SimplyCodes · Consumer Survey

Five Shopper Psychographic Segments

K-means clustering on attitudinal and behavioral variables. Demographics excluded from the clustering basis — applied afterward as descriptive overlays. These are trust and behavior segments, not demographic ones.

Key findings
55%
of U.S. online shoppers are Bargain-Focused Mainstream (28%) or Uncertain Mid-Journey Shoppers (28%) — the two largest segments, tied
74%
of High-Trust Power Shoppers would "definitely" use a code verification tool — more than double any other segment
5
distinct segments defined by trust orientation, savings behavior, and purchase confidence — not age, gender, or income
10%
High-Trust Power Shoppers are the smallest segment but have the highest code-seeking engagement, tool demand, and retailer-switching rate
Segment distribution — % of U.S. online shoppers
13%
28%
22%
28%
10%
Disengaged Skeptics (13%)
Bargain-Focused Mainstream (28%)
Savvy Confident Researchers (22%)
Uncertain Mid-Journey Shoppers (28%)
High-Trust Power Shoppers (10%)
Segment profiles
01
Disengaged Skeptics
13% of shoppers
n = 189
Behavior signals
36%
rarely or never look for promo codes
29%
abandon purchase after code failure
42%
searched for another code after failure (lowest)
Trust & tools
52%
believe retailers make codes harder on purpose
16%
would "definitely" use a verification tool
18%
say "definitely not" — highest rejection rate
Lowest trust Oldest skew (33% aged 65+) Passive Purchase confidence: 6 / 10 — second lowest. Skews older and slightly female (52%). Believe retailers are working against them but are the least likely to act on it.
02
Bargain-Focused Mainstream
28% of shoppers
n = 406
Behavior signals
34%
always search for codes before buying
76%
tried a code in the past 60 days
29%
experienced full code failure when they tried one
Trust & tools
55%
believe retailers make codes harder on purpose (2nd highest)
89%
would probably or definitely use a verification tool
71%
wouldn't trust AI for any purchase over $25 without checking
Active code users AI-averse Older skew (36% aged 65+) Purchase confidence: 8 / 10. Largest segment (tied). Active and frustrated — high code usage paired with high failure frustration. Very low AI spend tolerance.
03
Savvy Confident Researchers
22% of shoppers
n = 314
Behavior signals
15%
already use a browser extension (highest outside Power Shoppers)
68%
searched for another code after failure (highest of any segment)
19%
bought at full price after failure (lowest of any segment)
Trust & tools
93%
would probably or definitely use a verification tool
51%
say "definitely" use a tool (2nd highest)
69%
have switched retailers for a working code (highest)
Most resourceful AI-open ($51–$100 threshold) Younger skew (21% aged 25–34) Purchase confidence: 8 / 10. Broadest trust profile — elevated trust in all information sources, including AI. Gender-balanced. When codes fail, they adapt rather than give up.
04
Uncertain Mid-Journey Shoppers
28% of shoppers
n = 403
Behavior signals
33%
only use codes that arrive via email or notifications
24%
rarely or never look for promo codes
9%
always search before buying (lowest active rate)
Trust & tools
5/10
purchase confidence — lowest in the study
37%
believe retailers make codes harder on purpose (lowest)
5/10
every information source rated equally — no strong preferences
Passive code behavior AI-curious (high spend tolerance) Youngest skew (18% aged 18–24) Purchase confidence: 5 / 10 — lowest. Flat trust profile across all sources: AI, reviews, Reddit, YouTube all rated 5/10. Haven't formed preferences yet. Most likely to give retailers the benefit of the doubt.
05
High-Trust Power Shoppers
10% of shoppers
n = 151
Behavior signals
40%
always hunt for codes before buying
83%
tried a code in the past 60 days (highest of any segment)
78%
have switched retailers specifically for a working code
Trust & tools
74%
would "definitely" use a verification tool (2× next-highest segment)
72%
searched for another code after failure
9/10
retailer trust + AI trust — near-ceiling scores
Highest trust Most active Highest AI trust Purchase confidence: near ceiling. Smallest segment (10%) but most commercially engaged. When codes fail: most likely to search for another (72%), most likely to leave for a competitor (38%), least likely to buy at full price (16%) or abandon (14%).
Segment comparison — key metrics
Metric Skeptics Mainstream Savvy Uncertain Power
Segment share 13% 28% 22% 28% 10%
Used code in past 60 days 76% 83%
Always search before buying 34% 9% 40%
Purchase confidence 6 / 10 8 / 10 8 / 10 5 / 10 ~9 / 10
"Definitely" use verification tool 16% 51% 74%
Searched for another code after failure 42% 68% 72%
Bought at full price after failure 19% 16%
Switched retailers for working code 69% 78%
Believe retailers make codes harder 52% 55% 37%
How the segments relate — primary trust axis
General trust in the savings ecosystem → dominant organizing dimension
Disengaged
Skeptics
Uncertain
Mid-Journey
Bargain-Focused
Mainstream
Savvy Confident
Researchers
High-Trust
Power Shoppers
← Low trust High trust →
The three middle segments occupy similar positions on the trust axis but differ on a second dimension: purchase confidence + AI tolerance. Bargain-Focused shoppers are confident but AI-averse. Savvy Confident shoppers are confident and AI-open. Uncertain Mid-Journey shoppers lack confidence but are AI-curious.
Methodology note

Segments were identified using K-means clustering on attitudinal and behavioral variables: code-seeking behavior, trust in information sources, post-failure behavior, purchase confidence, and verification tool appetite. Age, gender, and income were excluded from the clustering basis and applied afterward as descriptive overlays.

A 28-year-old woman and a 52-year-old man who share the same trust orientation, savings behavior, and purchase confidence land in the same segment. These are attitudinal segments, not demographic ones.

SimplyCodes Consumer Survey · n = 1,463 U.S. online shoppers · K-means clustering on attitudinal/behavioral variables · Demographics applied post-hoc as descriptive overlays · "—" = specific value not provided in source data