In an increasingly competitive and complex environment, using data to offer the best customer shopping experience and increase the efficiency and profitability of points of sale is necessary.
On the one hand, customers are increasingly demanding. They want to buy when they want (24/7) and wherever they want (physical store/online store), receiving personalized treatment, the same quality of experience, and without disruptions depending on the channel. That is, they want to start a telephone conversation with customer service and continue it at the same point by instant messaging because it is the most appropriate for the customer. At this point, two key points will be knowing how to coordinate and align offline and online strategies and using data to have 360 knowledge of the customer and create personalized experiences to their tastes and needs.
On the other hand: the challenge of profitability. Today, with the increased costs and complexity of operations, making logistics chain processes more efficient is essential.
Retail companies capable of optimizing their supply chain from start to finish, from good demand and stock management to customer contact, will be more competitive and prepared for the challenges of the coming years.
New technologies such as Artificial Intelligence (AI) or Intelligent Automation (AI) systems allow the retail sector to maintain profitability, offer a great shopping experience, and act quickly on any possible change.
Many companies in the sector are already applying combinations of Machine Learning models, Mathematical Optimization algorithms, decision engines, and process optimization technologies. Figures that, according to McKinsey & Company, will double by 2030.
Several use cases in the retail sector combine these disciplines: Machine Learning models to predict the sales forecast, clustering of the different physical points of sale based on the history of sales and customer purchasing patterns, optimization systems to plan human resources, recommendation systems to offer the client those products that may be of greatest interest to them or rule-based engines that allow decision-making in an agile and consistent manner.
Also Read: How To Use Online Chat To Sell More In Your Ecommerce
A very clear example of the application of this technology in the retail sector that directly affects profitability and the customer experience is employee planning.
If the dimension of the team that we need needs to be improved and I have more employees than I need at any time, I am wasting resources and, therefore, losing profitability. On the contrary, if I have fewer employees than necessary, I will not be able to satisfactorily meet the demand, which generates long waiting times and directly impacts the customer’s shopping experience.
First, you must have a good forecast of future demand to know the number of employees needed at each moment. Machine Learning models are very effective in predicting sales by channel and product based on their sales history and crossing this data with other types of information, such as the holiday calendar or weather forecasts that significantly influence demand.
The rule engines use these sales forecasts to assign the number of needed employees at any given time. In other words, a calculation can be made of the employee needs for each day (in intervals of up to 30 minutes) based on the sales forecast for that day.
You can also go a step further and incorporate optimization algorithms to carry out optimal planning of the schedules and tasks of the different employees, considering all the variables and restrictions that come into play. For example, the abilities or skills of each employee, their availability, their performance in different tasks, their preferences, etc., always guarantee regulatory compliance and the service levels demanded by the market.
These models need to be supported by HATP (High Availability & Time Performance) architectures capable of acting quickly in critical time scenarios.
In this way, the IA+AI combination contributes positively to two key levers in the retail sector: profitability and customer experience.
In addition, the sales forecasts obtained with the Machine Learning models can be used for other purposes, not only to calculate the necessary staff to serve customers satisfactorily. They can also be used, for example, for efficient stock management.
There are other areas where Machine Learning models can be applied to improve the customer experience: recommender systems. They are models that predict customer preferences. These models filter the information according to the massive behaviour of customer preferences and recommend products with similar characteristics and, therefore, are in sync with customer preferences. These models can be used in each interaction with the customer using any of the communication channels established between the retailer and the user and are contributing significantly to the personalization of the customer offer and improving the shopping experience.
Suppose you want to learn more about recommender systems. In that case, we recommend you read the article “Multi-Armed Bandits Algorithms in recommender systems,” which explains in detail how one of these models works.
Retail is a sector in full transformation, and digitization and the efficient use of data are its levers of change. We are experts in developing different disciplines of Artificial Intelligence and Intelligent Automation, combining them, and applying them to the retail sector to respond to different problems and help make better business decisions.
These tools and technologies are currently implemented in retail companies so that they have the necessary capabilities to optimize their chain from start to finish. In this way, they can be more competitive and be prepared for the upcoming challenges.
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