PREDICTING THE UNPREDICTABLE:

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PREDICTING THE UNPREDICTABLE:

Investigating Customer Profitability over Time

author

Jantien Dekker

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PREDICTING THE UNPREDICTABLE:

Investigating Customer Profitability over Time

MSc Marketing Thesis - Marketing Intelligence 17 June 2018 author Jantien G. Dekker S3177874 Palembangstraat 1, 9715 LK Groningen (NL) j.g.dekker@hotmail.com +31 6 23653675

First supervisor: Prof. Dr. J.E. Wieringa Second supervisor: Dr. J.T. Bouma

University of Groningen Faculty of Economics & Business

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SUMMARY

Over two decades ago, Foster, Gupta, and Sjoblom (1996) acknowledged the challenge of tracking customer profitability (CP) over time: "a customer that is unprofitable now and is expected to remain unprofitable requires a different set of corrective actions than a customer that is unprofitable now but expected to be profitable in the foreseeable future". Measuring and predicting customer profitability has become a major topic within marketing. Estimating individual-level CLV has proven to be difficult, with sophisticated methods performing equally well as simple methods (Donkers, Verhoef, & de Jong, 2007). Perhaps this is the result of little attention to modelling changes in CP over time.

This thesis answers the management question of how we can identify profitable customers, and especially customers that become more or less profitable over time. The general research problem is how we can predict future profitability of individual customers, while accounting for changes in CP over time. We use transaction data of a supplier of non-food products to retail stores throughout The Netherlands over a three year period. Flowing from our discussion we should answer the following questions to being able to provide a solution to our research problem:

(1) What are drivers of customer profitability?

(2) How can we predict the future profitability of individual customers?

(3) To what extent are we able to predict changes in individual customer profitability over time?

(4) How can we identify profitable customers segments based on our predictions? We answer these questions by identifying CP drivers and components based on past research, and we develop a predictive model that captures these identified drivers and components to predict future CP over time.

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We measure the profitability of a customer i (CP_i) as:

CP_i= ∑ (GM_it-MC_it) T

t=1 where GM_it = gross margins in period t

MC_it = marketing costs in period t T = time horizon of our measurement

We model two separate components for our final CP model: a Negative Binomial count model for our number of visits components for our costs calculations, and a multiplicative fixed effects OLS model for our gross margins component. We combine both models by subtracting the number of visits multiplied by the average costs per visit from gross margins. For both our visits and gross margins component, we use a hurdle model with a binary logit component that models the zero-observations.

We found that past customer behavior, customer heterogeneity, and firm actions all drive CP through its costs and gross margins components. Our model is able to capture changes in CP over time, but these changes are not very accurate: our model does not offer a significantly better performance compared to a model that uses past average CP and projects it to future time periods. Also, a model without a separate cost component does not significantly provides a worse performance compared to our model. For segmentation purposes, our model could offer managers a tool to get a better understanding about what drives CP, especially within customer segments.

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PREFACE

Dear reader,

Hereby I present to you my thesis for the MSc Marketing Intelligence. Over ten years ago I graduated from secondary education, with no clue about what I wanted to do or who I wanted to be in ten years. Several years past in which I did not attend any form of education. Instead, I worked at multiple companies in several positions, to find out that my heart lies in marketing. After finishing my part-time Bachelor of Business Administration with a specialization in Marketing Management at the Hanze University Groningen, I realized that I was still missing “something”. When I learned about the MSc Marketing Intelligence, I quited my fulltime job to be a full-time student for the first time in my life. I have not regretted it for a single day: it has given me exactly the “something” that I felt I was missing two years ago.

The data and the research problem of this thesis comes from the company that offered me my first work experience. It therefore also carries a personal touch. Experiencing a bankruptcy of the company that you love working for on the age of nineteen was a very hard but informative experience. I want to thank the provider of the dataset, since it is very interesting, real-world data. Although the company does not exist anymore, it has given me the chance to put what I have learned during my education into practice.

My graditude goes out to Jaap Wieringa for supervising me during the process. I sometimes can get carried away in my enthusiasm, and he could get me back on track. And although my initial plan to combine my thesis with doing an internship did not follow true, I also want to thank Jelle Bouma for his conversations. The bumps in the road offered me a learning experience that goes beyond writing a thesis. Finally, I want to thank all lecturers from the (Pre-)MSc Marketing courses for their support and sharing their knowledge during my education at the University of Groningen.

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1 INTRODUCTION

Over two decades ago, Foster, Gupta, and Sjoblom (1996) acknowledged the challenge of tracking customer profitability (CP) over time: "a customer that is unprofitable now and is expected to remain unprofitable requires a different set of corrective actions than a customer that is unprofitable now but expected to be profitable in the foreseeable future". Measuring and predicting customer profitability has become a major topic within marketing. However, till date, many attempts to estimate individual-level customer profitability have been rather unsuccesfull, with simple models often performing just as good as more sophisticated ones. Perhaps this is the result of little attention to modelling the possible changes in customer contributions over time.

1.1 CHANGES IN CUSTOMER PROFITABILITY OVER TIME

Many CP models predict future profitability based on current contributions or the average past contribution from the customer, assuming that a customer’s margins stay stable over time. This assumption might be reasonable in some situations. However, there may be large variation within customer contributions over time. This variation is mostly addressed by incorporating the probability that the customer is still “alive” (i.e. retention probability).

Nevertheless, estimating individual-level CLV has proven to be difficult, with sophisticated methods performing equally well as simple methods (Donkers, Verhoef, & de Jong, 2007). Also, using current or past average margins in CLV calculations may lead to biases. For example in markets in which the cost to serve a customer takes up a large proportion of the gross margin, such as in B2B contexts with a high degree of personal sales. But also in markets with complex dynamics, especially if the (B2B) firm’s customers are operating within different industries.

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(5) What are drivers of customer profitability?

(6) How can we predict the future profitability of individual customers?

(7) To what extent are we able to predict changes in individual customer profitability over time?

(8) How can we identify profitable customers segments based on our predictions? We answer these questions by identifying CP drivers and components based on past research, and we develop a predictive model that captures these identified drivers and components to predict future CP over time.

1.2 DESCRIPTION OF THE ORGANIZATION

This thesis uses data of a supplier of non-food products to retail stores (e.g. supermarkets, drug stores, hardware stores, etc) throughout The Netherlands over the period 2006 up until 2008. The products were sold and distributed directly by the sales representatives, who put the goods in the store in a leased display. Goods that were not sold could be returned: the representative took them back at the next visit. Each representative was responsible for managing the customers within his region, from acquisition to retention.

