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									Notes                                     present short pieces which are research-based, experience-based, or idea-based.




Effective Retail Promotion Management: Use of
Point of Sales Information Resources
Arindam Banerjee and Bibek Banerjee
                                                                 India has witnessed a surge in organized retail-
                                                                 ing in the recent past. While the retailing
                                                                 industry is still in its infancy stage and growth
                                                                 opportunities are significant, the lessons from
                                                                 more developed markets suggest that increased
                                                                 competition in this area will demand better
                                                                 operational efficiencies to remain viable in the
                                                                 long run. To be able to support good decision-
                                                                 making for the future, retailing organizations
                                                                 need to equip themselves, among other things,
                                                                 with the infrastructure to develop and manage
   The increasing availability of customers'                     customer databases which can be effectively
   transaction level data at the point of sale                   mined in the future to help drive strategy-
   (POS) in electronic form in various stores                    building. Investing in Point of Sales (POS) data
   in India is opening up important arenas in                    has been a reasonable success story in the,
   marketing analytics that can enhance busi-                    western markets and this paper attempts to
   ness dicision-making process. This paper                      highlight some of the plausible applications of
   discusses the strategic role that manage-                     such data. It also emphasizes the areas in which
   ment of customer information resources can                    retailing majors could possibly invest to reap
   play for the growth and sustenance of                         the benefits of market information in the future.
   business. This paper also provides examples
   of simple analysis using POS data that can                         The use of POS data in developing analyti-
   directly aid practising managers in their                     cal models that provide insights into managerial
   strategic and tactical decision-making.                       decision-making in the context of retailing has
                                                                 been well-documented in marketing science
Arindam Banerjee and Bibek Banerjee are Associate                literature. A recent article on this issue (Bucklin
Professors in the Marketing Area of the Indian Institute         and Gupta, 1999) articulates the progress made
of Management, Ahmedabad.                                        in the consumer packaged goods industry in the
                                                                 US in the past 20 years in harnessing POS data
                                                                 both in academic research as well as in devel-
                                                                 oping effective industry practices in the area of
                                                                 retail promotion management. Some of the
                                                                 decision areas under retail promotions that have
                                                                 been extensively researched are:
                                                                 • Product pricing at retail outlets which in
                                                                   cludes decisions on timing and depth of
                                                                   discounts.
                                                                 • In-store display planning which includes issues

Vol. 25, No. 4, October-December 2000                       51                                                              Vikalpa
   on what brands/product categories to display                    POS data availability has been on the increase,
   and location of such displays.                                  especially with most retailing giants having
                                                                   electronic check out in their retail outlets which
• In-store communication and advertising                           facilitate easy recording and storage of data.
  management — issues on design of ceiling
  signs, mobiles, inflatable, floor graphics,
  danglers, instant redeemable coupons, etc.                       Why is POS Data an Important
                                                                   Source of Managerial Information?
• Local area advertising decisions to promote
  products that build store traffic.
                                                                   POS provides a rich source of behavioural data
• Decisions regarding shelf space allocation                       on the customers' purchase characteristics. It is
  and arrangement of brands to command                             the most disaggregate form of behavioural data
  optimal visibility.                                              available from the retail outlet. In comparison,
• Decisions regarding store coupon circulation                     the monthly retail audit data collected by ORG-
  for various product categories.                                  MARG aggregates brand sales over a period of
                                                                   a month. Therefore, the details of the purchases
                                                                   made on every transaction are not available at
What is POS Data?                                                  this higher order of aggregation. POS data has
                                                                   been used to develop various analytical frame-
As the name suggests, POS data is information                      works that have provided important insights into
collected at the retail store that provides vol-                   consumer retail purchase behaviour. In mar-
umetric information on every transaction made,                     keting science literature, a seminal paper on
i.e., the quantity sold, both in numbers and                       modelling brand choice (Guadagni and Little,
value, the price at which the transaction was                      1983) with marketing mix variables using con-
made, and any added information regarding                          sumer panel data marked the initiation of a spate
promotional programmes which the store ran                         of research on consumer behaviour using panel
at the time the transaction took place. POS                        as well as store level retail transaction data.
