- Email: [email protected]
An advertising evaluation system for retailers
An advertising evaluation for retailers Chitrabhanu Goizueta
Business
system
Bhattacharya School,
Emory
Universit_v,
Atlanta,
GA
30.322, USA
Leonard M Lodish The Wharton
School,
Univrrsit~~ of Pmmplvanirr
Extant research on assessing advertising productivity has mostly focused on developing methodologies for capturing the longer-term effects of ‘theme’ advertising. Measuring advertising productivity in the retailing industry, however, poses different challenges. For instance, in many retailing firms, the primary role of advertising is to announce ‘limited-time price specials’ and thereby attract customers to the store. In the absence of store traffic information, in order to assess advertising effectiveness for these businesses, there is a need to relate advertising expenditures to short-term sales changes. In this study, we design a decision support system aimed at aiding retailers in budgeting, tracking and evaluating their advertising. The overall objective of this system is to recommend more productive spending of the advertising dollar by market and by media type. We demonstrate a real-life application of the system for a national retail organization that specializes in electronic goods. Using daily sales and advertising expenditure data, we estimate different models to assess advertising effectiveness for different days of the week. We discuss the model findings, their implications for advertising decisionmaking, and the next steps in improving this system. Keywords:
advertising.
retailing.
dccislon
support
system\
Advertising continues to be a popular marketing tool: in 1990, over $128 billion was spent on advertising (Kim, 1992). Not surprisingly, numerous studies have endeavoured to improve advertising effectiveness by modelling the relationship between sales and advertising (e.g. Rao and Miller, 1975; Little, 1979: Eastlack and Rao, 1986; Stewart, 19%)). Almost without exception, the typr of advertising for which researchers have attempted to assess effectiveness has been ‘theme’ advertising, typically used by manufacturing firms. Very little research has focused on advertising in the retail industry (fol exceptions see Vaccaro and Kassaye. 1988: Kassaye and Vaccaro, 1991). This is especially interesting, because the money spent by manufacturing firms on theme or image-building brand advertising has generally been on the decline since the 196Os, and in fact one of the sectors that has witnessed major growth in advertising dollars is the retailing industry (Kim, 1992). In particular, as most of us are
aware from our daily observations, retailing firms have been committing substantial resources to their advertising budgets to announce temporary price reductions. These price reductions last for varying lengths of time: we typically come across announcements such as ‘one-day sale’, ‘for this week only’. and the like. These announcement ads appear in the usual media: newspapers, television and radio. An important distinction between theme advertising employed by manufacturing firms and these ‘sale’ announcements used by retail organizations is that in the latter context the ‘life’ of the ads is mostly limited’ by the duration of the ‘sale’. In other words, ads that announce time-bound price-offs are much more ‘action driven’. For the most part, the purpose of these ads is to attract customers to the store, during the tenure of the sale. Given this rather specific, well-defined role of this type of advertising in allocating resources, the retailer has to make a myriad decisions: how much
The authors are listed in alphabetical order. The study described in this paper was funded by the Wharton School. University of Pennsylvania.
‘In drawing this distinction, we are ignoring the role that these ads may play in the first three levels of the hierarchy of effects model: that is. in building awareness. interest and desire.
90
0969-6989/94/02090-l
1 0 1994 Butterworth-Heinemann
Ltd
An advertising
evaluation
to allocate to a particular market, how much to allocate to different media within a market, on which days of the week to advertise more heavily, and so on. While operating under resource constraints, each of these decisions involves tradeoffs, and the intense competition in the retail arena and consequent pressure on margins make it imperative for managers of these firms to maximize the marginal productivity of the advertising dollar. In the absence of store traffic information, the only way that managers of retailing firms can assess and consequently increase the productivity of such advertising spending is by measuring how advertising expenditures are related to short-term sales changes. Unlike traditional sales-advertising modelling contexts, where much emphasis is placed on deciphering the lagged effects of advertising (e.g. Bass and Clarke, 1972) retailers need to assess ‘short-term’ advertising effectiveness. Although researchers have responded to advertising decision support needs (e.g. Little and Lodish, 1969) to date there is no study that has attempted to help retailers make decisions in the area of advertising price reductions. The large resource commitment by retailers to announcing price-offs makes it important for researchers to design mechanisms that would improve the productivity of such spending. Our overall objective in this study is to develop a decision support system (DSS) that will aid retailers in budgeting, tracking and evaluating their expenditures on price-off advertising. In the course of developing this system, we investigate the following empirical research questions, which have hitherto not been addressed in the literature: (1) (2) (3)
Does price-based advertising impact sales positively? Does the effectiveness of such price-based advertising differ by days of the week? Does it differ across different media?
We design the system using sales and advertising data from a national retailer of electronic goods. The data are for a major city in the USA and span a period of three years. Our results suggest that for this retailer, in the market under study, advertising effectiveness exhibits tremendous variation both by day and by medium. The DSS we describe in this paper is a prototype of the kind of system that every retailer can easily implement for evaluating their advertising expenditures. As we describe in the following sections, the proposed system does not require highly sophisticated modelling methodologies or expensive software. Our belief is that an intuitively appealing, easy to understand, low-cost system that makes explicit the inevitable trade-offs involved in advertising decisions has a much better chance of ‘adoption’ than a more complex system. We shall proceed as follows. First, we review the relevant literature in this field. Second, we describe Journal of Retailing and Consumer Services 1994 Volume I Number 2
system for retailers: C Bhattacharya
and L M Lodish
the data available to us, and the challenges that we faced in the model development phase. Next, we describe the methodology and the empirical results of our analysis. We conclude with the implications of our study, and outline fruitful areas for future research. Related literature As mentioned in the introduction, very little research has been conducted in the area of retail advertising and sales. In the context of supermarkets, price-off announcements are popularly known as ‘features’. Basic profitability calculations that managers can perform to determine the efficacy of a feature ad are described in Blattberg and Neslin (1990); however, such calculations are contingent on our knowledge of the brand’s ‘normal’ or ‘baseline’ sales (Abraham and Lodish, 1993). Bemmaor and Mouchoux (1991) investigated the combined impact of retail advertising and in-store promotion on brand sales in an experimental framework. Overall, they found that the deal-advertising interaction is large: sales increase by a much greater extent when deals are advertised; moreover, these increases are smaller for leading brands. In the realm of specialty retailers, Vaccaro and Kassaye (1988) conducted a study among select urban and suburban communities to assess the relative effectiveness of newspapers and radio in reaching the specific target markets of small business retailers. Their methodology consisted of interviewing a random sample of households from both urban and suburban communities and asking them questions on utilization of the different media in choosing a liquor store. The authors conclude that the effectiveness of different media differs across urban and suburban communities. Kassaye and Vaccaro (1991) did a study in which they first surveyed households to assess their media habits, and then did a follow-up field experiment in which they manipulated the media mix (urban dailies, suburban weeklies and radio) in line with their survey findings. The authors report significant increases in store traffic and sales volume as a result of changes in the media mix. In the area of decision support, there have been a few important studies that have attempted to help managers with advertising decisions. The BRANDAID model developed by Little (1975) is a marketing mix model representing a classic example of Little’s decision calculus approach. Advertising is one of the submodels of this comprehensive model. Little describes an application of BRANDAID to a packaged good, where the fundamental problem facing brand management was the allocation of a budget to advertising and promotion. In part, the methodology involves a log-log regression model where sales are regressed against advertising and promotion expenditures to derive effectiveness 91
An advertising
evaluation
system for
retailers:
C Bhattachary
indices for each of these expenditure variables. which are then multiplied together to provide a sales forecast. MEDIAC (Little and Lodish. 1969) is one of the best-known media-planning models. The model assumes that an advertiser is seeking to buy media for a year with a certain amount of dollars so as to maximize his sales. MEDIAC is a comprehensive model, which includes the dimensions of market segments, sales potentials, diminishing marginal returns. forgetting and timing in a media-planning model. However, as reported by Lilien er crl. (1992). its use has been limited by its sophistication: it seems to be too complex for most media planners. Lodish (1982) developed a decision support system capable of planning marketing strategies and allocating resources for a multi-store retailer. The system combines well-established analysis tools, sophisticated computer software, and management’s implementation needs to help solve top-level strategy and forecasting problems. The system consists of a planning model, national campaign evaluation system, experimental analysis system, and an ongoing interactive database and reporting system. The campaign evaluation segment of the system is the one that is most relevant for our purposes. Examples of campaigns are White Sales and home furnishing sales. which typically include a price event and supportive advertising. In this instance, management wanted more detailed analyses of the sales effects of the campaigns. The system uses dollar and unit sales to routinely calculate percent changes between the ‘pre’, ‘during’ and ‘post’ periods of the campaign, and sales growth in the ‘during’ and ‘post’ periods. Our paper builds on the learnings provided by a number of the aforementioned studies. In contrast to the broad-based DSS developed by Lodish (1982), our interest is in developing a system specifically for investigating the advertising-sales relationship in the context of retailing. Although our system does not have an experimental analysis component as yet, in the same spirit as the field experiments conducted by Kassaye and Vaccar (1991), the proposed system will provide managers with response coefficients that they can use to alter their media mix. Some of the variables we shall use in our models will be operationalized in similar fashion to what was used in the BRANDAID application described in Little (1975). Finally. the models we shall develop will be considerably simpler than the methodologies used in MEDIAC, so as to encourage greater adoption in practice.
System overview The ultimate goal of a system built to measure advertising productivity is to recommend more productive spending of the advertising dollar. In our view, such a system should possess the following 92
and L M I_odish
general features and benefits. such systems to be able to: (1)
(2)
(3)
We want the users of
group advertising expenditures by any media type, for any market. for any time period, and relate these expenditures to sales changes: quantify advertising response by market and hence conduct a variety of ‘what-if‘ analyses. such as estimating the impact of different media and/or budget levels on sales: graphically understand the reasoning behind the response estimates.
