Westpac
personalized product offers to the existing customer base, measured against a control group
With its data and AI layer Westpac Intelligence, Australian bank Westpac sent personalized deposit offers to 500,000 retail customers in two weeks, with account uptake more than twice that of the control group, according to its investor presentation of September 15, 2026.
Key points
- Westpac pushes personalized savings offers to its retail customers through its Westpac Intelligence layer.
- 500,000 customers reached in two weeks by these deposit offers.
- Account uptake more than twice that of the control group.
- Figures published in the investor presentation of September 15, 2026.
Objective
Strengthen deposit gathering from existing retail customers by sending them the right savings offer at the right time, and surface to Business bankers the opportunities detected in the data.
The deployment
Westpac, Australia's second-largest bank, has built a data and AI layer called Westpac Intelligence, sitting on a single data foundation that aggregates 285 source systems. This layer feeds the mobile app, the digital banker and the digital contact center. Its first quantified use is commercial: personalized deposit offers sent to retail customers. According to the Data, Digital and AI Update presentation of September 15, 2026, these offers reached 500,000 customers in two weeks, with account uptake more than twice that of the control group. Westpac states in a footnote that the comparison is made against this control group, which makes it an incremental measurement and not a simple response rate. The same engine produces smart digital leads for the Business bank: 16,000 customer conversations and 2.8 times more settlements over the five months to the end of August 2026, compared with the same period a year earlier (period comparison, with no control group). In June 2026, Luis Uguina, general manager data, digital and AI of the Consumer division, described the intent: spot in behavioral signals the customer who is considering switching banks and send them a savings offer through the right channel, at the right time. The bank cites Snowflake as a partner for this layer. Outside the scope of this case but published the same day: an AI search engine on the public website, launched on August 21, 2026, with selection of the first result up 75% against the previous 12 weeks. Westpac discloses neither the amount of deposits gathered nor the model used.
Results Proof A
The figures come from the official investor presentation Data, Digital and AI Update of September 15, 2026, published on the investor site of Westpac (a bank listed on the ASX), with an explicit note on the comparison with the control group; Capital Brief covered the presentation the same day and Cyber Daily documented in June 2026 the intent to target savings offers through the Intelligence Layer.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- plateforme Westpac Intelligence In-house layer that connects data and AI and feeds the app, the digital banker and the digital contact center. Westpac reports 6 times faster activation of customer insights on the first use cases.
- infra Snowflake Cited by Westpac as a partner of the Intelligence Layer in the September 15, 2026 presentation.
How it runs, concretely
For ops teams-
1Data consolidation Data team
Data from the 285 source systems is brought together in a governed data foundation, on which the Westpac Intelligence layer relies.
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2Customer detection and selection AI
The layer processes behavioral signals to identify customers to send a deposit offer to, for example those considering switching banks.
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3Offer delivery Marketing and digital platform
The personalized offer goes out in the app and digital channels; a control group is held out.
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4Business leads AI then banker
For the Business bank, the same layer generates smart digital leads that open a conversation between banker and customer.
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5Measurement Data and marketing team
Account opening compared with the control group for the offers; conversations and settlements compared with the prior period for the leads.
Account opening by targeted customers, compared with a control group that does not receive the offer. Without this control group, the bank can no longer separate what the offer causes from what customers would have done on their own.
How your customers perceive this type of use
Sourced studiesLe paradoxe est documente des deux cotes : 71% des consommateurs attendent des interactions personnalisees et 76% sont frustres quand elles manquent (McKinsey, 2021), mais 75% declarent ne pas acheter aupres d'organisations auxquelles ils ne confient pas leurs donnees (Cisco, 2024). La « creepy line » est localisee : messages recus quelques secondes apres une recherche et suivi de localisation sont les pratiques qui mettent le plus mal a l'aise (Periscope by McKinsey, 2019).
Acceptance conditions
- La confiance dans le traitement des donnees precede l'achat : 75% ne achetent pas sans elle (Cisco 2024)
- Un cadre legal protecteur rassure : 59% des consommateurs disent que des lois fortes sur la vie privee les rendent plus a l'aise pour partager des informations dans des applications IA (Cisco 2024)
- La personnalisation elle-meme est attendue quand elle est consentie : environ la moitie des consommateurs (US 55%, UK 52%) disent s'inscrire souvent ou parfois a des services personnalises (Periscope by McKinsey 2019)
Red lines
- Le message declenche quelques secondes apres une recherche ou un achat : deuxieme ou troisieme cause de malaise selon les pays (Periscope by McKinsey 2019)
- Le suivi de localisation percu comme de la surveillance : 40% de malaise en Allemagne et au Royaume-Uni (Periscope by McKinsey 2019)
- Le mesusage des donnees personnelles par l'IA, devenu la premiere inquietude des consommateurs, a 53% et en hausse (Qualtrics 2025)
Sources: McKinsey & Company 2021 · Periscope by McKinsey 2019 · Cisco 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- unified customer view covering accounts, transactions and products held
- usable behavioral signals (balances, outgoing transfers, declining usage)
- history of responses to previous offers to train propensity
- control group mechanism built into the campaign tool
Org prerequisites
- a data and AI team that owns the activation layer, separate from infrastructure projects
- an agreement with the savings product teams on the offers and the margins allowed
- a measurement rule set before launch: incrementality against a control group
Possible stack
- cloud data warehouse (Snowflake, BigQuery, Databricks)
- in-house propensity models or next best offer platform
- CRM campaign tool able to manage a control group
- in-app notifications and digital channels
The plan, step by step
- Step 1Consolidate account and behavioral data in a governed base accessible to the data team.Deliverable: Unified customer view usable for targeting.
- Step 2Define the trigger signals (attrition risk, excess liquidity) and train a propensity model for opening a savings account.Deliverable: Propensity score per customer and list of triggers.
- Step 3Deliver the offer in the app to the targeted population while holding out a random control group.Deliverable: Campaign in production with a control group.
- Step 4Measure account opening against the control group, then extend to other products and to leads for bankers.Deliverable: Incremental read and extension decision.
First step: Pick a single savings product, build a propensity score on existing customers, and send the offer to a targeted population with a randomly drawn control group.
The same pattern, proven in other industries
Taobao
Real-time product recommendation with deep learning, served across hundreds of scenarios in the app
Amazon
Item-to-item collaborative filtering product recommendation engine, served across every touchpoint
A.S. Watson (Superdrug, ICI Paris XL, Watsons)
Selfie-based skin diagnosis and product routine recommendation
Sources
- S1 Westpac Banking Corporation - Data, Digital and AI Update, September 2026 (presentation investisseurs) Primary archive pending
- S2 Westpac goes all in on AI (Capital Brief, Rose Mackenzie) Established press archive pending
- S3 Westpac's AI use will see the bank shift to a 'digital-first service' (Cyber Daily, Daniel Croft) Secondary archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.