Optimizing

reward

scheme

impact

through

deep

behavioral

insights

@

How

I

identified

decision-ready

optimization

opportunities

and

proved

behavioral

impact

for

Reewild's

(now

Vela

Rewards)

sustainability

rewards

pilot

scheme

through

econometric

analysis,

theory-led

interviews,

and

a

behavioral

strategy

audit.

See what Mastercard had to say

Role:

Behavioral

Insights

Consultant

Duration:

6 months

Skills:

Quant

methods,

Qual

methods,

behavioral

diagnosis,

triangulation

Tools:

R

Studio,

Kumu,

Teams,

Excel

Languages:

R

Studio

My Role

While completing my MSc Behavior Change at University College London, I consulted for Reewild (now Vela Rewards), a Mastercard Start Path fintech, to evaluate their sustainability rewards pilot scheme: Planet Points.

The remit was broad with only two requirements:
1.  Tell us whether it's working
2. Tell us where we can optimize

The pilot

Reewild created Planet Points to reward people for purchasing lower-impact food and drink options. Users receive points per pound (£) spent depending on the eco-rating (A-E) of their purchased items.

The pilot ran for 6 months from January to June 2025 with 900 participating users and 23 participating locations across University College London and the surrounding Bloomsbury borough.

Problem & context

Reewild's

rationale

for

Planet

Points

is

to

target

the

sustainability

intention-behavior

gap:

Many

people

care

about

the

environment

to

some

degree;

however,

this

rarely

translates

into

consistent

action.

Pilot

evaluation:

Reewild

initiated

the

pilot

to:

1. Understand the degree and nuances of how rewards may influence sustainable purchasing
2. Identify and leverage optimization opportunities

Target behavior

Participants purchasing A and B-rated (low carbon impact) food and drink options at participating locations when hungry or thirsty.

Research questions
RQ 01
Impact

To what degree and across what food types and locations does the scheme influence performance of the target behavior?

RQ 02
Strategies

Which behavior change techniques are already operationalized in the scheme's digital and in-person experiences?

RQ 03
Barriers & Enablers

What barriers and enablers influence performance of the target behavior?

RQ 04
Optimization

How can the scheme's effect on target behavior performance be improved?

Research design
Methodology selection & rationale
01

POS quantitative analysis

Point-of-sale data from 187,000+ transactions were analyzed through a difference-in-difference econometric model to quantify the impact of Planet Points on purchasing behavior.

02

Behavior change technique (BCT) audit

185 unique components of the UI, messaging, and in-person experience were audited against the BCT Taxonomy (BCCTv1), identifying 10 integral and 14 subsidiary BCTs.

03

Participant interviews

15 45-minute interviews were conducted. Interview schedules were generated based on the components of the Theoretical Domains Framework. Transcript data was analyzed using the COM-B model, mapping behavioral barriers and enablers across: capability, opportunity, and motivation.

04

Triangulation

Findings from the quantitative analysis, BCT audit, and participant interviews were synthesized using the Behavior Change Wheel framework.

This triangulation allowed behavioral outcomes, intervention components, and participant experiences to be evaluated together, generating a clear diagnosis of how and why the intervention influenced behavior.

The combined evidence was then used to identify a series of informed recommendations to optimize the Planet Points scheme.

01

Difference-in-difference modelling

To gain a baseline understanding of the causal effect of joining Planet points on target behavior performance, I analyzed 200,000 transactions using difference-in-difference (DiD) econometric modelling. DiD tracks individuals' behavior before and after joining Planet Points as well as temporal trends, avoiding concerns of self-selection bias and supporting true causal inference.

DiD would also allow me to estimate the causal effect in different contexts, such as across food types and locations.

Sample:

900 users; 28,000 non-users; 200,000 transactions

02

Behavior change technique audit

To generate decision-ready recommendations, I first needed to understand the strategies already operationalized by the scheme. This would allow me to situate my recommendations around which strategies could be improved, added, or cut with reference to the identified barriers and enablers of the target behavior.

