Today’s consumers demand speed, convenience and value, and QSRs looking to attract customers are increasingly using technology to do so. Technology affords new and innovative advancements, both customer-facing and for backend services.
The QSR space is unique for several reasons
The revenue model and customer expectations in the QSR space are unique, including:
- Low average ticket values: Most meals cost much less than they do at other types of restaurants
- Combos and super-sized meals: Despite low ticket values, QSRs rely on combos, add-ons and extras to make their money
- Unchanging customer tastes: Customers generally know what they want, and stick to ordering it
- Higher guest frequency rates: It’s not uncommon for QSRs to see the same customer upwards of eight times in six months
In some ways QSRs are advantaged by their uniqueness, like having a reliable customer volume. But their distinctiveness also has drawbacks, including adaptability and more:
- QSRs can only rely on customer volume: Due to their low average ticket values, QSRs must drive traffic to their stores to remain profitable, and any reductions in this space significantly hurt their profits
- QSRs must price correctly: Low prices can attract customers, but if they’re too low, they can harm profits. QSRs must set prices that meet the restaurant’s profit goals, without driving customers away
- QSRs can’t change their offerings: Rigid customer expectations mean QSRs are less adept to change their menu offerings
How AI can help the QSR space overcome these challenges
These challenges don’t mean QSRs can’t adapt to industry changes and customer trends as they happen, but it does mean they should be more mindful about the changes they make.
Enter AI and advanced analytics. These tools take customer data, which QSRs are flush with, to help make better informed decisions on customer experiences, pricing and more.
Specifically, these tools further the following areas:
- Pricing analytics: QSRs can better understand price elasticities for base menu items, combos and extras. This means restaurants can confidently set prices without turning away customers
- Customer segmentation: AI and advanced analytics tools group customers into sophisticated segments from factors that include products purchased, time of day, extras bought and more. Consequently, restaurants can better position upselling and cross-selling efforts
- Store location analysis: Advanced analytics can help identify new, optimal locations for chains looking to expand. By taking customer data in a particular geographic area, and coupling it with street traffic and mapping information, new location options can be ranked by potential profitability
Laying the groundwork for the rise of AI
AI and advanced analytics bring many improvements to the QSR space. But before a restaurant can move forward with them, they need to have the right groundwork in place, including:
- Sufficient network bandwidth to handle the demands of AI
- Support from staff, mainly the understanding that AI will improve their jobs, not replace them
- Support from leadership for approving AI tools, while championing company support for them
AI and advanced analytics are increasingly a strategic imperative for businesses today, and QSRs are remarkably positioned to benefit from the analyses and recommendations they afford.
See how Antuit helped a leading restaurant chain increase sales by $5-7m a month with improved messaging, targeting and personalization.
