Seasonality coefficients in tourism, formula and examples. There is no life without sales planning

In practice, there are many large companies (especially in the regions of the country) that only after five to ten years of their existence come to the idea of ​​planning their activities. But before this insight, most companies were doing business as usual.

Why were they able to live without planning for so long? In my opinion, the turbulent pre-crisis economy with an abundance of credit resources is to blame. At any time, most companies could easily cover the cash shortage with the help of banks. However, times have changed, and we have to “make ends meet.” This is why planning is necessary. How can we calculate a company's liquidity for a certain period without planning sales, purchases, expenses, revenue receipts, etc.? Without planning - nothing.

So, the first important step in the budget of a trading company is sales planning. Although drawing up a sales plan is the responsibility of the sales department, I am committed to having sales plans drawn up by two departments: the sales department and the product management (purchasing) department, if there is one. Why complicate everything so much? This is a good cross-check of planned results, which are calculated by different methods.

The sales department draws up sales plans based on the capabilities of its clients, plans for the development of new markets, etc. However, sales people often do not have the analytical skills that are necessary when drawing up a sales plan. The product management department, whose functions include inventory management, pricing, budgeting, analytics and product promotion, should come to the rescue.

One way or another, below I provide for your consideration a practical methodology for calculating a sales plan:

1. Calculation of seasonality coefficients for past sales periods;

2. Calculation of sales targets based on:

• seasonality coefficients;
• growth (fall) trends;
• internal development plan of the enterprise;
• expert adjustment of calculated planned indicators.

Calculation of seasonality coefficients for past sales periods

Seasonality coefficients are determined by the formula:

k seasonality = Si / sS

where Si is the actual amount of sales for a particular month,

sS – average monthly sales amount for the year.

Table No. 1 shows a practical example of calculation.

Table 1. Calculation of seasonality coefficients

As can be seen from table No. 1, to calculate the seasonality coefficient, actual monthly sales for three years were taken. At the end of the table, average monthly sales were calculated for each year using the MS Excel formula: AVERAGE(). Then, in the lower block of the table, seasonality coefficients were calculated for each month of the year. Take, for example, the calculation of the coefficient for the first month of 2005, which was found as follows: 18,500 / 30,725 = 0.60.

To smooth out external factors that could affect seasonality in a particular year, we averaged the seasonality coefficients for each month (bottom row of Table 1). It is very important that the sum of all seasonality coefficients for the year is “12.00”, which indicates the correctness of the calculations.

Calculation of sales targets

Based on: seasonality coefficients, growth (decline) trends, internal development plan of the enterprise.

Economic growth (decline) trend

When calculating the amounts of planned sales, it is necessary to take into account the economic development trend. It is extremely wrong to rely only on internal company information (in our case, sales for past periods). Before the crisis, many companies made their plans solely based on their own historical data. And it worked until the crisis shook their minds. And the shock for most trading companies was inflated inventories, formed according to sales data, which in no way predicted a deterioration in the country’s economy. Yes, a company's actual sales reflect current trends that are embedded in the economy, but they do not predict its turning points. The trend coefficient is rather an expert indicator that cannot be accurately calculated. It is based on an analysis and forecast of the development of the economic situation. The topic of economic analysis is a separate large-scale issue that will not be discussed in this article.

Internal enterprise development plan

This is an enterprise plan for the next financial year, which contains information about company development strategies. Such information may include plans for developing sales channels, increasing the company’s customer base, etc., which are ultimately reflected in the planned level of sales growth.

So, after calculating the seasonality coefficients, we proceed to calculating the monthly amounts of planned sales.

Table 2. Calculation of monthly planned sales amounts

First, we calculate the total planned sales amount for the new period (line “Sales (plan) – 2008” in table No. 2). The company's management determined the sales growth rate for 2008 at the level of 1.2 - 20% increase in actual sales of the past year. Since actual sales for 2007 amounted to 495,545 USD, the planned level for 2008 will be: 551,454 USD. (495,545 USD + 20%).

Then in the “k trend” line we determine the coefficients for each month. In the above example (Table 2) it can be seen that in the second quarter we expect an increase in business activity in the country by 5% (coefficient 1.05), and in the third and fourth quarters by 10%.

In the line “k seasonality” we insert the previously calculated (Table 1) averaged seasonality coefficients.

To determine the amount of planned sales, it is necessary to make the following calculation for each month: the average monthly amount of planned sales (551,454 cu / 12 months = 45,955 cu) multiplied by k seasonality and k trend.

