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A VAR Model for the

Effect of Price Promotions

on Innovations’ Sales

A differentiation in radical and incremental innovations

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2 Master’s Thesis By Emma Eversdijk S1767321 Albert Cuypstraat 95-1 1072 CP Amsterdam +31 6 50266265 e.j.eversdijk@student.rug.nl

Supervisor Prof. M.J. Gijsenberg University of Groningen Faculty of Economics and Business

Marketing Department

A VAR Model for the

Effect of Price Promotions

on Innovations’ Sales

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Preface

This combined Master’s Thesis is written as a completion of the MSc Marketing Intelligence and Msc Marketing Management at the Rijksuniversiteit Groningen and concerned two main topics in marketing: price promotions and innovation. Price promotions are an often used tool for marketing managers, particularly in the FMCG sector, and are studied extensively. Still, debate exists about the long term effectiveness. Moreover, the frequent use of price promotions have led to changes in consumer behaviour, what makes it a topic worthwhile studying. In contrast, innovations, and especially the differentiation between incremental and radical innovations, are less studied. Yet, innovations provide a means for companies to create new profit potentials in a matured and / or saturated markets. Hence, this study contributes to both literature and practice.

A special thanks goes out to my supervisor at the RuG, Maarten Gijsenberg, who provided extensive feedback and introduced me to a new type of model. Not only did he help me to complete my MSc degree, but also extended my theoretical knowledge outside the studies’ scope.

The data used in this study was provided by a large brewery. and therefore, I would like to thank the company for providing me with the opportunity to study their products and use their databases. Specifically, I would like to thank my supervisor there, for supporting me in a turbulent time. Also, I would like to thank them for helping me with the A.C. Nielsen database. Finally, I want to express thanks to the Consumer and Marketing Intelligence Team and the several brand managers for taking the time to offer me their opinion on innovations.

I hope this Thesis offers insights for the use of price promotions as a marketing tool for innovations for the brewery and provides a basis to build future research on.

Emma Eversdijk

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Management Summary

This study examines the effects of both own price promotions and competitors price promotions on the sales volume of new product introductions in the market during the first year of the introduction in the retail sector. Moreover, it examines whether there is a moderating effect of the level of innovativeness in this relation. This will be done by examining 14 innovations of a large brewery in the Netherlands and one competitor on a SKU level with a Vector Auto-Regressive (VAR) Model.

Firstly, it was hypothesized that price promotions of the focal SKU would increase the sales volume of the SKU and that price promotions of a competing SKU would decrease the focal sales volume on the long term. However, the results of the analyses show that there is no long term effect, both positive or negative.

The immediate effects of a price promotion of the competitor are different for each SKU, some lead to a direct decrease in sales volume, possibly due to brand switching, whereas others lead to a direct increase in sales volume, possibly due to total category attraction. However, the results did not indicate an increase/decrease of sales volume on the long term. At the time of the price promotion of the focal brand there is an immediate positive effect, but this effect is followed by such a deep price promotion dip in the following week(s) that no positive (or negative) effect remains on the long term. Sales volume, therefore, remains stable regardless of the use of price promotions.

This result has an important managerial implication, as today the promo share of sales volume for most SKU’s in the FMCG sector is steadily increasing. When a larger part of the total sales volume is sold on price promotion, this means that profit margins are declining while no additional volume on the long-term is sold.

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In short, the current use of price promotions for an innovation do not lead to an increase in sales volume on the long term, whereas it does lead to a lower profit margin and possibly has the effect of changing consumer behaviour. It would therefore be worthwhile studying what the effect would be if less frequent and less deep price promotions would be used.

However, for two SKU’s, Jillz Regular and Wieckse Rosé 0.0%, the results did show a significant effect on the long term. For these two SKU’s the use of own price promotions indeed increased the sales volume on the long term, whereas competing price promotions decreased the sales volume on the long term. It could be insightful to study why these outcomes vary from the rest of the SKU’s outcomes and what differentiates them.

