A. INTRODUCTION Due to the competitive environment of retailing, it is not important to just sell the products to the customers but over and above a retailer has to be able to provide something extra which directly and / or indirectly enhances the shopping experience of the customer. Today's customer has the technology, the choice, and the inclination to construct personal assortments. Customers entering a retail store have a set of expectations and the store has to consistently exceed them, not just meet them. This can be achieved by creating an integrated shopping experience by understanding their shopping behavior. The "push" model of consumer marketing and interaction is being replaced with a "pull" model, in which consumers customise the purchasing space to meet their own needs. B. TRADITIONAL RETAILING Traditional retailing concepts considered retailing merely as a business, with choosing a product assortment that would possibly attract the largest group of customers and making the most profit possible out of it. Traditional retailing decisions were mostly judgmental, based on the understanding of the market trend only and not any kind of data analysis.
Even today, this strategy is prevalent in rural India. The consumer was not feature conscious and they did not have a wide variety of choices. The buying decision was primarily based on price and availability of the product. People used to buy from local super markets or haats. Products were pushed to the consumer rather than being pulled by the consumers. C. MODERN RETAILING The current century is characterized by the free flow of information and products. Consumers have become brand conscious.
Increase in purchasing power and decrease in import tariff have led to the development of the market of costly imported goods. Not all of the small kirana shops can afford those goods. Departmental stores and organized retail chains are increasing. Over 50 of the fortune 500 companies and about 25 of Asian top 200 companies have entered the retailing market.
The retailer's strategy is no longer based on qualititative judgment. It should rather be backed up by analytical viewpoint. D. RETAILING GROWTH FACTORS There are several factors responsible for the growth of retailing in the Indian market. The parameters can be broadly classified into two main categories, namely, socio-economic and consumer psyche. Socio-Economic Factors Following socio-economic factors are crucial for the growth of organized retailing. 1.
Buying Power: The buying power can be emulated using the GNP per capita. According to Statistical Outline of India 2001-02, the GNP Per Capita of India has increased to double in twenty years but the population has increased by 50% during the same period. Moreover, there is a wide gap in income distribution in the rural and the urban areas. While in the urban areas, dual income households are quite common, the rural areas are still characterized by one income member in a family of 5-6 members. Consequently, the disposable income and hence the purchasing power is more in the urban areas than in rural areas. The NCAER study released in 2001 shows the following income distribution.
Table 1: NCAER Household Income Classification Classification of Household % age of population in 2001% age of population in 1991 Very Rich (More than INR 3, 60,000) 1.5 0.8 Consuming class (INR 80000-3, 60,000) 25.9 20.1 Climbers (INR 40000-80000) 41.9 33.7 Aspirants (INR 28000-40000) 18.8 27.4 Destitutes (Less Than INR 28000) 13.7 20.5 Source: NCAER Study Released in 2001 2. FDI Regulations: Foreign Ownership of a local retailer is limited to 49% but the foreign players are permitted to enter India through the route of licensing, franchising and technology alliances. It is imperative that growth of organized retailing is related with the entrance of the foreign retail giants in India. Marks and Spencer has entered India through the licensee route. Food world Supermarkets has a 51: 49 stake distribution between RPG Group and the Dairy Farm International, Hong Kong. 3.
Space Constraint: There is a shortage of space in Indian cities and the cost of space is also high. Compounded, there are some other considerations as detailed below. a. Only Indians can own space in India. b. Stamp Duties are significantly high (8% in Delhi), increasing the cost. c. City urban planning, in most of the cases, projected smaller commercial plots and hence it is difficult to get bigger plots suitable for setting up big retail stores.
The above facts explain why the Indian organized retail stores are backed up by the real estate groups, as for example, K Raheja Group in Shopper " sample Stop, Pira mals for Crossroads etc. ). Again, this also explains why organized retailing is comparatively more widespread in south, where the land cost is comparatively lower. 4. Taxation System: Although VAT is going to be introduced in India, still complex taxation system still in operation in the various parts of the country make the management of supply chain in a large scale a difficult task. 5.