provides marketers with direct behavioural
information on what consumers buy.                                      In the developed countries, especially in the
     POS data has been recorded in a systematic                    US, research companies such as AC Nielsen and
                                                                   IRI have established businesses collecting both
manner in India for over a decade now, albeit
                                                                   store level (POS) and consumer panel level
at a modest scale. ORG-MARG has an' exten-
                                                                   information. The information collected is used
sive operation in India that conducts a monthly
                                                                   primarily for tracking studies of market share,
retail audit on a sample of stores for a limited
                                                                   but over the past 12 years, consumer data has
number of brands/Stock Keeping Units (SKUs).
                                                                   been systematically analysed to generate value
The information recorded has been primarily
                                                                   added insights on consumer reaction to various
volumetric data, which has been used by Fast
                                                                   marketing mix initiatives. The SCAN*PRO
Moving Consumer Goods (FMCG) companies
                                                                   model (Wittink et al., 1988) and its variants have
to track market share over time. While third
                                                                   been extensively used by practising managers,
party data collection practices at the retail store
                                                                   especially in the packaged goods industry in the
have been around for a while in India, the
                                                                   US to develop an understanding of the causal
fragmented nature of retail industry has inhib-
                                                                   effects of price and retail promotion. Some of
ited the collection of individual transaction level
                                                                   the areas which have been extensively re-
data at the retail store. With the coming of
                                                                   searched, both in academic institutions as well
organized retailing in India in the past three
                                                                   as in industry, are discussed below.
to four years and its projected spiralling growth
(Rs 160,000 crore by 20051 ), transactional level                  Analysis of Key Drivers of Sales
1.Economic Times, March 18, 2000. ET Interactive, Interview
with Simon Bell, Principal, A T Kearney, New Delhi.                Price and in-store promotion variables have

Vol. 25, No. 4, October-December 2000                         52                                               Vikalpa
traditionally captured the attention of both retail        • Optimal trade allowance package to be offered
store managers and brand managers. What                      to retailers by manufacturers with the objec-
brands to promote and when, for how long,                    tive of maximizing manufacturer's category
which will drive profitability of the store or the           contribution.
brand, has been of prime importance to man-                     While the first two areas are primarily retail
agers in this industry in the US for long. To
                                                           management issues and are of prime importance
evaluate the relative importance of various                to store managers, brand and category manage-
promotional vehicles, like temporary price                 ment specialists in manufacturing organizations
reductions, coupon offers, in-store special dis-           are necessarily concerned about optimizing their
plays, freebies, and local area feature advertising
                                                           own brand portfolio contributions. Sometimes,
of price-offs, the SCAN*PRO model (Wittink                 these objectives may work against each other,
et al., 1988) has been extensively used. Man-              for example, if promotion of brand A in the
agers across various companies ranging from
                                                           store brings in high volume share for the
Kraft, Coca Cola, Pepsi and Procter & Gamble,
                                                           particular brand, which is good for the manu-
to name a few, have used this modelling                    facturer of brand A; however, promoting brand
technique to examine the effect of various trade           A cannibalizes sale of other brands in the
promotion initiatives. The end objective has
                                                           category in the retail store to such an extent
been to develop normative models that help                 that it causes a fall in the total contribution for
make decisions regarding optimal promotional               the product category in the retail store.
spending — how much to spend, on what brand
to spend, and what specific promotions to run.                  With retailing dominated by large organized
Promotional price elasticity and base price                corporations in the US who have the leverage
elasticity are estimated using the SCAN*PRO                to negotiate better terms with manufacturers to
model output to make pricing decisions, wherein            maintain brand visibility in the store, manufac-
issues regarding the long-term impact of price             turers have been forced to devise promotion
changes are weighed against short run promo-               mechanisms that harmonize retailer's category
tional price effects.                                      profit objectives along with their own.
