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Operation managers often struggle with decisions to open or close stores due to limited visibility into footfall and sales potential at specific locations.
FootfallCam AI utilises built-in demographics data to offer catchment area insights, enabling operation managers to make data-driven decisions instead of relying solely on intuition.
In the realm of mixed store performance, sales managers grapple with unlocking the full potential of specific stores. They wonder if they've reached the pinnacle or if further potential remains untapped.
Each retail stores are unique. FootfallCam AI optimises sales targets for individual stores by intelligently categorise stores based on shared traits, benchmark their performance within their respective clusters, and recommend a set of achievable targets.
Challenges arise as marketing managers juggle data from various sources, aiming to track spending, footfall, and sales for each event effectively.
FootfallCam AI harnesses Big Data and AI-powered market intelligence to predict and quantify lift rates based on historical campaigns. It continuously learns, evaluates event effectiveness, and forecasts future outcomes intelligently.
Store managers face a challenge as they lack access to historical and projected footfall data, leading to uninformed staff roster planning solely based on guesswork.
With the diverse datasets like historical footfall and weather, FootfallCam AI recommends optimal staffing levels. By adopting AI recommendations for determining the ideal staff numbers, retail stores can avoid unnecessary labour cost wastage while enhancing the customer experience.
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