High Tariffs: In the post WTO Arena, the import tariffs have been reduced to a great extent. But still they are high enough to reduce the number of products available for Indian consumers to choose from. 6. Growth of Planned Township: In the planned towns growing across the country, the market is not available very near to the house. People have actually travel a lot to reach the unorganized market and do the shopping. If the distance between the unorganized market and the departmental stores be same, people may actually go for departmental stores.
Consumer Psyche 1. Buying Pattern: Buying pattern of the consumers guide the type of stores to be successful in that region. As for example, in US, consumers are habituated in bulk buying and there large departmental stores are a grand success. Whereas in Japan, the people are not habituated in bulk buying due to time and space constraint. Hence departmental stores are not very successful in Japan. 2.
Ownership of Cars: Ownership of cars is an essential growth factor for the evolution of the organized retail market. People may turn up in the nearby departmental stores with their cars, buy bulk with significant price advantage and then go back. The following population / vehicle chart gives an idea about average occupancy. Table City 1980 1994 Delhi 11.1 4.27 Calcutta 59.4 22.5 3. Brand Awareness: Indian consumers have become brand aware. This is a favourable condition for the growth of organized retailing.
4. Price Advantage: Consumers in a country like India are very price conscious. Departmental stores are also focused on low cost high volume mantra. So India offers an opportunity for the growth of the organized departmental stores. 5.
Oriental Mindset: Consumers in India feel themselves obliged to buy something whenever they enter a store. This leads to extensive window shopping, Indian consumers prefer to do window shopping rather than actually entering the shop and examining the product range. 6. Threshold Value and Perceived Value: The above phenomenon represents the concept of the threshold value and the perceived value. Threshold value can be taken as a virtual concept of the store the in the consumer mindset. The perceived value of a store may be defined as the image generated in the mindset of the consumer after examining the exterior of the shop.
The consumer will select the store only if the perceived value of the store exceeds that of the threshold value of the store. It may be noted that both the threshold value and the perceived value of a store are functions of the cultural background of the consumer. As for example, for a consumer with a high disposable income the perceived value of pantaloon will exceed his threshold value of a garment shop but the perceived value of local garment shop selling garments at economic prices will not. Whereas for a village customer it might be just the opposite.
With the above analogy, the store selection process of a consumer can be represented with the help of the following graph. 45 Threshold Value Figure 3. In the above diagram, the line OB represents the locus of all the points where perceived value is equal to the threshold value of the particular store. On the upper part of the line OB, the perceived value is more than the threshold value and hence represents the acceptance region, and the lower portion, on the other hand, with the same analogy, represents the region where the perceived value of the store is less than the threshold value and hence represents the rejection region. E. THE INDIAN RETAILING SCENERIO Evolution of Departmental Stores in Rural Area: From Haats to Super Stores The haats are a part and parcel of the rural village life. From different regions the buyers and sellers come together and transact various products. In case of the rural haats, a large range of products are available at a relatively cheaper price.
The low price strategy is obtained by the high volume of purchase and eliminating of middleman in the channel. The penetration of branded products in village is quite high because of the company's good supply chain management and the revolution of sachet converting the non-user to user. Superstores and organized retail chains also run on low price high volume strategy. Thus these superstores may be termed as a modified form of the haats and supermarkets. Hence we may infer that the conditions for the development of organized retail chain exist in India, even in the rural sector. According to the data from KSA, the Indian retail spending in 2000-01 was about Rs.
13, 50,000 crores and the expected growth is about Rs. 18, 00,000 crores by 2005-06. The share of different sectors in Indian Retailing may be detailed as follows. Figure 2: Retailing By Sectors Source: KSA Techno Pak Study on Indian Retailing Thus, we see, the food and beverages section has the lion's share in Indian retailing market. Still the organized retailing effort in India is concentrated most in sectors like apparels followed by durables and furniture segment. The data from euro monitor suggests that while in 1998 about 85% of the total retail; stores in US is organized, even in 2000, approximately 2% of India's retail chain is organized.