Expert adjustment of calculated planned indicators

After calculating the sales plan for the new period, it is necessary to logically comprehend the resulting figures. This allows you to identify places where the formulas may not have taken into account a number of factors that live in your head and are difficult to describe in numbers. The adjusted sales plan becomes a draft and is subject to review by company management. What management decides is a completely different story...

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Developing a sales plan and ensuring this plan is a pressing issue in the current economic situation. An incorrectly drawn up plan leads to direct losses - both in the case of excess storage of goods in the warehouse, and indirect losses - in the event of a shortage of goods in the warehouse, which leads to lost profits, deterioration of service, and even unnecessary bonus payments to sales managers.

One of the problems that greatly influences the preparation of the plan is the seasonality of sales of some goods. Some products, for example, running shoes, are more popular in summer than in winter. But heaters sell better in the cold season. These products are seasonal.

The confusion is also caused by the unstable macroeconomic situation, when inflation pushes prices up, and declining consumer demand forces sales to decrease in quantitative terms. In addition to negative factors, positive factors can also influence - both for the company as a whole - if the company is actively growing, and for specific product positions - if you invest a lot in product marketing, then the demand for them can grow faster than the company's growth. All this introduces a corrective element into the forecasts, because it is no longer so clear to rely on information about sales history without taking into account the real situation.

Therefore, when drawing up a sales plan, take into account the seasonal factor and trends in the company.

What is a seasonal factor - “seasonality”? This is a planned and regular deviation of product sales from average values. Seasonality is often calculated monthly for a calendar year relative to the previous calendar year for each product for which a sales plan is being built and for each outlet individually, and the final plan is compiled by consolidating the obtained values.

To calculate odds, I recommend calculating in unit terms. If you calculate in monetary terms, then the number of influencing factors increases many times over and this, in addition to increasing the volume of calculations, also greatly increases the chance of error.

Calculating annual seasonality coefficients is quite simple - you need to take the average monthly sales at the end of the year (the amount of sales for the year divided by the quantity), and then, for each month, calculate the deviation of the actual sales volume from the annual average.

(Consumption per month / Average annual consumption = Seasonal coefficient)

If our sales schedule is something like this:

Then, based on the calculation results, you should get something like this table for calculation (for 2010):

Seasonal odds:

But the task is not to calculate the coefficients as such, but to calculate the sales plan according to the current actual sales values ​​for the year. Let's assume that we conduct an analysis at the end of April 2011 and calculate the sales plan for May 2011:

And our plate will look like this:

The task is to understand how much we should sell for May, taking into account current actual sales volumes and seasonality. To do this, we will bring each of the months of the current year to a single base, removing from them the seasonal coefficient that we know.

(Actual Consumption per month / Seasonal coefficient = Ots Average annual consumption)

We get these values:

Which means that if we take into account seasonal factors, the expected average monthly for the year is 246 units/month.

From this, knowing the expected average for the year and the seasonal coefficient in May (calculated in the previous step), we calculate how many sales are expected in the month of May by multiplying the expected average annual sales by the calculated seasonal coefficient: 246 * 1.44 = 354.4 units.

Thus, we continue to formulate a sales plan for each month until the end of the year, adjusting according to actual sales data.

Unfortunately, these laconic calculations are not entirely correct...

We took into account the influence of seasonal fluctuations, but did not calculate the influence of the general trend. If your demand falls (or grows) by 10% every month for objective reasons, then without taking these movements into account, your newly drawn up plan will become untenable and, as we said above, will lead you to losses.

How to assess the impact of a trend?

The result of its calculation looks something like this (orange line):

The problem is that this method is difficult to use when calculating in Excel. But you can try to use simply linear functions, calculating the average monthly sales at the state “at the beginning of the year” and “at the end of the year” (taking into account seasonality), and assessing how it has changed over time. Or simply taking as a target value the one you would like to focus on (“I’m sure that sales volume should increase by 10%”).

Be that as it may, the result of the calculations is the resulting monthly trend “slope” coefficients for each product for each retail outlet for each month where you calculate the sales plan. The problem is that in a normal situation, within a year this is not a straight line, but bends smoothly.

The resulting coefficients are used to adjust the estimate of average annual sales, which, let me remind you, we rely on to estimate future sales.