As we describe in the following sections. the system we designed for our client possesses all the above features. But bear in mind that at the time of the study, the notion of advertising decision support. although appealing, was novel to our client. There had been no previous attempts in this organization to quantify the relationship between sales and advertising expenditure. Hence. in addition to the above general features, we had to define objectives and undertake a few tasks that were specific to the situation in hand, and geared towards convincing management that they were engaged in a valuable exercise. For instance, we knew that our primary goal was to highlight resource allocation options to show management the potential increase in effectiveness (and consequently profitability) of alternative decisions that could be made. But given that this was management’s first encounter with such a system, we wanted the models to merely provide managers with directional guidance (for example, advertise more heavily on television on Sundays), rather than identify optimal levels of expenditure. The proposed system would still enable marketing managers to compare advertising alternatives, to pick better alternatives than the ones that traditional methods had been selecting, and to track the impact on sales volume of any changes that they implement. In contrast to the implicit trade-offs that management were making in setting their advertising budget and in evaluating their advertising effectiveness, the system would encourage them to make such trade-offs in a more explicit fashion. Our second objective was to demonstrate to management the important role that computers could play in decision support. In this specific instance, we wanted to show management how the use of state-of-the-art computer languages facilitates early diagnosis of advertising problems and opportunities. In terms of advertising and sales information, the marketing department of the organization maintained Lotus l-2-3 files primarily for the purpose of preserving historical information. As we discuss in the data section, the files were not maintained in a manner that facilitated quantitative analysis. We built the system using PC Express. a fourth-generation, high-level command language (a Jmtmnl
of Hrtuiling rmi C~onsrcm~r Survicrs 1994 Volume
I Nunlhcr 2
An advertising evaluation system for retailers: C Bhattacharya and L A4 Lodish
Figure 1
How
the data were stored
product of Information Resources Inc). In demonstrating the use of the system, we wanted to impress on management that the same data, when stored and used in the appropriate way, could serve as ‘an early detection and warning system’.
The data As mentioned in the introduction, the organization is a national seller of household electronic appliances, such as TVs, VCRs, stereo systems and air conditioners. At the time of the study, the organization had 214 retail outlets nationwide, an annual sales turnover of about $1 billion, and an annual advertising budget of $6 million, The raw data that were made available to us comprised daily dollar sales and expenditures by different media. The media comprised television, print (that is, newspaper advertising), and radio. The print expenditures were further disaggregated by 20 different publications that served the market under study. As the firm added a number of retail outlets during the period of the study, we also obtained information on the number of retail outlets that the firm operated in the given market. The data were for three accounting/fiscal years, spanning May 1987 to April 1990, for a major market in the USA. Each year comprised 364 days (52 weeks x 7 days/week), so we had data for 1092 days in all. There were also some competitor’s advertising expenditure data available from syndicated sources, but they were so limited in nature that we have not used them in the existing system. Journal of Retailing and Consumer
Services 195’4 Volume I Number 2
Data reorganization was a challenge. The existing database management and reporting format can best be described as one that fostered ‘market status reporting’ (Little, 1979). Although the data were preserved on Lotus l-2-3 files, the format of the files was rather inflexible for our purposes. Figure 2 is an illustration of the way in which the data were stored. (The sales and advertising numbers in Figure 1 and Table 1 are disguised for purposes of confidentiality.) Note that there was a separate file for each planning period. The columns of the file comprised different days of the week, and the rows were the headings of interest, such as total expenditure this year, total expenditure last year, dollar sales this year and last year, sales as a percentage of budget, and so on. The cells contained information on (a) dollar sales for the current period, and for the same period in the previous year, and (b) type of medium used and corresponding expenditures; the total daily expenditures were again reported both for the current and the previous year. In contrast, we wanted a system that would facil: itate ‘market response reporting’ (Little, 1979; Abraham and Lodish, 1993). This necessitated providing the system users with a level of flexibility in data retrieval that the existing format did not provide. After considerable discussion, we reorganized the data in the format that is illustrated in Table
1.
Given that we had data only for one market, the values of all the variables for the three-year period are identified by a single dimension: ‘day number’ 93
An advertising evaluation system ,for retailers: C Bhattachuryu md L M Lodish Table 1 How the data look now Day number
Day of week
Sunda> Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday
4
s IO II
12 13 I4
TV 6) 50000 56000 43000 46000 300(X) 42000 sxooo 34OGi) 38000 43000 48000 35000 43000 X3000
(which goes from 1 to 1092). Having the day number as a dimension enables us to identify all details corresponding to a given day: the day of the week, the month, the year. the same day in the previous year, and so on. Once we have similar data for ‘markets’ would be a second multiple markets, dimension by which all the variables would be identified. Storing the data in this fashion enables us to group sales and expenditures by day of week, by media and by market, and look at summary statistics and run models in minutes.
The models The proposed model seeks to relate dollar sales to advertising expenditures at the market level. We emphasize that our modelling objective is not one of theory testing, but one of providing managers with insightful and reasonably valid response coefficients. This objective plays an important role in our model development. For instance, note that in the strictest sense, dollar sales is a surrogate for store traffic. The task of the ads is to bring customers into the store. at which point the salespeople take over. But at the time of system development. there were no records of daily store traffic and salesforce effort: hence we work without these variables. Further refinements are also possible in terms of incorporating information such as the location of a print ad in the paper. and the time of day a particular TV or radio ad was aired. But again such refinements are beyond the system’s objectives at this point. Once management gets comfortable about using the existing system, such refinements can easily be incorporated. In very broad conceptual terms, the model we propose to estimate is as follows: SALES,,
94
= f(TV, ,...., TV,,-,,, PRINT ,,,..., PRINT,,-,, RADIO, ,,..., RADIO,,-,,. NSTORES,,, DAY-OF-WEEK,, SEASON,,)
Print 6)
Radio 6)
8000 0
0 3000
0 0
0 0
7500
0
0
0
0
!mo
0
0 0
0
0 60(x) 0
5605 (I
0
0
where the subscript n refers to the day under consideration rf-n refers to the nth day prior to the day under consideration, SALES refers to the dollar sales for the market, TV, PRINT, RADIO are the respective expenditures on television, radio and newspapers. NSTORES is the number of stores operated by the firm, DAY-OF-WEEK refers to the day under consideration, SEASON denotes the time of year or special occasions during the year such as Christmas and President’s day when retailers typically offer ‘sales’. and fis a multivariate function. This model formulation assumes that there is no strong covariation between salesforce effort and advertising expenditures. Similarly, we are also assuming that depth of price reductions in the store and advertising expenditures are not strongly positively correlated. In going from the above general model to the final form, we encountered a number of challenges: the shape of the response curve, the lag structure for the media expenditure variables, level of disaggregation, seasonality and multicollinearity. We discuss each of them in turn. Shape
of the
response
curve
We had no a priori assumptions regarding the shape of the relationship between sales and the variables described above. But as we mentioned before, similar to Lodish (1982), our primary modelling goal was to provide managers with directional guidance (for example, advertise more heavily on television on Sundays), rather than identify optimal levels of expenditure. Hence we asserted that the shape of the response curve was not that critical for our purposes. We tried linear. semi-log and log-log versions of the model, and the linear model gave the best fits. Lag structure
As denoted by the PRINT,,..,,. RADIO,,,,)
lagged in the
variables (TV,,_,, model above, we
An advertising
evaluation
Multicollinearity
Recall from the data description that we had daily expenditure numbers for 20 newspapers. One of these was the leading daily for the metropolitan area, and enjoyed much higher levels of spending (about 80% of the total) compared with the other suburban newspapers. It so happened that the same pattern of print advertising was often followed across all the newspapers: that is, the print advertising variables were highly collinear. To reduce the incidence of multicollinearity, we aggregated the expenditures across all the newspapers and created a composite PRINT variable (Kennedy, 1992). Note that a better level of disaggregation for the data would be to have sales by store area (ideally even by item), by day, and expenditure on media vehicles allocated to the particular store area. But in this instance, as the bulk of the sales and expenditure pertained to a major metropolitan market (within which it was impossible to demarcate advertising expenditures to a particular store area), and as the adjoining small towns all received the radio and TV stations of this market, we believe that studying the sales-advertising relationship at the market level will not result in any great aggregation error.
Level of d&aggregation
As we all know from our observations in daily life, both sales and advertising of electronic household appliances are not uniformly distributed across the days of the week. Households have greater spare time at the weekends to shop for such items, and hence it is likely that the relationship between advertising and sales is different for different days of the week. This problem is somewhat similar to the problem of ‘calendar anomalies’ that has been dealt with in the finance literature. Researchers in finance (e.g. French, 1980; Kiem, 1985) have tried to explain abnormal stock returns in certain months and certain days of the week. The usual methodology is to use a dummy variable regression in which the dummy variable for the month/day of interest is left out of the estimation. Each of the dummies included in the regression variables then denotes the difference in the average return between a specific month/day and the month/day of interest, and the average daily stock return is regressed against these dummy variables. The hypothesis of the excess returns being the same for different months/days of the year is then tested through the F-statistic, which measures the joint significance of the dummy variables. The difference between the studies in finance and our study is that we are not interested in establishing that a particular day of the week is different from the other days. Rather, we are interested in estimating the differential effect that the media expenditure variables have on store sales on different days of the week. Given that different days of the week had certain patterns of advertising attached to them (for example, there was no radio advertising on Sundays, and prices advertised in Sunday newspaper inserts always held good through to the following Friday), we felt that estimating one differential effects model (although a theoretical possibility) would lead to Services 1994 Volume
I Number
and L M Lodish
problems of interpretation. Instead, an easier alternative (because we had enough degrees of freedom) was to estimate separate models for different days of the week. On attempting to estimate separate models for each day of the week, we observed (a) that there were too few non-zero observations during the week, and (b) that the advertising pattern was not very different across the weekdays. We therefore estimated three models, one that pooled observations for Saturday, one that pooled observations for Sunday, and one that pooled observations for Monday to Friday.
