scope of statistics in fashion industry

BUSINESS STATISTICS SCOPE OF STATISTICS IN THE FASHION INDUSTRY

SUHANI KAPOOR (FMS) Prof. GULNAZ BANU

SCOPE OF STATISTICS IN THE FASHION INDUSTRY: A BRIEF REPORT Suhani Kapoor National Institute Of Fashion Technology, Bangalore, India.

ABSTRACT Statistical techniques have been used in the fashion industry, production and quality management process and other industries for over more than 25 years. To achieve and analyse the objective decisions, statistical methods are used, based on data collected about a product or market. There are a number of practical and managerial issues related to the application of statistical techniques in studies aimed at improving the brand performance. This report is a summary of the application of statistical techniques on the several aspects of fashion industry. The objective is to learn the scope of statistical tools in analysing the pattern of sale, interrelationship between data variables and defining a successful business strategy in a very volatile industry like fashion industry.

INTRODUCTION Throughout history, fashion has greatly influenced the “fabric” of societies all over the world. What people wear often characterizes who they are and what they do for a living. As Mark Twain once wrote, “Clothes make the man. Naked people have little or no influence on society”. The fashion industry is a global industry, where fashion designers, manufacturers, merchandisers, and retailers from all over the world collaborate to design, manufacture, and sell clothing, shoes, and accessories. The industry is characterized by short product life cycles, erratic consumer demand, an abundance of product variety, and complex supply chains. What’s hot today is blasé tomorrow. Innovation becomes retro. Seasons change. Hemlines rise and fall and so do your sales figures. A celebrity makes a fashion statement on the red carpet and suddenly your financial statements are covered in red. Therefore launching a new fashion brand or streamlining a production process or evaluating current vs. prospective customers, today’s business managers face greater complexities than ever


before. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them.

STATISTICS IN FASHION INDUSTRY AND MANAGEMENT MARKET RESEARCH We are creatures of habit, especially when it comes to buying behaviour. We know the location of our favourite fashion brands in our vicinity, and we trust that they'll serve us year after year. People don't like change. It's a battle to get someone to switch to an unknown brand. Market research analysts devise methods and procedures for obtaining the data they need by designing surveys to assess consumer preferences. Market research is also helpful to develop advertising brochures and commercials, sales plans, and product promotions such as rebates and giveaways based on their knowledge of the consumer being targeted. The information also may be used to determine whether the company should add new lines of merchandise, open new branches, or otherwise diversify the company's operations (George Heinrich, 2006). For example Full price sales of luxury goods are dramatically down yearon-year, according to data collected by real-time analytics firm Edited in the wake of the UK's shocking Brexit vote. Noting this there is dramatic downfall in the market. Market research analysis is a procedure to design survey and to assess consumer performance (Robert P. Hamlin, 2000). Different methods and procedures are used to organize survey and to assess consumer performance. Data is collected through different channels such as Primary data and Secondary Data. It may be through primary data collection technique, which is conducted through the Internet and telephone, focus group discussions, mail responses, or setting up booths in public places, such as shopping malls. Trained interviewers usually conduct the surveys under a market


research analyst's direction. Use of secondary data is another statistical technique which is used in this research design. Following are widely statistical techniques for collection of data relating to market research and evaluating customer satisfaction in fashion industry.  Online Questionnaire and Survey SurveyMonkey is a powerful tool for creating in-depth surveys that will help you understand the market and consumer preferences.

 Scaling Technique A common feature of marketing research is the attempt to have respondents communicate their feelings, attitudes, opinions, and evaluations in some measurable form. To this end, marketing researchers have developed a range of scales. Each of these has unique properties. This technique allows companies to learn more about past, current and potential customers, including their specific likes and dislikes (Miriam Catterall, 1998). The following are the types of scales used generally. Dichotomous scales have two choices that are diametrically opposed to each other. Some examples would be: “Yes” or “No” “True” or “False” “Fair” or “Unfair” “Agree” or “Disagree” Rating scales are probably what we’re most familiar with. “On a scale of 1-10, how satisfied were you with our service today? The three most common rating scales are: 1-10 scale 1-7 scale


Likert scale (1-5) The image (1.1) below is an example of 1-7 rating scale conducted on beachwear products.

Image 1.1 Semantic differential scales are used to gather data and “interpret based on the connotative meaning of the respondent’s answer.” These two usually have dichotomous words at either end of the spectrum. They generally measure more specific attitudinal responses, such as the following image (1.2)

Image 1.2


 Census Data This tool helps fashion start-ups to understand an area's demographic information and lifestyle habits. One can find out which areas would be most receptive to a campaign or launch, which competitors are located nearby, and trends in the area that have shifted. Following image (1.3) is date collected from the U.S census.

Source: US Census Following are widely statistical techniques for analysis of data relating to market research and evaluating customer satisfaction in fashion industry.  Data Analysis Technique (Histogram in Excel)

1:

Frequency

Distribution


Frequency distribution is a simple data analysis technique which allows you to get a big picture of the data. From frequency distribution, fashion analysts can see how frequently the specific values are observed and what their percentages are for the same variable. For example: for variable of “age,” they can use frequency distribution to figure out how many people in the survey are aged 18 to 25, and how many are aged 26 to 33, etc.  Data Analysis Technique 2: Descriptive Statistics From the frequency distribution fashion analysts can figure out the frequency of the values observed, as shown in the “age example” above. They use the measures of central tendency and dispersion to learn more about the data for “age.” Mean, median and mode are the three measures of central tendency. The “Arithmetic Mean”, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. The mean is useful in determining the overall trend of a data set or providing a rapid snapshot of the data. “Median” is the value in the middle when all the values are lined in order (assuming there is an odd number of values). If there are even numbers of values, the median is the average of the two numbers in the middle. It is useful when the data set has an outlier and values distribute very unevenly. “Mode” is the value which is observed most often. It is useful when the data is non-numeric or when asked to find the most popular item. Range and standard deviation are the basics measures of dispersion. The bigger the range and bigger the standard deviation, the more dispersed the values are. “Range” is the difference of the maximum value and the minimum value for the variable. For the “age example”, the maximum value is 54 and the minimum value is 19. So the range is 35.