If we assume that in current economic realities, unit demand will fall by 10% by the end of the year, then the monthly adjustment factor should be approximately equal to 0.987. This means that by this coefficient we will change the estimated monthly average within the current year according to the trend coefficient for each month:

(Actual Consumption per month / Seasonal Coefficient * Trend coefficient = Ots Average annual consumption)

And the calculation of the current values ​​will look like this:

We noticed that the result was 349.8 pieces. instead of the previously calculated 354.4 pieces? It seems that this is not very much, but if you have billions of dollars in turnover, then such an error costs a lot.

To increase the quality of work with seasonality, it is necessary to recalculate the annual seasonal coefficients for the previous year, relative to the identified trends. But if you do not want to carry out a large amount of calculations, even such a small clarification for the current year can already qualitatively improve planning.

It is important that these calculations are carried out and adjusted regularly, according to actual data, in order to obtain the most adequate sales plan and understand how you will ensure and control it.

In real work, professionals usually use more complex approaches. The calculation is carried out not by months, but by weeks, or even by days. More factors influence target values. And the forecast model goes beyond the usual average calculations. But the approach presented above is something that anyone involved in planning can apply, even without special tools.

If this is too labor-intensive to do manually, and if you have 10 retail outlets and 15,000 products, then welcome to us. Our solution will do everything for you.

The seasonality index is calculated using the formula: Ic = t/ c where t - average monthly level of the indicator for three or more years, c is the average monthly value of the indicator for all years.

Seasonal phenomena include those phenomena that reveal certain patterns in their development, regularly repeating from month to month, from quarter to quarter.
Seasonality also refers to the unevenness of production activity in industries related to the processing of agricultural raw materials, the supply of which depends on the time of year. In addition, seasonality may arise due to the seasonal nature of demand for goods produced by industry, sold by trade, etc.
The study of seasonality for the purpose of developing a forecast poses the following tasks: to numerically express the manifestation of seasonal fluctuations; identify their strength and character in the conditions of individual sectors of the economy; reveal the factors causing seasonal fluctuations; find the economic consequences of seasonality.

Problems that need to be solved during the study of seasonality:

1. identify the presence of seasonality;
2. numerically express seasonal variations;
3. identify factors causing seasonal fluctuations;
4. assess the effects of seasonal fluctuations;
5. carry out mathematical modeling of seasonality.

Methodology for forecasting seasonal phenomena

1. Present graphically the actual values ​​of the phenomenon being studied in order to find out whether a seasonal wave is present and to identify the nature of the trend.
2. Calculate seasonality indicators (4-quarter totals, 4-quarter averages, centered averages, seasonality indicators).
3. Determine seasonality indices.
4. Calculate the parameters of the equation describing the trend of the phenomenon being studied (for example, through the construction of a multiplicative time series model).
5. Construct a forecast and calculate its error.

Methods for calculating seasonality indices

1. Constant average method
2. Moving average method

Constant average method

Seasonality is detected using:
1) absolute difference method;
2) relative difference method;
3) index method.
The absolute difference method involves calculating monthly averages and the overall average and then comparing them:

If seasonality is estimated using three years (thirty-six months) of data, then

where y i is the value of the level of the dynamic series.
The magnitude and sign of the absolute deviation values ​​determine the presence of seasonality.
The relative difference method is a continuation of the absolute difference method. As an indicator characterizing seasonal unevenness, the relative deviation indicator is used:

By the magnitude and signs of the relative deviations, one can judge the magnitude and strength of the influence of the seasonal factor.
The seasonality index is calculated:

where y t is the average monthly level of the indicator for three or more years,
y c is the average monthly value of the indicator for all years.
Calculation of the seasonality index using this formula does not take into account the presence of a trend.
The calculated seasonality index values ​​are compared with the value of 100%. If the seasonality index exceeds 100%, this indicates the influence of the seasonal factor in the direction of increasing the levels of the dynamic series.
If the seasonality index is less than 100%, then the seasonal factor causes a decrease in the levels of the time series.

Moving average method

Seasonality indices are calculated using the formula: i sij = x ij / x рi,
where x pi are the calculated trend levels, serving as a basis for comparison.
In addition to individual seasonality indices, we also calculate average seasonality indices i sjav. To do this, we average the individual seasonality indices of the intra-annual periods of the same name in the analyzed time series using the formula: i sjср = ∑i sij / 3
The range of the seasonality index is calculated using the formula: R i =i max – i min

Analytical alignment method

If there is a pronounced tendency to increase or decrease levels from year to year, other methods of measuring seasonal fluctuations are applicable, in particular, seasonality indices are determined on the basis of methods that eliminate the influence of the growth (decrease) trend. When using the analytical leveling method, the calculation process is as follows.
1) Calculate equalized levels for each month (quarter) using the corresponding analytical equation at time t.
2) Take the ratio of actual monthly (quarterly) data (y i) to the corresponding adjusted data as a percentage.