     A very effective managerial tool developed                 In the context of using POS data for
by market research organizations specializing in           developing analytical frameworks for resolving
the retail industry using POS data has been the            category management issues, brand and cate-
Sales Rate Chart. This is simply a distribution            gory managers in the US have constructed
of sales volume generated at a retail outlet across        causal models which determine not only the
various price points. The chart is simple but              direct effect of own price and promotional mix
provides important information on sales spikes             elements on sales of the brand, but also the cross
due to price changes. Sales rate charts have their         effects of marketing mix elements of other
limitations in terms of analytical rigour; how-            brands which are deemed to be direct competi-
ever, they have proven to be a successful tool             tors. Own price/promotional elasticities and
in making broad pricing decisions both in the              cross price/promotional elasticities elements are
long and short run.                                        inputs to build category profit simulators which
                                                           are typically scenario building tools which help
Analysis of Category Management Issues
                                                           in designing effective promotional programmes
Three specific decision areas that have benefited          which maximize contribution.
by the use of POS information analysis are:                     Such exercises are carried out quite regu-
                                                           larly at large packaged goods companies. In the
• Optimal product portfolio size at the retail
                                                           beer category, extensive use of this simulator
  outlet and retail shelf space optimization.
                                                           has been made by the second largest brewery
• Optimal retail promotion initiatives to max              in the US market. The specific purpose of this
  imize retail category contribution.                      project has been to convince retailers across the

Vol. 25, No. 4, October-December 2000                 53                                                Vikalpa
US markets that promoting their brand of beer             other agencies (presumably not having access
on key holiday weeks maximizes profits for the            to store level data) have argued for the appro-
retailers compared to promoting the market                priateness of using market level data to estimate
leader brand.                                             the impact of what they call market level
     Rationalizing on portfolio and shelf space           phenomenon such as advertising. Readers should
management requires additional inputs in terms            note that market level sales data is obtained by
of layout design of retail stores (planograms)            aggregating POS data across all stores in a
which when integrated with POS sales informa-             market over an appropriate time window. Using
tion can provide significant insights into optimal        appropriate projection factors that account for
portfolio size as well as the area and shelf              the population of stores the store level data can
location to be allotted to different product              be projected to get an estimate of the overall
categories with the objective to maximize the             market offtake.
retail sales or profits. Exercises of this nature              While brand managers are concerned about
are performed regularly with fair degree of               the effectiveness of their advertising budget,
success in developing appropriate marketing               retail managers also need to know about the
decisions. Several research companies have                impact of advertising on brands, especially
developed syndicated analytical models which              advertising campaigns of new brands and new
have had limited success in resolving issues              product categories.
across the board. They go by nomenclature such,           The Retailing Environment in India
as Portfolio Manager or Category Manager;
however, based on our experience, developing              With her population touching a billion, India
customized solutions using POS data has had               is working at the doorsteps of becoming one
significant potential in resolving product assort-        of the world's foremost consumer markets.
ment and shelf space management issues.                   About a quarter of this huge mass of consumers
                                                          live in towns and cities and the remaining in
Advertising
                                                          villages. Over the years, the retailing infrastruc-
POS data along with media exposure data as                ture that has proliferated in India is character-
collected by research agencies in the US, such            ized by a high degree of fragmentation as
as Nielsen Media Research (ORG-MARG col-                  compared to many developed nations. A recent
lects media exposure data in India) have been             estimate puts the figure at 10 million operational
used by brand managers to calibrate their long-           retail outlets in India, 32 per cent of these being
term advertising spendings. There have been               in urban areas. Small stores (about 300- 400
attempts at studying the short-term effects of            sq.ft.) accounted for 64 per cent of the retail
advertising on sales; however, the impacts                outlets in the country whereas very large stores
estimated have been fairly low (0.1 to 0.12 for           (about 800 sq.ft.) constituted only 3 per cent
established brands, 0.2 to 0.4 for new brands).           of the establishments. FMCG stores accounted
There has been moderate success at estimating             for nearly 75 per cent of these retail outlets.