contended that sales on a given day were also affected by advertising on previous days: hence we had to decide on the number of lags to include for each media expenditure variable. In other words, how short was ‘short-term’? Except for advertising in the Sunday newspaper, where the prices always held good through to the following Friday, the other price announcements either did not have a specific deadline, or held good for a maximum of three days. For all these ads (that is, except for those in the Sunday newspaper), we started with three lag variables (one for each of the three preceding days). In our trial runs, however, we noticed that at most the preceding day’s advertising expenditures had a significant impact on the sales for a given day. Therefore (except for Sunday’s print advertising), in the final model estimation, we retained only a single lagged variable (denoting expenditures on the previous day) for all three media. -
Journal of Retailing and Consumer
system for retailers: C Bhattacharya
Seasonality
As we would expect, there are weeks during the year when sales of household electronics are considerably higher than ‘normal’. Examples of such weeks include Christmas, Thanksgiving, Labor Day, and the like. Some of these events, such as Labor Day and Thanksgiving, have become traditional ‘sale’ weekends, when all retail stores offer large discounts. This apart, there may be other types of seasonal phenomenon: for example, higher sales of air conditioners in summer and of room heaters in winter. Needless to say, our methodology needs to control for all such seasonal effects. The modelling of seasonal effects is usually accomplished either by adjusting the data through some form of moving average (Hanssens et al., 1990), by using dummy variables to represent the seasons (Kennedy, 1992; Smith et al., 1994) or by 2
95
An advertising
evaluation
system for retailers: C Bhattacharya
using seasonal lag operators (Box and Jenkins, 1976). In the context of relating daily sales to daily advertising expenditures, capturing seasonal effects by dummy variables or through suitable lag operators seemed more appropriate. On inspecting the variety of items carried by the store, and the number of different types of sales offered during the year, we figured that the only way to control adequately for all seasonal effects would be to have separate dummy variables for each week. We estimated the models using weekly dummy variables, but on discussing the results with managers we discovered that they found it cumbersome to interpret all these weekly dummy coefficients. Hence we tried an alternative approach, which was to introduce a lagged sales variable as an independent variable in the model, to reflect the level of sales that took place on the same day in the previous year. As each planning year was always 364 days, if the sales of a given day of interest d are denoted SALES,,, this variable is simply this approach implied that SALESd-3e+ Although we would lose one year’s data for model estimation, managers were much more comfortable with this method. In sum, after debating and trying models, we decided to: (1)
(2) (3)
(4) (5)
alternative
estimate three separate models, one each for Saturday and Sunday and one for Monday to Friday; include a lagged sales variable to reflect sales on the same day in the previous year; include a lagged variable for each type of medium to reflect expenditure on the previous day; create a composite PRINT variable; and estimate additive linear versions for all the models.
The final model (1)
various
formulations
are as follows:
Sunday: SALES,{ = P,PRINT, + B,TV, + &PRINT,,_, B,RADIO,,_, + B,NSTORES, + P~SALES~~X,~ + ed
+
PRINT,, TV,,, RADIOcl refer to the dollar expenditures on these respective media on day d. NSTORES, is the number of stores owned by the firm on day d, and E(, is a normally distributed random error term. Note from the variables included in the model that the firm had no radio advertising on Sunday and no TV advertising on Saturday. (2)
Monday-Friday:
SALES,
96
= B,PRINT, + @*TV, + &RADIO, + B4 x I, X PRINTd_, + B,TV,,m, + &RADIO+,, + B,NSTORES,, + &SALES,,, + C:=,yl x D, x SUNPRINT,c+e,
and L M Lodish
Note that each day d, d E (1.2,..., 1092) corresponds to a specific week w, w E (1,2,...,156). Thus days l-7 correspond to week 1, days 8-15 correspond to week 2, and so on. SUNPRINT, denotes the expenditure on print in week w’s Sunday newspaper, where w is the week to which each of the following five d days belong. The D,s are indicator variables: D, is 1 when the day under consideration is Monday and 0 otherwise, D, is 1 when the day under consideration is Tuesday and 0 otherwise. and so on. As the effect of Sunday’s print advertising on Monday is already captured by the SUNPRINT variable. we multiply PRINT+, by an indicator variable I,, which assumes a value of 0 when the day under consideration is Monday and 1 otherwise. All the other variables of the above model have been defined previously. (3)
Saturday:
SALES,,
= P,PRINT,, + &RADIO, + &PRINT,_, + P,RADIO,,m, + &TV,-, +B,NSTORES,, + &SALES-XJ + e,/
All of the above models were estimated via ordinary least squares (OLS) regression. On estimating each of the models, we plotted the residuals to look for potential outliers. The rationale for looking for outliers is that they are influential ohservations, which often have a strong, undesirable influence on the OLS estimates (Kennedy, 1992). As expected, these powerful points always turned out to be special event days such as Christmas Eve or Ash Wednesday, when sales were abnormally high. These data points were dropped from the estimation data sets in consultation with the Advertising Manager. After determining the final datasets, we reestimated the models, and conducted extensive diagnostic tests including visual inspection of model residuals to verify that the assumptions of the classical linear regression model were not being violated. Inspection of the residuals suggested that the models were correctly specified; there was no evidence of either omitted variables or non-linearities. Moreover, the model residuals were plotted against each of the independent variables; the plots did not indicate that the disturbance terms were heteroskedastic.
Results Table 2 provides the results for the Sunday model. Given that we are dealing with noisy data, the R’ of 0.60 is quite impressive, and as per the F-statistic. the model is highly significant (p < 0.01). While the expenditure on PRINT and TV on Sunday is significant and positively related to SALES, the advertising expenditures on Saturday do not appear to help Sunday’s sales (as observed from the coefficients of RADIO,_, and PRINT,_,). Journal of‘ Retailing and Consumer Services 1994 Volume I Number 2
An advertising Table 2 Model: Sunday. advertising expenditures Independent
variable
INTERCEPT PRINT,, TV, RADIO,,_, PRINT,_, SALES,,,, NSTORES,, Summary
Relationship
between
evaluation
sales and
CoelTicient
t-statistic
-188223.91 0.77 9.11 1.29 -1.29 0.56 14581.98
-1.05 3.35*** 5.01*** 0.29 -1.50 11.20*** 2.68***
statistics
R2
0.60 24.7 97
F-statistic (6,90) No. of observations ***p < 0.01
Table 3 Model: and advertising Independent INTERCEPT PRINT,, TV,, RADIO,, PRINT,,-, TV,,., RADIO,,_, NSTORES,, SALE%,, SUNPRINT SUNPRINT SUNPRINT SUNPRINT SUNPRINT Summary
Monday to Friday. expenditures
variable
(T)
Relationship
Coetricient 212175.03 0.60 2.31 5.91 1.62 2.96 3.22 1763.41 0.54 1.28 0.91 0.83 0.85 0.42
between
sales
t-statistic 1.87* 1.01 1.89* 1.77* 1.33 _5.02*** 0.91 0.58 13.50*** 6.77*** 5.06*** 4.61*** 4.72*** 2.00**
statistics
R= F-statistic (13,490) No. of observations
0.38 24.2 504
*p
Table 4 Model: Saturday. advertising expenditures Independent
variable
INTERCEPT PRINT, RADIO, PRINT,_, RADIO,, TV,-, NSTORES, SALES-,, Summary
Relationship
between
sales and
Coefficient
t-statistic
-318772.94 -1.03 -3.91 -0.041 -5.38 0.59 29 523.38 0.56
-1.02 -0.55 a.34 -0.04 Xl.56 0.27 3.05*** 5.60***
statistics
R* F-statistic (7.91) No. of observations
0.27 6.2 99
*** p < 0.01
Journal of Retailing and Consumer Services 1994 Volume 1 Number 2
system for retailers: C Bhattacharya
and L M Lodish
For generating sales on the same day, TV appears to be more effective than print. Note that although for Sunday alone, $1 spent on print advertising generates only $0.77 in sales, this does not necessarily imply that print advertising on Sunday is ineffective, because Sunday’s print advertising affects sales through to the following Friday. In line with expectations, the covariates of previous years’ sales and the number of stores are positively related to SALES, and the relationships are significant. The system also has the ability to portray graphically a variety of diagnostics related to the model. For instance. one of the questions of interest is whether the productivity of the firm’s advertising spending is changing over time. An intuitively appealing way of investigating this issue is to plot the model residuals over time and observe whether the residuals are systematically becoming more positive or negative. Figure 2 shows a plot of the residuals over time for the Sunday model. Note that the residuals do not exhibit any systematic variation. Similar graphs are easily generated to show how each of the media variables affects sales, and they provide management with a better understanding of the results. In Table 3, we provide the results for the Monday to Friday model. The t-statistics of PRINT, and PRINTd_, suggest that print is not an effective medium during the week. Interestingly, TVd_, is much more significant than TV,, which makes sense because many more households have opportunity to watch TV in the evenings and then purchase the merchandise the following day. The opposite is true for radio; while RADIOd is significant at the 10% level, RADIO,_, is insignificant. This is perhaps attributable to people hearing the sale announcements in their car and deciding impulsively to go to the store. The most interesting aspect about the Monday to Friday model is how Sunday’s print advertising affects sales through the week. Typically, there is an insert or ‘tab’ in Sunday’s paper, the advertised prices in which hold good through the week. Advertising expenditures vary according to the number of pages included in the insert. Figure 3 shows graphically how spending $1 on print advertising on Sunday impacts on sales on each of the following five weekdays. In line with conventional knowledge in advertising, we find that the effect of advertising declines over time. It was indeed reassuring to uncover a reasonably wellbehaved decay curve from real data. Finally, Table 4 gives the results for Saturday. What do we see? First, the summary statistics of R2 and the F-statistic (although significant at the 1% level) suggest a much poorer relationship between advertising expenditure and sales. The coefficients of the media expenditure variables show that, in sharp contrast to Sunday and the weekdays, advertising expenditures incurred on Saturdays do not 97
An advertisirq
evaluation
and I, M Lodish
system for retailers: C Hhattacharya
RESIDUAL. (Thousands of Dollars)
.
. . . .
.
-100
.
.