3) Find the average of these ratios for the same months (quarters) as a percentage.

n – number of months (quarters) of the same name
4) From the obtained monthly or quarterly relative values, the average monthly level is calculated.
5) Determine seasonality indices using the formula

where y i are the initial levels of the series, are the leveled (theoretical) levels of the series, n is the number of annual periods

Example No. 1. There is data on the average egg production of one laying hen by month over three years (pieces).
Define:

1. indicators of seasonal fluctuations in the average egg production of one laying hen based on the monthly average for three years;
2. graphically depict changes in seasonal fluctuations in the productivity of laying hens. Draw brief conclusions.

Example No. 2. Using the constant average method, construct a seasonal wave of consumer demand for meat products in city markets (thousand tons).

Quarters	1st year	2nd year	3rd year	4th year
I II III IV	24,4 19,7 15,9 13,21	26,23 22,7 16,4 12,9	25,7 20,4 14,9 11,0	27,7 18,9 17,91 14,4

An objective sign of seasonality is the concentration of morbidity in a short period of the year. When analyzing seasonality, it is necessary to provide a quantitative description of the characteristics of the distribution of diseases throughout the year, determine the beginning and duration of the seasonal increase in morbidity, and determine the proportion of diseases that are caused by seasonal factors.

Most often, extensive indicators are used to analyze seasonality, that is, the proportion of diseases of each month in the annual number of diseases is calculated. It is assumed that the share of the average monthly level with an even distribution of diseases throughout the year is 8.33%. Months in which the share exceeds this number are considered seasonal increase months.

More precisely, seasonality is revealed when calculating indicators of seasonal fluctuations (the ratio of the average daily monthly number of diseases to the average daily annual number, as a percentage). If the monthly seasonal fluctuation rate is less than 100%, then the influence of seasonal factors on morbidity is absent or minimal. When exceeding 100%, the influence of seasonal factors is significant and sometimes decisive.

Seasonality factor– the ratio of the number of diseases that occurred in the months of increase to the total number of diseases for the year as a percentage.

Kc = number of diseases in the months of seasonal growth / number of diseases per year * 100.

Conventionally, increase months include those in which the number of diseases exceeds the monthly average.

Seasonality index– the ratio of the number of diseases in the months of seasonal increase to the number of diseases that arose in the off-season period.

Is = number of diseases in the months of seasonal growth / number of diseases in other months.

Table 2.2. Example of calculating seasonal fluctuations

The monthly average (1456 / 12 = 121) is exceeded in the 6th, 7th, 8th and 9th month. Thus, the seasonality coefficient (169+275+272+165) / 1456 x100% = 60%.

Seasonality index = 169+275+272+165) / (27+65+55+101+96+88+64+79) = 1.5.

This indicator answers the question: how many times the number of diseases in the months of increase exceeds the off-season level.

The distribution of incidence between months (weeks) of a year or months of several years makes it possible to detect the time of risk. Studying the seasonality of morbidity allows us to draw conclusions regarding the ways of infection spread and changes in people’s behavior throughout the year that increase the risk of morbidity. Analyze the seasonal incidence of the total population, as well as age, occupational and other groups of the population and groups. The months with the maximum and minimum number of diseases, the beginning and end of the seasonal rise, and the proportion of diseases that are recorded during the rise are noted. To exclude randomness in determining seasonality, the duration of the period for which it is determined should be several (3-5) years.

When analyzing the annual dynamics of morbidity, determining the time of risk and the reasons that predetermine it allows appropriate measures to be taken ahead of time in order to achieve a reduction in the morbidity rate during the months of seasonal growth.

Analysis of incidence by territory determined by administrative and geographical boundaries. The morbidity rate is analyzed and compared by medical districts, medical associations, districts, cities, regions, and countries. Infectious respiratory diseases spread faster in cities than in villages. Cities have higher population densities and more intense communication between people. Zoonotic diseases that humans contract from animals are predominantly found in rural areas and natural hotspots. In order to visually depict and analyze the uneven distribution of infectious diseases across the territory, it is advisable to use a cartogram on which intensive morbidity indicators or cases of diseases are plotted according to the places where the disease is registered. The reasons for the uneven territorial spread of intestinal infections may be the unfavorable sanitary condition of the locality, the presence or absence of food establishments, water supply and sewerage, the possibility of infection of products during their transportation, manufacturing and storage. Therefore, it is necessary to identify risk areas, that is, areas in which social and natural factors predetermine a high level of morbidity. The unevenness of the epidemic process across the territory may depend on the volume and quality of preventive and anti-epidemic measures and the completeness of registration of infectious diseases. To determine the reasons for such unevenness, it is advisable to analyze the long-term dynamics of incidence in different territories. Important practical conclusions can be drawn from the analysis of information in a given territory about the sources of infectious agents, transmission routes and epidemic foci. For example, when studying focality, attention is paid to the number of outbreaks with single and multiple diseases, indicators that characterize focality in apartments, preschool institutions and schools. The focality indicators for a number of years are compared. The following indicators of focality are determined:

Fociality index= number of diseases / number of all outbreaks.

Fociality index with multiple diseases = number of foci with 2 or more cases / total number of foci * 100.

To analyze morbidity by population groups, characteristics such as age, profession, gender, living conditions, and immunization are distinguished. Analysis of morbidity by age, profession and among other population groups, as well as in teams, is carried out according to intensive indicators per 1 thousand, 10 thousand, 100 thousand persons of a given age, profession, etc. In addition, the proportion of morbidity of a given group or collective in the overall morbidity is determined (extensive indicator). The most significant feature of the population with which the possibility of the disease is associated is its age composition. Age groups are allocated according to the research program, the purpose of which is to identify the causes of the prevailing morbidity in people of a certain age. For example, a possible distribution of the population into the following age groups: 0-1, 1-2, 3-6, 4-7, 7-14, 15-19, >19 years. Morbidity in age groups indicates which age group is most effectively affected by a particular pathogen transmission mechanism, how effective immunoprophylaxis is, and what features of life and behavior of this population contribute to an increase in morbidity.

Occupational risk groups for intestinal infections include employees of food production and processing enterprises, water supply systems, trade and public catering establishments.

Within the boundaries of social groups of the population, risk groups are distinguished. In particular, these are teams of preschool institutions and schools. In some risk groups, morbidity due to respiratory or intestinal infections may be observed for a long time. In order to identify the causes of morbidity in groups, they compare it among different groups, analyze the number of outbreaks that have arisen in them, the number of cases in each outbreak, and thus establish the reasons for the high incidence in them. Identification of risk groups and communities makes it possible to establish epidemic cause-and-effect relationships of morbidity in these groups and communities with risk factors.

Infectious diseases are identified by a doctor at the patient’s place of residence or at an appointment in a clinic. For each patient, on the day he is identified, the clinic sends an emergency message to the epidemic department of the SES. An epidemiological examination of the outbreak is carried out by an epidemiologist or his assistant. The purpose of an epidemiological examination of outbreaks is to identify the source of infection from which the infection occurred, the factors and routes of transmission of the pathogen. The following areas of work in the outbreak are distinguished:

· identification of the causes and conditions of outbreak occurrence;

· development and implementation of anti-epidemic measures to eliminate the outbreak;

· medical observation of the outbreak;

· analysis of the effectiveness of measures taken to eliminate the outbreak.

To identify the causes and conditions for the occurrence of an outbreak, the following methods are used:

· interviewing the patient (collecting an epidemiological history);

· carrying out laboratory tests (for the patient and contacts);

· study of medical documentation about morbidity in the outbreak area 1-4 weeks before identifying the patient (to find the source - the patient or the carrier).

The survey is carried out in the form of a conversation, for which it is necessary to know the features of the epidemiology of this infectious disease. During the inspection of the outbreak, attention is paid to those features that are important in the epidemiology of this disease: living conditions, the sanitary condition of the outbreak, the nature of the water supply. To identify the pathogen, laboratory methods (bacteriological, immunological) are widely used. The data obtained during the epidemiological survey of the outbreak is entered into the epidemiological surveillance map, and the results of the team survey are drawn up in the form of a report. All materials from the epidemiological survey are analyzed and, on their basis, conclusions are formulated about the causes of the outbreak and its approximate boundaries. Taking into account the characteristics of the epidemic focus, a specific plan for its elimination is being developed in the following areas:

· hospitalization of the patient or his isolation at home;

· measures for healthy individuals who are in the outbreak (laboratory examination, immunoglobulin prophylaxis, observation by local personnel);

· disinfection, disinsection, deratization.

An epidemic outbreak is considered eliminated if, during the maximum incubation period, no new cases of disease have arisen in the outbreak and all necessary anti-epidemic measures have been taken in it. Investigating an outbreak or epidemic has its own challenges because it involves a number of people being infected over a short period of time. The first cases of the disease at the beginning of the epidemic, the increase in the number of diseases, the peak of the epidemic and the decrease in incidence are determined.