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Table of Contents

Preface ... 3 Management Summary ... 4 1. Introduction ... 7 2. Theoretical Framework ... 9

2.1 Own Sales Promotion ... 10

2.2 Competitor Promotion ... 12

2.3 Control Variables ... 14

3. Methodology ... 14

3.1 Data ... 14

3.2 Model & Method ... 17

4. Results ... 20

4.1 Unit Root Test ... 20

4.2 Vector Autoregressive Model ... 20

4.3 Structural Vector Autoregressive Models ... 20

4.4 Impulse Response Functions ... 25

4.5 Regression ... 29

5. Discussion and Conclusion ... 29

6. Limitations and Future Research Possibilities ... 32

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1. Introduction

As Peter Drucker (1954) once famously said: “The business enterprise has two and only two basic functions: marketing and innovation. Marketing and innovation produce results; all the rest are costs” (Story, Hart & O’Malley, 2009). Extensive research over the years has shown that innovations offer several benefits to firms: amongst others they can improve brand value and profitability (Srinivasan, Pauwels, Silve-Risso & Hanssens, 2009; Sriram, Balachander, & Kalwani, 2007) and strengthen a firm’s competitive position with regards to competitive tactics and changing consumer preferences (Carpenter & Nakamoto, 1989). Moreover, in maturing markets innovations offer a possibility for firms to extend the product life cycle or reshape existing category boundaries (Barone & Jewell, 2013).

However, due to the high costs and risks involved with innovations, firms are constantly seeking ways to maximize return on investment in this area (Barone & Jewell, 2013). Literature in the marketing area mainly evolved around process models (Story, Hart & O’Malley, 2009), diffusion models and consumer adoption decisions (Moreau, Lehmann & Markman, 2001). Yet, often the factors that firm’s can actually control, e.g. the traditional marketing mix elements, are hardly considered. As such they are classified by Aboulnasr, Narasimhan, Blair & Chandy (2008) as an opportunity for further research.

Moreover, only few studies make a distinction between the effects for radical versus incremental innovations (e.g. Moreau et al. 2001). Many studies address issues regarding radical innovations (Souder & Song, 1997; Collarelli O’ Conner, 1998; Veyzer, 1998; Kessler & Chakrabari, 1999; Chandy & Tellis, 2000), but only few acknowledge incremental innovations. Also, many studies use samples of radical innovations but draw conclusions about innovations in general. Hence, current theory possibly gives a biased view about innovations and its effects.

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This study continues with filling the current gap in literature by studying whether results of an innovation are different for both radical and incremental product innovations. A product innovation is “an innovation in the offering to the market, the object of market exchange, and the basis upon which money from a customer flows in into the firm” (Story, Hart & O’Malley, 2009). This definition will be used because it acknowledges that an innovation can be both incremental as radical, and because it addresses one of the main motives to innovate: creating cash flow into the firm.

Finally, this study will account for how competitors’ promotion response is when introducing an innovation. Redefined market boundaries offer potential for competition as well. As such competitor reactions can be expected.

The main research question of this study is the following: Does the effect of a firm’s own sales promotion level increase less or more for radical product innovations compared to incremental product innovations accounting for competitors’ sales promotion level?

This research question will be studied by using a Vector Auto-Regressive Model (VAR Model), which is an econometric model that captures linear interdependencies in multiple time series (Meyer-Waarden & Benavent, 2009). Originally its application stems from models in monetary policy (Inoue & Kilian, 2013), but, over the years, VAR models have been extensively used in other areas and have become a popular analyzing tool in marketing as well (Horvath, Leeflang & Otter, 2002). As demonstrated by Lautman & Pauwels (2009), VAR Models are a proper tool for resolving causal ambiguity and they are shown to be consistent with and complementary to traditional marketing mix analyses.

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The innovations are NPI from in the beer market and will be studied by looking at how one of the traditional marketing mix elements, sales promotion, is used in the retail sector during the first year of the introduction.

The structure of the paper is as follows: firstly a theoretical framework will be provided in with the hypotheses are outlined. Secondly, the research design is given which contains the research method, data collection method and plan of analysis. This will be followed by a results section that analyses and discusses the empirical data. Finally, conclusions and recommendations will be given.