advertising impact on sales in the long run (1.5          According to a CMIE2 forecast, total retail sales
to 2 times more than short run effects).                  in India is likely to exceed Rs 9200 billion by
     An explanation for these lukewarm results            the year 2002, of which about 73 per cent is
 may rest with the characteristics of data re-            expected in the food sector. Therefore, it is
 sources used to construct the analysis. Research         obvious that the great Indian retailing revolution
 agencies such as AC Nielsen and IRI favour               is not waiting for the size of the business
 the use of store level data (POS) to study               opportunity. The challenge lies in identifying the
 advertising effects, which they claim is consist-        key drivers that steer the Indian consumers'
 ent with the analysis conducted to study the             perception and behaviour when it comes to their
 effect of store promotions (Bucklin and Gupta,           shopping needs.
 1999). This point is, however, debatable since           2
                                                              Centre for Monitoring Indian Economy.

Vol. 25, No. 4, October-December 2000                54                                               Vikalpa
     In this context, market enthusiasts are crys-              Herein lies the opportunity of utilizing the
tal gazing on the 'fate' of large format and/or            POS data to model consumer response to
organized retailing in India. There are interest-          marketing mix variables at the retail level. With
ing trends by way of statistics, e.g. the friendly         increasing competition, both at the manufac-
neighborhood mom&pop stores have increased                 turer level and the retail level, it is obvious that
per 1000 population as per an ORG study, and               there are distinct advantages of being the first
large format and specialty retailing is also on            mover in harnessing information resources to
the increase (though their absolute numbers are            drive the marketing strategy building exercise,
still quite small). This is at the expense of              both for retailers and manufacturers. The latter,
perhaps the middle-sized-middle-value shops3 .             it is hypothesized, will increasingly feel the
The reality is that every retailer has to "under-          pressure of building strategies that are in con-
stand his customers" more discerningly than                gruence with the retailers' business objectives.
ever before and make strategic choices to pursue
the right target (customer) with the right propo-                The rapid changes in the retailing environ-
sition. Also, the reality is that every retailer           ment are currently quite evident at least in some
today is unanimous in his appreciation that he             of the metropolitan pockets of the country. A
needs to "deliver value" to his customers.                 fairly extensive review of the expanding retail-
                                                           ing sector in India is available in prominent
     The final reality is that in today's retailing        trade publications.4 Competition has set in from
environment in India, the sheer complexity of              multiple sources. In the Chennai market, com-
the product-market matrix is posing 'mental-               petition in food retailing is multi-faceted with
model' based decision-making a real challenge.             the neighborhood (kirana) stores facing direct
This exponential change, started during the                competition from more than one organized
early 90s, is arising essentially out of two               retailing chain: FoodWorld, Nilgiri's, Subhiksha,
sources. First, in pursuit of the proverbial 200           and Vitan. With such intense competition in the
million strong Indian middle class, the manu-              market, newer retailing chains are forced to
facturers have been continually adding new                 adopt significantly differentiated POS strategy
products in the market place. For instance, in             to make customers change their shopping habits.
the FMCG sector, there were 57 core categories             For instance, the specific trade publication
of products in 1990, which grew to 76 by 1996              reports that Subhiksha adopts a blanket policy
(and the trend continues). These 19 new cat-               of discounting prices by selling less than MRP,
egories boasted of 1378 brands and 2579 SKUs.              similar to the textbook definition of an Everyday
Furthermore, the number of SKUs in the erst-               Low Pricing (EDLP) strategy. "You have to give
while 57 categories also grew from 7715 to 15160           customers a solid reason to change their shop-
during the same period (Banerjee et al., 1999).            ping behaviour. One which conventional stores
                                                           like the kirana can't duplicate," says the director
     The second change agent, and the most                 of Subhiksha chain of supermarkets.
important, is the consumer who has become
vastly discerning through rapid exposure to the                 The above instance corroborates our asser-
global business environment. The Indian con-               tion that retailers of the future will have to
sumer today reflects a strong preference for               rapidly employ differentiated marketing strategy
imperatives such as evaluating choices from                based on customer response information. Uti-
among large assortments of products, a plea-               lizing customer behaviour data at POS to deve-
surable shopping experience, and a shopping                lop effective pricing strategies, EDLP or other-
experience that would provide the maximum                  wise, will certainly be an imperative for long-
"value" per rupee spent.                                   term survival and growth.