After collating 185 unique components of the UI, UX, messaging, and in-person experience, I conducted deductive thematic analysis against the Behavior Change Technique (BCT) Taxonomy (BCCTv1) to identify integral and subsidairy operationlized BCTs.

Sample:

185 touch points against 93 distinct research-backed BCTs

03

Participant interviews

To determine which BCTs are relevant to the target behavior, I first needed to identify its barriers and enablers. To do so, I conducted 15 45-minute interviews with a range of users. Interview schedules were generated based on the components of the Theoretical Domains Framework (an extension of the COM-B model). I then conducted inductive then deductive thematic analysis using the COM-B model.

Sample:

28 participants recruited & screened, 15 interviewed

01

POS quantitative analysis

Point-of-sale data from 187,000+ transactions were analyzed through a difference-in-difference econometric model to quantify the impact of Planet Points on purchasing behavior.

02

Behavior change technique (BCT) audit

185 unique components of the UI, messaging, and in-person experience were audited against the BCT Taxonomy (BCCTv1), identifying 10 integral and 14 subsidiary BCTs.

03

Participant interviews

15 45-minute interviews were conducted. Interview schedules were generated based on the components of the Theoretical Domains Framework. Transcript data was analyzed using the COM-B model, mapping behavioral barriers and enablers across: capability, opportunity, and motivation.

04

Triangulation

Findings from the quantitative analysis, BCT audit, and participant interviews were synthesized using the Behavior Change Wheel framework.

This triangulation allowed behavioral outcomes, intervention components, and participant experiences to be evaluated together, generating a clear diagnosis of how and why the intervention influenced behavior.

The combined evidence was then used to identify a series of informed recommendations to optimize the Planet Points scheme.

Triangulation

The methodologies and frameworks were specifically selected to support triangulation. The BCCTv1 provides literature-supported links between each BCT and one more component of the COM-B Model. By deductively coding participant interview transcripts against the COM-B model, I determined the relevance of each operationlized BCTs to the barriers and enablers of target behavior performance. I was also able to identify which BCTs should be added to the scheme. Finally, by bringing in the DiD model results, I could prioritize certain recommendations against the estimated magnitude of their effect.

RQ 01

To what degree and across what food types and locations does the scheme influence performance of the target behavior?

From difference-in-difference modelling

17 pp

increase

in

target

behavior

performance

for

hot

meals

Planet Points increased the likelihood of choosing low-carbon hot meals by 17 percentage points, showing stronger behavioral effects in high-emission food categories.

p < 0.05

From difference-in-difference modelling

11 pp

increase

in

target

behavior

performance

with

on-menu

eco-labels

When eco-labels were displayed on menu cards, sustainable purchases increased by 11 percentage points (though not significant at the 5% level), suggesting visible prompts may influence decisions.

p < 0.1

To support Reewild's external communications and investor relations, I also ran t-tests on emission reductions, basket value, and transaction frequency.

From t-tests analysis

16.8%

reduction

in

emissions

amongst

users

p < 0.05

From t-tests analysis

9.8%

more

transactions

per

month

amongst

users

p < 0.05

From t-tests analysis

5.5%

higher

basket

value

per

purchase

amongst

users

p < 0.1

RQ 02

Which behavior change techniques are operationalized in the scheme's digital and in-person experiences?

Integral

Feedback on outcome(s) of behavior

Instruction on how to perform the behavior

Information about consequences

Prompts/ cues

Material incentive (behavior)

Incentive (outcome)

Reward (outcome)

Conserving mental resources

Adding objects to the environment

Subsidiary

Goal setting

Discrepancy between current behaviour and goal

Feedback on behaviour

Social support

Anticipated regret

Social comparison

Credible source

Non-specific reward

Future punishment

Remove reward

Verbal persuasion about capability

Imaginary reward

Vicarious consequences

RQ 03

What barriers and enablers influence performance of the target behavior?

After thematic analysis, I mapped each theme in a behavioral systems map. This allowed me to see how each theme was connected and identify key chains, loops, and parent behaviors. Chains and loops of behaviors are integral as they offer multiple entry points to influence downstream behaviors.