“Standard deviation” shows how much variation the value exits from the mean. Variance is the average of squared difference from the mean. Standard deviation is the square root of variance.

 Data Analysis Technique 3: Correlations Correlations are used when a brand wants to know about the relationship between two variables. For example, you want to know consumers’ willingness to pay and their ratings for the product quality. If the correlation is 1, meaning the willingness to pay and the ratings for the product quality are completely positively correlated and if the correlation is 0, meaning there is no correlation between these two variables. If the correlation is -1, it shows they are completely negatively correlated, meaning the higher one variable, the lower the other variable.

FASHION PRODUCTION & QUALITY CONTROL Manufacturers rely on statistics and data reports to assess production and quality and is therefore an efficient means to evaluate a manufactured product. Statistical quality control helps maintain the consistency of how a product is made. Statistical quality control methods can include cause-and-effect analysis, check/tally sheets, histograms, Pareto and scatter analyses, data stratification, defect maps, events logs, progress centres and randomization. Graphical charts and graphs, the part of statistical process control that monitors the manufacturing process, help decipher the statistics and data from quality control reports. One important method of statistical quality control is acceptance sampling. In acceptance sampling, a sample of a product is randomly taken to determine whether or not to continue making the product. If the percentage of "good" or acceptable product is higher than "bad" or defective product in the sample, the product is approved and manufacturing continues. If not, the product is


rejected and production stops. Any evaluation process that involves samples and human judgement usually assumes a margin of error. With statistical quality control, human error is reduced. FASHION RETAILING Fashion retailing has always been a tough, highly competitive business, and many chains rise dramatically and then fail as price pressure from major discounters like Wal-Mart, Target and Kohl's keep profit margins thin at stores that sell moderately priced apparel. The global apparel market is always changing, attempting to adapt to customer trends and new technology that will allow the consumers shopping experience to be more enjoyable and ergonomic. Sales forecasting is crucial for many retail operations. It is especially critical for the fashion retailing service industry in which product demand is very volatile and product’s life cycle is short. In fact, a lot of statistical methods have been used for sales forecasting, which include linear regression, moving average, weighted average. Most of the existing forecasting models are suitable for middle-term and long-term forecasting. However, shortterm forecasting, including the very short term forecasting such as real-time forecasting, is not yet fully explored. This kind of shortterm forecasting is very important given the nature of the fashion industry (the fashion trend is unpredictable, and the lead time is very short). Since the fashion industry has changed and fast fashion companies like ZARA, H&M, and Mango are adopting quick response strategy with a very short lead time (e.g., 2 weeks in Zara for some products). ORIGIN OF THE CLOTHING SIZES At one time or another, most of us have found ourselves in a changing room, tugging at ill-fitting jeans or frowning at a snug top. We blame ourselves, our shape, and our genetics. But these accusations may be mislaid. After all, very few of us are a perfect fit


for ready-to-wear sizing, and even if we find one brand that works for us, that rarely translates to other brands or stores. At least sewing patterns allow us to customize sizing to create a perfect fit. But where did our sizing system even come from, and whose proportions are they based on? Before mass production clothing there was made-to-measure, where garments were tailored to specifically fit each customer. Women’s clothing, in particular, always required a precise fit and plenty of detail. After World War I, things began to shift. Money was tight and women wanted access to affordable, on trend fashion, regardless of their class. Manufacturers were keen to adapt to this changing market, but sizing was a major problem. In 1939, the US Department of Agriculture (USDA) launched a yearlong study titled Women’s Measurements for Garment and Pattern Construction. Working with the Bureau of Home Economics under a federal project grant, they studied the weight and 58 body measurements of 14,698 women across seven states in the US. They even went so far as to measure elbow girth and ankle height; they were thorough, to say the least. Once the final data was collated, statisticians analysed the results and decided that just five measurements were enough to determine the size and shape of a woman: weight, height, bust girth, waist girth, and hip girth.


CONCLUSION Statistical techniques are widely used in the fashion industry. It’s a well known fact that using the obtained statistics about certain parameters, future business strategies can be decided. By gathering specific information about product and sale via marketing research and statistical analysis, knowledge of the best marketing, manufacturing and retailing practices can be applied to provide ideas that are both based on the feedback of customers and proven to work by the experience.


REFERENCES  http://www.ipublishing.co.in/jarvol1no12010/voltwo/EIJAER2218 .pdf  BLS Spotlight on fashion statistics, June 2012.  NPD, Apparel Market Research & Business Solutions.  John T. Williams, “Small business powered by StudioD”.  Jami Oetting,2016, 17 Tools & Resources for Conducting Market Research.  www.businessoffashion.com  FAO Corporate Document Repository, Chapter 3 Level of measurement and scaling.  Alex Birkett, “Survey Design 101 Choosing Survey Response Scales”. ‘  Jiafeng Li, April 2013, “Market Research”  www.reference.com

SCOPE OF STATISTICS IN THE FASHION INDUSTRY: A BRIEF REPORT Suhani Kapoor National Institute Of Fashion Technology, Bangalore, India.  Plunkett Research Ltd, “Apparel, Textiles, Clothing & Fashions Industry Market Research”  Hindawi Publishing Corporation, “Sales Forecasting for Fashion Retailing Service Industry: A Review”.  Seamwork Magazine, “The origins of clothing sizes”.

scope of statistics in fashion industry

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