3 The Hindu Business Line, "Responding to Modern           4 Brand Equity, The Economic Times, April 26 - May 2,
Retailing Formats," May 23, 1998.                          2000.

Vol. 25, No. 4, October-December 2000                 55                                                     Vikalpa
Status of POS Data in Retail                               Table 1: Type of POS Data Available
Management in India                                       * Item Files
                                                            • Bill Date, Bill Number, Bill Time, Product
As was pointed out earlier, ORG-MARG has                       Code, Cost, MRP, Sold Value, Quantity
operated a retail store audit in India for many           * Product Master
years. The data primarily consists of actual stock          • Product Code, Product Name, MRP, Group
movement data at sample stores. Collection of                  Code
corresponding marketing mix elements has been             * Group Master
minimal, and only very recently has some effort             • Group Code, Group Name
been made to collect price and promotion data.            * Campaign Files
AC Nielsen is also reported to be planning to               • Product Code, Campaign Start Date, Campaign
develop an audit infrastructure. However, none                 End Date, Cost, MRP, Selling Price, Campaign
of these organizations is capable of fully col-                Number
lating the rich transaction level data that exists
                                                          Source: Raghuram et al, 1999.
at the retail point of sales. AC Nielsen and IRI
claim to provide census data on store sales, but          the investment in infrastructure that organized
only in a few geographic regions in the US.               retailing chains have made in collecting trans-
                                                          action level data. An ad hoc survey in the
     A typical source of POS transactional data
                                                          Ahmedabad market, which is in the infancy
will have information on the following:                   stage of the retailing revolution compared to its
•   Wider range of SKUs.                                  counterparts in the south, revealed that a sig-
                                                          nificant number of-supermarket stores collected
•   Tracking of generic/store brand sales.                transaction data at the cash register. Yet, no
•   Ability to handle consumer panels through             significant efforts are made currently to "mine"
    loyalty programmes.                                   this database to reveal insights on customer
                                                          behaviour. Even with limited information avail-
    Ability to conduct product basket analysis.           ability, these databases have enough data to
•   Ability to generate information on optimal            understand market level drivers of store sales.
    "loss leader" promotion strategy.                     The reasons for this perceived apathy may be
                                                          varied; lack of expertise in mining data may
     A recent study document prepared on this             be an important reason but more often it is the
topic (Raghuram et al., 1999), highlights some            issue of prioritization of initiatives that has kept
of the analytical capabilities of this type of            developmental projects such as database mining
information. However, a detailed assessment of            a back burner. This is most unfortunate since
a typical POS transactional level database                we predict that with rapid entry of competition,
available today shows the limited amount of               players who understand customer psyche,
information collected currently (Table 1). This           through its manifestation of overt behaviour,
is not surprising given that use of the data has          will dictate the future course of the retailing
been primarily to generate simple accounting              industry.
reports. In order to develop the full potential
of POS data for decision-making, the future
managers have to proactively think of informa-
                                                          An Example of a Potential Analysis
tion bytes that need to be systematically col-
                                                          We used a database obtained from a store of
lected which can be used along with the
                                                          a large retail food chain based in South India
transaction level data in the future to help
                                                          to identify specific "category-driven" transac-
making better store level decisions.
                                                          tions that generate more revenue for the store.
      One area that conventional grocery stores           The purpose of the study was to identify
like the small kirana stores cannot replicate is          customer segments that, on an average, gener-

Vol. 25, No. 4, October-December 2000                56                                                Vikalpa
ated more revenue for the store compared to               Table 2: Average Transaction Value (in Rs)
others. Customer segments were identified based           Category           Key Purchase1      Minor Purchase
on their main purchase category as exhibited
                                                          Staple                 517               160
by the nature of their transaction.