The company offered three main services/product categories:

(1) Regular: non-food products that were placed into the store in a display that was provided by the supplier (e.g. socks, toys, cleaning accessories);

(2) Loyalty programs: are used by the customer for consumer loyalty programs (i.e. consumers saved loyalty stamps for discounts on products). One program usually covered a period of 4 to 8 weeks. The distributor delivered promotional material (e.g. posters and vouchers) and made sure that there was always enough stock within the store;

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more than they yielded. They therefore wanted to gain insights in the profitability of their customers. They used simple summary statistics aggregated on the segment-level (e.g. segmented on retail chain), and found that some segments appeared to be highly profitable, but the company only had a few customers within these segments. After that, the sales forces put in a lot of effort to acquire more customers within these segments. However, by that time, it was already too late. The company was already in a rough patch and the theme displays were a final hope. They seemed a success at first, but after a few months, a disasturous amount of theme displays were returned, after which the company had no choice than to file for bankruptcy in March 2009.

1.3 SCOPE AND CONTRIBUTION

The model can be used diagnostic, for assessing the performance of the company’s customer-base and aspects that drive customer-customer-based profits, and also normative, as input for managerial decision-making for the selection and targeting of profitable customers. This research contributes to theory by developing and testing a model that predicts CP on the individual customer level in a context with a high degree of uncertainty and changes in customer profits over time, which has been proven to be extremely difficult.

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2 CUSTOMER PROFITABILITY

In this chapter we first discuss customer management, which will answer the question of why it is relevant to measure CP. We then define customer profitability, and discuss the different research streams that have developed within CP. Next, we discuss the measurement and general components of CP, followed by an identification of CP antedecents to get a better understanding of the measure. We end the chapter with a conceptual model for our research, in which all the identified components of customer profitability are present.

2.1 MANAGING CUSTOMER PROFITABILITY

Customer management can be defined as the processes through and actions by which the contribution or value from each customer to the firm’s overall profitability is maximized, by making use of individual data on customers (Kumar, Ramani, & Bohling, 2004; Verhoef & Lemon, 2013). Customer management involves making decisions on (a) selecting customers for targeting, (b) allocating resources to these selected customers, and (c) nurturing customers to increase future profitability (Kumar, Venkatesan, Bohling, & Beckmann, 2008). Customer profitability can be increased by acquisition, up-selling, cross-selling, reducing customer costs, and retention (Verhoef & Lemon, 2013). The underlying philosophy of is that to derive value

from customers, an organization should first be able to provide value to customers.

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In his recent work, Kumar (2018) provides the Customer Valuation Theory, in which he attempts to integrate the concepts of customer value and customer assets. His theory connects individual-level customer value to the performance and valuation of the entire firm by “(1) valuing customers as assets, (2) managing a portfolio of customers, and (3) nurturing profitable customers”.

Whichever of these approaches or actions a firm takes to manage its customers, it requires a thourough understanding of its customer profitability and drivers of this profitability to be able to determine the optimal courses of action. This allows firms to better identify and target profitable customers, and optimize resource allocations to profitable customers and activities, which leads to an increased marketing ROI (Reinartz, Thomas, & Kumar, 2005; Venkatesan & Kumar, 2004).

2.2 DEFINING CUSTOMER PROFITABILITY

Pfeifer, Haskins and Conroy (2005) define customer profitability as “the difference between the revenues earned from and costs associated with the customer relationship during a specified period”. The authors state that if CP is viewed strictly from an accounting perspective, then CP should focus on past and current contribution of a customer to the firm. Hence, CP is backward-looking by definition. However, a firm cannot change or control its past, only its future. Therefore, it needs to be able to anticipate on expected customer profitability by making predictions about the future if it whishes to take appropriate actions. In literature, expected future customer profitability is often referred to as customer lifetime value (CLV), which is the expected future customer profitability during the entire relationship of a customer with the firm, discounted by the current value of future capital (Holm, Kumar, & Rohde, 2012; Pfeifer et al., 2005). If a customer is seen as an asset, then customer value can be viewed as the price that someone would be willing to pay to acquire that asset (Pfeifer et al., 2005).

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discussion we may conclude that CP and CV are used interchangeably, and that it is important to define and specify any measurement of the concepts, and being aware of the differences between them. In this thesis our main goal is to not only measure past CP, but also to make predictions about the future. We use sources from both customer profitability and customer value research, as long as it is relevant for the current research.

2.3 MEASURING CUSTOMER PROFITABILITY

A lot of different models for measuring CP exist, and each model seems to capture and/or focus on different components. Holm, Kumar, and Rohde (2012) argue that model specification and sophistication should depend on the complexity of the context, which they view as consisting of customer behavioral complexity and customer service complexity. They define customer behavioral complexity as “the degree of variation in retention durations (relationship length), transaction frequency and value of transactions (relationship depth), and cross-buying behavior (relationship breadth) across the total number of customer relationships a firm serves”. Customer service complexity is defined as “the degree of variation in service needs and requirements that invoke differential activities on an organization across customer-facing functions in terms of the number of activities performed as well as the time spent on each activity”.

The model should capture all aspects that are relevant for the specific context, as long as the benefits for measuring each aspect is higher than its costs. We adopt the view of Holm, Kumar, and Rohde (2012) on measuring CP, since it is highly flexible while still capturing all components that are commonly used within CP literature, and it seems to bridge a gap between several CP research streams (CPA and CLV). We will next discuss three aspects of customer profitability that seem to be important to distinguish: customer relationship, customer revenues and risk, and customer costs.

2.3.1 CUSTOMER RELATIONSHIP

According to Gupta et al. (2006), marketing actions of the firm lead to customer behavior, which in turn leads to CP. They distinguish between three customer behaviors that represent the lifetime stages of a customer:

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(2) Customer margin: the purchase behavior during the customer-firm relationship (i.e. up- and cross-selling);

(3) Customer retention: repeat-purchases and/or customer defection.

These three behavioral components seem to be the focus of many CP models (Gupta et al., 2006; Gupta, 2009; Venkatesan & Kumar, 2004), and capture relationship length, depth, and breadth (Bolton, Lemon, & Verhoef, 2004). Relationship depth and breadth refers to the revenues that are associated with the customer relationship, including purchase frequency/up-selling (depth) and cross-buying (breadth). We go into more depth on customer revenues in the following subsection. In the remainder of this subsection on customer relationship, we will discuss the relationship length in more detail.

