If you assume the typical "coupon lover" is a lower income, bingo jockey housewife with her hair in curlers and a cigarette hanging out of her mouth, think again. Here's what the study found:
Demographic and Psychographic Profile of the Coupon "Lover"
- High household income (those pulling in over $100K per year are roughly 2x more likely to be coupon lovers)
- College educated (78% more likely)
- "Extroverted" and "imaginative" personality types (47% and 25% more likely, respectively)
- Female (no surprise here, 67% more likely than males)
- "Green" conscious (37% more likely, is it because online coupon codes are better for the environment)
- Believe that social responsibility is the most important element of a good work culture (a whopping 151% more likely)
- Parents (48% more likely)
- Northeasterners (66% more likely than West Coasters)
- Devoutly religious (31% more likely)
- Libertarians (47% more likely and that's libertarians, not librarians)
In addition, coupon lovers are 220% more likely than coupon avoiders to highly value tradition, and 96% more likely to highly value self-direction.
Demographic and Psychographic Profile of the Coupon "Avoider"
- "Insecure" or "Temperamental" personality types (33% more likely to be coupon avoiders)
- Detached, sophisticated, careless and procrastinators (each at least 20% more likely)
- Men (27% more likely)
- Insecure, low-income men (130% more likely)
The researchers were surprised at the findings, expecting to see more online coupon lovers among lower income earners and more avoidance with higher incomes and education.
What does this mean to you?
If you work with personas to optimize your online marketing campaigns, web design and conversion rate improvement efforts - you may want to re-think your assumptions about your customer segments. Don't assume that higher income people will always be happy to pay full price. Don't expect coupon lovers to be tight on cash.
In fact, when working with personas, don't assume anything. Try to base your customer profiles on factual data, rather than gut feel or stereotypes.
You may also apply this data to your campaigns in an A/B split test. For example, segment your email list by zipcode (or postal code, if you're outside the US). Test sending email offers with coupon codes to the highest income neighbourhoods vs. simply featured items. Or, test merchandising featured items to men vs. discount codes. Slice and dice your data to see if West Coasters respond more often to discount offers or new item emails. Do the same for Northeasterners. And so on, and so on.
The takeaway is to use the data in available consumer research to generate testing ideas (and to challenge your assumptions). Conduct tests to understand your own customer behavior. When you learn something about your customer, make changes to marketing campaigns and web personalization accordingly.