.
.
-
.
-200
I 100
0
I 200
I 300
I 400
I
I
wl
600
l
.r
_
.
a .
1
700
l
.
.
.
-
.
.
.
.
.* I 800
I
900
I
lmo
I 1.100
I
1.200
TIME
(DAYS)
Figure 2
Model:
Sunday.
Advertising
Impact ofS1 Spent on Sunday
Figure
I
I
MOtt
Tue
3 Model: Monday impact during the week
I
Wed
to Friday.
I
I
Thu
Fri
Sunday’s
ä
print
seem to be helping sales at all! In fact. although not statistically significant, the PRINT and RADIO variables are even directionally negative. As expected, year-ago sales and the number of stores are positively related to sales, and the relationships are significant. The question was: why were we getting such a weak correlation between sales and the media expenditure variables in the Saturday model‘? In discussing the results with management, it surfaced that the company had been using the same type of sale for a majority of the Saturdays for many years. 98
over time
On any given Saturday. people living in this area knew with a high degree of predictability that the company had a ‘one-day sale’ on every Saturday. Variations in advertising expenditures therefore had less of an impact on the sales levels. In sum. our modelling efforts showed that print advertising on Sunday not only impacts sales positively on Sunday, but also has a positive impact during Monday to Friday. However. both during the week and especially on Saturday, print advertising seems relatively ineffective. Overall, TV seems to be an effective medium, particularly on Sundays. During the week, TV advertising is effective in generating sales the following day. Radio seems effective during Monday to Friday, particularly in generating sales on the same day.
4
I
effectiveness
Implementation Before taking any action, management wanted a sense of the total incremental sales associated with $1 of advertising on a particular day in a particular medium. As the least-squares estimators of the regression models are assumed to be normally distributed random variables. under the assumption that these normal distributions are independent of one another. we could use standard statistical principles to compute the average impact and the associated standard error of each of the media variables. The combined impact of a media variable is simply the sum of the relevant model coefficients, and the combined standard error is given by the square root of the sum of the estimated variances of the respective coefficients. In computing the average impact. we eliminated those media variables that had turned .lorrmul of Retailing unti Consumrr Serviws 1994 Volume I Number 2
An advertising evaluation system for retailers: C Bhattacharya and L M Lodish Table 5 Estimated advertising
incremental
sales associated
TV Sunday Radio during week TV during week Print Sunday Print during week Radio Saturday Print Saturday
with $1 of
12.07*** _5.91* 5.27*** 5.06*** Non-significant Non-significant Non-significant
to conduct similar analyses in other geographic markets. The flexible and multidimensional data storage system designed by us should make such analysis quite feasible to conduct. There are also a number of ways in which the system we designed can be improved:
(1)
“pcO.10 *** p < 0.01
(2) out to be insignificant in the original analysis (for example, in calculating the incremental sales generated by $1 of radio advertising during the week, we ignored the coefficient associated with RADIO,_, in the Saturday model). Moreoever, in instances where it is impossible to tell the differential effect of a certain media variable on different days of the week2, we assume that the impact of that variable is the same across those different days of the week (for example, we add 2.31, the coefficient for TV,_, in the Monday to Friday model, not only in computing the impact of advertising on TV during the week but also in computing the impact of advertising on TV on Sunday). Table 5 gives the results of this analysis. The table shows that advertising is most effective on Sunday and least effective on Saturday. Across media, TV appears to be more effective than print. The difference of advertising effectiveness across days of the week and across media prompted management to make some changes in their media schedules. Basically, the firm decided to cut back Saturday’s advertising to maintenance levels. Note that as our models did not incorporate the impact that competitive advertising had on the firm’s sales, managers, in their wisdom, decided to maintain some advertising on Saturday to prevent loss of sales to competition. Interestingly, our situation was similar to that described in Little (1975) where managers tempered the model recommendations with their own understanding of the market. What is gratifying to us is that the decision support system development process gained credibility with management.
Directions
for future research
To validate the model findings, similar exercises need to be conducted using data for adjoining small towns in which there may be only one or two stores, and hence greater possibilities of establishing causality. To be able to allocate advertising resources across markets, there is an obvious need
2For example, the coefficient of TV,., in the Monday model reflects the impact of TV advertising on Sunday during Monday-Thursday. Journal of Retailing and Consumer
to Friday as well as
Services 1994 Volume I Number 2
(3)
(4)
Similar to Lodish (1982) an experimental analysis component could be added to the current system. Such a component would be useful for validating the model findings in one location, before taking large-scale media mix and scheduling decisions. Put seasonality into the models as moderator of advertising effect on sales. Make the system capable of handling nonlinear response functions, and incorporate aspects of optimization into the models. Store data on competitive advertising in the system, so that competition’s impact on the firm’s sales can be ascertained.
Similar decision support systems can also be developed for other retailing contexts. For instance, in the realm of supermarkets, there are no studies that have related total store sales to total featuring expenditures by day and by medium. On the other hand, a system such as the one developed by Abraham and Lodish (1993) for evaluating promotion profitability for supermarket goods would be extremely helpful for speciality retailers and department stores. Conceivably, methodologies can be developed to arrive at better estimates of ‘baseline’ sales both for advertising and for promotions done by specialty retailers. Once these baseline estimates are derived, we can compute the incremental sales and causally relate them to the advertising and promotional variables. Such a system would be more versatile than the one we have described in this paper.
Conclusion All the components of the system we designed (data reorganization, modelling methodologies and report formats) have been used elsewhere in practice. Our contribution lies in putting the pieces together to aid decision-making for an important managerial problem. Imperfections notwithstanding, the managers of this national retail organization had their first exposure to quantitative evaluations of advertising efforts. The instant credibility of the system and its adoption by top management was a very satisfying experience. In fact, the person who was the CEO of the company at the time we designed the system has recommended it to many of his peers in the retail business. Although every organization will no doubt have its own set of challenges, we believe that our system design is simple, low-cost, and flexible enough for it to be fruitfully adapted to measure the productivity of advertising spending in other retailing firms. 99
advertising
An
evaluation
system for retailers: c’ Bhattacharya
References Abraham, M.M. and Lodish. L.M. (1993) ‘An implemented system for improving promotion productivity using store scanner data’. Marketing Science. 12 (3). 248-269 Bass. F.M. and Clarke, D.G. (1972) ‘Testing distributive lag models of advertising effect’. .Journul of Marketing Rc.se~trch. 9 (August). 2YX-30X Bemmaor. A.C. and Mouchoux. D. (1991) ‘Measuring the shortterm effect of in-store promotion and retail advertising on brand sales: a factorial experiment’, .lormurl of Mrrrkrritlg Rexarch, 28 (May). 202-2 14 Blattberg. R.C. and Neslin. S.A. (1990) S~lcs Pror,~o/io/r Concepts, Merhods and Sfrrrtegie.s, Englewood C‘liffs. NJ. Prentice-Hall Box, G.E.P. and Jenkins. G.M. (1976) 7%r Srrirs Atur/w.\: Forecasting untl Control. 2nd edn. San Francisco: HoldenDay Eastlack, J.O. and Rao. A.G. (lYX6) ‘Modeling response to advertising and pricing changes for “V-X” cocktail vegetable juice’. Marketing Science, 5 (3). 245-259 French, K.R. (1980) ‘Stock returns and the weekend effect‘. Journal o,f Financiul Economics. 8 (March), SS-69 Hanssens. D.M.. Parsons. L.J. and Schultz. R.L. (IYYO) Mrrrl\c~r Re.sponse
Models:
Econometric
und
Boston: Kluwer Academic Publishers Kassaye, W.W. and Vaccaro, J.P. (lY91) effectiveness through better selection Rusinexss. 13 (3). 40-44
100
Time
Serie.s
Ar~ul~.siv.
arid L_ M lzodish Kennedy. P. ( I YY2) it (;~tirlr 10 f~~,ottonlc,tri~,.\. (‘ambridge. MA: MIT Press Kiem, D.B. (19X5) ‘Dividend yields and stock returns: implicalions of abnormal January returns’. .Jorrrrltr/ of Fit~nncirrl Ec~onortzi~.s. 14 (September). 473-4XY Kim, P. (1002) ‘Does advertising work? A review 01 the evidence’. .Jorrrtd of’ Con.swner Markcrin,y, 9 (4) 5-2 I Lilien. G.L.. Kotlrr. ‘P. and Moorthg, K.S. (lYY3) MNrkCfitlg Mode1.s. Englewood Cliffs. NJ: Prentice-Hall Little, J.D.C. 6975) ‘BRANDAID: a marketing mix model. Part\ 1 and 2‘. 0perdon.v Resetrrch, 23 (July-August), 62X-673 Little. J.D.C. (1979) ‘Decision support systems for marketing managers’. .Jotrrttcr/ of Marketing. 43 (Summer). Y-26 Little. J.D.(‘. and Lodish. L.M. (1969) ‘A media planning calctlIus’. Oprrrrriotrs Rc.secrrdr. 17 (Jan-Feh). I-35 I,odish. L.M. (19X2) ‘A marketing decision support \p\tcm 101 retailers’, Mtrrkrriq Scicvfw. 1 ( 1). 3 I-56 Rae. A.(;. and Miller, P.B. ( 1975) ‘Advertising/sales rc\ponsc functions‘, .Journal of’ Advertising Rrsenrch. 15. 7-l 5 Smith. S.A.. McIntyre. S.H. and Achabal, D.D. (IYYJ) ‘A twostage salts forecasting procedure using discounted lcast squares’. Jolrrtrrrl of Marketing Research, 31 (February). 44-55 Stewart. D.W. (lYX9) ‘Measures, methods, and models in advertising research’. .Jocwntr/ q/‘Adverrising Resecrwh. 29 (2). 54-60
‘Increasing of media’.