Source: Euromonitor Study on Retailing There are about 20 million retail outlets in India employing about 12 million people. India has the highest number of retail outlets per capita in the world but the retail space per capita is about 2 sq. ft. one of the lowest in the world. F. PERCEPTUAL MAPPING OF INDIAN RETAILING SCENERIO Based on the factors detailed above we have depicted the position of India with respect to Organized Retailing. We have taken the base year as 1990 and the current year as 2002. Environmental Factors. Buying Power To classify the buying power, we adapt the income classification model of NCAER. Keeping in view that higher the purchasing power, better is the opportunity, we assign following weights to different categories.
Very Rich Class - 4 Consuming Class - 3 Climbers - 2 Aspirants - 1 Destitutes - 0 We then multiply the respective ratings with the respective proportion of population and get the weighted score for buying power. According to our formula, buying power in 1991 = (0.8 4 + 20.1 3 + 33.7 2 + 27.4 1) /100 = 2.346 buying power in 2001 is = (1.5 4 + 25.9 3 + 41.9 2 + 18.8 1) /100 = 2.94 FDI Regulation Higher the foreign stake is allowed, more favourable is the environment for the setup of organized retail chains, as setup of retail chains typically require high investment. We assign the following weights. So, the scores for 1991 and 2001 would be (-5) and (-1). Taxation System Simpler is the taxation structure better would be the supply chain management for organized retailing. We assign the following weights Unified taxation structure - +1 Differential taxation structure across states - (-1) In 1991, there were individual tax structures across the states.
But now, with the implementation of VAT, the tax structure has become unified and simpler. Hence the scores would be (-1) and 1 for 1991 and 2001 respectively. Township development This is the proportion of planned township in India. In our model, the total score function is the sum of the individual scores.
In 1991, it was 2.346 -5 -1 = -3.654 The total score in 2001 is 2.94 -1 +1 = 2.94 Consumer Psyche Factors Brand Awareness We assign +1 if consumers are brand aware and (-1) if not. So, the scores in 1991 and 2001 would be (-1) and +1 respectively. Ownership of Cars More is the ownership of vehicles per persons, more favourable is the environment for organized retailing. As an example we have taken the weighted average of ownership of cars per persons in Delhi.
If no. of persons / car be less than 6 (the average no. of members in a family), we may consider the situation as each family owning a car. In this case, the weight is considered as +1, otherwise (-1). The total score would be -1-1 or (-2) in 1991 and 1+1 = 2 in 2001. The perceptual map will look like the following. The first quadrant represents the most favorable condition for retailing development. We see from the perceptual map that India has entered this region. G. Neural Network Architecture Neural network is basically an emulation of the biological neural network.
The artificial neurons may be thought of a flexible signal processing unit with some inputs and some outputs. The input at each node is multiplied with some weights and then summed up to produce an activation signal. Every artificial neuron has a predetermined threshold value. If the activation is more than the threshold value, then the neuron gets fired up and produces the output. This functionality of the artificial neuron is called Threshold Logic Unit or TLU and is originally proposed by McCulloch and Pitts, 1943. The diagram of a TLU may be represented as follows.
As for example, if the inputs are represented by x 1, x 2, ... xn etc., and the weights be w 1, w 2, ... wn etc., the activation signal w may be represented as a = ∑ wi. xi If the threshold signal is represented by, the output will be represented by y = 1 if a and y = 0 if a It may be noted that the input and the output may be approximated either as digital signal or as continuous signal. Advantage of Neural Network Based Models The advantage of the neural network over traditional statistical methods in case of data analysis can be summarized as follows. 1. Neural Network based model is an adaptive model, i. e., neural network based models can be trained with the actual data pattern.
These models do not require any assumption to work upon. 2. Neural network based models are flexible models. The parameters can be dynamically changed with the change in the actual data pattern, thus making them most suitable for the analysis of the complex retailing data. 3. Neural Network based models are robust, i. e., these models are not affected by the presence of the disturbance in the actual data collected.
