They asked for ads. The constraint was knowing their customers and their calendar.

Google Ads.

• The store treated every customer the same, but its sales data showed a small group of buyers driving a large share of revenue.
• Buying was concentrated on predictable days, and Sundays sold far less than staffing assumed.
• The store was barely visible in search, and its products weren't in Google Shopping.

Use the existing sales data to understand valuable customer groups and buying patterns, then align acquisition, repeat-purchase activity and staffing recommendations with that picture.

• Sales data analysis from the store's POS.
• Distinct buyer groups, including highest-value buyers and salon professionals, each with its own campaigns.
• Promotions timed for the day before peak buying days.
• Google Merchant Center connected for Shopping visibility.
• Local search fixes.
• Email and SMS automations: win-back, replenishment, and loyalty.
• A recommendation to staff Sundays lighter, based on actual sales patterns.

The work brought customer analysis, acquisition activity and repeat-purchase workflows into a more connected approach. A staffing recommendation was also made from the sales patterns.

No financial or growth outcome is published for this project.