Relationship length relates to the customer retention component of CP. Let us first discuss the possible natures of a customer-firm relationship. Fader and Hardie (2009) distinguish between contractual and non-contractual relationships. Within a contractual relationship, it is relatively easy to observe the relationship termination. The customer needs to let the firm know that he is terminating the relationship, or he does simply not extend its contract. Within a non-contractual relationship, this customer defection is usually unobserved, making it harder to determine whether a customer is still “alive” at a certain point. CP models usually model customer retention by means of the probability that a customer will still be active at a certain point in time (Gupta et al., 2006). Another distinction that Fader and Hardie (2009) make is between the transaction opportunities. These can either occur continuously (i.e. at any given time) or discretely (i.e. only at certain points in time). They presented a quadrant for both the relationship type and transaction opportunities dimensions, and each setting asks for a different modelling approach.

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the always-a-share seems to be more appropriate for noncontractual relationships (Rust, Lemon, & Zeithaml, 2004).

Finally, one should specify the period over which CP is measured or estimated. If it is modelled for the entire (expected) relationship with the customer (i.e. its entire lifetime), the time horizon should generally be set to infinity. It is however difficult to predict for a long period in time, since markets are usually dynamic in nature. Besides, most companies set their strategies for the next three to five years, which makes it reasonable to set a limited time horizon for CP predictions (Kumar et al., 2008).

2.3.2 CUSTOMER REVENUES AND RISK

Instead of customer revenues, we can also refer to customer gross margins, which are revenues minus the costs of goods sold (COGS) (Pfeifer et al., 2005). Using revenues and COGS separately or using gross margins should depend on the variability in product margins. Many models use average contribution margin of a customer to project future CP (Gupta, Lehmann, & Stuart, 2004; Reinartz & Kumar, 2003). This might be reasonable if the company offers relatively few service propositions and margins are relatively stable over time, but might be biased if large variations in cash flows between customers exist. That is why several authors have argued for more attention to risk in CP models (Bolton et al., 2004; Holm et al., 2012; Kumar, 2018; Nenonen & Storbacka, 2016).

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Based on the previous discussion we hypothesize that there is a significant improvement in model performance when changes in customer revenues are taken into consideration, compared to a model that is based on average past contribution:

H1. A model that incorporates changes in customer revenues over time predicts future customer profitability significantly better than a model that predicts future customer profitability based on the average past contribution of a customer.

2.3.3 CUSTOMER COSTS

Several scholars stress the importance of explicitly specifying costs in CP calculations, since most calculations of CP seem to focus on demand resulting from customer behavior, while the costs related to serving customers are an important part of the customer margin (Blattberg, Malthouse, & Neslin, 2009; Gupta, 2009). Pfeifer et al. (2005) describe three accounting methods to allocate costs to customers: (1) divide the costs by the number of customers, assuming that all customers use the same amount of resources, (2) assign costs to customers relative to their size (e.g. revenues), and (3) based on their use of resources. The latter is referred to as Activity-Based Costing (ABC), which is a common theme within CP analyses. ABC was developed by Cooper and Kaplan (1988) with the underlying philosophy that costs should be attributed to the activities proportional to their use of resources, i.e. splitting costs and tracing them to individual products, instead of simply dividing costs by the number of units. ABC can also be used to trace back costs to individual customers (Niraj, Gupta, & Narasimhan, 2001), which works the same way, but with a different unit of interest. Costs can first be divided in “pools”, and then into “drivers”, after which they are attributed to customers (Foster et al., 1996). Take, for example, distribution costs as a cost pool. These costs may depend on the number of product units sold, which is the cost driver. The total distribution costs are then divided by the product units sold, which can then be attributed to customers, relative to their units bought.

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used for comparing customers within the company’s customer-base (Pfeifer et al., 2005). Blattberg et al. (2009) refer to including all company costs as a “full-costing” approach, and only including the variable costs of serving customers as a “marginal-costing” approach. Again, the choice depends on the context and goals at hand.

We hypothesize that there is considerable difference in model performance when the costs to serve customers are attributed to individual customers compared to a model that does not includes a cost component:

H2. A model that attributes customers costs to individual customers predicts future customer profitability significantly better than a model without separate cost component.

2.4 UNDERSTANDING CUSTOMER PROFITABILITY

It is essential that a firm is not only able to measure CP, but also understands what drives CP in order to being able to control it. Based on past research, we identified customer behavior and characteristics, firm actions, and control variables that are found to influence CP or its components (i.e. costs and revenues) in B2B settings. We chose to only investigate observed behavior and characteristics, and thus we do not investigate perceptions or attitudes that were found to influence CP. We discuss subsequently each identified driver and how it influences CP. 2.4.1 CUSTOMER BEHAVIOR AND CHARACTERISTICS

Customer behaviors that are found to be highly predictive of future behavior (and with that, CP) are past purchase behavior, cross-buying behavior, and product returns behavior. In terms of customer characteristics, we identified customer size and location as important predictors of future CP. We subsequently discuss each CP driver that is related to customer behavior and customer characteristics.

Past purchase behavior

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Several other metrics can be derived from RFM measures, such as interpurchase time (i.e. average time between transactions) and the average spend per transactions (i.e. M/F). Although RFM metrics are amongst the most studied antecedents of customer profitability, findings on the direction of the effect between RFM and profitability remain inconclusive. Most studies show a positive link: customers who purchased more (often/recently) in the past are also more likely to purchase more in the future, which is positively related to future profitability (Reinartz et al., 2005).

Niraj et al. (2001) found that frequency was actually negatively related to profits, because it adds complexity to the purchases. They found that frequency does not translate in significantly higher average gross margins, but it does significantly increase costs. Compared to many other CP studies, their model can be considered as one of the most detailed in terms of attributing costs to individual customers. They did not only assign marketing costs to customers, but also costs of each individual product. For example, costs are first attributed to separate items (e.g. warehousing, distribution, negotiation with suppliers), and then to customers based on their unit purchases of each item. Thus, especially if the costs per order is high relative to the gross margins, and this is appropriately captured within the profitability model, we would expect similar results to Niraj et al. (2001).

