Overview
Quick-service restaurants (QSRs) cater to the fast-paced lifestyles of modern individuals and families seeking delicious and satisfying meals on the go.
Of course, customer satisfaction and loyalty are huge focus areas, but marketing teams often face dozens of challenges along the journey of managing multiple store locations, maintaining customer loyalty, driving foot traffic, and attracting new customers.
To address these pain points, QSR distributed marketing using Snowflake and Census in conjunction with your email service provider (ESP) can help consolidate fragmented customer data and create personalized, targeted offers that reward loyal customers and encourage them to bring friends.
By integrating Snowflake, Census, and your ESP, marketing teams can provide a seamless experience for their chain owners and operators with a centralized data store and reusable segments based on store locations. This approach enables a highly effective marketing strategy that drives in-store visits and boosts customer loyalty.
To send your loyal customers "thank you" offers, you'll need these customer fields:
With these fields in hand, you can start highlighting the unique features and differentiators of your QSR to:
- Increase loyalty program participation: Utilize loyalty_points and visit_count to identify and reward loyal customers with special offers, encouraging repeat visits.
- Drive referrals: Use referral_code to create BOGO offers for loyal customers, enticing them to bring friends and expand your customer base.
- Improve Customer Lifetime Value (CLV): Leverage visit_count and last_visit_date to identify high potential customers, sending personalized offers that encourage future visits and increase lifetime value.
- Enhance customer segmentation: Apply location_id to segment customers by their preferred store location, enabling more targeted and personalized marketing campaigns.
QSR distributed marketing streamlines the process of sending localized offers to customers, increasing loyalty and driving in-store visits. With this approach, marketers can effectively support chain owners and operators, ensuring a seamless experience and maximizing ROI.
| Field Name | Description |
|---|---|
| customer_id | Unique identifier for each customer |
| first_name | Customer's first name |
| last_name | Customer's last name |
| Customer's email address | |
| phone | Customer's phone number |
| location_id | Store location associated with the customer |
| visit_count | Number of store visits by the customer |
| last_visit_date | Date of the customer's last store visit |
| loyalty_points | Current loyalty points balance for the customer |
| referral_code | Unique referral code for the customer |
Use Case
Here's how to get started with a QSR distributed marketing campaign:
- Connect Snowflake as your data source, consolidating all customer information, such as their purchase history, loyalty points, and preferred store location.
- Use Census data activation to sync customer fields like loyalty_points, visit_count, and location_id with your chosen ESP, such as Braze, Mailchimp, or SendGrid.
- In your ESP, create customer segments based on different criteria, such as those with a visit_count of more than 10 or loyalty_points above 500.
- Develop a targeted BOGO offer campaign for these loyal customers, where they can enjoy a free item when they bring a friend. Personalize the offer email by including the customer's first_name and preferred location_id store address.
- To encourage referrals, include the referral_code in the email, allowing customers to share the offer with their friends. This way, you're not only rewarding your loyal customers but also attracting new ones.
- Monitor the success of your campaign through metrics like open rates, click-through rates, redemptions, and referral-driven sales. Adjust your marketing strategies based on the results.
What's Next?
Now that you've successfully implemented QSR distributed marketing, consider exploring these other use cases to help achieve your marketing goals:
- Personalized marketing campaigns: Use your customer data to create targeted campaigns based on individual preferences or behaviors.
- Churn prediction & prevention: Analyze customer data to predict churn and create targeted offers to retain at-risk customers.
- Localized event promotions: Promote local events and special offers to customers based on their preferred store location.
- Customer satisfaction analysis: Collect and analyze customer feedback to improve your products, services, and marketing strategies.
