Convenience stores can benefit from basket analysis to understand customer purchasing patterns and optimize their product offerings, promotions, and store layouts. Here are some examples of basket analysis in convenience stores.
Product affinity analysis
- Objective: Determine which products are often purchased together.
- Analysis: Analyze transaction data to find patterns where certain items are frequently bought together. For example, customers buying chips are likely to buy soda or customers purchasing bread might also buy peanut butter and jelly.
- Action: Place complementary products near each other on shelves or run promotions that bundle these items together for a discounted price.
Basket size analysis
- Objective: Understand the typical number of items customers purchase in a single transaction.
- Analysis: Group transactions by the number of items purchased. This helps identify the most common basket sizes, such as solo shoppers buying a quick snack versus families doing larger grocery runs.
- Action: Tailor marketing efforts, store layouts, and checkout lanes to accommodate different basket sizes.
Time-of-day analysis
- Objective: Determine if purchase patterns vary by the time of day.
- Analysis: Analyze transaction data to see if there are certain items or product categories that are more popular during specific hours. For instance, coffee and breakfast items might sell more in the morning.
- Action: Adjust staffing levels, inventory, and promotions to match customer preferences throughout the day.
Promotion effectiveness analysis
- Objective: Evaluate the impact of promotions on basket composition.
- Analysis: Compare transaction data from periods with and without promotions to see if certain products or categories experience increased sales during promotional periods.
- Action: Refine promotion strategies to focus on items that respond well to promotions and avoid discounting items that customers regularly purchase at full price.
Seasonal basket analysis
- Objective: Identify seasonal purchasing patterns.
- Analysis: Examine transaction data over different seasons to determine which products or categories have varying demand. For example, ice cream and cold beverages may sell more in the summer.
- Action: Adjust inventory, marketing, and store layouts to align with seasonal trends.
Customer Segmentation Analysis
- Objective: Segment customers based on their purchasing behavior.
- Analysis: Cluster customers into groups based on what they typically buy. This can help identify different customer personas, such as “health-conscious shoppers” or “late-night snackers.”
- Action: Tailor marketing messages and product selections to cater to specific customer segments.
Cross-selling analysis
- Objective: Encourage cross-selling opportunities.
- Analysis: Identify products that are frequently purchased alone and explore ways to pair them with other items to increase sales. For example, suggest buying a drink with a sandwich.
- Action: Implement suggestive selling techniques at the checkout counter or through digital displays.
EV purchasing behavior analysis
- Objective: Lengthen time spent in store for EV charging customers
- Analysis: Evaluate what shoppers purchase while their cars are charging and examine ways to attract their time and focus.
- Action: Place preferred items close to tables and chairs; consider installing phone and laptop charging stations inside as well.
Basket analysis in convenience stores can provide valuable insights into customer behavior and help store owners make data-driven decisions to improve their operations, customer experience, and profitability.
To gain more insights into how market basket analysis can support you, check out these resources: Market basket analysis in data mining
Harnessing basket analysis to evaluate buy-now-pay-later (BNPL) services
How basket analysis empowers merchandisers
Causation vs. correlation: Basket analysis in the retail industry