Combined with a deep understanding of the product's touch points gained via the behavioral strategy audit, the behavioral systems map and thematic analysis, I uncovered 8 targetable levers for furthering Reewild's positive impact on sustainable purchasing. These levers are enumerated below.

RQ 04

How can the scheme's effect on target behavior performance be improved?

After identifying which behavior change techniques (BCTs) were operationlized in the digital and in-person experiences as well as which factors were most influential to users' sustainable purchasing under the reward scheme, I generated clear decision-ready optimization recommendations.

Reflective motivation

Value Framing

Users associated sustainability with health and ethics, suggesting wider value framing could strengthen motivation.

“For me, it's more of a health choice rather than a sustainable choice.”

Implemented

Adapt existing BCT: Information about social and environmental consequences

Frame sustainable choices within broader value narratives (e.g. health & ethics) to leverage additional sources of motivation for the same outcome.

Reflective motivation

Reward schedule

Users often could not accumulate enough points quickly enough to redeem rewards, weakening motivational impact.

“it took so long <that I> gave up on trying to redeem again”

Implemented

Adapt existing BCT: Reward (outcome)

Recalibrate the reward schedule so users can earn their first reward quickly, experience the ah-ha moment, and form the engagement habit.

Automatic motivation

Reward Preference

Points reinforced sustainable behavior when users had a particular reward option in mind but failed to do so otherwise.

“I feel actually is motivates me to use more of this app”

Implemented

Adapt BCT: 
Material incentive (behavior)

Situate the reward pool within users' existing purchases (e.g. gift cards for participating food service providers) to maximise the number of users pursuing a meaningful reward goal.

Physical opportunity

Recall friction

Participants frequently forgot to scan their Planet Points card at checkout, reducing engagement with the intervention.

“I think it’s easy to forget and maybe there could be more advertising for it.”

Implemented

New BCT: Environmental restructuring

Add additional modes of proving purchase that aren't reliant on in-situ recall: debit card integration via Mastercard partnership and/ or receipt scanning.

Adapt BCT: Adding objects to the environment

Maximize instances of in-situ or point-of-sale eco-labeling at partner locations.

Social opportunity

Collective contribution

Users indicated stronger engagement with peer participation, suggesting social norms could reinforce adoption.

“When you see people using it you think... I'm generating more of an impact”

TBD

New BCT: 
Information about others’ behavior

Introduce social proof signals from other users, highlighting participation and the collective environmental impact of the user base.

Psychological capability

Food emissions knowledge gaps

Effects were stronger in high-impact food categories where differences between options were more intuitive.

“I know that meat production is terrible and that I should not eat it.”

TBD

Adapt existing BCT: Conserving mental resources

Prioritize engagement in high-emission categories where differences between choices are most salient and have higher environmental impacts.

Psychological capability

Rating tangibility

Eco-ratings were understood at a high-level but left user wondering what the actual tangible impact of there decisions were.

“It would be really nice to be able to see what the impact actually is”

TBD

Adapt existing BCT: Information about social and environmental consequences

Translate impact ratings into relatable environmental equivalents (e.g., “choosing A over C saves as much CO₂ as 10 plastic bottles”).

Psychological capability

Perception of personal impact

Many users underestimated the impact that an individual’s food and drink choices can have on the environment.

“I don't know how much of an impact I as an individual can make”

TBD

Adapt existing BCT: Information about social and environmental consequences

Provide heuristic-based cues (framing, anchoring, availability) on how individual purchasing decisions contribute to collective environmental impact.

RQ 03

What barriers and enablers influence engagement?

01
Reflective motivation

Value Framing

Users associated sustainability with health and ethics, suggesting wider value framing could strengthen motivation.

“For me, it's more of a health choice rather than a sustainable choice.”

02
Psychological capability

Rating tangibility

Eco-ratings were understood at a high-level but left user wondering what the actual tangible impact of there decisions were.