An outbreak (epidemic) is investigated by a doctor-epidemiologist, but if necessary, other specialists (infectious disease specialist, bacteriologist, hygienist) also take part in it.

Epidemiological significance The disease is determined by its prevalence, frequency of registration (indicators of morbidity, mortality, lethality are studied and compared), the trend of the epidemiological process, the duration of the period of epidemic trouble are determined, the maximum and minimum levels of morbidity are compared, the ratio of manifest and asymptomatic forms is calculated.

Social significance infection is associated with the harm it causes to human health and the disorganizing effect of morbidity on various forms of life and activity of the population.

Economic significance infection is assessed by the losses that are caused to the national economy through the limitation of labor resources, the diversion of forces and funds to combat infectious diseases. Economic losses - direct and indirect (outpatient examination, inpatient treatment, payments for sick leave, loss of production by society due to illness, disability, mortality).

QUESTIONS FOR SELF-CHECK:

1. What is the difference between intensive and extensive indicators?

2. What is the difference between case-control and case-control analytic techniques?

"cohort study"?

3. What types of experimental research in epidemiology do you know?

4. Define the concept of “epidemiological diagnosis” and “retrospective epidemiological analysis”.

5. What manifestations of the epidemic process include cyclicity,

seasonality, epidemic trend?

6. What is the ultimate goal of a retrospective epidemiological analysis?

7. How can you visualize long-term and annual dynamics?

morbidity?

8. What is the purpose of an epidemiological survey of an outbreak?

9. At what levels are anti-epidemic measures aimed at in the outbreak?

10. Documentation that is completed in connection with the identification of a source of infection.

If consumer activity for a product is observed only at some point in time, they speak of strict seasonality. A striking example is Christmas tree decorations. They are eagerly sold out only on New Year's Eve.

There are goods or services for which demand exists for a longer period. For example, tourism business falls under this category. There are also products that are in demand in any month or season of the year. These are necessary food products, medicines, and household appliances.

What affects seasonality?

The level of consumer interest in products depends on various factors. These include:

• Seasons. With the onset of the next season, weather conditions change, which plays a significant role in fluctuations in demand. There are business sectors that are most susceptible to a decrease or increase in consumer activity depending on the time of year. This is, for example, the sale of street lighting equipment. It is carried out more successfully in winter. Soft drinks, on the other hand, sell out faster in the summer. And the need for various types of heaters appears with the onset of autumn cold weather. Then the demand for such equipment increases.
• Holidays and significant periods. For example, on the eve of March 8, you can count on more successful sales of jewelry and perfumes. The flower business also experiences a peak in sales at this time. Gifts are usually not given on May 1st. But the demand for food for picnics is increasing, because many people prefer to spend these days outdoors. School bags and school supplies are in high demand at the end of summer. Lent is a time when people buy less meat, and before Easter consumers stock up on eggs.
• Allocation of budget funds. Orders placed by state-owned enterprises involve large volumes of work. However, budget funds are allocated at certain periods. Typically the unit is a quarter. Therefore, payment for projects, which is carried out using budget funds, is made in most cases at the end of the quarter.

How to smooth out the seasonality factor

If it is not the season to run its business, the company tries to at least reduce costs. Sometimes drastic measures are taken. For example, staff quit. However, you can use other methods to help you stay afloat and even make a profit during this period.

An effective measure is diversification. In other words, efforts are being made to expand the range. If the business is based on producing knitted sweaters and selling them, it will be more successful during the cold season. In the summer you can make openwork napkins, tablecloths, and soft toys. The demand for these things does not depend on the time of year.

Another method that allows you to keep your business at the desired level is discounts on goods that are not relevant this season. A classic example is the sale of clothing and shoes. Fur coats not purchased in winter can be sold cheaper in spring and summer. At the same time, profit will decrease by some percentage, but this way you can get rid of old products and replenish your assortment with new models.

All kinds of promotions are carried out with out-of-season goods. It is often sold “in addition” to what is currently in demand. You can attract buyers by providing free services in case of purchasing out-of-season products. For example, helping with deliveries or installing equipment.

Many companies have a loyalty program that offers benefits to regular customers. Common options are a discount card and offering discounts based on the total purchase price.

Seasonality can play a positive role in doing business or, on the contrary, reduce sales. However, in many cases there are opportunities to bypass it and turn the matter to your advantage.