Shelf space for what you need. Category management: experience and practical lessons. POS look around: sales from shelf boxes

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System tools for optimizing retail and shelf space

It is sometimes argued that the literature on the efficiency of retail and shelf space organization falls into three categories. This opinion, in particular, is shared by the outstanding researcher in this field, Marcel Corstiens. These three categories are: empirical research reports (such as those we have reviewed in this chapter); books on the commercialization of research, namely: software products electronic merchandising; and academic work by mathematicians and statisticians seeking to optimize shelf and retail space models.

Planogramming software products often rely on the empirical principle that placement of products is determined based on the profit or sales they generate. Similar solutions have been on the market since the 1970s; one of the first were SLIM systems (Store Labor and Inventory Management - management inventory store staff) and COSMOS (Computer Optimization and Stimulating Model for Supermarkets – computer model optimization and sales promotion for supermarkets). I didn't set out to give full review of all algorithms developed since then, up to modern instruments on the construction of planograms, but considered it necessary to describe the most important milestones in their evolution. It should be noted that many of the commercial tools are created by researchers who prefer the accumulation of capital to the accumulation of knowledge, so their developments are often simplified versions of the optimization models that can be found in academic works.

I will focus on the latter here as they always precede commercial decisions. To be useful in practice, programs like Spaceman and Appollo must be based on a significant simplification of reality, a detail that seems to be of little concern to research circles.

Three key milestones, which will be briefly described below, show how the researchers gradually solved the optimization problem by incorporating the following new factors:

Different elasticity of the layout of different product lines;

Cross-elastic layout;

Direct commodity costs.

Different product lines have different display elasticity

Evan Anderson and Henry Amato (1973) developed one of the first algorithms to solve the shelf space optimization problem. As they say among marketers, they approached the problem "from the side of demand." The researchers proceeded from the knowledge that was available at that time, namely from the fact that different product lines have different calculation elasticity. Simply put, their model was based on logistic regressions that calculated betas for different product lines. It is this type of computation that underlies the aforementioned SLIM and COSMOS systems.

Cross elasticity of layout and direct commodity costs

The next important step was taken by the Frenchman Marcel Corstiens and the Englishman Peter Doyle (1981). The same Peter Doyle, who, if you remember, criticized retail marketing research for its lack of progress. The model they proposed was more extensive than the previous ones and is still being discussed today. Among other things, they included the ability to calculate direct commodity costs (associated with the acquisition, storage, as well as the lack of goods on the shelves, the so-called out-of-stocks), demand effects and the cross elasticity factor. It was the inclusion of the latter indicator that brought fame to their model.

They tested their model on five product lines at 140 $30 million-a-year candy, ice cream, and gift card stores. According to the researchers, the elasticity of the calculation was of the order of 0.19, and thus was in line with what had been shown by earlier experiments. It was also found that the cross elasticity of the layout was negative between various types sweets (if stores sold more chocolate, the demand for caramel fell) and positive between sweets and gift cards.

In addition, direct merchandise costs associated with acquisition (ordering and transport), handling (storage, insurance, and loss of goods) and out-of-stock items were calculated. The calculations were made on the basis of average data for 10 stores, but were used for all outlets covered by the study. The data showed that items with a higher turnover (such as chocolate versus gift cards) had higher processing costs.

Next, M. Corstiens and P. Doyle performed calculations for planograms (1) currently used in stores, (2) developed from sales data, and (3) developed from gross margins; to compare them with the results of their new model. The comparison showed that the latter potentially generates $128,000 more net income than currently used planograms, $104,000 more than planograms based on sales data alone, and $97,000 more than planograms based on sales data. gross profit. This was primarily due to the fact that rule-of-thumb models gave too little space for additional goods such as ice cream and gift cards. In percentage terms, this meant an increase in net income of less than 0.5%.

Lack of items on shelves out-of-stock) is a major problem for retailers. Customers react to this in one of five ways: 1) go to another store, 2) delay the purchase, 3) abandon the purchase, 4) buy a different size package or a similar product of the same brand, or 5) switch to another brand. David Grant and John Fernie (2008) report that a 2003 IGD study found that 65% of UK shoppers choose one of the first three options when a product they want is out of stock.