2. Theoretical Framework

Over the years many definitions of product innovativeness have been used: innovativeness can be seen as the relative consumer advantage (Srinivasan et al. 2009), the relative newness to the market (Barone & Jewell, 2013), and the degree to which they incorporate a new technology (Sorescu, Shandy & Prahbu, 2003). Additionally, the degree of innovativeness can be defined in terms of the extent of the allocated budget. In general, studies agree that the level of new product innovativeness depends on the extent to which the introduction of it creates a potential discontinuity, in marketing and technological processes (Garcia & Calantone, 2002). Moreover, several distinctions have been made about incremental (continuous) and radical (discontinuous) innovations. This study builds on the notion that radical and incremental innovations differ in their degree of novelty, the benefits they bring to consumers, and the effect they can have on a category (Chao & Cavadias, 2008). Moreover, for this study it is assumed that incremental innovations have the effect of redistributing market shares within an existing market, whereas radical innovations have the effect of redefining the boundaries of a category (Aboulnasr et al. 2008).

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One of the marketing mix variables that is often used for innovations is sales promotions, which consist of offering temporary price reductions to consumers (Blattberg & Fox, 1995; Nijs, Dekimpe, Steenkamp & Hanssens, 2001). Sales promotions have been one of the most popular research topics in marketing science for the last two decades (Freimer & Horsky, 2008). However, literature usually adopts either a retailer-centric or a consumer centric perspective and do not address brand management (Dass, Kumar, Peev & Plamen, 2013).

Sales promotions are usually an effective demand booster as they have a recognizable direct effect on sales volume and profit (Dekimpe & Hanssens, 1999; Srinivasan et al. 2009). Hence, an important opportunity lies in studying the possible moderation of radical and incremental innovations to the relation between sales and sales promotions. Moreover, the relation between type of innovation and sales promotion is important as firms that have greater depth and breadth in their product portfolio will gain more from innovating (Sorescu et al. 2003). This is due to the fact that a firm with greater product portfolio can extend the innovation to its other products/brands. For example, currently Holy Soda is introduced to the Dutch market which includes a new replacer of sugar, the Stevia Extract. This innovative ingredient is now also used for other brands like Pepsi Cola that now introduced Pepsi Next. The relation between the radical innovation and sales promotion is now not only important for the one brand or product, but for several.

Further exploring theory, this study investigates 1) how sales level of NPI’s is affected by its own promotions and by competitors’ promotions and 2) how this relation is moderated by type of innovation.

2.1 Own Sales Promotion

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promotion have a positive effect on sales volume due to purchase reinforcement (Ailawadi, Gedenk, Lutzky & Neslin, 2007), whereas others suggest a negative effect as consumers can perceive a sales promotion as a signal that a product is performing below target, which can lead to a lower perceived value of the product (Srinivasan et al. 2009) and thus lower sales in the long term. Moreover, accelerated purchases can lead to a post-promotion dip (Nijs, et al. 2001).

Recent studies have found that pricing is one of the strongest drivers to increase brand purchases (Harvey, Herbig, Keylock, Aggarwal & Lerner, 2012). Moreover, in a study by Lautman & Pauwels (2009) it was shown that advertising and promotion in the FMCG have a significantly larger effect than any other marketing mix tool on base sales. When consumers are regularly confronted with sales promotions they elicit that the future price of a product will be higher. These expected higher future prices result in an increased choice for the product currently under promotion (Tsiros & Hardesty, 2010). Additionally, studies have shown that promotions can have the result of increasing the consumption of the product class (Freimer & Horsky, 2008) and therefore lead to an increased volume of product sales. Hence, here it is argued that an increase in promotion indeed has a positive effect on sales.

H1a: A higher (lower) level of own sales promotions will lead to a higher (lower)

level of sales volume for the NPI

The innovative attributes of a new product introduction need to be communicated to the market to create a perception of differentiation (Barone & Jewell, 2013). Hence, sales promotions are often used when introducing new products, as they lower the barrier for consumers to try the innovation. A consumer learns from such a “try” and updates his or her brand purchase probability (Freimer & Horsky, 2008). Moreover, it is often stated in literature that sales promotions are informative about a deviation from industry norms (Raghubir & Corfman, 1999) as is often the case for NPI’s.

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the product or service, but they also consider whether the actual price is higher or lower than that previous set reference price (Weaver & Frederick, 2012).