advertising Revirn
of
Vaccaro. J.P. and Kassayc. W.W. (IYXX) ‘Increasing the advzrtising cffectivenrss of small retail businesses‘. Etrtreprertc,/tr.\lti/~ Theory rrnd P rrrctiw, (Fall) 4 I-47
Business
system
Bhattacharya School,
Emory
Universit_v,
Atlanta,
GA
30.322, USA
Leonard M Lodish The Wharton
School,
Univrrsit~~ of Pmmplvanirr
Extant research on assessing advertising productivity has mostly focused on developing methodologies for capturing the longer-term effects of ‘theme’ advertising. Measuring advertising productivity in the retailing industry, however, poses different challenges. For instance, in many retailing firms, the primary role of advertising is to announce ‘limited-time price specials’ and thereby attract customers to the store. In the absence of store traffic information, in order to assess advertising effectiveness for these businesses, there is a need to relate advertising expenditures to short-term sales changes. In this study, we design a decision support system aimed at aiding retailers in budgeting, tracking and evaluating their advertising. The overall objective of this system is to recommend more productive spending of the advertising dollar by market and by media type. We demonstrate a real-life application of the system for a national retail organization that specializes in electronic goods. Using daily sales and advertising expenditure data, we estimate different models to assess advertising effectiveness for different days of the week. We discuss the model findings, their implications for advertising decisionmaking, and the next steps in improving this system. Keywords:
advertising.
retailing.
dccislon
support
system\
Advertising continues to be a popular marketing tool: in 1990, over $128 billion was spent on advertising (Kim, 1992). Not surprisingly, numerous studies have endeavoured to improve advertising effectiveness by modelling the relationship between sales and advertising (e.g. Rao and Miller, 1975; Little, 1979: Eastlack and Rao, 1986; Stewart, 19%)). Almost without exception, the typr of advertising for which researchers have attempted to assess effectiveness has been ‘theme’ advertising, typically used by manufacturing firms. Very little research has focused on advertising in the retail industry (fol exceptions see Vaccaro and Kassaye. 1988: Kassaye and Vaccaro, 1991). This is especially interesting, because the money spent by manufacturing firms on theme or image-building brand advertising has generally been on the decline since the 196Os, and in fact one of the sectors that has witnessed major growth in advertising dollars is the retailing industry (Kim, 1992). In particular, as most of us are
aware from our daily observations, retailing firms have been committing substantial resources to their advertising budgets to announce temporary price reductions. These price reductions last for varying lengths of time: we typically come across announcements such as ‘one-day sale’, ‘for this week only’. and the like. These announcement ads appear in the usual media: newspapers, television and radio. An important distinction between theme advertising employed by manufacturing firms and these ‘sale’ announcements used by retail organizations is that in the latter context the ‘life’ of the ads is mostly limited’ by the duration of the ‘sale’. In other words, ads that announce time-bound price-offs are much more ‘action driven’. For the most part, the purpose of these ads is to attract customers to the store, during the tenure of the sale. Given this rather specific, well-defined role of this type of advertising in allocating resources, the retailer has to make a myriad decisions: how much
The authors are listed in alphabetical order. The study described in this paper was funded by the Wharton School. University of Pennsylvania.
‘In drawing this distinction, we are ignoring the role that these ads may play in the first three levels of the hierarchy of effects model: that is. in building awareness. interest and desire.
90
0969-6989/94/02090-l
1 0 1994 Butterworth-Heinemann
Ltd
An advertising
evaluation
to allocate to a particular market, how much to allocate to different media within a market, on which days of the week to advertise more heavily, and so on. While operating under resource constraints, each of these decisions involves tradeoffs, and the intense competition in the retail arena and consequent pressure on margins make it imperative for managers of these firms to maximize the marginal productivity of the advertising dollar. In the absence of store traffic information, the only way that managers of retailing firms can assess and consequently increase the productivity of such advertising spending is by measuring how advertising expenditures are related to short-term sales changes. Unlike traditional sales-advertising modelling contexts, where much emphasis is placed on deciphering the lagged effects of advertising (e.g. Bass and Clarke, 1972) retailers need to assess ‘short-term’ advertising effectiveness. Although researchers have responded to advertising decision support needs (e.g. Little and Lodish, 1969) to date there is no study that has attempted to help retailers make decisions in the area of advertising price reductions. The large resource commitment by retailers to announcing price-offs makes it important for researchers to design mechanisms that would improve the productivity of such spending. Our overall objective in this study is to develop a decision support system (DSS) that will aid retailers in budgeting, tracking and evaluating their expenditures on price-off advertising. In the course of developing this system, we investigate the following empirical research questions, which have hitherto not been addressed in the literature: (1) (2) (3)
Does price-based advertising impact sales positively? Does the effectiveness of such price-based advertising differ by days of the week? Does it differ across different media?
We design the system using sales and advertising data from a national retailer of electronic goods. The data are for a major city in the USA and span a period of three years. Our results suggest that for this retailer, in the market under study, advertising effectiveness exhibits tremendous variation both by day and by medium. The DSS we describe in this paper is a prototype of the kind of system that every retailer can easily implement for evaluating their advertising expenditures. As we describe in the following sections, the proposed system does not require highly sophisticated modelling methodologies or expensive software. Our belief is that an intuitively appealing, easy to understand, low-cost system that makes explicit the inevitable trade-offs involved in advertising decisions has a much better chance of ‘adoption’ than a more complex system. We shall proceed as follows. First, we review the relevant literature in this field. Second, we describe Journal of Retailing and Consumer Services 1994 Volume I Number 2
system for retailers: C Bhattacharya
and L M Lodish
the data available to us, and the challenges that we faced in the model development phase. Next, we describe the methodology and the empirical results of our analysis. We conclude with the implications of our study, and outline fruitful areas for future research. Related literature As mentioned in the introduction, very little research has been conducted in the area of retail advertising and sales. In the context of supermarkets, price-off announcements are popularly known as ‘features’. Basic profitability calculations that managers can perform to determine the efficacy of a feature ad are described in Blattberg and Neslin (1990); however, such calculations are contingent on our knowledge of the brand’s ‘normal’ or ‘baseline’ sales (Abraham and Lodish, 1993). Bemmaor and Mouchoux (1991) investigated the combined impact of retail advertising and in-store promotion on brand sales in an experimental framework. Overall, they found that the deal-advertising interaction is large: sales increase by a much greater extent when deals are advertised; moreover, these increases are smaller for leading brands. In the realm of specialty retailers, Vaccaro and Kassaye (1988) conducted a study among select urban and suburban communities to assess the relative effectiveness of newspapers and radio in reaching the specific target markets of small business retailers. Their methodology consisted of interviewing a random sample of households from both urban and suburban communities and asking them questions on utilization of the different media in choosing a liquor store. The authors conclude that the effectiveness of different media differs across urban and suburban communities. Kassaye and Vaccaro (1991) did a study in which they first surveyed households to assess their media habits, and then did a follow-up field experiment in which they manipulated the media mix (urban dailies, suburban weeklies and radio) in line with their survey findings. The authors report significant increases in store traffic and sales volume as a result of changes in the media mix. In the area of decision support, there have been a few important studies that have attempted to help managers with advertising decisions. The BRANDAID model developed by Little (1975) is a marketing mix model representing a classic example of Little’s decision calculus approach. Advertising is one of the submodels of this comprehensive model. Little describes an application of BRANDAID to a packaged good, where the fundamental problem facing brand management was the allocation of a budget to advertising and promotion. In part, the methodology involves a log-log regression model where sales are regressed against advertising and promotion expenditures to derive effectiveness 91
An advertising
evaluation
system for
retailers:
C Bhattachary
indices for each of these expenditure variables. which are then multiplied together to provide a sales forecast. MEDIAC (Little and Lodish. 1969) is one of the best-known media-planning models. The model assumes that an advertiser is seeking to buy media for a year with a certain amount of dollars so as to maximize his sales. MEDIAC is a comprehensive model, which includes the dimensions of market segments, sales potentials, diminishing marginal returns. forgetting and timing in a media-planning model. However, as reported by Lilien er crl. (1992). its use has been limited by its sophistication: it seems to be too complex for most media planners. Lodish (1982) developed a decision support system capable of planning marketing strategies and allocating resources for a multi-store retailer. The system combines well-established analysis tools, sophisticated computer software, and management’s implementation needs to help solve top-level strategy and forecasting problems. The system consists of a planning model, national campaign evaluation system, experimental analysis system, and an ongoing interactive database and reporting system. The campaign evaluation segment of the system is the one that is most relevant for our purposes. Examples of campaigns are White Sales and home furnishing sales. which typically include a price event and supportive advertising. In this instance, management wanted more detailed analyses of the sales effects of the campaigns. The system uses dollar and unit sales to routinely calculate percent changes between the ‘pre’, ‘during’ and ‘post’ periods of the campaign, and sales growth in the ‘during’ and ‘post’ periods. Our paper builds on the learnings provided by a number of the aforementioned studies. In contrast to the broad-based DSS developed by Lodish (1982), our interest is in developing a system specifically for investigating the advertising-sales relationship in the context of retailing. Although our system does not have an experimental analysis component as yet, in the same spirit as the field experiments conducted by Kassaye and Vaccar (1991), the proposed system will provide managers with response coefficients that they can use to alter their media mix. Some of the variables we shall use in our models will be operationalized in similar fashion to what was used in the BRANDAID application described in Little (1975). Finally. the models we shall develop will be considerably simpler than the methodologies used in MEDIAC, so as to encourage greater adoption in practice.
System overview The ultimate goal of a system built to measure advertising productivity is to recommend more productive spending of the advertising dollar. In our view, such a system should possess the following 92
and L M I_odish
general features and benefits. such systems to be able to: (1)
(2)
(3)
We want the users of
group advertising expenditures by any media type, for any market. for any time period, and relate these expenditures to sales changes: quantify advertising response by market and hence conduct a variety of ‘what-if‘ analyses. such as estimating the impact of different media and/or budget levels on sales: graphically understand the reasoning behind the response estimates.