Cannibalization effect

The idea of the existence of this kind of cross elasticity was put forward by the French researcher Alain Boultes, and his attempt to include the effect of cannibalization in the planogram model was successful. In other words, he was the first to come up with a good solution to calculate brand B's sales decline as a result of brand A gaining more shelf space and showing an increase in sales. The model of A. Bultes is called SH.A.R.P. and still works (see below) as tested in Belgian grocery stores.

At first glance, the inclusion of the cross-elasticity factor in the model seems to be a trivial matter, but everything is not as simple as it seems. How complementary and/or competitive are, for example, rice and spaghetti? Add millet, potatoes, french fries, other grains, and root vegetables to this equation, and the difficulty increases exponentially. Keep in mind, however, that cross elasticity varies across product pairs and across time and situations. Products that compete with each other in one situation (hamburgers and meatballs can be considered as alternatives for dinner) are complementary in another (if you are going to invite friends to a barbecue).

Rivalry with rules of thumb

Once the fundamental models were developed, the researchers threw their energies into further optimization, often by removing various limiting conditions. For example, if the earlier model included a cross elasticity factor ( M. Corstiens and P. Doyle 1981, 1983), the later one excluded it from consideration in order to focus on another aspect, such as, for example, vertical or horizontal placement ( A. Lim, B. Rodriguez and K. Zang, 2004). A lot of time has been spent trying to mathematically solve the problem of why the packaging of different products looks the way they do. Of course, mathematicians are not accustomed to take into account some facts, for example, that some goods (the same pack of coffee) by definition must have a larger package than others (a bag of yeast).

On the one hand, the models suffered from the fact that they were not simple enough to be used in practice. On the other hand, interest in them has never dried up. Researchers continued to try to create efficient algorithms for optimizing shelf space that could compete with rules of thumb that emphasize the number of faces depending on the share of total sales or gross profit. In 1988 Frenchman Alain Boultes and Belgian Philippe Naert introduced a model called the SH.A.R.P. (Shelf Allocation for Retailers' Profit - distribution of shelf space for retailers' profit). They argued that it was far superior to the empirical principle " display area / share of sales". However, a year later A. Bultes had to swallow a bitter pill. It turned out that after the inclusion of the cannibalization effect (SH.A.R.P. II) in the model, most of its advantages over this rule of thumb disappeared ( A. Bultes et al., 1989). However, according to A. Bultes, the store will lose approximately 2.7% of gross profit if it does not optimize shelf space with SH.A.R.P. II. As a result, due to their ability to provide a level of profitability comparable to the total net profit received by the retailer, optimization models continue to be of great interest to researchers.

The return on efforts to ensure effective use shopping space

So far, I have introduced you to scientists who have pioneered a new approach to planogram development. Among their modern followers, I would like to name the talented researcher Min-Hsien Yang from Taiwan. He developed models to reduce the need for computing power in the calculation of optimization algorithms, and conducted studies on the cost-effectiveness of working with planograms.

Together with his colleague M.-H. Young and W.-C. Chen, 1999) he conducted a study that looked at how retailers work to improve the efficiency of their retail space. Store owners were asked to answer questions about how much time and effort they spend on (1) strategic and (2) operational work. Each item (strategic/operational work) contained five questions. The researchers then correlated the responses with economic indicators retailer activities: total sales, sales per square meter, profit per square meter.

A clear pattern was found: the quality of operational work to ensure the efficient use of retail space was most reflected in sales per square meter, while the quality of strategic work influenced the gross margin per square meter of selling space.

Table 3.13. The table shows the results of the ANOVA study using f-values (and p-values). Retailers' efforts at both the strategic and operational levels to improve the efficiency of retail space appear to be paying off.

In a recent study, Chase Murray, Abhijit Talukdar and Debu Gosavi (2010) developed an optimization model that takes into account factors such as product prices, shelf position, number of faces (display area), and package orientation. C. Murray and his colleagues report that their shelf space management techniques have improved sales in the same ranges as in the studies we have previously reviewed. However, they argue that many of the models in use today are a significant abstraction compared to the actual context in which the retailer makes decisions. The development of 3D modeling for planogramming is certainly an important step forward, especially for those product categories where packaging does not have a natural face.