Cross-buying

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Perhaps the reason why Niraj et al. (2001) did not find a significant effect is again because of their cost attribution method. Cross-buying could be associated with higher spending levels, but also with higher order complexity and thus higher costs, which may lead to diminishing returns. Also, Shah, Kumar, Qu, and Chen (2012) found that approximately 10 to 35% of cross-buying customers are in unprofitable relationships, and that the unprofitability increases with the degree of cross-buying. They found that this is due to other unprofitable behaviors that these customers show, such as excessive service requests (which is in line with our reasoning that cross-buying may add to the costs) and promotion purchase behavior (i.e. lower gross margins).

The contradictory findings on cross-buying behavior suggest that on an aggregated level, cross-buying seems to be related to higher profits. However, large differences between customers can exists, interactions with other behaviors are likely to be present, and cost-attribution methods might influence the result.

Product returns

Research on the effect of product returns on CP shows contradictory findings (Petersen & Kumar, 2015; Reinartz & Kumar, 2003). At one hand, product returns result in higher costs and lower revenues, which has a negative consequence for profitability (Reinartz & Kumar, 2003). However, returns could also decrease risk/price perceptions and therefore enhance future spending, which in turn may positively influence CP (Petersen & Kumar, 2015). Kumar et al. (2004) suggest that there is an optimal level of product returns, and thus shows a U-shaped relationship with CP.

Customer size

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customers are reported to be most unprofitable. Reinartz and Kumar (2003) and Rust et al. (2011) found a positive relationship.

Population density

Reinartz and Kumar (2003) found that population density had a negative effect on customer profitability within B2C contexts. There was no effect present within B2B settings. However, within a B2B context that deals with B2C retailers, and thus is indirectly related to a B2C context, we could argue that the effect might be present.

Thus, to conclude, we hypothesize that past customer behavior and customer characteristics are important drivers for CP, with implications and interactions for both revenues and costs:

H3. Past customer behavior and customer characteristics are significant drivers of CP and both its costs and revenues components.

2.4.2 FIRM ACTIONS

Firm initiated contacts are found to positively influence the length of the customer-firm relationship and individual profitability (Kumar et al., 2008; Reinartz et al., 2005; Reinartz & Kumar, 2003). However, Blattberg et al. (2009) suggest that there is an optimal number of marketing contacts. Above a certain point, there are diminishing returns, which is referred to as wearout. Thus, marketing contacts are believed to show an inverted U-shaped relationship with profitability. Rust et al. (2011) find evidence that marketing contacts do not only drive customer behavior, but that the number of contacts in turn is also driven by past customer behavior.

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we should control for the effect of sales persons on customer profitability when reps make their own decisions for visiting customers.

To conclude, we hypothesize that firm actions drive both revenues and costs: there is a “point of diminishing returns” after which CP declines:

H4. Firm actions drive both revenues and costs, and they show a diminishing return on CP, implicating that there is an ideal point of firm actions.

2.4.3 MARKET VARIABLES

The market of retailers is highly dynamic, with a large amount of mergers & acquisitions, changing customer demands, increased competition (both off- and online) and increasing strategic alliances (Grewal, Roggeveen, & Nordfölt, 2017; Kumar, Anand, & Song, 2017). These high dynamics imply that market dynamics could potentially affect customer profitability, and thus, although they cannot be controlled by the firm, they need to be accounted for.

2.5 CONCEPTUAL MODEL

Our main goal is to measure and predict individual customer profitability, which is the revenues derived from a customer minus the costs to serve that customer. In this thesis we test the following hypotheses:

H1. A model that incorporates changes in customer revenues over time predicts future customer profitability significantly better than a model that predicts future customer profitability based on the average past contribution of a customer.

H2. A model that attributes customers costs to individual customers predicts future customer profitability significantly better than a model without separate cost component.

H3. Past customer behavior and customer characteristics are significant drivers of CP and both its costs and revenues components.

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We test these assumptions by including the identified antecedents in our CP model, and determine their individual effects on the components of CP (i.e. revenues/gross margins and customer costs). Also, we compare our model to simpler model variants without changes in CP over time and without a cost component.

To summarize, we present an overview of all identified antecedents of CP and their relationship with costs, revenues, and profitability in table 2.1. We present the most important drivers of CP and relationships between concepts in our conceptual model (figure 2.1). We expect that firm actions are driven by past firm actions, customer characteristics, and past customer behavior. Customer behavior is driven by both current and past firm actions, customer characteristics, and past customer behavior. Customer profitability is driven by both firm actions and customer behavior, and this relationship is influenced by market dynamics.

Figure 2.1: conceptual model Customer profitability

Market dynamics

Firm actions Customer behavior

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Antecedents ∩ = inverted U-shaped relationship U = U-shaped relationship C = control variable / = no significant effects Ni ra j et al. (20 01 ) Van R aa ij et al. (20 03 ) Reinartz & Ku ma r ( 20 03 ) Bo wm an & Nara yand as (2 00 4) Ku m ar et al. (20 04 ) Reinartz et al. (20 05 ) Ku m ar et al. (20 08 ) Ru st et al. (20 11) M ul lins et al. (20 14 ) Peter sen & Ku ma r ( 20 15 ) Gre wel et al. (20 17 ) Ku m ar et al. (20 17 ) Rev enu es Cos ts Pro fit ab ilit y B2B setting* X X X X X X X X X R R Customer behavior Frequency - + + + + + ∩ Interpurchase time ∩ ∩ _- ∩ ∩ ∩ Spending level + + + + + + Cross-buying / + + + + + + + ∩ Product returns ? ∩ ₊ _{- + U} Firm actions Marketing contacts + + + + + + + Extra services - + - + + ∩ Customer characteristics Customer size ∩ ₊ ₊ _{+ + ∩} Location / ? Control variables Sales representative C C C C C Market dynamics C C C C C C Table 2.1: antecedents of CP

* Only the study Petersen and Kumar (2015) investigated CP within a B2C context. Grewel et al. (2017) and Kumar et al. (2017) did not study CP, but discussed the future within retailing. All other

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3 MODEL

In this chapter we develop our model to measure and predict CP. We first define our specification of CP, followed by the specification of its two components: costs and gross margins. We then discuss the data available for the research, and the procedure that we follow to answer our research questions and test our hypotheses.