As we describe in the following sections. the system we designed for our client possesses all the above features. But bear in mind that at the time of the study, the notion of advertising decision support. although appealing, was novel to our client. There had been no previous attempts in this organization to quantify the relationship between sales and advertising expenditure. Hence. in addition to the above general features, we had to define objectives and undertake a few tasks that were specific to the situation in hand, and geared towards convincing management that they were engaged in a valuable exercise. For instance, we knew that our primary goal was to highlight resource allocation options to show management the potential increase in effectiveness (and consequently profitability) of alternative decisions that could be made. But given that this was management’s first encounter with such a system, we wanted the models to merely provide managers with directional guidance (for example, advertise more heavily on television on Sundays), rather than identify optimal levels of expenditure. The proposed system would still enable marketing managers to compare advertising alternatives, to pick better alternatives than the ones that traditional methods had been selecting, and to track the impact on sales volume of any changes that they implement. In contrast to the implicit trade-offs that management were making in setting their advertising budget and in evaluating their advertising effectiveness, the system would encourage them to make such trade-offs in a more explicit fashion. Our second objective was to demonstrate to management the important role that computers could play in decision support. In this specific instance, we wanted to show management how the use of state-of-the-art computer languages facilitates early diagnosis of advertising problems and opportunities. In terms of advertising and sales information, the marketing department of the organization maintained Lotus l-2-3 files primarily for the purpose of preserving historical information. As we discuss in the data section, the files were not maintained in a manner that facilitated quantitative analysis. We built the system using PC Express. a fourth-generation, high-level command language (a Jmtmnl
of Hrtuiling rmi C~onsrcm~r Survicrs 1994 Volume
I Nunlhcr 2
An advertising evaluation system for retailers: C Bhattacharya and L A4 Lodish
Figure 1
How
the data were stored
product of Information Resources Inc). In demonstrating the use of the system, we wanted to impress on management that the same data, when stored and used in the appropriate way, could serve as ‘an early detection and warning system’.
The data As mentioned in the introduction, the organization is a national seller of household electronic appliances, such as TVs, VCRs, stereo systems and air conditioners. At the time of the study, the organization had 214 retail outlets nationwide, an annual sales turnover of about $1 billion, and an annual advertising budget of $6 million, The raw data that were made available to us comprised daily dollar sales and expenditures by different media. The media comprised television, print (that is, newspaper advertising), and radio. The print expenditures were further disaggregated by 20 different publications that served the market under study. As the firm added a number of retail outlets during the period of the study, we also obtained information on the number of retail outlets that the firm operated in the given market. The data were for three accounting/fiscal years, spanning May 1987 to April 1990, for a major market in the USA. Each year comprised 364 days (52 weeks x 7 days/week), so we had data for 1092 days in all. There were also some competitor’s advertising expenditure data available from syndicated sources, but they were so limited in nature that we have not used them in the existing system. Journal of Retailing and Consumer
Services 195’4 Volume I Number 2
Data reorganization was a challenge. The existing database management and reporting format can best be described as one that fostered ‘market status reporting’ (Little, 1979). Although the data were preserved on Lotus l-2-3 files, the format of the files was rather inflexible for our purposes. Figure 2 is an illustration of the way in which the data were stored. (The sales and advertising numbers in Figure 1 and Table 1 are disguised for purposes of confidentiality.) Note that there was a separate file for each planning period. The columns of the file comprised different days of the week, and the rows were the headings of interest, such as total expenditure this year, total expenditure last year, dollar sales this year and last year, sales as a percentage of budget, and so on. The cells contained information on (a) dollar sales for the current period, and for the same period in the previous year, and (b) type of medium used and corresponding expenditures; the total daily expenditures were again reported both for the current and the previous year. In contrast, we wanted a system that would facil: itate ‘market response reporting’ (Little, 1979; Abraham and Lodish, 1993). This necessitated providing the system users with a level of flexibility in data retrieval that the existing format did not provide. After considerable discussion, we reorganized the data in the format that is illustrated in Table
1.
Given that we had data only for one market, the values of all the variables for the three-year period are identified by a single dimension: ‘day number’ 93
An advertising evaluation system ,for retailers: C Bhattachuryu md L M Lodish Table 1 How the data look now Day number
Day of week
Sunda> Monday Tuesday Wednesday Thursday Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday
4
s IO II
12 13 I4
TV 6) 50000 56000 43000 46000 300(X) 42000 sxooo 34OGi) 38000 43000 48000 35000 43000 X3000
(which goes from 1 to 1092). Having the day number as a dimension enables us to identify all details corresponding to a given day: the day of the week, the month, the year. the same day in the previous year, and so on. Once we have similar data for ‘markets’ would be a second multiple markets, dimension by which all the variables would be identified. Storing the data in this fashion enables us to group sales and expenditures by day of week, by media and by market, and look at summary statistics and run models in minutes.
The models The proposed model seeks to relate dollar sales to advertising expenditures at the market level. We emphasize that our modelling objective is not one of theory testing, but one of providing managers with insightful and reasonably valid response coefficients. This objective plays an important role in our model development. For instance, note that in the strictest sense, dollar sales is a surrogate for store traffic. The task of the ads is to bring customers into the store. at which point the salespeople take over. But at the time of system development. there were no records of daily store traffic and salesforce effort: hence we work without these variables. Further refinements are also possible in terms of incorporating information such as the location of a print ad in the paper. and the time of day a particular TV or radio ad was aired. But again such refinements are beyond the system’s objectives at this point. Once management gets comfortable about using the existing system, such refinements can easily be incorporated. In very broad conceptual terms, the model we propose to estimate is as follows: SALES,,
94
= f(TV, ,...., TV,,-,,, PRINT ,,,..., PRINT,,-,, RADIO, ,,..., RADIO,,-,,. NSTORES,,, DAY-OF-WEEK,, SEASON,,)
Print 6)
Radio 6)
8000 0
0 3000
0 0
0 0
7500
0
0
0
0
!mo
0
0 0
0
0 60(x) 0
5605 (I
0
0
where the subscript n refers to the day under consideration rf-n refers to the nth day prior to the day under consideration, SALES refers to the dollar sales for the market, TV, PRINT, RADIO are the respective expenditures on television, radio and newspapers. NSTORES is the number of stores operated by the firm, DAY-OF-WEEK refers to the day under consideration, SEASON denotes the time of year or special occasions during the year such as Christmas and President’s day when retailers typically offer ‘sales’. and fis a multivariate function. This model formulation assumes that there is no strong covariation between salesforce effort and advertising expenditures. Similarly, we are also assuming that depth of price reductions in the store and advertising expenditures are not strongly positively correlated. In going from the above general model to the final form, we encountered a number of challenges: the shape of the response curve, the lag structure for the media expenditure variables, level of disaggregation, seasonality and multicollinearity. We discuss each of them in turn. Shape
of the
response
curve
We had no a priori assumptions regarding the shape of the relationship between sales and the variables described above. But as we mentioned before, similar to Lodish (1982), our primary modelling goal was to provide managers with directional guidance (for example, advertise more heavily on television on Sundays), rather than identify optimal levels of expenditure. Hence we asserted that the shape of the response curve was not that critical for our purposes. We tried linear. semi-log and log-log versions of the model, and the linear model gave the best fits. Lag structure
As denoted by the PRINT,,..,,. RADIO,,,,)
lagged in the
variables (TV,,_,, model above, we
An advertising
evaluation
Multicollinearity
Recall from the data description that we had daily expenditure numbers for 20 newspapers. One of these was the leading daily for the metropolitan area, and enjoyed much higher levels of spending (about 80% of the total) compared with the other suburban newspapers. It so happened that the same pattern of print advertising was often followed across all the newspapers: that is, the print advertising variables were highly collinear. To reduce the incidence of multicollinearity, we aggregated the expenditures across all the newspapers and created a composite PRINT variable (Kennedy, 1992). Note that a better level of disaggregation for the data would be to have sales by store area (ideally even by item), by day, and expenditure on media vehicles allocated to the particular store area. But in this instance, as the bulk of the sales and expenditure pertained to a major metropolitan market (within which it was impossible to demarcate advertising expenditures to a particular store area), and as the adjoining small towns all received the radio and TV stations of this market, we believe that studying the sales-advertising relationship at the market level will not result in any great aggregation error.
Level of d&aggregation
As we all know from our observations in daily life, both sales and advertising of electronic household appliances are not uniformly distributed across the days of the week. Households have greater spare time at the weekends to shop for such items, and hence it is likely that the relationship between advertising and sales is different for different days of the week. This problem is somewhat similar to the problem of ‘calendar anomalies’ that has been dealt with in the finance literature. Researchers in finance (e.g. French, 1980; Kiem, 1985) have tried to explain abnormal stock returns in certain months and certain days of the week. The usual methodology is to use a dummy variable regression in which the dummy variable for the month/day of interest is left out of the estimation. Each of the dummies included in the regression variables then denotes the difference in the average return between a specific month/day and the month/day of interest, and the average daily stock return is regressed against these dummy variables. The hypothesis of the excess returns being the same for different months/days of the year is then tested through the F-statistic, which measures the joint significance of the dummy variables. The difference between the studies in finance and our study is that we are not interested in establishing that a particular day of the week is different from the other days. Rather, we are interested in estimating the differential effect that the media expenditure variables have on store sales on different days of the week. Given that different days of the week had certain patterns of advertising attached to them (for example, there was no radio advertising on Sundays, and prices advertised in Sunday newspaper inserts always held good through to the following Friday), we felt that estimating one differential effects model (although a theoretical possibility) would lead to Services 1994 Volume
I Number
and L M Lodish
problems of interpretation. Instead, an easier alternative (because we had enough degrees of freedom) was to estimate separate models for different days of the week. On attempting to estimate separate models for each day of the week, we observed (a) that there were too few non-zero observations during the week, and (b) that the advertising pattern was not very different across the weekdays. We therefore estimated three models, one that pooled observations for Saturday, one that pooled observations for Sunday, and one that pooled observations for Monday to Friday.
