Nando’s Order at Table

02 – SETUP

The brief was two-fold: a brand problem — consistency, recognizably Nando's, across markets — and a revenue opportunity we surfaced once we dug in: increase basket size, purchase frequency, recency, and personalization.

During the pandemic, each of Nando's markets built its own order-at-table QR experience with no shared standard between them. The global team commissioned the audit to bring them into one consistent Nando's experience.

Through stakeholder interviews, we also learned that order at table is uniquely placed to increase basket size, purchase frequency, recency, and personalization, because selling ancillary items, bundles, and acting on data is easier in a digital environment.

The brief said audit four apps, but what it needed was someone willing to find out where each market was falling short, and why, before recommending anything.

CONTENTS

03 – PROCESS

Grounding first:

Lived experience helps me close the distance between me and the problem – something unattained from analyzing flows and data.

I dined at Nando’s in London with our client first, giving me key observations I couldn’t have picked up from assumptions, data analysis, or user flow analysis.

Felt the wait time

Staff scramble to find a table, chaos of takeout orders coming in at the same time, lots of movement and energy at the entrance

Reliance on staff and training to make order at table work

Not all staff point out the QR when they seat you; awareness becomes harder

Major gaps: allergies and customizations are not covered

Customers with allergies or customized items have to go up to the counter to order

Group ordering felt clunky

Awkward to pass around 1 phone, everyone in the party is ready to order at different times so others are waiting if someone is slower to decide or new to Nando’s

South African culture is integrated through music and art

Nando’s is one of the largest exporters of South African art. The art adds more color and liveliness to the space. Playlists are curated per country and follow the rhythm of the restaurant – slower during lulls and faster in busier times.

Part 1: The full audit

Went market by market, screen by screen, scoring each against usability, brand, functionality and navigation out of 5 per category, averaged into a total, plotted on a perceptual map.

This version went to the global team, and it was genuinely sensitive, close to marking each market’s homework.

Example: instead of asking “how was ordering with our app?”, reframe toward the actual taste preference, so the experience can start personalising around it.

Part 2: Sanitized and actionable

Same audit, reworked for the markets themselves rather than the global team. No grade this time.

Categories expanded (experience design, content, navigation, functionality) and everything reframed as a specific opportunity per screen, per market: something each market could actually act on.

Part 3: Into the journey, then the synthesis

Recommendations moved from a list into the actual journey, placed exactly where they’d land as someone orders. Tags are the clean example: not instinctive enough across markets, so we explained why tagging mattered, then pointed to best-in-class for what good looks like.

That fed the final move: a “Frankenstein” journey, the best ideas from all four markets plus patterns borrowed from best-in-class (Sweetgreen, Airbnb), combined into one new, cohesive customer journey.

CONTENTS

04 – AI IN THE WORKFLOW

Across four markets, this project generated a huge volume of qualitative feedback from 17 stakeholders. Analyzing that much material by hand would have taken weeks.

Using AI — ChatGPT and FigJam's AI — to synthesize it into themes let me move much faster. But more than that, it let me go deeper: spotting patterns across markets I'd have missed skimming transcripts alone, and freeing up time to sit with what actually mattered and sharpen my judgment on what to prioritize and how to frame it back to stakeholders.

CONTENTS