What Men and Women Buy

Kevin managed marketing for a grocery chain. He treated all customers the same—same promotions, same emails, same aisle layouts. He assumed everyone shopped similarly. But sales data told a different story. Men and women spent roughly the same amount overall, but they bought different things. Kevin needed to understand these differences to target his marketing better.

Kevin hired a data analyst to examine purchase patterns by gender. The analyst studied 135 customers—69 female (51%), 66 male (49%)—and analyzed what they bought across six product categories: bakery, beverages, dairy, fruits, snacks, and vegetables. The goal was to find meaningful differences that could guide targeted campaigns.

Total spending was nearly identical. Women averaged $290.45 per transaction, men $274.64—a difference of just $15.81. The p-value was 0.497, meaning this gap wasn't statistically significant. Quantity purchased was also the same: women bought 5.51 items per transaction, men 5.48 (p=0.963). Overall behavior looked identical.

But when Kevin looked at categories, clear patterns emerged. Women spent 23.90% of their grocery budget on snacks ($4,788.91 total), making it their top category. Men spent 23.36% on vegetables ($4,386.05), making it their highest category. The data showed that men and women spent the same amount overall but allocated it differently across categories.

Kevin ran statistical tests on each category. Most showed no significant gender differences: bakery (p>0.05), beverages (p>0.05), dairy (p>0.05), fruits (p>0.05), vegetables (p>0.05). But snacks were different. Women spent significantly more on snacks than men (p=0.018). This was the only category with a statistically significant gender gap.

The confidence intervals confirmed it. Male spending ranged from $242.36 to $306.92 (95% confidence), female spending from $257.19 to $323.72. The ranges overlapped substantially, proving no overall difference. Quantity intervals also overlapped: males 4.84-6.13 units, females 4.80-6.21 units. Gender didn't predict total spending or basket size—but it did predict what went into the basket.

Kevin realized he'd been asking the wrong question. The question wasn't "Do men and women spend differently?" (they don't). The right question was "Do men and women buy different things?" (they do). This shifted his entire marketing approach from budget-based targeting to category-based targeting.

He restructured his campaigns around category preferences. For women, he created snack-focused promotions emphasizing convenience, indulgence, and lifestyle appeal. Display ads showed snacks as treats, quick energy, or stress relief. In-store placement put snacks near checkout lines, magazines, and beauty products where female shoppers browsed.

For men, Kevin focused on vegetables and beverages. Vegetable promotions highlighted health, simplicity, and meal prep—pre-cut options, recipe kits, ready-to-cook bundles. Beverage marketing emphasized multi-pack value deals and placed them near ready meals, sports nutrition, or grilling sections where male shoppers spent time.

Kevin kept dairy and vegetables as broad-appeal categories since both genders bought them heavily. These got universal promotions during high-traffic periods—holidays, back-to-school, summer grilling season. He didn't gender-target these categories because the data showed no meaningful difference.

Kevin tested the new approach with email campaigns. Women received snack-heavy newsletters with lifestyle imagery. Men got vegetable and beverage promotions with value messaging and health benefits. Overall newsletters still went to everyone, but the targeted messages went to customers most likely to respond based on their demonstrated preferences.

Today, Kevin segments his marketing by product category, not customer demographics alone. He knows that gender predicts category preference (especially for snacks) but doesn't predict total spending. His campaigns target what people buy, not who they are. The data showed him that same spending doesn't mean same shopping—and that difference is where targeted marketing works.