Despite the efforts of researchers such as C. Murray and colleagues (2009) to create more realistic models, many retailers and manufacturers use planograms only as preliminary sketches of how shelf space can be organized, but never fully rely on them. Store owners refine them by incorporating factors such as storage specifics, type of outlet, communication goals (e.g., the best place and more space can be allocated to products that retailers want to sell rather than those that are already in demand), the adjacent placement of related product categories, etc. All this ultimately has a big impact on the final appearance of planograms. As well as the empirical principle of the distribution of shelf space depending on the share of goods in sales (or gross profit).

Conclusion

An overview of the study by M.-H. Young and V.-Ch. Chen, I conclude this section on optimization models. Based on the foregoing, we can conclude the following: efforts to optimize retail space certainly pay off in terms of increased profits and sales, but there is a certain limit to how much time and effort a retailer can spend on this activity. Therefore, despite the emergence of more and more advanced commercial software tools for retail space planning, challenges remain. In particular, they are related to the estimated values that the model requires as input. For example, these estimates are about cross elasticity or any strategic decisions where the marketer must enter subjective data. An interesting study was conducted by Norm Borin and Paul Farris in 1995. The scientists wanted to test how wrong numbers could be entered into the model without noticeably affecting the result. After testing SH.A.R.P. II, they found that input values that depend on the subjective judgment of decision makers can deviate significantly from the real values (up to 50%) without the model succumbing to rule-of-thumb methods.

Another, perhaps more important, problem is that optimization models do not take strategic decisions into account. The algorithms are based on historical data, but a retailer may want to influence the behavior of its customers by refocusing them from the products they buy today to some other ones. We will discuss this problem in the next section.

Strategic decisions regarding private label stores

Research in the field of shelf space optimization is becoming more and more applied. As an example, two of them are related to private label networks and their placement on the shelf space. The first study was conducted by Marcel Corstiens and Rajiv Lal (1994). They described the difference in approach between European and American grocery stores to work with your own trademarks. The first are singled out private label the most advantageous places, often exceeding the share of these products in the local market, while the latter mainly concentrate their brands in low-price segments.

In their study, M. Korstjens and his colleagues clearly showed that in most markets it is preferable to work according to the European model. This imposes certain requirements on the quality of products. private label and the pricing policies of their national competitors, however, in the light of our discussion, the most important point is that the strategic solution proposed by M. Korstjens cannot be implemented using existing planogram software tools. Meanwhile, the researchers insist that such a solution should entail all the necessary steps for its implementation up to the appropriate organization of the shelf space.

I prefer to keep the role of an objective observer and not express my opinion on this issue here. So, in another applied study, Fernandez Nogales and Gomez Suarez (2005) compared the amount of shelf space allocated by various stores for their brands (the study covered the periods from 1998 to 1999 and 2003). The obtained results confirmed the conclusion of M. Korstjens and his colleagues that private label get more shelf space than they "deserve" based on their market share. Interestingly, the researchers also looked at the overall performance of those product lines in which stores heavily promoted their own brands and found that this had an impact on their profitability, although not all outlets were affected by this negative trend. As a result, some retailers have begun to reduce the display area of their own brands in order not to lose sales. On the other hand, they still continued to allocate a lot of space for new private labels.

The conclusion is that it is not difficult to influence the decisions of buyers through the appropriate organization of shelf space, the main thing is not to abuse this tool, even though buyers are rarely aware of the changes taking place.

Comparison of display elasticity between different store departments

Of course, this issue is closely related to the topic of planning. trading floor, which will be considered in chapter 8, but I decided to discuss it here because it directly relates to the elasticity of the layout.

Two French researchers Pierre Desme and Valerie Renaudin, following R. Kerhan (1972), conducted a large-scale study to try to establish the reasons for the elasticity of the calculation. But, unlike R. Kerkhan, the French decided not to compare product lines, but different departments within one outlet. In 1998, they published an article on the relationship between allocated retail space and sales across different store formats and departments. The study covered about 200 universal shops in France.