                                                          Spices                 445               130
    Transaction data over a four-month time               Cooking Oils           620               153
period across all recorded product categories in
                                                          Pulses                 556               140
the store was used for the analysis. Five major
category purchases were identified for the study          Soap                   475               152
                                                          1
based on the frequency of purchases made                      When the key purchase is of the corresponding category.
(weighted by the value of the purchase). These            Similarly, the average transaction, which was
categories were: a) staple, b) spices, c) cooking         driven by spice purchase, was valued at Rs 445
oils, d) pulses, and e) toilet soap. A sixth              compared to Rs 130 that was spent by custom-
category was formed clubbing sales from all the
                                                          ers5 who did not have spices as a major category
other categories. The number of categories
                                                          in their shopping list.
chosen for the study was kept at six to avoid
unnecessarily complicating the study, but the                  The largest difference in the transaction
analysis could be done at any level of detail.            value occurs between transactions that record
It must be pointed out that while this type of            significant purchase of cooking oils versus the
data has been available in India only in the past         transactions that do not record significant
three to four years, there does not seem to be            purchases of cooking oil (Rs 620 versus Rs 153).
a scarcity of transaction data in electronic form
                                                               This result throws some light on the type
in the organized retailing sector currently.
                                                          of customers that bring in more value to the
     In our analysis each individual transaction          store in terms of revenue (Chen et al., 1999).
in the store (identified as all purchases made            If store managers were to make decisions about
during one store visit based on a unique bill             promotions in the local area to draw more
number) was categorized as either a particular            customers, the typical output obtained from this
category driven purchase or not (e.g. staple              analysis may be used to identify the "right"
driven purchase or not staple driven purchase)            destination category to be featured in the local
based on the rupee value spent on the category            area promotion to increase store traffic of high
in the specific transaction. To control for vari-         valued customers. In the present case, high
ation in average value spent across categories,           propensity of using cooking oil as a promotion
the purchases were categorized based on their             category will attract the highest revenue cus-
relative value compared to the maximum value              tomer.
recorded in that category across the entire               Designing Proper Information Resource to
database. This is a limitation, since without any         Help Better Decision-Making in the Indian
customer-related characteristics in the database,         Context
it is impossible to identify the drivers of indi-
vidual purchases and hence one has to resort              There are limitations in the analysis described
to approximations.                                        above given the approximations made in cate-
                                                          gorizing transactions into specific category driven
      SAS software was used to manipulate the             purchases due to the lack of customer descrip-
data and the results obtained are presented in            tors. Also, the results obtained are not statis-
Table 2. The direct interpretation of the results         tically robust enough to pass a test of technical
is that customers that came into the store                rigour. Although technical rigour is needed,
primarily to buy staples spent on an average
Rs 517 compared to customers who did not have             "The same transaction may be classified as driven by
staples as a major category in their shopping             multiple category purchases — staples, spices, etc. This is
list (average spending per transaction is Rs 160).        true since many consumers make monthly purchases in
                                                          bulk across major consumption categories.

Vol. 25, No. 4, October-December 2000                57                                                        Vikalpa
often, appropriate data analysis (even without            true value of this loyalty programme is to
passing the litmus test) provides a higher level          provide individualized promotions to customers
understanding of consumer behaviour which can             which enhances perceived service quality at the
shape better decision-making.                             store.
     What is critical to drive the process is                  There are a number of non-food departmen-
availability of the right information/data re-            tal store chains in Mumbai and Delhi, which
source that can be mined. In this respect,                have introduced loyalty programmes and are
FoodWorld (C) (Banerjee et al, 1999) touches              tracking purchase data on these customers.
on the need to "IT-enable" operations for                 Proper design and mining of these databases can
smooth running of the retail operations. We               yield significant insights to develop direct mail
would suggest that retail organizations of the            promotion campaigns.
future do need to go beyond envisioning IT                     There is tremendous opportunity to enhance
integration. They will benefit by adopting a              the scope of in-store promotions and to capture
proactive stance in designing and developing an           the information at the most disaggregate level.