Training The Neural Network Models Training the Neural Network Models essentially means adjusting the weights of the individual inputs as well as the threshold. If the threshold is also modified, it is approximated as weight and the resultant weight vector is called augmented weight vector. The learning can be primarily of two types, supervised and unsupervised. In case of supervised learning, the network is trained with some preexisting data pattern. In case of unsupervised learning, the existence of a preformatted pattern is not an absolute necessity. There are numerous training algorithms available like hebb's rule, gradient descent etc. and all them may be used with fair accuracy.
In Error Minimization technique, an error function is defined typically in terms of the network parameters and then the error is minimized at each pass. Thus the values of the weights also keep on changing with each pass and ultimately the final output is obtained. G. PROCESS MODEL OF RETAILING We suggest that retailing should be perceived as a process. A process has some inputs and some outputs. In order to obtain the desired output based on the given set of inputs the process parameters need to be tuned up. A process may have some sub-processes.
The process should be robust, i. e, the accuracy of the process should not be affected by the presence of noise in the system. Since retailing involves real world inputs full of uncertainty, the process modeled should be dynamic enough to align itself with the changing input data pattern. Instead of applying statistical techniques, we suggest retailing to be analyzed by a network based model. The traditional statistical models require a lot of assumptions to work upon and Secondly, traditional statistical models are inflexible and rigid. Once the parameters are tuned up, they can not be changed dynamically. Neural network based models are flexible and require no assumption to work on.
They can be trained with the actual data pattern. The parameters can be changed any timed if the input data pattern is changed. The retailing can be modeled as a process consisting of three sub-processes as follows. Figure 1: Process Model of Retailing H. CHOICE OF THE STORES Choice of a store depends on the threshold value on the mindset of the consumer and the perceived value of the store in concern. If the perceived value of the store is greater than the threshold value in the mindset of the consumer, the consumer selects the store, otherwise not. Choice of a store is related to the targeting of consumer segments.
As for example, if a gift shop retailer targets the 20-25 age group, he / she has to ensure that the store ambience should be such that no potential customer in this segment would be missed out. The first sub process of the process model of retailing is modeling of the store choice parameters. This process is simulated with the help of a neural network. The input to the process is a weighted sum of three groups of inputs, namely, 1. In Store Philosophies 2. Type of Involvement 3.
Consumer Psyche In store philosophies involves the store parameters like store ambience, store decoration, stock keeping units, check out period, shelf apace organization etc. Type of involvement determines the type of good the consumer is going to buy - the low involvement products include grocery items, middle involvement products include items like textiles and the high involvement products may include items like consumer electronics. Consumer psyche is typically the attitude and expectation of the consumer on different parameters under consideration. The consumer psyche can be modeled with the help of the three attribute model, as shown below. + Figure: Tri-Component Attitude Model The cognitive component is the knowledge and perception of the consumer towards the retail outlet, created by the experience with the store, either directly or indirectly. This cognitive component often takes the form of a belief or personal judgment.
The affective component, on the other hand consists of the attitude of the consumer towards the store, the attitudes being based on the cognitive components. Depending on the attitude, a consumer may see the store as favourable or unfavorable, as good or bad. The conative component is concerned with the action of the individual - whether the consumer will select the store or not. The consumer has a general reservation about the nature of the store, created by the cognitive component. If the value perceived by the consumer be greater than this reserved value, the consumer will select the store, otherwise not. These components can be modeled with the help of a threshold value and the perceived value.
The cognitive component is essentially the threshold value, or the reservation in the mind of the consumer. The affective component is the perceived value, it signifies how the consumer perceives the store. If the cognitive and the affective component be symbolized by t and p respectively, then the conative component may be symbolized by a = f (p - t), p t = 0, otherwise Where a is termed as the activation signal. The function can take any form depending on the context. If p t, i. e, the perceived value is more than the threshold value, the consumer will select the store and will take action accordingly. The threshold value of the consumer can be quantified with the help of multi-attribute model as detailed below.
