Analyze and leverage your data for valuable insights
Data required by the retail industry can be structured and unstrucured, multisource and complex. It is therefore important to be able to gather data from the web (google reviews, facebook …) but also to integrate the results of in-store surveys in a synthetic and directly actionable way by managers.
There are other sources, such as data extracted from cameras and cashier solutions to analyze in real time the attendance, the conversion rate, the average ticket and even to see the most popular areas in a store.
Artificial Intelligence at the retail's service
Our Natural Language Processing (NLP) algorithms allow you to assess customer satisfaction in real time, regardless of the original platform, including open-ended questions and unqualified transcripts. Our Sentiment Analysis technology assigns a rating to their feedback.
Topic Modeling techniques categorize information and facilitate the identification of new action drivers to explore new growth opportunities.
Pocket Result solutions provide AI-based recommendations for action plans that are best suited to your organization, business unit or store to improve your customer journey.
We also integrate an automated translation process, allowing you to have a global vision of all your data, as it comes from a multitude of sources, both digital and geographical.
Need to collaborate, share best practices and implement action plans?
The purpose of analyzing and augmenting your data with AI is to implement relevant action and remediation plans within your organization.
The most effective way is to implement a collaborative action plan. For example, improving the referencing of a brand with an immediate link between on-site observations and sales and marketing departments.
Our solutions summarize and categorize the data, allowing you to identify the levers requiring priority action: in-store advice, product range, parking lot accessibility, waiting times, etc. Managers are naturally encouraged to implement relevant action and remediation plans.
The solution then includes the possibility for managers to share best practices, to “like” them, copy them, modify them, and comment on them to promote collaboration.
The need for anticipation
The ability to anticipate, forecast, and manage the future evolution of your data is critical to your success as a manager in the retail industry.
Within a couple of weeks, your historical data can be used to build your predictive model, but it can also be augmented with external data to have even more reliable and accurate models: macro-economic data, for example, or even meteorological data to forecast the occupation of your terraces. Once built, the model is in your hands and you can create your own scenarios to optimize the execution of your strategy and reach your goals.