Scientists hypothesized that the type of outlet, as well as the product, affects the elasticity of the display. They looked at the differences between three different store formats in the selected chain (small, medium and large) and also divided the entire range into departments (from jewelry, fashion and home goods to six types of food departments).

The study showed a pretty clear picture. As it turned out, there are significant differences in the elasticity of the calculation between departments. The highest values of this indicator are typical for such goods as underwear, jewelry, fruits and vegetables. Therefore, allocating more space to these departments is the most cost-effective. For fashion products, negative elasticity was found, and most of the assortment was found to be relatively inelastic. In the theoretical part of the article, reference was made to a dissertation by a German researcher, in which, based on the results of more than twenty experiments, it was concluded that approximately 40% of the assortment in German supermarkets has a display elasticity of less than 5% (if you remember, the rule of thumb says about 20%.

Lessons for retailers

Retailers can also draw some important lessons from this group of studies. As with private labels, success here depends on making the right strategic decisions. For example, a study by P. Desme and V. Renaudin showed that fashion products have a positive elasticity of layout in more big stores. In all likelihood, the latter can create an atmosphere that encourages people to buy clothes. At the same time, in smaller outlets, the same category of goods shows negative elasticity. Thus, it is important for a retailer to know exactly what to expect from a particular store format. If the researchers did not share outlets by type, the above differences might not be so obvious and go undiscovered. One lesson for retailers is that current shelf space optimization tools can work well for some store formats and assortment categories, but not for others.

Kerkhan (1972) has given another reason to be careful when calculating the elasticity of the layout. The fact is that in many cases this indicator increases with a decrease in the laying area and decreases with its increase. A similar trend is found in the case of price elasticity, where empirical studies indicate the non-linear nature of the demand function. This means that an increase in sales due to a price decrease is not the same as a decrease in sales due to an increase in price. In our case, this indicates that it is often impossible to reduce the area of any section, even if it has a low elasticity of the layout.

Facing is a unit of product laid out on a shelf frontally, facing the customer.

Facing is a unit of product that is visible (in self-service stores - available) to the buyer. Thus, each assortment position can occupy several faces at the point of sale. But it is necessary to distinguish between facings and the stock of products on the shelf for each item.

Shelf space goal setting- this is the definition of the number of faces that the manufacturer wants to present at the point of sale.

It should be noted that the facing has two functions: a demonstration function and a shelf space retention function. Depending on the tasks set by the manufacturer, one of the functions comes to the fore.

The first and most obvious reason to set shelf space targets is to optimize the turnover rate (shelf space hold function). Most often, such decisions need to be made for self-service stores, where the buyer is left alone with the goods. The goal is to ensure a uniform decrease in product from the point of sale and to ensure that there is a close to 100% chance that each customer will leave with a purchase.

The primary and simple requirement should be the following: the facing of priority positions should be greater than the facing of the main and additional ones. This will allow the product to evenly leave the shelf, this will reduce the labor costs of sellers and merchandisers to maintain the display.

In foreign merchandising practice, there is such a rule: SPACE TO SALE. It says that the brand should occupy the same percentage of shelf space that it takes in sales from all products displayed in a certain area. If 3 cherries and 10 oranges are sold for 1 package of plum juice, then approximately the same proportions must be maintained in the faces so that all customers find the right product at any time they are in the store.

However, fast-moving goods often fall into a trap here. If priority positions are given as much space as they should be in proportion, then there may simply be no room for additional positions. Therefore, on the store shelf for the above example, we will most likely see 1 plum juice faces, 2 cherry juice faces, and 4 orange faces. This adjustment is needed so that buyers of the additional assortment also find their product. But such an adjustment will have to "pay". The seller or merchandiser will need to replenish the stock of priority items more often. In the long run, this approach justifies itself in those stores where the emphasis is on a wide range. In other stores, the decision to reduce the number of SKUs in favor of increasing the facing of priority and main positions has a simple explanation. Of two evils, the lesser is chosen - it is better to lose one buyer of plum juice than to lose 6 buyers of orange.

It should be noted that sales analysis and shelf space allocation decisions are much more complex. What is SALE in the SPACE TO SALE rule? Someone means a market share, someone - a share in turnover, someone - a share in profit. In my opinion, as a starting point, it is necessary to take a share in profit (total margin).