Threshold t = bie i, where bi is the behaviour and ei is the expectation on the behaviour. These behaviors are the consumer attitude formation components like in store philosophies. As for example, the behaviour may be the store ambience and ei may be the expectation of the consumer towards the store ambience. The threshold value of the consumer can be changed by changing the expectations on the behaviors. Modeling the Store Choice Process with Neural Network Based Model The store choice behavior can be modeled with the help of the neural network based model as detailed below.
In store philosophy components, the consumer perception components and the type of involvement components will be fed to the net as input, each multiplied by a weight. The weight will represent the amount of importance given on that particular component. The threshold will be represented by a weighted sum of the in store philosophy components and the attitude components, as threshold is the function of both as discussed above. The factor which can not be modified by the retailer will be represented with a zero weight and will be excluded from modification.
Organized retailers can afford to change the threshold by advertisement or other forms of consumer learning and hence for organized retailer, the threshold weights will be treated as variables. For small kirana shops, variation of the threshold is almost impossible and hence the threshold will be represented by zero weights. The neural net will modify the weights depending on the required output. The output of the net will be a set of weights or the final weight matrix. The final weight matrix will give the amount of importance to be placed on each parameter for success. These values will vary from store to store.
The term 'activation' refers to the stimulus that leads to the consumer to choose the store. The activation function will be represented by a sigmoid function of the following shape instead of a step function. This is so because, the step function represents activation only if the perceived value is more than the threshold value. However, there may be cases like the non-availability of a particular product, so that the consumer chooses the store even if the threshold value is marginally higher than the perceived value. Sigmoid: f (x) = (1 + e-ssx) -1 Here x is represented by the difference between the perceived value and the threshold value. The network is represented as follows.
Industry Matrix The industry matrix we suggest is a modified form of that suggested by T.L. Wheeler and J.D. Hunger. The matrix will take the following shape. Key Success Factor Rating Firm A Weight Firm A Score Firm B Weight Firm B Score The key success factors represent the factors like in store philosophies, consumer psyche and consumer threshold, which can be modified and controlled by the retailer. Once the weights are assigned by running the neural network based model the above matrix can be filled up for a number of firms. The next step is to get the perceptual positioning of different firms in the competitive environment. In this case we run a principal component analysis, wherein we would be able to classify the factors into two major groups and get the individual discriminate functions of each group.
Once the individual group functions are known from the component analysis, the individual group scores of the firms will be determined. Let us elaborate the same with the help of an example. Say, there were initially 20 factors namely x 1, x 2, ... , x 20. Now we propose to run a principal component analysis wherein we get two principal groups namely G 1 and G 2.
The discriminant function may be determined by the coefficients of the groups with respect to the factors. + 0.95 x 10 and the discriminant function for the group G 2 may be represented as G 2 = 0.89 x 11 + 0.764 x 12 +... + 0.76 x 20 The variables x 1, x 2, ... , x 20 represents the values of the weights for the factors. It would be possible for us to obtain individual group scores for the firms.
We can then plot the scores in a two dimensional graph, a technique known as multidimensional scaling and hence identify the competitive positioning of different firms. The correlation discriminant function will give us an idea about the nature of the groups G 1 and G 2. Say, for example, the group G 1 is associated with in store philosophy variables and hence termed as in store philosophy and the group G 2 is associated with consumer psyche variables and hence termed as consumer psyche. The ultimate graphical mapping will also tell us the strengths and the weaknesses of different firms, and hence will also give us an idea about the areas where the firms can possibly improve to attract more customers.
