Executive Summary
This research report is based on the information gathered from consumers in London High Street stores such as H&M, Primark, Gap, Topshop and Zara.
The London retail shops that form part of the report include West End, Kensington High Street, Covent Garden, Knightsbridge, Bromley, Croyron, Lakeside, Brent Cross, King’s Road and Kingston. Most of the information was acquired from loyalty or credit card schemes and scan data. The data analysis gives an accurate picture of the spending patterns by consumers and the particular characteristics of consumers who shop in these high streets. It finds that;
- Most of the shoppers in London High Street stores are below the age of 30 years.
- Owning a car has a huge aspect on the consumer’s spending habits.
- The income level of the consumer limits where he/she shops and how much.
- Most of London’s High Street retail stores shape their marketing approach based on consumer spending patterns.
Introduction
Consumer behaviour forms a very important subject in marketing. Store and retail shop owners need to know which particular type of consumers shop for a particular type of product and if they do, where they do it and why. In this study, the retail stores in London provided a very good example of the need for information due to the high level of competition between the retail stores in the City.
Most of the retail stores went out of their way to attract a particular segment of the population by being unique while others just provided simple products at affordable rates hoping that this was enough to attract customers. However, from the volumes of sales and consumer spending patterns, the unique stores seem to be attracting more customers especially in the High Street areas.
Consumers usually spend according to their tastes and preferences. However, there are various factors that seem to influence the consumer more when it comes to choosing a particular retail store. These factors vary but the most significant are; age, owning a car, income level, location of the store, special needs, having a family and the locality where the customer lives. These factors were all considered in this report and they informed most of the consumer spending trends and retail sales.
Data Collection
As earlier stated, most of the data was acquired from loyalty or credit card schemes while the rest was obtained from scan data. In most retail stores in London, there are various reward programmes that are initiated with the intention of attracting and retaining customers. These programmes usually involve the issuance of credit cards or loyalty cards which usually offer discounts or free services to their loyal customers.
The most common kind of these schemes usually involves the awarding of points to customers every time they shop at that particular shop. The more one shops or spends, the more the points increase. These points can be ‘redeemed’ for free items or for discounted prices. Another popular method is the issuance of a credit card to the most loyal customers. These cards allow the customers to obtain the goods on credit upon an agreed repayment scheme.
In all these reward schemes, the customer is usually required to fill in some information which gives details about his or her preferences, location, income level and age in a standard form. These forms are later used by the retail store to establish the shopping habits of the customer and the factors that are most likely to cause the customer to shop in a particular way.
The other kind of secondary data used here is scanner data. In most retail stores and supermarkets, shoppers are usually issued with a card which they are encouraged to use while shopping. Interestingly, most consumers are usually willing to divulge much of their personal information at the time of issuance of the card.
Using the card, the retail store can now monitor the customer’s shopping record and compare it with the demographic information previously acquired such as education level, occupation, income, children’s ages, residence e.t.c. Nowadays, technology e.g. that run by A.C Nielsen allows the retail store owner to recognize the family’s TV watching habits including where individuals sit as they do so.
Using the scanner data, the store owner or supermarket can determine which brands were bought and when, how many times the customer had seen an advertisement related to the brand purchased, whether the brand was properly displayed in a nearby store, what method of purchase was used e.g. a coupon and finally, the effect that family size and income have on spending patterns.
Finally, using what is known as “split cable” technology, an advertiser will determine how to allocate advertisements among the members of a community. The bias is usually on a household level since using a neighbourhood approach would bring about greater sampling errors. The trick is usually to present the advertisement on as many channels as possible since it has been found that the advertisements that stick are those the viewer sees as he “zaps” through the channels.
While scanner data seems to be quite useful, it is limited only to fast moving commodities such as toilet paper, beverages, food items, detergents, cooking oil etcetera. It is not useful for products that are not frequently purchased such as MP3 players, printers and other electronic products.
This is because the data obtained would not sufficiently correlate with the important factors of shelf space, display, pricing, effect of competitors and discounts, due to the fact that the purchase may be informed by other consumer needs e.g. a need to add more capabilities to the existing product, need to replace broken down product, dissatisfaction with existing product e.t.c.
Summary of information gathered
The information used was acquired from five leading retail stores in London; H&M, Primark, Gap, Topshop and Zara. These stores kept accurate data in the form of standard forms for credit cards and loyalty schemes. The data was then divided into clusters depending on what information the consumer researcher needed. The bias of the research was on three clusters; age, owning a car and income level.
Age
According to Bowman and Ambrosini, “customer perceptions of a value of a good are based on their beliefs about the good, their needs, unique experiences, wants, wishes and expectations”. This aspect is very true when age is considered as a factor. Without exception, persons of different ages clearly have different tastes and preferences when it comes to shopping. In the four retail stores, the age of the shoppers seemed to be the most important factor judging from the data collected.