The second reason for setting shelf space goals is to increase the visibility of products at the point of sale (Visibility). Here, the faceting performs a demonstration function. From this point of view, this tool is necessary for all stores, regardless of the mode of service.

What is high visual perception goods at the point of sale? This is the case when, when approaching the point of sale, the customer first of all sees this particular product (brand block, packaging, etc.). From the point of view of the store, high visual perception allows customers to easily notice all the goods present at the point of sale. To achieve this goal, apply various ways grouping and blocking.

It is known that a person can perceive information quite consciously in a field that is 30 degrees from the point where his gaze is focused. If a person moves along the place of sale to study the presented assortment, then these conditional 30 degrees also move. If a company wants to take a visual dominant position at the point of sale, then it is necessary to fill with its products a place exceeding 30 degrees. Within this space, incremental facing will have a big effect.

But the further we go beyond this space, the less effect each added face will bring. Therefore, sometimes companies set themselves the goal of achieving specific quantity faces at the point of sale of one brand. Since all packages of the same brand most often have a single design style, the same color spot is created that instantly attracts attention.

Christina Udalova

Facing and SKU are two concepts that play a key role in setting goals for distribution and shelf space. This article will be of particular interest to those who have a wide product range and those who have already exhausted the possibilities of extensive sales development due to an increase in the customer base.

If you look at the goals of most manufacturers operating in the consumer goods market, then most often they are formulated quite simply: you need to sell X tons or get Y money in a certain period of time. Expanding the customer base, increasing the number of distributions, expanding the product range in retail outlets, installing additional points of sale - all this is seen as tools to achieve one big goal.

However, if you go down to the level of a sales representative, then it is far from always enough to voice the goal or sales plan. Such employees often need to be explained what needs to be done in order to fulfill the plan. Let's leave the issue of expanding the customer base aside and consider setting quality distribution goals (as many companies call the goal of having a certain number of positions in the outlets).

The saga of 6 acres (from the book "Modern Supermarket").

One person bought 6 acres of land for himself and decided not to build a banal house-bath-flower bed on them, but to go into business. He went to the market, talked to the sellers and decided to grow and sell apples. No sooner said than done, I bought and planted an apple orchard on my 6 acres. The harvest was good, in the autumn the man earned a lot of money. But when he was selling his apples in the market, he noticed that pears were also popular. With a part of the proceeds, the man bought pear trees and planted them near the apple trees. The next autumn came, pears (like apples) gave a good harvest. The man thought that expanding the range is a good idea. And bought cherry bushes. And planted them between pears and apple trees…. It is not difficult to guess that the next year he had apples, and pears, and cherries were the same size (with a cherry). Therefore, he could not sell his apples and pears, and he did not earn much on cherries.

Any gardener will say: from the very beginning it was necessary to think about what assortment you want to sell, then ask how much land is needed for each tree so that it bears fruit well, and, finally, do not plant more than it should on these 6 acres.

Shelves in stores very often resemble this picture, when merchandisers try to present too wide an assortment on a limited shelf space. This makes it difficult for the buyer to choose and reduces turnover. Let's take a look at how to maximize revenue per unit of shelf space.

Definitions.

SKU (Stock Keeping Unit, literal translation from English - stock holding unit) In fact, this is an assortment position (a unit of one product group, brand, variety in one type of packaging of one container).

Example: the brand of milk "House in the village" contains several assortment items: 0.5%, 1.5%, 3.2%, 3.5%, 6%, etc.

Facing is a unit of product that is visible (available in self-service stores) to the customer. Thus, each assortment position can occupy several facings at the point of sale. But it is necessary to distinguish between facings and the stock of products on the shelf for each item.

Assortment goal setting- This is the definition of the number of SKUs that the manufacturer wants to present at the point of sale. Assortment targets for different sales channels can be significantly different from each other. For what reason? Most often there are two. Firstly, the size of the shelf space varies in different trade channels, and they are limited (there are conditional 6 acres). Secondly, buyers come to different trading channels with different needs and assortment requirements. This is where you need to answer the question: what to present on the shelf?

