and messaging
Does a Loyalty Program Actually Work?
Members of a named loyalty program (Everyday Rewards, Flybuys, Costco, Harris Farm Rewards) vs. respondents in "None" — based on the Open Banking-connected subset
Share of wallet at primary retailer
Switch likelihood (0–10)
Actual weekly spend
Retailer Vulnerability
Which retailer's customer base is most exposed to churn — average switch likelihood, and the Sean Ellis-style "very disappointed if closed" product-market-fit score, by claimed primary supermarket
Avg. switch likelihood by primary retailer
% "very disappointed" if retailer closed (score ≥ 9/10)
Cross-Shop Leakage Network
Which other retailers each primary shopper base also shops at (Q6) — ribbon colour = the primary (claimed) retailer sending spend elsewhere. Click a retailer's arc to filter.
Biggest cross-shop flows
Passive Data Participation
Which additional data sources respondents chose to connect after the survey (Q18)