01 Case Study
QuickShop.
Reducing weekly grocery friction through personalised basket building
A personalised basket-building experience designed to help customers rebuild their regular shop faster using behavioural data and recommendation confidence.
- Role
- Senior Product Designer
- Company
- Waitrose & Partners
- Timeline
- 6 months
- Focus
- Personalisation / Behavioural UX / AI-assisted commerce
My role
I led the experience strategy, interaction design, prototyping and validation approach for QuickShop, working across repeat shopping behaviour, recommendation logic and personalised basket-building flows.
- Defined the behavioural problem around repeat weekly grocery shopping
- Designed and compared personalised basket-building interaction models
- Created prototypes to test recommendation structure, confidence and customer control
- Worked with product, data and engineering teams to shape a focused Top Regulars MVP
02 Overview
Routine shops were still too manual.
Online grocery shopping involves high-frequency repeat behaviour. Customers regularly repurchase staple groceries, household essentials, familiar brands and recurring weekly products.
Despite this, rebuilding a weekly basket still required repeated searching, category navigation and manual basket building.
QuickShop explored whether personalisation could make routine shopping faster, clearer and more effortless.
Manual basket building
Decision-heavy browsing
Slower basket completion
03 Problem
Repeat shopping still felt like starting again.
Most online grocery experiences treated every shopping session as a new browsing journey.
But grocery shopping behaviour is highly habitual. Customers often wanted to repeat previous behaviour, replenish essentials quickly and complete routine shopping efficiently.
Traditional navigation forced customers to search for known products, move across multiple categories and rebuild baskets from scratch.
How might we intelligently streamline repeat shopping behaviour without overwhelming customers or reducing confidence?
The goal wasn't discovery. It was reducing effort for repeat purchasing.
04 Behavioural insights
Customers wanted speed, not more choice.
Research showed that grocery shopping is deeply habitual. Customers often shop around predictable routines, familiar products and replenishment cycles.
Grocery shopping is deeply habitual
Customers consistently purchased recurring products on predictable cycles, including weekly staples, household products, repeat brands and replenishment items.
Speed mattered more than exploration
During routine weekly shops, customers wanted efficiency, familiarity and predictability rather than discovery-led browsing.
Over-personalisation created friction
Recommendations became frustrating when they felt irrelevant, overly broad or difficult to scan.
Confidence shaped trust
Customers responded better when recommendations felt highly relevant, structured, transparent and confidence-driven.
05 Constraints & trade-offs
Prioritising what we could prove in the MVP.
QuickShop needed to validate predictive basket building without blocking roadmap delivery. Real constraints around engineering effort, category scale and customer trust shaped what shipped first.
Constraints
Fast validation required
We needed to prove the predictive shopping experience quickly without delaying broader roadmap delivery.
Engineering cost of the full vision
A fully guided multi-step experience demanded significant build effort before value was proven.
Relevance at category scale
Maintaining useful recommendations across large product ranges added complexity to logic and presentation.
Risk of recommendation overload
Too many layers could overwhelm customers and weaken confidence in what was being suggested.
Speed, familiarity and discovery
The journey had to balance routine efficiency with enough discovery without slowing repeat shops.
Evidence before scale
Behavioural and commercial metrics needed to justify investment before expanding the experience.
Trade-offs
Top Regulars as MVP
We launched a focused entry point first rather than the full multi-step basket-building experience.
Less exploration upfront
Category breadth was reduced initially to improve delivery speed and learning clarity.
Structured steps over joy scrolling
Testing showed continuous feeds caused cognitive fatigue and loss of orientation, so we prioritised step-based navigation.
Inspiration deferred
Recipe-led and inspirational experiences waited until core behavioural assumptions were validated.
Customer control over automation
Selections stayed reviewable rather than relying too heavily on automated basket creation.
06 Design principles
Reduce effort, not control.
These principles shaped how the experience balanced behavioural intelligence, trust and customer control.
We believed that if we could identify highly predictable shopping behaviour, prioritise confidence-based recommendations, simplify basket rebuilding and structure recommendations around customer mental models, we could reduce friction and increase repeat-purchase efficiency.
1. Prioritise confidence over quantity
Highly relevant recommendations were more valuable than large recommendation sets.
2. Reduce decision fatigue
The experience should minimise unnecessary browsing and searching.
3. Reflect natural shopping behaviour
Customers think in routines, categories and replenishment patterns, not algorithmic outputs.
4. Keep the experience lightweight
The interaction model needed to feel fast, focused and easy to scan.
07 Concept exploration
Three ways to rebuild a basket.
A major part of the project focused on testing different interaction models for personalised basket building.
01Rejected
Joy Scrolling
A continuous feed of recommended products displayed within a single long page.
What we learned: Customers struggled to maintain context. Recommendations felt overwhelming, category switching created friction and lower-confidence products reduced trust.