contended that sales on a given day were also affected by advertising on previous days: hence we had to decide on the number of lags to include for each media expenditure variable. In other words, how short was ‘short-term’? Except for advertising in the Sunday newspaper, where the prices always held good through to the following Friday, the other price announcements either did not have a specific deadline, or held good for a maximum of three days. For all these ads (that is, except for those in the Sunday newspaper), we started with three lag variables (one for each of the three preceding days). In our trial runs, however, we noticed that at most the preceding day’s advertising expenditures had a significant impact on the sales for a given day. Therefore (except for Sunday’s print advertising), in the final model estimation, we retained only a single lagged variable (denoting expenditures on the previous day) for all three media. -
Journal of Retailing and Consumer
system for retailers: C Bhattacharya
Seasonality
As we would expect, there are weeks during the year when sales of household electronics are considerably higher than ‘normal’. Examples of such weeks include Christmas, Thanksgiving, Labor Day, and the like. Some of these events, such as Labor Day and Thanksgiving, have become traditional ‘sale’ weekends, when all retail stores offer large discounts. This apart, there may be other types of seasonal phenomenon: for example, higher sales of air conditioners in summer and of room heaters in winter. Needless to say, our methodology needs to control for all such seasonal effects. The modelling of seasonal effects is usually accomplished either by adjusting the data through some form of moving average (Hanssens et al., 1990), by using dummy variables to represent the seasons (Kennedy, 1992; Smith et al., 1994) or by 2
95
An advertising
evaluation
system for retailers: C Bhattacharya
using seasonal lag operators (Box and Jenkins, 1976). In the context of relating daily sales to daily advertising expenditures, capturing seasonal effects by dummy variables or through suitable lag operators seemed more appropriate. On inspecting the variety of items carried by the store, and the number of different types of sales offered during the year, we figured that the only way to control adequately for all seasonal effects would be to have separate dummy variables for each week. We estimated the models using weekly dummy variables, but on discussing the results with managers we discovered that they found it cumbersome to interpret all these weekly dummy coefficients. Hence we tried an alternative approach, which was to introduce a lagged sales variable as an independent variable in the model, to reflect the level of sales that took place on the same day in the previous year. As each planning year was always 364 days, if the sales of a given day of interest d are denoted SALES,,, this variable is simply this approach implied that SALESd-3e+ Although we would lose one year’s data for model estimation, managers were much more comfortable with this method. In sum, after debating and trying models, we decided to: (1)
(2) (3)
(4) (5)
alternative
estimate three separate models, one each for Saturday and Sunday and one for Monday to Friday; include a lagged sales variable to reflect sales on the same day in the previous year; include a lagged variable for each type of medium to reflect expenditure on the previous day; create a composite PRINT variable; and estimate additive linear versions for all the models.
The final model (1)
various
formulations
are as follows:
Sunday: SALES,{ = P,PRINT, + B,TV, + &PRINT,,_, B,RADIO,,_, + B,NSTORES, + P~SALES~~X,~ + ed
+
PRINT,, TV,,, RADIOcl refer to the dollar expenditures on these respective media on day d. NSTORES, is the number of stores owned by the firm on day d, and E(, is a normally distributed random error term. Note from the variables included in the model that the firm had no radio advertising on Sunday and no TV advertising on Saturday. (2)
Monday-Friday:
SALES,
96
= B,PRINT, + @*TV, + &RADIO, + B4 x I, X PRINTd_, + B,TV,,m, + &RADIO+,, + B,NSTORES,, + &SALES,,, + C:=,yl x D, x SUNPRINT,c+e,
and L M Lodish
Note that each day d, d E (1.2,..., 1092) corresponds to a specific week w, w E (1,2,...,156). Thus days l-7 correspond to week 1, days 8-15 correspond to week 2, and so on. SUNPRINT, denotes the expenditure on print in week w’s Sunday newspaper, where w is the week to which each of the following five d days belong. The D,s are indicator variables: D, is 1 when the day under consideration is Monday and 0 otherwise, D, is 1 when the day under consideration is Tuesday and 0 otherwise. and so on. As the effect of Sunday’s print advertising on Monday is already captured by the SUNPRINT variable. we multiply PRINT+, by an indicator variable I,, which assumes a value of 0 when the day under consideration is Monday and 1 otherwise. All the other variables of the above model have been defined previously. (3)
Saturday:
SALES,,
= P,PRINT,, + &RADIO, + &PRINT,_, + P,RADIO,,m, + &TV,-, +B,NSTORES,, + &SALES-XJ + e,/
All of the above models were estimated via ordinary least squares (OLS) regression. On estimating each of the models, we plotted the residuals to look for potential outliers. The rationale for looking for outliers is that they are influential ohservations, which often have a strong, undesirable influence on the OLS estimates (Kennedy, 1992). As expected, these powerful points always turned out to be special event days such as Christmas Eve or Ash Wednesday, when sales were abnormally high. These data points were dropped from the estimation data sets in consultation with the Advertising Manager. After determining the final datasets, we reestimated the models, and conducted extensive diagnostic tests including visual inspection of model residuals to verify that the assumptions of the classical linear regression model were not being violated. Inspection of the residuals suggested that the models were correctly specified; there was no evidence of either omitted variables or non-linearities. Moreover, the model residuals were plotted against each of the independent variables; the plots did not indicate that the disturbance terms were heteroskedastic.
Results Table 2 provides the results for the Sunday model. Given that we are dealing with noisy data, the R’ of 0.60 is quite impressive, and as per the F-statistic. the model is highly significant (p < 0.01). While the expenditure on PRINT and TV on Sunday is significant and positively related to SALES, the advertising expenditures on Saturday do not appear to help Sunday’s sales (as observed from the coefficients of RADIO,_, and PRINT,_,). Journal of‘ Retailing and Consumer Services 1994 Volume I Number 2
An advertising Table 2 Model: Sunday. advertising expenditures Independent
variable
INTERCEPT PRINT,, TV, RADIO,,_, PRINT,_, SALES,,,, NSTORES,, Summary
Relationship
between
evaluation
sales and
CoelTicient
t-statistic
-188223.91 0.77 9.11 1.29 -1.29 0.56 14581.98
-1.05 3.35*** 5.01*** 0.29 -1.50 11.20*** 2.68***
statistics
R2
0.60 24.7 97
F-statistic (6,90) No. of observations ***p < 0.01
Table 3 Model: and advertising Independent INTERCEPT PRINT,, TV,, RADIO,, PRINT,,-, TV,,., RADIO,,_, NSTORES,, SALE%,, SUNPRINT SUNPRINT SUNPRINT SUNPRINT SUNPRINT Summary
Monday to Friday. expenditures
variable
(T)
Relationship
Coetricient 212175.03 0.60 2.31 5.91 1.62 2.96 3.22 1763.41 0.54 1.28 0.91 0.83 0.85 0.42
between
sales
t-statistic 1.87* 1.01 1.89* 1.77* 1.33 _5.02*** 0.91 0.58 13.50*** 6.77*** 5.06*** 4.61*** 4.72*** 2.00**
statistics
R= F-statistic (13,490) No. of observations
0.38 24.2 504
*p
Table 4 Model: Saturday. advertising expenditures Independent
variable
INTERCEPT PRINT, RADIO, PRINT,_, RADIO,, TV,-, NSTORES, SALES-,, Summary
Relationship
between
sales and
Coefficient
t-statistic
-318772.94 -1.03 -3.91 -0.041 -5.38 0.59 29 523.38 0.56
-1.02 -0.55 a.34 -0.04 Xl.56 0.27 3.05*** 5.60***
statistics
R* F-statistic (7.91) No. of observations
0.27 6.2 99
*** p < 0.01
Journal of Retailing and Consumer Services 1994 Volume 1 Number 2
system for retailers: C Bhattacharya
and L M Lodish
For generating sales on the same day, TV appears to be more effective than print. Note that although for Sunday alone, $1 spent on print advertising generates only $0.77 in sales, this does not necessarily imply that print advertising on Sunday is ineffective, because Sunday’s print advertising affects sales through to the following Friday. In line with expectations, the covariates of previous years’ sales and the number of stores are positively related to SALES, and the relationships are significant. The system also has the ability to portray graphically a variety of diagnostics related to the model. For instance. one of the questions of interest is whether the productivity of the firm’s advertising spending is changing over time. An intuitively appealing way of investigating this issue is to plot the model residuals over time and observe whether the residuals are systematically becoming more positive or negative. Figure 2 shows a plot of the residuals over time for the Sunday model. Note that the residuals do not exhibit any systematic variation. Similar graphs are easily generated to show how each of the media variables affects sales, and they provide management with a better understanding of the results. In Table 3, we provide the results for the Monday to Friday model. The t-statistics of PRINT, and PRINTd_, suggest that print is not an effective medium during the week. Interestingly, TVd_, is much more significant than TV,, which makes sense because many more households have opportunity to watch TV in the evenings and then purchase the merchandise the following day. The opposite is true for radio; while RADIOd is significant at the 10% level, RADIO,_, is insignificant. This is perhaps attributable to people hearing the sale announcements in their car and deciding impulsively to go to the store. The most interesting aspect about the Monday to Friday model is how Sunday’s print advertising affects sales through the week. Typically, there is an insert or ‘tab’ in Sunday’s paper, the advertised prices in which hold good through the week. Advertising expenditures vary according to the number of pages included in the insert. Figure 3 shows graphically how spending $1 on print advertising on Sunday impacts on sales on each of the following five weekdays. In line with conventional knowledge in advertising, we find that the effect of advertising declines over time. It was indeed reassuring to uncover a reasonably wellbehaved decay curve from real data. Finally, Table 4 gives the results for Saturday. What do we see? First, the summary statistics of R2 and the F-statistic (although significant at the 1% level) suggest a much poorer relationship between advertising expenditure and sales. The coefficients of the media expenditure variables show that, in sharp contrast to Sunday and the weekdays, advertising expenditures incurred on Saturdays do not 97
An advertisirq
evaluation
and I, M Lodish
system for retailers: C Hhattacharya
RESIDUAL. (Thousands of Dollars)
.
. . . .
.
-100
.
.
.
.
-
.