It is important how the price is set when a new product enters the market as consumers have to define the value of the product from scratch and price often serves as an indicator of quality for consumers (Gneezy, Gneezy & Olié Lauga, 2014). For a radical innovation the reference price for the product is less established as the more radical innovations are, the larger the extent to which they deviate from standard norms and are unfamiliar for consumers (Aboulnasr et al. 2008). Hence, the psychological barrier to try a product is larger for more radical innovations than for incremental innovations. The category and/or products and its value still have to be defined and especially for more radical NPI’s, the introduced price will define the reference price for both the product and the category (Lowe & Alpert, 2010).

When confronted with a price promotion, a consumer sets a reference price for the new product based on the sales promotion price, a price that is lower than the actual regular price. This regular price will afterwards be seen as high by consumers as they have based their price and value perceptions on the price promotions (Lowe & Barnes, 2012). Hence, although direct sales volume will be larger due to the lower price, over time sales volume will be lower as consumers will believe the innovation to be expensive (Kalwani & Yim, 1992). Therefore, the more radical an innovation is the smaller the increase in sales over time will be as consumers believe the innovation to be too expensive.

H1b: The effect of own sales promotions on own sales level will be less strong for

radical innovations than for incremental innovations

2.2 Competitor Promotion

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promotions to other firms’ actions (Pauwels, 2007). Usually, due to the immediate effects, competitors also respond with sales promotions (Steenkamp et al. 2005).

In literature it is often argued that due to the use of such marketing mix elements, brand switching occurs (Lam, Ahaerne, Hu & Schillewaert, 2010). As such, sales promotions will induce price sensitive consumers to switch to the competitor’s offer (Freimer & Horsky, 2008) which will cause a decrease in the focal firms sales volume.

Other studies have also argued that a brand is more vulnerable when (strong) competitors offer (large) promotions (Dass et al. 2013). Increased competitor reactions with promotions have the effect of accumulating price elasticity’s, which means that a price promotion of a competitor does not only have a negative effect in week t, but sales in subsequent periods are also affected by it (Horvath & Fok, 2013). Hence, it is expected that the perceived ‘price unfairness’ for an NPI increases when a competitor increases promotions as the category reference price changes.

Thus, here it is argued that the focal firm’s sales will be lower when a competitor uses price promotion. Therefore:

H2a: A higher level of a competitor’s promotion level will have a negative effect on

own sales

By introducing more radical innovations firms take high risks as they take a chance of losing shares by disrupting the market (Heide & Weiss, 1995; Montaguti, Kuester & Robertson 2002). Such a disrupted market or a newly opened category offers possibilities for competitors as well who face the possibility to gain market share. Hence, introducing a new product to the market does not only benefit the focal brand, but also their competitors (Fosfuri & Giarratana, 2009). As competitors also see this opportunity, it is expected that when an innovation is introduced, they will also use more sales promotions. Such increased and deepened competitor promotions usually lead to a higher brand switching probability (Freimer & Horsky, 2008) and therefore a lower sales volume of the focal firm.

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larger their motivation will be to react to it (Steenkamp et al. 2005). This effect is enlarged for more radical innovations as consumers face a higher risk with the focal brand’s NPI as explained above. Therefore, for a more radical innovation, the switching probability increases.

Hence, the more radical an innovation is the more regular and/or deep the competitor’s sales promotions will be.

H2b: The effect of a competitor’s sales promotions on own sales level will be

stronger for radical innovations than for incremental innovations

2.3 Control Variables

Firstly, in this study is accounted for two control variables that are both related to seasonal influences. Seasonality can be decomposed into regular cyclical patterns, such as the seasons we encounter every year, and an irregular component, such as patterns in data due to special events (Fok, Franses & Paap, 2007).

The first component is controlled for in this study as the beer market is one that is susceptible to seasonal influences, especially for some of the new product introductions that see large increases in sales in for example summer.

Moreover, as beer is a product that is often consumed to enjoy and to celebrate, the market is subjected to influences of large events like the World Cup Soccer. Most of the products sold in this category see a large increase in sales volume during such events. Hence, control variables will be included for events that occurred during the measurement time of the NPI’s.