Owning a car
While owning a car may not seem like an important factor in general, in London, it seems to have a very huge impact particularly on where and how people shop. Most retails store with ample parking space and ‘car-friendly’ environment seemed to attract consumers who owned cars more.
On the other hand, the consumers who used public transportation seemed to prefer shopping in stores closer to the bus stops or train stations. When it comes to making purchases, those who owned cars were more likely to ‘window shop’ than those who used public transportation. Additionally, the latter group seemed to prefer cheaper and lighter goods unlike their car-owning counterparts who were less restricted in their shopping habits.
Income level
This is another important factor that hugely affected the shopping habits of customers. In the High Street stores, customers who were eager to spend more came from what could be termed as an ‘affluent’ background. This is because the prices in these stores are quite high and they are less attractive to low income earners.
Data Analysis
This part of the research analyzes the data collected from the five High Street retail stores over a period of time in a bid to determine the consumer habits of shoppers in these stores. The following is an analysis of individual retail stores.
Topshop
Fig. 1.1 shows that most of Topshop’s clientele comprises of young people between 18 and 30 years. The retail store has several stores in the High Streets all which sell different types of apparel and fashion accessories mostly for women. The main flagship store in Oxford Street attracts close to 200,000 shoppers in a week making it the world’s largest fashion store.
Shoppers below 30 years take the lion’s share of the demographics with 67%. Due to its convenient locations, most car owners are comfortable shopping at Topshop and they form 65% of all shoppers (Fig 1.2). Lastly, looking at income level, we find that persons with an income level of over 40,000 pounds per annum formed the larger part of shoppers with a 51% showing (Fig 1.3). Topshop sells its products online which has helped to raise its portfolio.
Zara
Unlike Topshop, much of Zara’s clientele is composed of older persons with the largest age segment being between 30 to 45 years with 48% (Fig 2.1. It has also invested more in male fashion and clothing; however, just like Topshop, it attracts more affluent customers. Its stores are also more common and conveniently located and are thus more attractive to car owners as well as users of public transportation. It is also more attractive to the ‘affluent’ population (Fig 2.3).
H & M
The company has 192 stores in the UK alone and has been at the forefront in designing children and adult clothing. Most of its customers are middle aged with the age segment of 25 to 35 years taking the largest target group (Fig 3.1). The retail stores in London are quite accessible and thus owning a car is not a huge factor in determining shopping habits. Finally, most of the clientele earns more than 10,000 pounds annually (Fig 3.3).
Primark
As a retailer in the apparel and fashion market, Primark prides itself in serving what is termed as the ‘budget end’ of the market. The retail store has various stores in London but most of them are not ‘car friendly’. The largest age group that shops there is between 30 to 45 years since the retail store is convenient for family needs (Fig 4.1). Owning a car is again not a significant factor but income level is (Fig 4.2). The majority of those who shop at Primark earn below 10,000 pounds annually (Fig 4.3).
Gap
Gap prides itself in selling clothing for the whole family. It does not focus on a particular segment of the market. The retail store attracts mostly young customers who form around 43% of the total shoppers (Fig 5.1). It offers a wide selection of products for clients to choose from and thus attracts a wider segment of shoppers.
When it comes to convenience, most of the stores are located along the main streets and thus it is friendly to both car owners and users of public transportation (Fig 5.2). Fig 5.3 shows that the income level for Gap’s clientele is medium range with 51% of the customers earning over 10,000 pounds.
Conclusion
From the above data, we can clearly get an accurate picture of the characteristics of shoppers in London’s High Streets. The information available shows that these shoppers are mainly young, own cars and have a high-income level of over 40,000 pounds. The report is consistent with other reports that have reported on London’s high spending ways.
Appendices
Fig 1.1
Analysis of the age of shoppers at Topshop.
Fig 1.2
Analysis of shoppers at Topshop based on car ownership.
Fig 1.3
Analysis of shoppers at Topshop based on their income level
Fig 2.1
Analysis of the age of shoppers at Zara.
Fig 2.2
Analysis of shoppers at Zara based on car ownership.
Fig 2.3
Analysis of shoppers at Zara based on their income level.
Fig 3.1
Analysis of the age of shoppers at H&M.
Fig 3.2
Analysis of shoppers at H&M based on car ownership.
Fig 3.3
Analysis of shoppers at H&M based on their income level.
Fig 4.1
Fig 4.2
Analysis of shoppers at Primark based on car ownership.
Fig 4.3
Analysis of shoppers at Primark based on their income level.
Fig 5.1
Analysis of the age of shoppers at Gap.
Analysis of shoppers at Gap based on car ownership.
Analysis of shoppers at Gap based on their income level.
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