“It would be really nice to be able to see what the impact actually is”

03
Psychological capability

Reward schedule

Users often could not accumulate enough points quickly enough to redeem rewards, weakening motivational impact.

“it took so long <that I> gave up on trying to redeem again”

04
Reflective motivation

Category context

Effects were stronger in high-impact food categories where differences between options were more intuitive.

“I know that meat production is terrible and that I should not eat it.”

05
Psychological capability

Knowledge gaps

Many users underestimated the impact that an individual’s food and drink choices can have on the environment.

“I don't know how much of an impact I as an individual can make”

06
Physical opportunity

Recall friction

Participants frequently forgot to scan their Planet Points card at checkout, reducing engagement with the intervention.

“I think it’s easy to forget and maybe there could be more advertising for it.”

07
Automatic motivation

Reward Motive

Points reinforced sustainable behavior when users had a particular reward goal but failed to do so in lieu of a desired reward.

“I feel actually is motivates me to use more of this app”

08
Social opportunity

Social proof

Users indicated stronger engagement with peer participation, suggesting social norms could reinforce adoption.

“When you see people using it you think... I'm generating more of an impact”

RQ 04

How can the intervention’s efficacy be improved?

Frame sustainable choices within broader value narratives (e.g., health, ethics) rather than environmental outcomes alone.

Adapt BCT: Information about social and environmental consequences

Translate impact ratings into relatable environmental equivalents (e.g., “choosing A over C saves as much CO₂ as 10 plastic bottles”).

Adapt BCT: Information about social and environmental consequences

Calibrate the reward schedule so users can earn their first reward quickly, experience the ah-ha moment, and start engaging more deeply.

Adapt BCT: Reward (outcome)

Prioritize engagement in high-emission categories where differences between choices are most salient and have higher environmental impacts.

Adapt BCT: Conserving mental resources

Provide clearer educational cues explaining how individual purchasing decisions contribute to environmental impact.

Adapt BCT: Information about social and environmental consequences

Remove the need to scan at checkout and instead leverage institutional partnerships to link users’ payment cards.

New BCT: Environmental restructuring

Calibrate reward thresholds and expand the reward pool to maximize the number of users pursuing a meaningful reward goal.

Adapt BCT: 
Material incentive (behavior)

Introduce social proof signals from other users, highlighting participation and the collective environmental impact of the user base.

New BCT: 
Information about others’ behavior

Scheme impact

-16.8%

Lower emissions amongst users

+9.8%

Higher purchase frequency amongst users

+5.5%

Higher avg. basket value amongst users

17pp

Higher target behavior impact for hot meals

11pp

Higher target behavior impact with labels present

Recommendations impact

With my recommendations as guidance, Reewild transitioned into Vela Rewards to expand its value framing by incorperating nutritional scoring and broaden its reward pool to align more closely with users' existing purchasing patterns. Now, Vela Rewards has scaled drastically from its initial pilot scheme with 138,000+ points-earning transactions per month across 6000+ retailers.

Reewild

Pilot Scheme

Purely sustainability scoring

Slow to first reward

QR code at checkout to earn points

Limited to sustainability-focused rewards

Vela Rewards

£400M+ GMV run-rate; 138,000+ transactions/ month across 6000+ retailers.

Broader value model utilizing sustainability and nutritional data

New user bonuses & faster reward loops

QR code + receipt scanning

Wider reward pool aligned with users' shopping interests including Ikea, Starbucks, Uber, Ticketmaster, and more.

From the COO

"Your recommendations and data analysis from the pilot have genuinely been invaluable.

The recommendations helped shape Vela's evolution from a sustainability-led rewards proposition into a broader health and sustainability loyalty model, including item-level nutritional scoring, broader everyday earning, faster reward loops and reduced checkout friction. These recommendations were subsequently incorporated into the live consumer product as it scaled.

We’ve used [your research] extensively in joint external communications with Mastercard, and was also central to testing the new emissions avoidance framework developed by the World Resources Institute and the World Business Council for Sustainable Development."

Kit Nicholl - COO of Vela Rewards