3. Methodology

3.1 Data

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brewery and their main competitor were taken (see table 1). This large Dutch brewery has a wide portfolio of several brands and over the last years they have introduced both radical innovations and incremental innovations.

The endogenous variables consist of the sales volume of the focal NPI, the sale volume of the main competitor, the price promotion depth for the focal brand and the price promotion depth for the competitor brand. Exogenous variables include the baseline price for both brands and in order to control for seasonal effects, a sinus and co-sinus of were included. In order to control for other seasonal effects a deterministic dummy variable was included for two events: the World (European) Cup Soccer and the Olympic Games (Summer/Winter).

In Market Since Introduction Competitor

Wk 10 2009 SKU 1 Competitor 1 Wk 12 2011 SKU 2 Competitor 2 Wk 22 2011 SKU 3 Competitor 3 Wk 9 2012 SKU 4 Competitor 4 Wk 9 2012 SKU 5 Competitor 5 Wk 9 2012 SKU 6 Competitor 6 Wk 10 2012 SKU 7 Competitor 7 Wk 11 2012 SKU 8 Competitor 8 Wk 26 2012 SKU 9 Competitor 9 Wk 9 2013 SKU 10 Competitor 10 Wk 11 2013 SKU 11 Competitor 11 Wk 11 2013 SKU 12 Competitor 12 Wk 11 2013 SKU 13 Competitor 13 Wk 11 2013 SKU 14 Competitor 14

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Finally, in order to determine whether an SKU was a radical or an incremental innovation, 10 (brand) managers of the brewery were interviewed and asked for their opinion on innovations. They were each asked to rank the 14 innovations from low to high innovative (1 most innovative, 14 least innovative). Based on these rankings a weight for innovativeness was created (see Table 2). These values were inverted, so that the highest value represented the most innovative SKU and the lowest value the least innovative, and were used in the regression analysis.

SKU Level of Innovativeness

Most Innovative 12,2 12 8,6 8,2 8 7,9 6,4 6,1 5,6 5,3 3,5 3,1 2,4 Least Innovative 1,7

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3.2 Model & Method

The approach in this study is twofold: as mentioned in the introduction, this analysis will use a Vector Autoregressive (VAR) Model to assess the effect of price promotions on sales. This will be done by using the software JMulti, developed by Lüttkepohl & Krätzig (2004). Thereafter, with linear regression the influence of innovativeness on the relation between promotion and sales will be tested with IBM SPSS Statistics 20. For each SKU a general VAR model will be used, as displayed on page 18.

The VAR Models will be estimated with the stationary variables in levels and the non-stationary variables in differences. In order to determine what endogenous variables are stationary and which are non-stationary, the Augmented Dicky Fuller Test will be used, which tests for the absence of cointegration (Pesavento, 2004). When all the variables for a SKU are found to be non-stationary, a Vector Error Correction Model (VECM) will be estimated. A VECM accounts for cointegration by adding error correction features (Dungey & Osborn, 2014). However, cointegration is rarely found among sales and marketing actions in the FMCG sector (Nijs, et al. 2001; Srinivasan, Pauwels, Hanssens & Dekimpe, 2004)

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To assess whether price promotions indeed have a significant effect and to determine the extent of the effect, a Structural Vector Autoregressive (SVAR) model will be estimated by imposing restrictions on the interactions. The SVAR approach assumes that the εt are

orthogonal structural shocks, whereby the structural disturbances are uncorrelated and the variance-covariance matrix Σε is constant and diagonal (Raghavan, Silvapulle &

Athanasopoulos, 2012). By imposing restrictions, specific effects of one variable on another can be determined. In the model used in this study, only the effects on the sales of the focal brand will be allowed for. The coefficients resulting from this analysis will be used in the regression analysis.

In order to estimate the long term effect of a price promotion, impulse response functions will be extracted. The impulse response functions derived from the SVAR analysis trace the path of the response of variable i over time to a shock to variable j. Subsequently, these responses are plotted over s periods (Raghavan et al. 2012). As such, the net result of all the modelled actions and reactions over time can be derived (Pauwels, 2004).

