4. Sales analytics
It is a given that all restaurants want to predict demand and sales. By analyzing historical sales trends and taking into account seasonal variances and other constraints, restaurants can effectively do that. These analyses empower restaurateurs to optimize their inventory and better forecast demand swings. The data can also optimize staffing by considering shift preferences and maximum-hour limits for crew members.
When combined with location-based analytics, sales analytics can be taken to a whole new level. By combining primary and secondary data on restaurant sales, companies can also analyze the sales of other restaurants in the same vicinity. This comparison can benchmark one’s own performance against competitors and identify the factors responsible for any sales dips.
5. Promotional and feedback analytics
By combining reviews, feedback, and social media data, restaurant managers can effectively evaluate and gauge their service levels. These analytics can also help them understand whether customers are receptive to special offers, and if so, which ones. Furthermore, managers can identify the key influencers by running algorithms. Instant feedback from analytics also allows for timely intervention in resolving customers’ complaints and preventing poor impressions. Analysis on cumulative data can also unearth hidden insights which otherwise could be missed, such as which food item is losing its popularity or which particular food chain is receiving negative remarks and for what reasons.
Given that the restaurant business has low margins but is cash-intensive, analytics can offer a definitive business advantage. While the aforementioned techniques are just a starting point, organizations stand to benefit by making analytics integral and pervasive in their ecosystem.
With the right ecosystem in place, restaurants can look to people with the right analytical tools and techniques to sieve through data and draw actionable insights. Across the spectrum, the analysis should be: descriptive, or telling them what happened or is happening; inquisitive, or helping them understand why it’s happening; predictive, or informing them of what’s going to happen; and prescriptive, or giving clear recommendations for next steps. These efforts all become futile if companies discount the importance of real-time decision-making on the basis of insights generated. This is perhaps the most critical part of the whole analytics equation.
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