Understanding the Shopper's Buying Mission and their behavior in the store is now much easier than in the past. The sales ticket stores are very valuable information for the teams responsible for managing categories and key accounts.
Today, we don't need to ask questions to learn about customer expectations and preferences; it is possible to optimize the assortment, the location of the products in the store, design promotional activities that are more appropriate to what the customer is willing to buy and use relevant stimuli that activate the desire to purchase. Some analyzes allow us, additionally, to identify the best times to carry out the training and qualification of the personnel or the logistics operation of replenishing inventories in shelves or refrigerators without inconveniencing customers.
The 4 most common analyzes that we usually carry out with the tickets of a commercial establishment are Pareto, Time, Correlation and Incidence.
1. Pareto Analysis:
Vilfredo Pareto Federico Damaso 1848-1923 was an Italian engineer, sociologist, economist, political scientist, and philosopher who introduced the concept of 80-20 in 1906 helping to describe the distribution of income in Italy and the development of the field of microeconomics.
This analysis focuses attention on those products that account for 80% of the total volume of the category or business. It is through this principle that we determine, for example, the best day to carry out a promotional activity in the store, to minimize waste on days with little influx of the target Shopper. Additionally, it allows us to identify the way in which most products are purchased to avoid designing offers that are outside the purchase pattern:
What day of the week concentrates the highest volume of the category?
What time of day?
What is the most demanded product format?
What is the price range?
What is the segment? – example: light, gluten-free, lactose-free…
What is the number of products for each ticket?
What is the average value of the ticket that contains the category vs. store average?
2. Time Analysis:
This analysis is built considering the evolution of the billing and the total number of receipts of the store over a given period: the days and times of greater effectiveness and efficiency of the operation.
To start we build a simple matrix that combines the time the ticket was issued with the number of tickets issued. For example, we can find a store that is visited mainly in the morning.
If we consider that the infrastructure of the store is designed to operate efficiently with the largest number of transactions, it means that we have a large number of hours a day in which the operation is inefficient, because we have products that are not selling. This may be the case, for example, in stores that are used as the preferred breakfast spot, and during the rest of the day, the flow of customers decreases considerably.
If we build a second matrix, but now using the Ticket Value on the vertical axis to identify the period of the day when Shoppers spend more money in the store, we can see that the customers who leave more money in the store per transaction are the of night time.
And what do we do with this? If we define the average value of the ticket as a target for the periods that are below, we can establish a clear reference of the store's sales potential during hours that are not being effective.
By combining both graphs we can build a 4-quadrant matrix using the averages of each variable to define the strategic priority per turn of the operation:
For example, the quadrant with a high number of tickets and value above the billing average is receiving a high number of strategic customers, and to avoid losing sales, attention to customer processes and services must be reinforced, that is, we will focus on EFFICIENCY and as a consequence, we will give priority to aspects such as reducing waiting time in lines.
The quadrant with a high number of tickets but with a purchase value significantly lower than the average of the store should be focused on promotional actions to motivate the Shopper to buy a greater number of products or more expensive versions (Increase the VALUE OF THE TICKET).
In the hours of less traffic of shoppers with low ticket value, the focus may be on the implementation of training programs for the staff, to improve the quality of customer service, replenish the inventory or fix the Planogram on the shelves avoiding, for example, fill all the beverage refrigerators at the same time, assigning a specific time for beer and a different one for non-alcoholic beverages, which are purchased at different times.
3. Correlation Analysis:
It is used to identify the products that are normally purchased in the same visit of the Shopper to the store. It is very useful to determine the products that should be promoted together by day and time to increase the value of the ticket or determine the most effective secondary locations for each category and thus optimize the distribution of spaces within each store.
It is important to emphasize that the correlated products are not necessarily part of the same consumption occasion of their category, that is, we can obtain as a result that the category "toilet paper" has a high correlation with "beer" in a specific supermarket during weekends. week, indicating that the products are purchased at the same time by the same Shopper.
When the objective is to increase the visibility of categories that are in development, it is necessary to go beyond the products that we consume and consider those that we buy in the same visit to the store. So, as unconventional as it sounds, we could expect a higher return on investment if we put the extra toilet paper display near the beers, for example.
The correlation index varies between [-1 and +1], indicating the following:
If the correlation index is equal to 1: positive and perfect! The two categories are always purchased on the same ticket.
If the correlation index is 0: there is no relationship between the two categories analyzed.
Negative correlation close to -1 indicates products that are substitutes, that is, when one category is purchased, the other is not, and vice versa.
In a client that sells the “rice” and “pasta” categories, we detected in one of its studies of consumption habits that in the lower socioeconomic classes these two categories are substitutes, since both are considered carbohydrates that accompany the main protein , such as meat, chicken, or fish.
Subsequently, in an observational study of shopping behavior in supermarkets, we were able to corroborate that in those stores where pasta and rice were in the same aisle, generally next to each other, the shopper from the lower classes compared prices between both categories and opted, in general, for the purchase of the cheapest option.
Finding that in most supermarket chains the two categories were together in the same aisle, one next to the other, it was recommended to carry out a pilot test in 10 stores, moving the pasta to a different aisle, together with other products such as sauces and olives, among others and compare the results for 8 weeks.
The results were very clear. Where the two products were separated, their sales increased, as did the number of versions purchased, especially the pasta ones. These results helped create a success story to convince other stores to take the same action and improve the performance of both categories.
4. Incidence Analysis:
The fourth and last type of analysis that we recommend doing at this stage is critical to support the definition of the strategic role of each category in the store.
In this case, we build a Matrix considering the Incidence, or relationship of each product version of the category in the total number of tickets sold and its Net Operating Margin, that is, the total value that remains for the store after deducting all the costs. directly associated with the sale of the category.
The quadrant of low incidence in tickets and net operating margin is made up of COMPLEMENTARY products, which serve to fill the spaces and bring news to the store's customers.
The totally opposite quadrant is made up of those products present in the majority of sales receipts and that represents a good part of the money produced in the store. These products are considered essential and cannot be missing, so some of them can be used to POSITION the store as the preferred place to buy those categories.
Those others that for some reason cannot be used to differentiate themselves from other stores, such as mass consumption products whose offer is common between channels and stores, can be used as CASH FLOW generators. Any failure in the availability of these products generates a high impact on the performance of the category.
Those products present in most of the tickets, but with low participation in the net money resulting from the operation, are normally used as generators of TRAFFIC, since the retailer usually lowers its margin to a minimum to attract customers to the store.
This quadrant is normally formed with high-turn products. The focus in this quadrant is space efficiency, as products must rotate many times to reach the average turnover for each square meter of the store.
The last quadrant (PROFIT) is built with products with high representativeness in the operating net margin, but with low presence in tickets, that is, they are products with high value for money and gross margin that are sold only to some specific clients. In a convenience store, for example, cigars, wine and whiskey can be located in this quadrant. The focus in this quadrant should be inventory management so as not to end up with a lot of product on hold, affecting the cash flow of the business.
As you can see, with these 4 types of analysis we can obtain a large number of INSIGHTS to maximize the operation of the store and the sales of the categories.
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