Finally, the parameters obtained in the SVAR analysis, will be used in the regression analysis, which will be performed with OLS estimation in IBM SPSS Statistics 20. The level of innovativeness will be regressed on both the parameters of the focal price promotions and the competitor price promotions. In such way, it can be determined whether the level of innovativeness strengthens the effect of a price promotion on sales level or not. Hence, the following two regression equations will be estimated:

Pf = α1 + α2I and Pc = β1 + β2I

Where

Pf = the parameter of the price promotion depth of the focal brand

Pc = the parameter of the price promotion depth of the competitor brand

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4. Results

4.1 Unit Root Test

Table 3 displays which variables were found to be stationary and which ones were non-stationary. The variables that were found to be stationary, were taken in levels. For the variables that were found to be non-stationary, the first difference was taken for further analysis. Taking the first difference eliminates fixed effects and, in the case of unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of non-stationarity (Han & Phillips, 2013). As for none of the SKU’s all variables were found to be non-stationary, there was no need to estimate a VECM.

4.2 Vector Autoregressive Model

Table 4 displays the Schwarz Criterion for a one lag model, and for a k-lag model (depending on the number of lags as determined by computation of the info-criterion). The Schwarz Criterion has proved to be an accurate selector and an accurate evaluator of the relative likelihood that the selected model is best (Rust, Simester, Brodie & Nilikant, 1995). For simplicity, for further analysis it was determined to proceed with one lag models.

4.3 Structural Vector Autoregressive Models

For each SKU a SVAR model was estimated with 1 lag. The restrictions were set in such a way, to only allow for direct effects of the endogenous variables on focal sales. Table 5 displays the results and the coefficients that were later used for the regression analysis. Table 6 displays for which SKU the hypotheses are rejected.

As Table 6 shows, for 11 SKU’s the first hypothesis (H1a) has to be rejected and it can be

concluded that a price promotion of the focal brand has no significant long term effect on sales. Yet, for 3 SKU’s a positive long term effect was found. For 12 SKU’s the second hypothesis (H2a) also has to be rejected, what shows that there is no significant long term

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Table 3 – Results of Unit Root Test

Test Stat. FD/L Test Stat. FD/L SKU 1 SKU 8 Sales F 0,0565 FD Sales F -4,2738 Levels Sales C -0,2103 Levels Sales C -2,3642 FD Promo F -0,2332 Levels Promo F -2,9015 FD Promo C -0,1183 FD Promo C -3,4353 Levels SKU 2 SKU 9 Sales F 0,0474 FD Sales F -4,4647 Levels Sales C -0,4379 Levels Sales C -5,7798 Levels Promo F -2,3447 Levels Promo F -3,4238 Levels Promo C -0,20511 Levels Promo C -2,9651 FD SKU 3 SKU 10 Sales F -6,4186 Levels Sales F -3,1768 Levels Sales C -1,9111 FD Sales C -2,5727 FD Promo F -3,4919 Levels Promo F -3,3454 Levels Promo C -3,8016 FD Promo C -3,3487 Levels SKU 4 SKU 11 Sales F -10,5235 Levels Sales F -2,9068 FD Sales C -5,4769 Levels Sales C -2,8240 FD Promo F -4,9995 Levels Promo F -2,7830 FD Promo C -4,826 Levels Promo C -1,6982 FD SKU 5 SKU 12 Sales F -3,3126 FD Sales F -5,6394 Levels Sales C -3,4261 Levels Sales C -4,788 Levels Promo F -3,7144 Levels Promo F -4,1996 Levels Promo C -4,4942 Levels Promo C -5,4997 Levels SKU 6 SKU 13 Sales F -2,5592 FD Sales F -14,0894 Levels Sales C -0,3944 FD Sales C -0,4281 FD Promo F -4,1399 Levels Promo F 3,4052 Levels Promo C -3,0609 FD