All assortment positions (SKU) of each product group and brand can be divided into priority, main and additional. The criterion for determining is the popularity of the position among buyers. Let's say orange juice and green apple juice are sold 4 or more times more often than any other juice. At the same time, regardless of price category. Such positions are called priority. In the total number of SKUs of one brand, they usually make up about 20%.

The next group of SKUs are the main positions that allow you to keep a place on the shelf. The main assortment includes those positions that have a consistently large number of regular customers. In the group of juices, such positions are most often tomato, cherry, pineapple, peach, apricot. They make up about 60% of the total number of brand SKUs.

Additional positions have their loyal customers and they are much less than the main and priority positions. The number of additional positions should not exceed 20%. Moreover, the fulfillment of this condition must be observed at the level of production planning in the first place.

Shelf space goal setting- this is the definition of the number of faces that the manufacturer wants to present at the point of sale.

The first and most obvious reason to set shelf space goals is to optimize the turnover rate (shelf space hold function). Most often, such decisions need to be made for self-service stores, where the buyer is left alone with the goods. The goal is to ensure that the product leaves the point of sale evenly and to ensure that there is a close to 100% chance that each customer will leave with a purchase.

In foreign merchandising practice, there is such a rule: SPACE TO SALE. It says that the brand should occupy the same percentage of shelf space that it takes in sales from all products displayed in a certain area. If 3 cherries and 10 oranges are sold for 1 package of plum juice, then approximately the same proportions must be maintained in facings so that all customers find the right product at any time they are in the store.

However, fast-moving goods often fall into a trap here. If priority positions are given as much space as they should be in proportion, then there may simply be no room for additional positions. Therefore, on the store shelf for the above example, we will most likely see 1 facing plum juice, 2 facing cherry juice and 4 facing orange juice. This adjustment is needed so that buyers of the additional assortment also find their product. But such an adjustment will have to "pay". The seller or merchandiser will need to replenish the stock of priority items more often. In the long run, this approach justifies itself in those stores where the emphasis is on a wide range. In other stores, the decision to reduce the number of SKUs in favor of increasing the facing of priority and main positions has a simple explanation. Of two evils, the lesser is chosen - it is better to lose one buyer of plum juice than to lose 6 buyers of orange.

The second reason for setting shelf space goals is to increase the visibility of products at the point of sale (Visibility). Here facing performs a demonstration function. From this point of view, this tool is necessary for all stores, regardless of the mode of service.

What is a high visual perception of a product at the point of sale? This is the case when, when approaching the point of sale, the customer first of all sees this particular product (brand block, packaging, etc.). From the point of view of the store, high visual perception allows customers to easily notice all the goods present at the point of sale. To achieve this goal, various methods of grouping and blocking are used.

But the further we go beyond this space, the less effect each added facing will bring. Therefore, sometimes companies set themselves the goal of achieving a specific number of faces at the point of sale of one brand. Since all packages of the same brand most often have a single design style, the same color spot is created that immediately attracts attention.

Facing or centimeter?

Suppose a company has set a goal for shelf space of 50% of a product group. It is necessary to decide how the achievement will be evaluated. There are two ways. The first way is to measure the size of the point of sale with a tape measure and calculate the percentage of your corporate block. The second way is to measure the number of faces at the point of sale and calculate the percentage of your corporate block. Let's figure out which way to use?

Consider an example.

How many product facings can be installed on a 3 meter section with 5 shelves if the facing width is 10 cm and if the facing width is 7.5 cm? If these two companies divide this area in half, then one company will be able to present 75 faces (1500cm/2/10 = 75), while the second company will be able to present 100 faces (1500cm/2/7.5 = 100). Thus, when counting in centimeters, the first company really takes 50%, but when counting in faces, it is only 43%. features and price).

(This example is made for two companies whose products are similar in terms of consumer characteristics and price category).

The choice of measurement method depends on the goals of the company.

The first path can be used if it is necessary to strive for VISUAL dominance in the first place. The unit, measured in centimeters, gives an idea of what the size of the color spot is.

In the second case, potential SALES are measured: after all, the buyer purchases a unit of production (facing, and not a certain number of centimeters).