3.1 MODEL SPECIFICATION

We measure the profitability of a customer i (CP_i) as:

CP_i= ∑ (GM_it-MC_it) T

t=1 where GM_it = gross margins in period t

MC_it = marketing costs in period t T = time horizon of our measurement

We chose to only include marketing costs in our model and not general overhead costs, because our main goal is to compare CP between customers, instead of determining the CP of the entire customer-base. Thus, to estimate future CP we must predict two components: the number of visits and the gross margins for each period:

CP̂_i= ∑ (GM̂_it_-V̂_it_C) T

t=1

where GM̂_it_{= predicted gross margins (in euros) in period t} V̂_it = predicted number of visits in period t

C = costs per visit

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since sales representatives are responsible for maintaining the relationships with their own customer-base, and they delivered products straight from their car on each visit (section 1.2). Thus, the costs to serve a customer depends on the number of orders derived from that customer.

Also, the costs of the sales force are only known on an aggregated level. We therefore divide the total costs of the sales force over our time horizon, divided by the total number of orders within that time horizon, to arrive at the average costs per visit. Individual customer costs are thus a function of the number of orders (i.e. the number of visits) placed by that customer, multiplied by the average costs per order over our entire time horizon. We have estimated the average costs per visit at € 70,39 based on the average order costs in 2008 (total costs of the sales force divided by the total number of orders: 544,396.26 / 7,734).

3.1.1 MODEL FOR THE NUMBER OF VISITS

We are interested in predicting the number of visits for our costs predictions. The number of visits takes on discrete values from 0 to 26, with mean 1.83 and variance 5.82. Since the variance is much larger than the mean (i.e. overdispersion), we assume a Negative Binomial distribution (NBD) for our count data. Also, a relatively large part of our observations have zero values (32.3%). We expect that a regular count model would not handle these zero-observations very well. We therefore estimate a zero-inflated and a zero-hurdle model, and choose the model that offers the best fit.

We hypothesized that the number of visits is a function of past purchase behavior, past marketing contacts, market variables, and customer characteristics. Therefore, our initial estimation of our visits component will have the following functional form:

V_it= α + β₁Rec_it+ β₂V_it-1 + β₃V.sum_it-1 + β₄ V.avg_it-1 + β₅GM_it-1 + β₆GM.sum_it-1 + β₇GM.avg_it-1 + β₈PR_it-1 + β₉PR.dum_t + β₁₀GDP_t + β₁₁Cat_i + β₁₂Pop_i + u_it where α = intercept

Rec = periods t since last purchase V = number of visits

GM = gross margins

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avg = cumulative average from t=1 till t-1 PR = number of premium orders

PR.dum = dummy indicating that no order details were recorded GDP = GDP of consumers

Cat = number of categories purchased over the entire time horizon Pop = population density

u = error term 3.1.2 MODEL FOR GROSS MARGINS

Our response variable gross margins (i.e. revenues derived from a customer minus the costs of goods sold) can take positive and zero values, and its distribution is somewhat skewed to the right (mean = 359.58, sd = 878.88). Also, there are considerable outliers present within our data. All these characteristics are possible issues that can bias our estimations. To somewhat account for these issues, and to accommodate potential interactions between our variables, we estimate our gross margins model as a multiplicative (log-log) model. To account for the zero-observations, we fit a zero-hurdle model to our data, in which we allow the variables and parameters for the zero-hurdle part to differ from the positive gross margins model. Our initial gross margins model takes the following specification:

GM_it* = α + β₁V_it* + β₂Rec_it* + β₃V_it-1* + β₄V.sum_it-1* + β₅V.avg_it-1* _{+ β} 6GMit-1

* ₊ β₇GM.sumit-1* + β8GM.avgit-1* + β9PRit-1* + β10PR.dumi* + β11GDPit-1* +

β₁₂Cat_it-1* + β₁₃Pop*_it-1 + β₁₄Returns_it* + εit where α = intercept

Rec = periods t since last purchase V = number of visits

GM = gross margins

sum = cumulative sum from t=1 till t-1 avg = cumulative average from t=1 till t-1 PR = number of premium orders

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Cat = number of categories purchased over the entire time horizon Pop = population density

Returns = gross margins of product returns ε = error term

3.2 DATA

In total we have 3 years of observations (2006 - 2008), which we divided into 12 quarters. We only model customer profitability for customers that have purchased within the first period of the data (Q1 2006) to avoid potential problems with left-censoring. Because of variable transformations (e.g. lagged variables of our response variables) we lose the first period of our data, which leaves us with a total of 11 time periods. We then excluded all customers that did not make any purchase in the remaining 11 time periods. In total we have observations of 349 customers over each period, which results in 3839 observations. We use the first 9 quarters of our data for estimating our model (Q2 2006 to Q1 2008), and the last 3 quarters for assessing its predictive validity (Q2 2008 to Q4 2008).

In the first five periods of our observations (Q1 2006 – Q1 2007), the company did not keep track of order details, only of order totals. As a result, we do not have data on which products were ordered (and also not the number of premium programs or product categories), nor on the products that were returned. We therefore added a dummary variable that indicated missing data for premium programs (i..e the variable Premium Dummy), and we set the cross-buying variable as a fixed customer-specific variable, that does not change over time (i.e. the variable categories, which is the sum of the bought product categories over the entire time horizon).

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(1) For each period in which the returns were registered (from Q2 2007), and where the return ratio was less than 1 (i.e. the customer bought more than he returned) and higher than 0, we multiplied gross margins by the return ratio (e.g. if gross margins was 100, and the return ratio 0.5, then gross margins was set at 50); (2) For the resulting negative gross margins, we subtracted these from the gross

margins in the previous period, and repeated this step until every gross margins value was zero, or negative for our first period of observations. We chose to subtract them from previous periods, because gross margins can only be negative if a customer returned products that he bought in an earlier period;

(3) We then set negative values for the first period of observations at zero, since these were returns of products that were bought before our observation periods.

Thus, product returns are not included within our gross margins response variable. We therefore included product returns as a predictor within our gross margins component to still account for the effect of product returns. Note that, just as with our premium program orders, product returns were not registered before Q2 2008.

We had 7% missing values for postal code, which we assume to be random errors because of administrative errors. This led to missing values for our variable population density. Also, the data obtained from Statistics Netherlands could not completely be matched to every postal code, possibly because of changes within the municipalities within the last decade. This led to 17% missing values for Population Density. Thus, in total we had 24% missing values for population density. We imputed these values based on predictive mean matching, with 5 imputed datasets and 50 iterations.

The R-code for our data preparation and manipulation are included in Appendix A.