Promo C n/a n/a

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22 Schwarz Criterion Schwarz Criterion SKU 1 SKU 8 Lag 1 -9,49E+00 Lag 1 -9,23E+00 8 -1,31E+01 8 -1,56E+01 SKU 2 SKU 9 Lag 1 -1,36E+01 Lag 1 -1,15E+01 8 -1,76E+01 8 -1,23E+01 SKU 3 SKU 10 Lag 1 -1,37E+01 Lag 1 -1,14E+01 8 -1,75E+01 8 -1,51E+01 SKU 4 SKU 11 Lag 1 -1,08E+01 Lag 1 -8,89E+00 8 -1,21E+01 8 -1,39E+01 SKU 5 SKU 12 Lag 1 -1,08E+01 Lag 1 -1,12E+01 8 -1,34E+01 8 -1,44E+01 SKU 6 SKU 13 Lag 1 -1,13E+01 Lag 1 -6,79E+00 8 -9,88E+00 - SKU 7 SKU 14 Lag 1 -9,35E+00 Lag 1 -1,19E+01 9 NaN 8 -1,52E+01

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SKU Coef. SE Criterion 5%* Criterion 10%**

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SKU H1a H2a

1 R R 2 R R 3 R R 4 R R 5 R R 6 R R 7 R R 8 R R 9 A A 10 A R 11 R R 12 A A 13 R R 14 R R

Table 6 – Rejected/Accepted Hypotheses

4.4 Impulse Response Functions

For each SKU two impulse response functions were estimated: one with the effect of its own price promotion on sales and one with the effect of the competitor’s price promotion on the focal brand’s sales. A 95% Hall Percentile was used to determine the (non)significance of the effects for n=20 periods. For the SKU’s for which focal sales were in first difference a cumulative impulse response function was estimated.

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The positive effect in t=0 is also shown in 2 other SKU’s, however, for those two it is followed by an even increased effect in t=1. For the other SKU’s there is a direct negative effect of competitor price promotion on focal sales. Again, for none of the NPI’s a significant effect of competitor price promotion on focal sales was found.

Figure 1 – IRF SKU 1

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Figure 3 – IRF SKU 2

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Figure 5 – IRF SKU 3

Figure 6 – IRF Competitor 3

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4.5 Regression

The results of the regression analysis are shown in table 6. It was found that, contrary to what was hypothesized, there is a positive effect of innovativeness on focal promo. This means that the more innovative a SKU is, the stronger the effect of a price promotion of the focal brand on sales will be. In contrast, a negative effect of innovativeness on competitor promo was found, which was also opposite to what was hypothesized. The negative effect implies that the more radical an innovation is, the smaller the effect of competitor promo on sales will be.

For both focal promo and competitor promo the overall models are non-significant and have a low explanatory value (R2 of respectively .072 and .280). Additionally, the intercept and the predictors are also non-significant. Hence, both hypothesis H1b and H2b have to be rejected.

Model 1 – Focal Promo Model 2 – Competitor Promo

Intercept 0.07 0.103

Innovativeness 0.03 -0.01

F 0.062 1.019

R2 0.072 0.280

Table 6 – Regression Analysis Results

5. Discussion and Conclusion

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promotions are best explained by an 8-lag model, for one SKU a 9-lag model would give the best explanation.

A SVAR model was estimated to determine the long-term effect of price promotions on sales. The results of the SVAR analysis show that, for most NPI’s, there is no positive long-term effect of both its own price promotions and the price promotions of the competitor on the total sales. Yet, it also shows that there is no long-term negative effect as argued by some theorists (Abu-Shalback Zid, 2004). Sales volume, therefore, remains stable regardless of the use of price promotions by the focal brand or a competitor. This result has an important managerial implication, as today the promo share of sales volume for most SKU’s in the FMCG sector is steadily increasing. When a larger part of the total sales volume is sold on price promotion, this means that profit margins are declining while no additional volume on the long-term is sold.

There are several possible causes for the non-promotion effect of price promotions on sales. The first stems from the post-promotion dip that is often cited in literature. In general it is often found that a price promotion leads to an acceleration of purchase, e.g. consumers buy earlier and/or purchase larger quantities than they would have in the absence of a promotion (Van Heerde, Leeflang & Wittinck, 2000). This means that the duration to the next period in which they will buy again is longer (Hendel & Nevo, 2003), e.g. a post-promotion dip. Taking this acceleration and dip together, the overall volume that is bought by these consumers stays the same.