IT-enabled information warehouse for both                 In-store displays, whether they are end of aisle
customer and operational information, which               displays, within aisle or front of store, have been
can significantly drive strategic decision-making         recorded to have varying effects on sale spikes.
in the future. As a starting point, an assessment         A deeper understanding of the drivers of in-
of the type of decisions that one needs to take           store sales will help retail store managers plan
in more advanced stages of the business life              their space utilization more effectively in con-
cycle is necessary. Lessons from the developed            junction with the most appropriate marketing
markets of the west and the type of decision              programme. It is important to invest in an initial
problems that they have faced may provide                 period of testing various innovative marketing
leads to the type of information requirements             options and recording them religiously, such as
of the future.                                            one would do in conducting a pilot test pro-
                                                          gramme. After the initial phase of testing,
Some Broad Level Initiatives                              managers would have a sufficiently large infor-
                                                          mation base to fine tune their programmes
Initiating customer loyalty programmes is a               according to the market needs. However, this
pragmatic way of collating customer level trans-          latter stage does make the created database
actional data that can be linked to customer              redundant. Effective ongoing decision-making
characteristics. Segment level analysis of cus-           demands continuous building and mining da-
tomer likes and dislikes drive most marketing             tabases as well.
decision-making and this type of database
provides ample opportunity to customize store             Conclusions
promotional programmes based on the type of
consumer segments who frequent the store. An              Retailing is entering an active phase in its
effective way of utilizing this behavioural infor-        business life cycle in India. This note has
mation is to generate customized coupons and              attempted to address a dimension of this busi-
promotional features at the check-out counters            ness, which has a strategic role to play in the
based on the customer characteristics and cur-            growth and development of its constituents —
rent purchases made. Catalina Corporation, a              the management of information resource. Like
Chicago-based research agency has evolved                 many other service industries which have be-
such coupon generating machines that have                 come intensely focused on information resource
been set up at several retail food stores across          management, for example, consumer finance,
the US. Manufacturers have also availed of this           insurance and hospitality, survival in the long
service at retail counters to induce brand                run will depend upon smart management of
switching with limited success. However, the              market information resources. Unfortunately,
Vol. 25, No. 4, October-December 2000                58                                               Vikalpa
developing marketing information resources                   Chen, Yuxin; Hess, James D; Wilcox, Ronald T and
needs a significant lead-time and there is a                   Zhang, John Z (1999). "Accounting Profits vs
natural advantage of being the first off the block.            Marketing Profits: A Relevant Metric for Category
Large retail chains have a definite advantage                  Management," Marketing Science, Vol 18, No 3, pp
                                                               208-229.
with respect to the available infrastructure that
they have in place. They simply have to initiate             Raghuram, G; Banerjee, Bibek; Jain, A K; Koshy,
a proactive process of investing in appropriate                 Abraham and Bhatt, Gunjan (1999). Retailscope '99,
information that will yield the right kind of                   Retail Sales Data: The Hidden Treasure, Draft
marketing insight for the future.                               Report, Ahmedabad: Indian Institute of Manage-
                                                                ment.
References
                                                             Guadagni, Peter M and Little, John D C (1983). "A
Banerjee, Bibek; Raghuram, G and Koshy, Abraham                Logit Model of Brand Choice Calibrated on
  (1999). "FoodWorld (C): The Road Ahead,"                     Scanner Data," Marketing Science, Vol 2 (Summer),
  Working Paper, Ahmedabad: Indian Institute of                pp 203-238.
  Management.
                                                             Wittinik, Dick R; Addona, Michael; Hawkes, William
Bucklin, Randolph and Gupta, Sunil (1999). "Com-                and Porter, John (1988). SCAN*PRO: The Estima-
   mercial Use of UPC Scanner Data: Industry and                tion, Validation and Use of Promotional Effects Based
   Academic Perspectives," Marketing Science, Vol 18,           on Scanner Data, Working Paper, AC Nielsen,
   No 3, pp 247-273.                                            Schaumburg, IL.




Vol. 25, No. 4, October-December 2000                   59                                                    Vikalpa

								
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