3.3 PROCEDURE

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or a zero-hurdle Binary Logit component significantly improves our model by comparing models based on AIC scores.

Once we have fitted each model component, we investigate heterogeneity by comparing the model with a model that includes effects for individual customers, customer groups, and/or sales representatives. For our gross margins component, we test whether considerable heterogeneity between individual customers is present by performing an F-test between the pooled version and a fixed effects model. We then estimate a random-effects model, and perform a Hausman test to assure that the heterogeneity between customers is endogeneous to our predictors, which is an important assumption of a random-effects model.

For our gross margins component we assess whether our residuals are normally distributed by both a Shapiro-Wilk and a Kolmogorov-Smirnov normallity test. We test for autocorrelation using the Durbin-Watson test, and assess whether heteroskedasticity is present within our data before and after the company started to register order details, by means of a Breush-Pagan test. Selection bias may be present within our model. We therefore re-estimate the model by using the Heckman procedure. A significant Inverse Mills Ratio indicates that selection bias is present, and that we have to apply a Heckman correction to our gross margins expectation.

We test the predictive accuracy of our models by testing our models on both our estimation and holdout sample. We use the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Relative Absolute Error (RAE) for assessing predictive validity.

Once we have validated each model component and have made predictions for our holdout sample, we calculate both the observed and the predicted customer profitability. We divide customers into profitability segments for both the validation and the holdout sample, and we check whether we observe and predict changes in individual customer profitability based on shifts from and to profitability segments. We then compare our model to a model that takes the average past contribution and projects it on the future and to a model without cost component. Finally, we investigate differences in CP between customer segments by performing a cluster analysis by using the Ward method based on Euclidean distance.

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4 RESULTS

In this chapter we present the results of our analyses. We first estimate and predict our visits and gross margins components, and present both model’s results. Then, we combine the results of both models to arrive at our customer profitability predictions. We then investigate whether our model is able to predict the changes in customer profitability over time, and compare our model to simple variants. Finally, we inspect whether we can find differences between customers for the purpose of customer management.

4.1 NUMBER OF VISITS

For our visits component we first estimated a Poisson model and deleted variables that showed a high collinearity and a relative poor fit compared to correlated predictors (i.e. the cumulative sum of both visits and gross margins, the direct lag of visits, and the cumulative average of gross margins). We then performed a dispersion test, that showed significant results (dispersion = 1.617, z = 4.188, p = .000). Therefore, we tried fitting a Negative Binomial distribution to our data, which performed significantly better than our Poisson model (LL Poisson = -4352.0, LL Negative Binomial = -4243.3, Chi squared = 217.32, p = .000). We continued optimizing our model assuming a Negative Binomial distribution.

Since 32.3% of our observations are zero-observations, we estimated a zero-inflated and a hurdle model to our data. Both models show a large improvement in AIC compared to the regular NBD model (regular NBD = 8506.6, zero-inflated = 8310.7, hurdle = 8306.6). The hurdle model provides a better fit, and offers more flexibility in estimating the zero-observations using different predictors. We therefore continue fitting the hurdle variant of our model.

Till now, we have neither considered heterogeneity between customers, nor have we investigated the effect of sales representatives. A model with a customer-specific intercept provides a perfect fit, and is therefore not an option. We fitted variants of our model by adding the effects of sales reps, customer industry/retail chain, and both. Our results (table 4.1) indicate that customer industry or retail chain and sales reps both have a significant effect on the number of orders placed by a customer.

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Let us first discuss the zero-model: the binary logit model. We observe the strongest effect for recency, which is negative. For each unit increase of recency, keeping all else equal, the probability of purchase decreases with 65.3%. We observe the strongest, positive for the lag of visits: for each unit increase, keeping all else equal, the probability of purchase increases by 61%. For the number of visits we observe that for each unit increase of the cumulative average of visits, the number of visits increases by 20.1%. Above a certain point, this effect diminishes, as we observe a significant effect for the squared term of the cumulative average of visits. Thus, customers with a higher average past number of visits are also predicted to show a higher number of visits in the future. For each unit increase of the lag of premium oders, the number of visits increases by 5.2%. During the period in which order details were not registered, the number of visits was 18.4% higher. The GDP of consumers also shows a significant effect: for each unit increase, keeping all else equal, the number of visits increases by 10.2%. We do not observe any negative effects of predictor variables on the number of visits, only the strength of the increase of the cumulative average past visits diminishes above a certain point.

Our model shows a Relative Absolute Error (RAE) of 0.59 (out-of-sample), which means that it outperforms a naïve model where the estimated number of visits equals the number of visits in the previous period. We observe a Root Mean Squared Error (RMSE) of 1.665 in our estimation sample, against 1.358 in our validation sample. The Mean Absolute Error (MAE) of 1.358 of our holdout sample indicates that, on average, the predicted value for visits deviates 1.358 from the observed value for visits.

Model k LL Chisq AIC

Model without qualitative IVs 14 -4082.3 8192.6

Sales Rep 24 -4058.7 47.255 *** _8165.4

Industry/Chain 26 -4039.9 37.615 *** _8131.8

Industry/Chain + Sales Rep 36 -4019.8 40.266 *** _8111.5

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Figure 4.1: distribution of Visits

Count-model (Negative Binomial)

Variable Estimate Std. error z-value p-value 2.5% 97.5% Marginal Visits avg 0.183 0.016 11.498 0.000 *** _0.152 _0.214 _1.201

Premium t-1 0.051 0.017 2.932 0.003 ** _0.017 _0.084 _1.052

Premium dum 0.169 0.046 3.701 0.000 *** _0.079 _0.258 _1.184

GDP Cons. 0.097 0.014 8.352 0.000 *** _0.069 _0.125 _1.102

I(Visits avg2) -0.044 0.006 -7.048 0.000 *** _0.053 _0.086 _1.072

Zero-model (Binary Logit)

Variable Estimate Std. error z-value p-value 2.5% 97.5% Marginal Intercept -0.429 0.172 -2.487 0.013 * _{-0.767 -0.091} _0.651 Recency -0.427 0.064 -6.701 0.000 *** _{-0.552 -0.302} _0.653 Visits t-1 0.476 0.064 -6.701 0.000 *** _0.374 _0.578 _1.610 Visits sum -0.024 0.006 -3.835 0.000 *** _{-0.037 -0.012} _0.976 Categories 0.281 0.019 14.899 0.000 *** _0.244 _0.318 _1.325 I(Categories2₎ _-0.320 _{0.069 -4.647} _0.000 *** _{-0.455 -0.185} _0.726

Log-likelihood = -4019.8, LR test: Chi squared (33) = 2191.1***_{, AIC = 8111.51}

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4.2 GROSS MARGINS

We estimated a multiplicative model for our gross margins (GM) component, with again a zero-hurdle component for our zero-observations that has the same specification as for our Visits model. We dropped the cumulative sum of both frequency and gross margins due to high collinearity. Our initial model is significant (F = 114.4, df = 10; 1878, p = .000) and explains 37.9% of the variance within gross margins.