02Preferred direction
Step-by-Step Shopping
A structured basket-building experience organised into focused stages such as Top Regulars, Food & Drink, Household & More and Inspirational Meals.
What we learned: This aligned more closely with customer mental models. Customers described it as faster, clearer, easier to trust and more predictable.
Top RegularsFood & DrinkHouseholdInspiration
03Useful learning
Netflix-style navigation
A vertically stacked interface using horizontal product carousels grouped by recommendation themes.
What we learned: It improved category separation but created excessive scanning, fragmented focus and weaker progression through basket building.
Customers preferred recommendations that felt structured, transparent and confidence-driven.
08 Solution
A faster way to rebuild regular shops.
The final direction focused on a simplified Step-by-Step basket-building experience.
Recommendations were prioritised using behavioural confidence scoring and grouped into structured categories aligned with shopping habits.
The experience surfaced highly predictable repeat purchases, household staples, personalised product suggestions and category-specific recommendations within a fast, lightweight flow.
Top Regulars
High-confidence repeat purchases formed the foundation of the basket-building experience.
Structured recommendation groups
Recommendations were grouped around behavioural patterns rather than algorithmic outputs.
Lightweight interaction design
The flow prioritised quick selection, minimal decision-making, rapid progression and easy basket refinement.
Behavioural dataConfidence scoreProduct groupingQuick basket action
09 Key product decisions
The decisions that shaped the experience.
The product direction was shaped by customer behaviour, recommendation trust and delivery constraints.
01 We prioritised recommendation confidence over volume
Large recommendation sets reduced trust and increased cognitive load. Surfacing fewer, more relevant products created stronger customer confidence.
02 We avoided endless recommendation feeds
Continuous feeds created scanning fatigue and reduced orientation. Structured progression aligned more closely with grocery shopping behaviour.
03 We designed around behavioural patterns, not categories alone
Customers think in routines and replenishment behaviour. Grouping recommendations around shopping intent improved usability.
04 We shipped a focused MVP
Although broader recommendation structures tested positively, the initial MVP focused on Top Regulars. This allowed the team to validate behavioural assumptions quickly, reduce implementation complexity and accelerate delivery.
10 Validation and iteration
Testing helped simplify the direction.
The concepts were tested with customers across different shopping behaviours and levels of online grocery familiarity.
Our validation process
- 01
Research
Behavioural insight and concept framing
- 02
Prototype
Low-fi to hi-fi interaction models
- 03
Test
Usability testing with customers
- 04
Iterate
Refine, simplify and focus the MVP
Structured flows increased confidence
Customers responded positively to the Step-by-Step model because it reduced overwhelm, improved clarity, created stronger progression and aligned with existing shopping habits.
Recommendation transparency mattered
Trust improved when recommendations felt understandable, relevant and behaviourally logical.
Simplicity outperformed novelty
While more exploratory browsing models appeared visually engaging, customers ultimately prioritised speed, predictability and efficiency for routine shopping tasks.
Poor matches reduced trust quickly
Customers lost confidence when lower-confidence recommendations appeared too prominently.
Proof
What proved the direction was right.
The strongest signal came from comparing different recommendation models against real shopping behaviour. Customers did not want more ways to browse. They wanted a faster way to rebuild the shop they already had in mind.
Structured shopping outperformed novelty
The Step-by-Step model tested better than endless feeds because it gave customers clearer progression, better orientation and a stronger sense of control.
Poor matches damaged trust quickly
Lower-confidence recommendations made the experience feel less reliable. This reinforced the decision to prioritise fewer, higher-confidence products rather than a larger set of suggestions.
MVP scope protected delivery
Although broader recommendation groups tested well, focusing the first release on Top Regulars reduced delivery complexity and allowed the team to validate the highest-confidence behaviour first.
The winning direction was not the most visually novel. It was the one that best matched routine shopping behaviour.
11 Results
The impact of reducing repeat shopping friction.
QuickShop delivered measurable gains in speed, basket value, engagement and repeat behaviour, showing the value of data-informed personalisation when it is designed around customer confidence and control.
25%
faster completion
26 mins → 21 mins
5 minutes saved per shop
Completion time dropped from 26 minutes to 21 minutes, saving customers around 5 minutes per shop.
45% reduction from Favourites
Navigational add-to-basket actions from Favourites dropped by 45%.
10% reduction from Search
Add-to-basket actions from Search dropped by 10%, showing customers relied less on manual searching.
Browse and discovery remained stable, showing QuickShop reduced repeat-shopping friction without stopping spontaneous shopping behaviour.
12 Reflection
Personalisation only worked when it reduced effort.
The most important learning was that personalisation alone does not reduce friction.
Recommendations only became valuable when they aligned with customer expectations, felt highly relevant, reduced cognitive effort and maintained customer confidence.
Designing for repeat grocery behaviour required balancing prediction with clarity, speed with trust and automation with human shopping habits.