-200
I 100
0
I 200
I 300
I 400
I
I
wl
600
l
.r
_
.
a .
1
700
l
.
.
.
-
.
.
.
.
.* I 800
I
900
I
lmo
I 1.100
I
1.200
TIME
(DAYS)
Figure 2
Model:
Sunday.
Advertising
Impact ofS1 Spent on Sunday
Figure
I
I
MOtt
Tue
3 Model: Monday impact during the week
I
Wed
to Friday.
I
I
Thu
Fri
Sunday’s
ä
seem to be helping sales at all! In fact. although not statistically significant, the PRINT and RADIO variables are even directionally negative. As expected, year-ago sales and the number of stores are positively related to sales, and the relationships are significant. The question was: why were we getting such a weak correlation between sales and the media expenditure variables in the Saturday model‘? In discussing the results with management, it surfaced that the company had been using the same type of sale for a majority of the Saturdays for many years. 98
over time
On any given Saturday. people living in this area knew with a high degree of predictability that the company had a ‘one-day sale’ on every Saturday. Variations in advertising expenditures therefore had less of an impact on the sales levels. In sum. our modelling efforts showed that print advertising on Sunday not only impacts sales positively on Sunday, but also has a positive impact during Monday to Friday. However. both during the week and especially on Saturday, print advertising seems relatively ineffective. Overall, TV seems to be an effective medium, particularly on Sundays. During the week, TV advertising is effective in generating sales the following day. Radio seems effective during Monday to Friday, particularly in generating sales on the same day.
4
I
effectiveness
Implementation Before taking any action, management wanted a sense of the total incremental sales associated with $1 of advertising on a particular day in a particular medium. As the least-squares estimators of the regression models are assumed to be normally distributed random variables. under the assumption that these normal distributions are independent of one another. we could use standard statistical principles to compute the average impact and the associated standard error of each of the media variables. The combined impact of a media variable is simply the sum of the relevant model coefficients, and the combined standard error is given by the square root of the sum of the estimated variances of the respective coefficients. In computing the average impact. we eliminated those media variables that had turned .lorrmul of Retailing unti Consumrr Serviws 1994 Volume I Number 2
An advertising evaluation system for retailers: C Bhattacharya and L M Lodish Table 5 Estimated advertising
incremental
sales associated
TV Sunday Radio during week TV during week Print Sunday Print during week Radio Saturday Print Saturday
with $1 of
12.07*** _5.91* 5.27*** 5.06*** Non-significant Non-significant Non-significant
to conduct similar analyses in other geographic markets. The flexible and multidimensional data storage system designed by us should make such analysis quite feasible to conduct. There are also a number of ways in which the system we designed can be improved:
(1)
“pcO.10 *** p < 0.01
(2) out to be insignificant in the original analysis (for example, in calculating the incremental sales generated by $1 of radio advertising during the week, we ignored the coefficient associated with RADIO,_, in the Saturday model). Moreoever, in instances where it is impossible to tell the differential effect of a certain media variable on different days of the week2, we assume that the impact of that variable is the same across those different days of the week (for example, we add 2.31, the coefficient for TV,_, in the Monday to Friday model, not only in computing the impact of advertising on TV during the week but also in computing the impact of advertising on TV on Sunday). Table 5 gives the results of this analysis. The table shows that advertising is most effective on Sunday and least effective on Saturday. Across media, TV appears to be more effective than print. The difference of advertising effectiveness across days of the week and across media prompted management to make some changes in their media schedules. Basically, the firm decided to cut back Saturday’s advertising to maintenance levels. Note that as our models did not incorporate the impact that competitive advertising had on the firm’s sales, managers, in their wisdom, decided to maintain some advertising on Saturday to prevent loss of sales to competition. Interestingly, our situation was similar to that described in Little (1975) where managers tempered the model recommendations with their own understanding of the market. What is gratifying to us is that the decision support system development process gained credibility with management.
Directions
for future research
To validate the model findings, similar exercises need to be conducted using data for adjoining small towns in which there may be only one or two stores, and hence greater possibilities of establishing causality. To be able to allocate advertising resources across markets, there is an obvious need
2For example, the coefficient of TV,., in the Monday model reflects the impact of TV advertising on Sunday during Monday-Thursday. Journal of Retailing and Consumer
to Friday as well as
Services 1994 Volume I Number 2
(3)
(4)
Similar to Lodish (1982) an experimental analysis component could be added to the current system. Such a component would be useful for validating the model findings in one location, before taking large-scale media mix and scheduling decisions. Put seasonality into the models as moderator of advertising effect on sales. Make the system capable of handling nonlinear response functions, and incorporate aspects of optimization into the models. Store data on competitive advertising in the system, so that competition’s impact on the firm’s sales can be ascertained.
Similar decision support systems can also be developed for other retailing contexts. For instance, in the realm of supermarkets, there are no studies that have related total store sales to total featuring expenditures by day and by medium. On the other hand, a system such as the one developed by Abraham and Lodish (1993) for evaluating promotion profitability for supermarket goods would be extremely helpful for speciality retailers and department stores. Conceivably, methodologies can be developed to arrive at better estimates of ‘baseline’ sales both for advertising and for promotions done by specialty retailers. Once these baseline estimates are derived, we can compute the incremental sales and causally relate them to the advertising and promotional variables. Such a system would be more versatile than the one we have described in this paper.
Conclusion All the components of the system we designed (data reorganization, modelling methodologies and report formats) have been used elsewhere in practice. Our contribution lies in putting the pieces together to aid decision-making for an important managerial problem. Imperfections notwithstanding, the managers of this national retail organization had their first exposure to quantitative evaluations of advertising efforts. The instant credibility of the system and its adoption by top management was a very satisfying experience. In fact, the person who was the CEO of the company at the time we designed the system has recommended it to many of his peers in the retail business. Although every organization will no doubt have its own set of challenges, we believe that our system design is simple, low-cost, and flexible enough for it to be fruitfully adapted to measure the productivity of advertising spending in other retailing firms. 99
advertising
An
evaluation
system for retailers: c’ Bhattacharya
References Abraham, M.M. and Lodish. L.M. (1993) ‘An implemented system for improving promotion productivity using store scanner data’. Marketing Science. 12 (3). 248-269 Bass. F.M. and Clarke, D.G. (1972) ‘Testing distributive lag models of advertising effect’. .Journul of Marketing Rc.se~trch. 9 (August). 2YX-30X Bemmaor. A.C. and Mouchoux. D. (1991) ‘Measuring the shortterm effect of in-store promotion and retail advertising on brand sales: a factorial experiment’, .lormurl of Mrrrkrritlg Rexarch, 28 (May). 202-2 14 Blattberg. R.C. and Neslin. S.A. (1990) S~lcs Pror,~o/io/r Concepts, Merhods and Sfrrrtegie.s, Englewood C‘liffs. NJ. Prentice-Hall Box, G.E.P. and Jenkins. G.M. (1976) 7%r Srrirs Atur/w.\: Forecasting untl Control. 2nd edn. San Francisco: HoldenDay Eastlack, J.O. and Rao. A.G. (lYX6) ‘Modeling response to advertising and pricing changes for “V-X” cocktail vegetable juice’. Marketing Science, 5 (3). 245-259 French, K.R. (1980) ‘Stock returns and the weekend effect‘. Journal o,f Financiul Economics. 8 (March), SS-69 Hanssens. D.M.. Parsons. L.J. and Schultz. R.L. (IYYO) Mrrrl\c~r Re.sponse
Models:
Econometric
und
Boston: Kluwer Academic Publishers Kassaye, W.W. and Vaccaro, J.P. (lY91) effectiveness through better selection Rusinexss. 13 (3). 40-44
100
Time
Serie.s
Ar~ul~.siv.
arid L_ M lzodish Kennedy. P. ( I YY2) it (;~tirlr 10 f~~,ottonlc,tri~,.\. (‘ambridge. MA: MIT Press Kiem, D.B. (19X5) ‘Dividend yields and stock returns: implicalions of abnormal January returns’. .Jorrrrltr/ of Fit~nncirrl Ec~onortzi~.s. 14 (September). 473-4XY Kim, P. (1002) ‘Does advertising work? A review 01 the evidence’. .Jorrrtd of’ Con.swner Markcrin,y, 9 (4) 5-2 I Lilien. G.L.. Kotlrr. ‘P. and Moorthg, K.S. (lYY3) MNrkCfitlg Mode1.s. Englewood Cliffs. NJ: Prentice-Hall Little, J.D.C. 6975) ‘BRANDAID: a marketing mix model. Part\ 1 and 2‘. 0perdon.v Resetrrch, 23 (July-August), 62X-673 Little. J.D.C. (1979) ‘Decision support systems for marketing managers’. .Jotrrttcr/ of Marketing. 43 (Summer). Y-26 Little. J.D.(‘. and Lodish. L.M. (1969) ‘A media planning calctlIus’. Oprrrrriotrs Rc.secrrdr. 17 (Jan-Feh). I-35 I,odish. L.M. (19X2) ‘A marketing decision support \p\tcm 101 retailers’, Mtrrkrriq Scicvfw. 1 ( 1). 3 I-56 Rae. A.(;. and Miller, P.B. ( 1975) ‘Advertising/sales rc\ponsc functions‘, .Journal of’ Advertising Rrsenrch. 15. 7-l 5 Smith. S.A.. McIntyre. S.H. and Achabal, D.D. (IYYJ) ‘A twostage salts forecasting procedure using discounted lcast squares’. Jolrrtrrrl of Marketing Research, 31 (February). 44-55 Stewart. D.W. (lYX9) ‘Measures, methods, and models in advertising research’. .Jocwntr/ q/‘Adverrising Resecrwh. 29 (2). 54-60
‘Increasing of media’.
advertising Revirn
of
Vaccaro. J.P. and Kassayc. W.W. (IYXX) ‘Increasing the advzrtising cffectivenrss of small retail businesses‘. Etrtreprertc,/tr.\lti/~ Theory rrnd P rrrctiw, (Fall) 4 I-47