Therefore, if the goal of visual dominance is achieved, it is necessary to move on to the goal of dominance in sales and evaluate in faces, and not in centimeters.

The relationship between facing and SKU (targets for assortment and shelf space)

Facing plays the role of some conventional unit of measurement of shelf space. In this regard, when setting goals for the assortment, it is necessary to understand the CAPACITY of the shelf space, measured in facings. If the assortment goal (number of SKUs) exceeds the shelf space goal (number of faces), then the store will not be able to display the entire assortment at the point of sale. This will entail lost profits and freezing money in products that are not sold.

There is also another extreme: when the goal is high, but the company does not have enough assortment positions to occupy this area. In this case, serious work is required to increase the facing and maintain the existing positions, or adjust the goals for shelf space downwards.

There are outlets where a large number of SKUs are prerequisite success, such as pharmacy, bookstore, auto parts. At the same time, the area of the trading floor does not always allow placing all of them available to the buyer. What to do in such cases? Is it necessary to strive to place each position "facing the buyer" in a small area?

In my opinion, in such product groups it is absolutely not necessary to lay out all positions in the window. If a person has a prescription for a drug, then most often in a pharmacy a person will simply go to the pharmacist and ask him about the availability of this drug. If this drug is not on sale, then the person will go to another pharmacy or decide to replace it with the help of a pharmacist.

In this case, it makes sense to put those SKUs behind the counter, the impulse purchase of which is minimally likely. Thus, the place on the showcase will be distributed among those product groups and positions in which the buyer can make his own decision without the help of the seller.

Product groups such as fabrics, clothing, shoes, stationery, dishes, wallpaper, ceramic tiles, etc. also contain a large number of SKUs. But in such product groups, SKUs are rarely sold if their faces are not visible to the buyer. Here you will have to make a difficult decision about optimizing the assortment. Whatever is not laid out or exhibited, whatever one may say, is not for sale, but only takes up space in the warehouse.

Thus, when setting goals for quality distribution, it is recommended to consider both shelf space and the number of assortment positions. This will allow you to set achievable goals and avoid the situation of setting goals that have already been achieved.

Economy

Keywords:

1 -1

The initial saturation of the demand of the domestic buyer occurred by extensive methods of development retail such as expanding retail space or expanding the range. Modern economic realities contribute to the implementation of intensive retail business development programs through more efficient use of retail space or. Thus, shelf space has become a tool for intensifying the growth of turnover.

Nowadays, the art of selling a product directly depends on how competently the sales staff will show the product “face”, or, as merchandisers say, how many faces of each SKU can be placed on the “golden shelf” of the trading floor. " " of the store consider the trading space the most convenient for buying goods, located at the level of the eyes and hands of the buyer, that is, at a height of 1.2 - 1.7 m from the floor. "Golden Shelves" are the most effective trading space and carry out the sale of goods without a seller. Also, the most profitable retail space includes shelves to the right of the planned customer flow.

The distribution of the area of the shelf space for product categories and assortment groups is carried out by category managers or merchandisers, middle managerial staff of the store. Together with merchandisers, they organize the display of goods on commercial equipment. Assortment items (SKU) can be conditionally divided into priority, basic and additional items according to the degree of product turnover. The most popular positions are prioritized, the main ones include positions that stably hold a large number of regular customers, additional positions have a low turnover, but they have their own loyal customers. When distributing the shelf area by positions, the percentage ratio will be 20:60:20. Further, when distributing the shelf space, the number of facings is determined. To solve the problem of a uniform decrease in goods from the shelf, the correct placement of the facings of assortment items is required, the facing of priority positions should be greater than the facing of the main and additional ones.

With a large assortment of goods, full facing is impossible and then it makes sense to remove those SKUs behind the counter impulsive buying which is unlikely, and put on the counter the goods of the main demand. There is a fairly extensive range of products that are not sold without facing, in which case the task is to optimize the assortment for more efficient use of shelf space.

To solve the problem of shelf space management, it is necessary to study customer demand, partners, commercial equipment and many other things that make up the daily work of a store manager, the implementation of internal reserves and, as a rule, an increase in turnover and profit growth.

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