5 4 3 2 1 G 1 -1 -2 -3 -4 -5 -6 -7 -8 -9 -9 -8 -7 -6 -5 -4 -3 -2 -10 11 12 13 14 15 16 G 2 Figure: A Sample Output in Multi-dimensional analysis The above output suggests that the firm A is very good in G 2, but not so good in G 1, whereas firm B is very good in G 2 but not so good in G 1. The firm C on the other hand is reasonably good in both G 1 as well as G 2 and is likely to attract more customers. I. CHOICE OF PRODUCT ASSORTMENT The choice of product assortment will also be modeled by a similar neural network based model where the weights assigned on individual inputs will determine the nature and the type of products to choose. This will be followed by an industry matrix and perceptual mapping of competitors as done before. As we see from the process diagram the input parameters in this process are Store Selection o This parameter is determined by the store choice sub-process. It takes a value of 1 if the store is selected and 0 otherwise. Interaction Between demand and supply Consumer Psyche o The same parameters as discussed in the above sub-process.
Consumer perception and threshold plays a crucial role in the buying process and hence retailer has to give due importance while choosing the right product assortment. Interaction between demand and supply The interaction between demand and supply is an important component in the selection of the product assortment of the retailer. The term demand means the aggregate demand of the persons having adequate purchasing power and the term supply means the supply of the product by the manufacturers. Needless to say, it also takes into account the supply of the product by the competitors in that region. In our model we define demand by the household spending in different sectors, whereas supply is characterized by the retail sales in that area. In a particular area, if the household spending is more than the retail sales then it signifies that the demand of that product is more than the supply of that particular product in that area.
This additional demand is met by buying the same products from the neighborhood area. If the demand is less than the retail sales it signifies that there is a surplus demand of that product in that area. The retailer should concentrate on products having surplus demand. If the household expenditure is represented by h and the retail sales is represented by r, then the interaction between demand and supply is represented as a function of (h-r). ids = f (h-r) if h r = 0, otherwise. The variable ids essentially represents the market potential of a particular product in a particular region This variable is fed into the neural network model. The function, however, may be linear as well as non-linear depending on the context of the problem.
The function will assume the linear form where demand increases at a constant rate, given the supply is constant. Example is low involvement items like grocery items. The function will take the non-linear form where the demand increases at a decreasing or increasing rate, given the supply to be constant. Example is the luxury segment where often demand increases at an uneven rate. The final weight matrix, as in the case before, will determine the amount of weights to be given to the individual components. These weights combined with the weights in the first sub-process As for example if the analysis shows that there demand for toothpaste J. PURCHASE DECISION SUBPROCESS The input to this process are purchase environment, product environment selection (input from the previous process) and last but not the least, consumer psyche, The final weight matrix, as before suggests the amount of importance to be placed on different factors.
Purchase Environment The in store parameters are already covered in the store choice sub-process. This parameter represents the interaction between the customers and the sales people in the store. Customer Segmentation for a retail store Customer segmentation is a vital ingredient in a retail organization's marketing recipe. It can offer insights into how different segments respond to shifts in demographics, segments fashions and trends.
For example it can help classify customers in the following 1. Customers who respond to new promotions and new product launch and discount. 2. Customers who are not planned what to buy 3.
Customers who show propensity to purchase specific products 4. The customers who compare the products before buying... Salespersons Segmentation in retail store (i) Information kiosks: These are individuals who behave like kiosks. They are passive providers of information. Information is obtained only after a button is pushed. (ii) Box pushers: These salespeople are out to sell anything, without knowing the shopper's requirements. They push the merchandise available in the store.
( ) The warriors: These salespeople consider shoppers as the enemy that needs to be conquered. It is a 'we and they's itu ation. ( ) The shopper-friendly: This type is not found in large numbers, but are liked by shoppers. They consider and ask for the requirements of the shoppers and make shopping a rewarding endeavour. Knowledge of Sales Person High The warrior and the customers who compare product before purchase Shopper-friendly, customers with specific product need. Low Box purchase and the customers who are not planned what to buy Information kiosk and the customer who responses on discount, new product launch and new promotion.