The second possible cause is based on consumer perceptions. Consumers decision’s on brand an purchase quantity may depend on the size of the price reduction and the time until the next price reduction (Krishna,Currim & Shoemaker, 1991). As price promotions are used more often and more deeper promotions are used, consumers get used to buying their products when it is on promotion and will stop buying it on the regular price. This means that using promotions will never have the effect of increasing sales volume on the long term, as no additional consumers are reached.

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However, for two NPI’s the hypotheses cannot be rejected and focal price promotions do have a significant effect on sales. Competitor price promotions also have a significant effect on sales, yet, they have a positive effect as opposed to the theorized negative effect. Overall, the IRF’s showed that for the several NPI’s there was no significant effect of both focal price promotions and competitor price promotions.

The research question proposed in this study cannot be answered based on the results of this study, but only an indicative answer can be given. The results show that the level innovativeness has a stronger effect for focal price promotions, but a less strong effect for competitor price promotions. However, as the results are non-significant they cannot be generalized.

The effect of innovativeness on focal price promotions is opposite to what was hypothesized in this study. Here, it was hypothesized that the effects of a focal price promotion would be less strong for a more radical innovation, as consumers would perceive it to be more expensive due to a previous set reference price based on the price promotions. However, the results of this study show that the exact opposite is the case. A possible explanation of this effect could be found in the assessment of risk. Consumers often face considerable uncertainties about price and quality of product (Lam, Vandenbosch, Hulland & Pierce, 2001) and this risk will be higher for more radical innovations; hence, consumers need to compensate for such increased uncertainty (Chen, 2010). Price promotions can have the effect of lowering the barrier to buy such a “risky” product, as due to the decreased price, the possible loss if the consumer does not like the innovation is also lower.

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tempted to switch brands when a competitor offers a price promotion as the competitor’s product does not offer the same benefits.

By studying the effects of price promotions on sales of innovations and making a distinction between incremental and radical innovations, this study adds to current literature, but also shows an important consideration for (brand) managers when introducing new innovations. Based on the findings of this study it would be recommended not to use price promotions as a marketing tool to increase long term sales of innovations. The study shows that price promotions do not significantly improve long term sales volume. As price promotions decrease the profit margins but do not lead to additional sales volume, it is a mere cost that decreases long term profitability.

Moreover, several studies have shown that frequent price promotions can lower a consumer’s quality perception of a brand (Ortmeyer & Huber, 1991; Erdem, Keane & Sun, 2008). Hence, the use of price promotions for brands that a firm wants to position as (premium) quality would not be recommended as no additional volume is sold on the long term, while at the same time the quality perception of the brand could be (negatively) affected.

6. Limitations and Future Research Possibilities

The findings of this study are subject to several notable limitations, some of which point out future research possibilities. Firstly, the data used in this study was aggregated to a national level where it would be desirable to extend the research to chain or even store level. As the reduction of observations reduces power and adds the potential of aggregation bias, this study is a conservative test of the hypotheses. As well, it does not allow for the modelling of cross-store heterogeneity and strategic retailer behaviour (Sudhir, 2001; Pauwels, 2004).

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Secondly, this research was performed with data from the retail sector in the Netherlands which has its distinct characteristics from other countries. Also, this study was performed for SKU’s of the FMCG sector; studying other sectors may lead to different results. Hence, future research should expand to different markets and competitive conditions to examine whether this study’s results can be generalized.

With regards to the model used in this research, there is a notable limitation which is found in the high standard errors in this study and when using VAR Models in general. Future research should focus on finding ways to increase VAR estimation efficiency and reduce the high standard errors (Pauwels, 2004).

Also, the results of the regression analysis were found to be non-significant, which is probably due to the fact that only 14 observations are used. Future studies could extend this research by including more observations. Parameters resulting from such a study, will be more likely to be generalizable.

Finally, this study focused on the effects of price promotions when they are actually being used. However, especially in the FMCG sector, consumers are used to phenomenon of price promotions and some consumers even buy their products only when they are on a price promotion. The regular sales promotions of the brands rather encourage them to wait for the next price promotion instead of purchasing the product at its regular (full) price (Lodish & Mela, 2007). Hence, it would be worthwhile studying what the effects would be if an SKU would use less or even no price promotions.

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