Since we expect considerable heterogeneity between customers, we have modeled three variants of our model: (1) a pooled model, (2) a model with fixed customer effects, and (3) a model with random customer effects. For the fixed effects model we deleted the variable categories because it is customer-specific and time invariant, and therefore not allowed. An F-test between the pooled and fixed effects model shows that there are significant individual differences present (F = 2.082, df1 = 346, df2 = 1532, p = .000).

We then estimated a random-effects model, but a Hausman test showed that the random effects are endogenous to our predictors, from which we must conclude that a random effects model is not allowed (Chi squared = 631.59, df = 8, p = .000). Thus, we estimate our model with a fixed effect for each customer, but we do not allow for customer-specific error terms. We further fitted our model by deleting recency, premium dummy, GDP consumers, and the lag of gross margins. Including quadratic effects of our variables did not improve the model’s performance.

Since we predict a two-stage model, selection bias may be present. We therefore re-estimated our model using the Heckman procedure. The Inverse Mills ratio was not significant (IMR = 0.050, t = 0.394, p = .900), indicating that we can estimate our model without applying the Heckman correction. No autocorrelation was detected between the residuals of each customer (Durbin-Watson = 2.193, p = 1), but we did find significant heteroskedasticity (Breusch–Pagan = 36.594, df = 5, p = 0.000). Also, the residuals of our model failed to meet the normality assumption (Shapiro-Wilk = 0.894, p = .000; Kolmogorov-Smirnov = 0.131, p = .000). We therefore obtained robust standard errors by t-tests of the coefficients using the Arellano method that accounts for heteroskedasticity in fixed effects models.

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table 4.2. Our overall model is significant, and explains 36.2% of the variation in gross margins. Except for the number of premium orders, all estimates are significant. Since we estimated a multiplicative model, estimates are presented as elasticities. We calculated the original estimates by multiplying the elasticities by the variance within the standard errors divided by two.

The number of visits shows the largest, positive effect on gross margins: for each 1% increase in visits, gross margins increases by 1.8%. Visits, the lag of visits, and product returns show negative effects, with the largest effect resulting from product returns. For each 1% increase in the value of product returns, gross margins decreases by 0.3%. The weighted mean of the fixed effects is 4.767 with (robust) standard error 0.418. Except for 16 of the customers, all fixed effects are significant and positive.

We predict the values for gross margins by taking the exponential of our log-transformed predictions and multiplying these predictions by the probability of purchase. We present our measures of accuracy in table 4.4. Our model shows an RAE of 0.529 on our validation sample, which indicates that it ourperforms a naïve model. The out-of-sample MAE is 186.34, which means that, on average, our predictions are 186.34 off the true values of gross margins.

Variable Estimate Std. error z-value p-value Elasticity

Visits 5.157 0.142 12.897 0.000 *** _1.826 Visits t-1 -0.215 0.089 -2.722 0.007 ** _-0.243 GM avg -0.217 0.070 -3.529 0.000 *** _-0.245 Premium 0.211 0.144 1.343 0.179 0.193 Returns -0.284 0.021 -15.656 0.000 *** _-0.334 Unbalanced Panel: n = 349, T = 1-8, N = 1889 𝜎̂ = 1.253, RSS = 2965.8, ESS = 4649.3, R2_{= 0.362, Adj. R}2_{= 0.215} F-statistic (5, 1535) = 174.258, p-value = .000

Table 4.3: estimates gross margins model

Sample MAE RAE RMSE

Estimation (Q1-Q8) 184.99 0.422 521.07 Holdout (Q9-Q11) 175.22 0.498 506.84

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4.3 CUSTOMER PROFITABILITY

Now that we have estimated our individual model components, we predict customer profitability. Means and standard deviations of measured and predicted V, GM, and CP, for both our estimation and validation sample are presented in table 4.5. Especially the predicted number of visits within the validation period appears to be far off its observed values. This is probably because of observations with relatively high values that could be considered outliers (figure 4.1).

On average, the absolute deviation between the observed and predicted values for CP is 204.03 within the estimation period, and 187.11 within the validation period. Our model shows an RAE of 0.579 and 0.646 in the estimation and validation period respectively, and thus performs better than a naïve model. With an RMSE of 511.13 (estimation period) and 521.92 (validation period) and a standard deviation of 468.72 in the prediction errors in the estimation period.

4.3.1 CHANGES IN CP OVER TIME

We now investigate changes in CP over time. For this purpose we have divided customers into three profitability segments: low (0-25%), middle (25-75%), and high (75-100%). We examine the shifts within segments by comparing the average CP in the year prior to the validation period to the average CP in the validation period. We chose to only take the year prior to the validation period (Q5-Q8) for our comparison to prevent large changes in CP within the estimation period to disturb our comparisons. Also, we deleted the customers that did not make any purchases within the year prior to the validation period, because (a) this resulted in a very high percentage of zero observations that prevented us from dividing customers into realistic high percentage of zero observations that prevented us from dividing customers into realistic profitability segments, as 41.6% of the customers showed a CP of zero within the validation period, and (b) we did not believe that this would bias our comparison too much, since only 3 out of the 70 customers that did not purchase within Q5-Q8 eventually did make a purchase in Q9-Q11. We will refer to Q5-Q8 as period 1, and to Q9-Q11 as period 2.

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segment, while 10% of the highest segment (7 customers) shifts to the lowest segment. When examining the predicted shifts, these do not look far off: 56.5% of the customers are predicted to stay within the same CP segment.

If we compare the predicted CP segments with the observed CP segments in period 2 (table 4.7), 66.6% of the segments are classified correct. The largest errors seem to take place between the lowest and the middle segment. 21.7% of the customers in the middle segment are predicted to be in the lowest segment, and 34.8% of the customers in the lowest segment are predicted to be in the middle segment. A possible explanation for these errors is the distribution within each segment. For example, the difference between the first and the third quartile of the predicted CP is only € 151.24, while the total range is € 5356,30. Also, there is a considerable overlap between the lowest observed CP segment, and the middle predicted CP segment. Thus, we could conclude that our model is predicting high CP reasonably well, but it has difficulty in predicting lower and average CP.