Not pre determined Pre determined Type of customers in terms of buying behavior K. PUTTING IT ALTOGETHER The ultimate strategy of the retailer will depend on the selection of weights from all sub-processes. It may be clarified with an example, Say, in the first sub-process analysis, a retailer sees storing kid's product and related in store environment would be helpful for selection of that stores. In the second sub-process analysis, suppose he sees that more stress should be given on bicycle segment. The type of salespeople to be employed would be determined in the third process with the help of the matrix suggested. This, ultimately leads to the success of the retailer. L. EXAMPLE: RURAL / URBAN STRATEGY The following matrix would identify the gross buying behavior of the rural and urban consumers and help retailers choose a set of factors to be banked upon. The amount of importance to be placed on these individual factors would be determined by placing these factors in the above neural network based process model.
The following psyche matrix illustrates the buying habit of the rural and urban consumers. High Involvement Price conscious Word of Mouth Faith on Retailer Low Brand Comparison Brand Comparison Quality Consciousness Store Perception Medium Involvement Low price. Faith on the retailer No Brand Comparison Eye catching posters Price Quality Brand Comparison Low Involvement Low Price Low Price Attractive Packaging Visual Merchandising Rural consumers Urban consumers Rural Consumer Analysis Low Involvement Products The consumers are more price conscious. As for example, a rural consumer will go for unbranded salt rather than going for branded salts. The challenge of the retailer in this segment is thus to offer standard products at low price. The margin of the retailer should be obtained from low price high volume strategy.
Medium Involvement Product Rural consumers are not very choosy on the brands. The retailer should help the customer in choosing the brand by providing information on the features of the brands, recent fads and values associated with the product. High Involvement Product In this segment, the consumers are not brand conscious. Word of mouth plays a very crucial role in store selection. The retailer should help the consumers to select the right product.
The retailer should try to offer more value for money by combining different brands or models so that it is attractive for the rural consumers. Urban Segment Low Involvement Product Consumers are price conscious, but visual merchandising plays a crucial role in this segment. Consumers are likely to select the stores having good shelf display and low price offerings. Medium Involvement Segment Consumers are brand conscious and they also go for value for money.
The consumers are likely to select the stores having wide range of products. However, the urban consumer class also consists of the rich class segment and hence the product range should contain both the value for money brands and the up-market brands. High Involvement Product The consumers are choosy about the brands and hence the product basket should contain a wide range of brands. At the same time the product width is also a necessity. The service offered by the retailer and other intangible benefits like special attention to kids and ladies, proper seating arrangement, TV for engaged shopping also play a crucial role in the selection. Based on the above analysis, the retailer strategy may be presented in the form of the following matrix.
High Involvement Help Consumers Choose a Brand. Provide Adequate Information. Try to cash on different offers. Build Trust with the Consumers.
Wide Range of Brands Product Depth in each range. Make the Shop Visually Attractive. Offer Excellent Service. Offer Intangible Benefits.
Medium Involvement Help Consumers to choose the Product. Stock 'Value For Money Range' products with proper depth and width. Placing eye catching posters in proper place Short Check Out Time. Shelf Space Management. In Store Communication Proper Brand Assortment Low Involvement Increase Low Price Product Width. Minimize Checkout Time.
Visual Merchandising Management. Increase Low Price Product Depth and Width. Rural Urban Since the rural consumers are mostly price conscious than feature conscious, the large scale discount stores are likely to be a great success in the rural areas. These stores can afford to offer the low price based on standardized product assortment as well as high volume. Suman please include this point on model if u can and modify I made the model Model 2 Relate customer segmentation and type of salesperson in a superstore 1. web Involvement has been defined along different dimensions.
Laurent and Kapferer (1985) define involvement as a multifaceted construct having five dimensions of perceived importance and risk of the product class, subjective probability of making a mis-purchase, symbolic or sign value attributed by the consumer to a product class, hedonic value of the product class, and interest. Purchase involvement is expected to affect consumer decision processes from pre-search to post-search as well as attitudes and behaviours towards purchasing (Slam a and Tas chian, pp. 73). They define involvement as the self-relevance of purchasing activities to the individual. The degree of relevance or 'involvement' determines the consumer's level of motivation to search for knowledge or information about a product or service (Schiffman & Kan uk, 1997).