Data Analysis: How to Make Decisions That Help Your Business

The management of mass product sales in the traditional channel can greatly benefit from data and artificial intelligence. To put this into practice, it is necessary to have technological tools for data collection and analysis that open a new door to more effective implementation of business strategies. How is this achieved?

The digitization of various human activities allows us to capture and analyze an unprecedented volume of quantified information that is forever changing the strategic decision-making process.

The days when choices were made based on instinct and approximate calculations are long gone. And the interesting thing is that the data has always been there, but today, thanks to artificial intelligence and machine learning, managers have the opportunity to work with precise information to minimize margins of error.

The traditional channel’s best ally: applying data analysis and machine learning

The traditional channel is deeply rooted in Latin American culture, where it is also known as store-to-store (TAT) or simply the neighborhood store. In fact, it has been reinforced in recent times: in 2020 it represented more than 40% of consumption in Chile and, due to the social crisis plus the rise of the pandemic, this channel has had a strong boost in the last two years, where projections indicate that it will surpass the modern channel in sales.

The trend shows that the final customer’s inclination to prefer the store over the supermarket is an indicator that the traditional channel provides benefits that go hand in hand with the well-being of society.

However, given the personalized nature of the traditional channel, what often has the greatest impact is that the sales process is not efficient. According to data collected by YOM, 30% of the SKUs of companies are never offered to retailers.

Useful data

There are several reasons for this. In most cases, salespeople have less than 10 minutes to make the sale and offer what is easy for them, becoming more of an order taker than a purchasing advisor for the store. So, how can technology improve sales efficiency? Data and machine learning offer some answers.

Let’s assume there was a way to collect precise and detailed information on traditional channel sales. What types of data would be useful for the company? Here are some examples:

• What products the customer (the storekeeper) usually buys
• How often they buy them and in what quantities
• What products they are not buying and which could be beneficial for the business
• What the drop size is
• What the product mix and breadth are
• What the product stock is

The cross-referencing and analysis of all this data provides precise insights into product sales, and allows decision-makers to more effectively guide sales for each of their businesses.

For example, if a mass consumer company, also known as a CPG (Consumer Packaged Goods), is engaged in the sale and distribution of alcoholic beverages, it can obtain information on the type of product the customer habitually buys, in what formats, quantities and frequency, segmenting each neighborhood store and making customized decisions that bring greater benefits to both parties.

1 on 1 customization

Being able to serve each customer individually according to their needs is highly beneficial, both for the CPG and the storekeeper, as the store is supplied correctly with products that rotate efficiently.

If this information is obtained immediately and updated on what each storekeeper needs, restocking could be done quickly. This ensures supply in the neighborhood stores, prevents stock breakage and ensures that products are effectively rotated, displayed and sold. It also allows better establishment of discount modalities, offers and promotions.

One of the principles of machine learning is that the more it is used, the better its results will be, as the system itself «learns» from the use given to it by users. In product sales, this also applies: if there is more data collection in stores, the system will provide better predictions and exploration possibilities on new products that may be attractive for the storekeeper to sell.

One app is no longer enough to optimize sales

How can data be collected in the traditional channel? How can the commercial department get to know and provide solutions to the stores? An app is no longer enough, and this is due to the multiple deficiencies that the traditional channel has.

With a mobile sales application, designed for the salesperson to take orders from those who run the neighborhood stores, they will probably be able to sell, but not in the right way. The ideal is to acquire an intelligent digital platform that is capable of capturing the greatest value of each business and translating it into personalized recommendations, where the salesperson no longer places orders based on the last invoice, but advises their storekeeper to stock up optimally.

Through this type of intelligent platforms, the mass consumer company (CPG) can visualize the updated data, which will allow it to generate efficient actions and thus personalize the sale of products.

Technology has come to become an essential tool with the purpose of improving commercial performance, and the sale of mass consumer products is in the midst of change. What was once reserved for large businesses and supermarkets is now within reach of the neighborhood store.

In this sense, those companies that resist adapting to the transformations will be relegated compared to others who see digital transformation as an opportunity to efficiently manage commercial processes.