Adapt your stores to your customers, not the contrary !

One-size-fits-all Action Plans don't Work Well

Challenges orchestrated by the headquarters are often designed for the stores network average and therefore difficult to apply to stores that deviate from it. The main reason is that not all stores have the same customer profiles in the same proportions at the same times of the year. It is therefore legitimate to expect different performances.

Each Store has its Own Context and Potential

Each store needs an adapted action plan that adds the most value to its activity depending on its potential (the customers who will pass through its doors) and its means (human, products). With perfectly adapted actions and means, it becomes possible to set ambitious but achievable objectives, which will motivate the teams and maximize the store's performance.
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Each store has its own weekly actions

The modeling of customer behavior and dynamics makes it possible to know in advance which customer profiles will be the most numerous in each store, and which products and baskets will make their behavior evolve in the direction desired by the brand (loyalty, annual spending, responsible consumption...)

Top-down Segmentations don't Help Stores

Traditional segmentation techniques are strongly influenced by the vision of the top management and the past performance. The best performing stores continue to receive more resources, and the others miss out on their optimal performance because of the lack of resources adapted to their context and their true potential.

Real Similar and Comparable Stores

What could be more similar and legitimately comparable in performance than two stores visited by the same customer profiles in similar numbers? They have the same performance potential, as long as they are given the same resources, even if their performance may have diverged in the past.

A Truly Customer Centric Segmentation

Our approach groups together sales outlets that will be exposed to similar customer inflows in terms of numbers and profiles. These groups of outlets have the same performance potential, similar objectives, and identical means to achieve them.
Our tool thus proposes a clustering that evolves over time and is the most adapted to maximize the performance of each store.

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A must for the customer experience... and the employee experience

For both, the right staffing is a key contributor to the customer experience. If employees are understaffed, they are not able to provide the necessary efforts to nurture the customer relationship. As a result, the store's commercial performance suffers in the short term, the brand perception deteriorates and the employees are exhausted.
On the other hand, if the staff is over-staffed, they are bored, risk not reaching their individual objectives and are therefore penalized financially. On the other hand, customers feel excessive pressure, which degrades their experience. Of course, another consequence is that the profitability of the store deteriorates.

Historical approaches no longer suffice

Since the Covid crisis, the fall of international tourism and the development of the home office, traffic has become more difficult to predict. Modern time-based prediction tools are now far beyond the capabilities of an analyst for this kind of prediction.
Moreover, classical match-to-traffic approaches ignore the fact that customer service expectations vary, depending on the customer profile and the time of the year, week and day.

Predicting traffic and expected service level‍

Our algorithms simultaneously predict the hourly traffic of each store, and the optimal ratio of the number of employees per 100 visitors according to the most relevant seasonal variables as learned from historical data. Our tool thus recommends the optimal number of employees to schedule hour by hour in each store, with sufficient anticipation for the construction of the staff schedule.