On average, CP decreases by 232% from period 1 to period 2, while our model predicted an average increase of 220%. We found that investigating these relative changes in CP is not useful, since our data contains many values close zo zero. For example, customer X showed a relative CP increase of 80,000%, because his CP in the first period was - € 0.0625, while he showed an average CP of € 86.11 in the second period.

4.3.2 MODEL VARIANTS

To what extent does our model outperform a model that predicts future CP based on the past average CP and to a model without a separate cost component? We took the average observed CP of period 1 (Q5-Q8) to predict CP of period 2 (Q9-Q11), and compared it to the observed CP of period 2. Also, we compared our model to several simpler variants, that included less model components than our main model. For example a model that is based on the past average of gross margins with a correction for predicted purchase probability (∅).

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Thus, our model does not provide a significantly better performance compared to models based on past average contribution and to models without a separate cost component.

In figure 4.3 we show observed and predicted CP for 6 customers, and also predicted CP based on past average CP multiplied by the probability of purchase. Customers A and B showed a large under-prediction, customers C and D a large over-prediction, and E and F relatively a good performance. As we can see, the simple model does not predict large changes over time. Our main model does show changes in CP over time, but often too much or in the wrong direction. Therefore, the simple model is often just as close to the observed value as our main model.

Visits Gross Margins CP

Mean SD Mean SD Mean SD

Q1-Q8 Actual 1.83 2.41 360 879 231 752 Predicted 1.83 1.81 280 732 151 667 Q9-Q11 Actual 1.92 1.84 245 648 161 568 Predicted 1.01 0.90 194 575 123 546

Table 4.5: summary statistics CP

Observed Q9-Q11 Predicted

Low Middle High Low Middle High Q5-Q8

Observed

Low 8.3% 15.9% 0.7% 9.4% 14.5% 1.1%

Middle 14.1% 28.3% 7.6% 11.6% 30.8% 7.6% High 2.5% 5.8% 16.7% 4.0% 4.7% 16.3% Table 4.6: shifts in CP segments from Q5-Q8 to Q9-Q11

Predicted Low Middle High Observed Low 12.3% 8.7% 4.0%

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Model MAE t-value Main model 187.11 ∅ GM.past.avg_it - V̂_itC 197.67 -0.507 GM.past.avg_it - V̂_itC 223.41 -1.696 . ∅ GM.past.avg_it 220.41 -1.596 ∅ CP.past.avg_it 190.69 -0.173 CP.past.avg_it 208.36 -1.009

Table 4.8: predictive accuracy CP model variants (holdout sample)

A B

C D

E F

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4.4 CUSTOMER SEGMENTS

We now investigate differences between customers. We divided customers into clusters based on their average number of orders and spending levels in both Q5-Q8 and Q9-Q11 (Ward method, Euclidean distance): this way, both components of CP are used for clustering, and also the changes from the first to the second period are captured. Six segments were identified, of which we present averages across multiple variables in table 4.9 and the distribution of CP within each segment in figure 4.3.

The segments with the highest average visits and gross margins also show the highest CP. The ordering also holds for the returns ratio: customers in the highest profitability segment return the least of their products, while the customers in the lower segments return a large ratio of the products. Segment 6 shows a return ratio of above 100%, which indicates that they returned more than they bought. We can only explain this by the fact that the customer returned products that they bought in the first observed periods, in which the company did not yet register product returns. The least profitable segments show relatively the highest rate of product returns. Segments 3 and 4 show why it can be helpful to segment on both visits and gross margins over both periods. Both segments start out really close to eachother. However, the profitability of segment 4 increases from period 1 to period 2, because of a large increase in gross margins. Our model predicted this increase in gross margins, but it was not able to predict the increase in CP. Instead it predicted that segment 3 would show a large increase in CP, while that segment stayed relatively stable.

When performing the cluster analysis on clusters based on predicted instead of observed visits and gross margins in period 2, 73.2% of the customers were classified within the same segment as for our first cluster analysis. The fourth cluster seems to be the problem: only 5.8% of the customers in segment 4 are predicted correctly. Thus, we could again conclude that our model components are not able to predict changes in CP over time very well, which was the main purpose of our model.

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Figure 4.3: distribution of CP within customer segments Segment 1 2 3 4 5 6 n 24 22 18 52 70 90 Visits Q5-Q8 5.0 3.6 2.1 1.8 1.5 0.7 Q9-Q11 4.9 2.8 1.9 2.6 0.8 0.4 Predicted 2.7 2.2 1.5 1.6 1.0 0.6 Gross margins Q5-Q8 1698 861 317 282 168 33 Q9-Q11 1766 502 239 758 48 17 Predicted 1277 541 235 441 50 15 CP Q5-Q8 1346 607 166 153 63 -19 Q9-Q11 1421 305 104 573 -7 -9 Predicted 1085 387 326 128 -21 -27 CP Segment Q5-Q8 3.0 3.0 2.2 2.2 1.9 1.4 Q9-Q11 3.0 2.6 2.0 3.0 1.6 1.6 Predicted 2.9 2.6 2.6 2.1 1.7 1.7 MAE 3097 1293 1536 769 198 89 Premium orders 8.5 4.0 1.9 1.6 0.4 0.1 Returns ratio 0.05 0.14 0.17 0.29 0.48 1.32 Categories 11.5 10.4 10.1 9.1 6.7 5.3

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Figure 4.5: customer profitability per customer industry/retail chain Industry/chain A B D E F G I O X Y n 9 19 49 7 6 9 12 82 36 6 Visits 2.8 3.5 2.2 3.3 2.6 1.6 2.2 0.9 1.6 1.1 Gross margins 521 1151 407 698 751 158 397 101 336 146 CP 321 903 252 466 566 47 245 38 224 69 CP Predicted 83 647 154 482 201 188 140 7 259 103 CP Segment 2.3 2.4 2.2 2.5 2.6 1.7 2.5 1.7 2.1 1.9 CP Seg. Pred. 1.6 2.5 2.0 2.3 2.3 2.1 2.1 1.8 2.1 2.0 MAE 1151 1789 780 2073 1609 844 547 213 887 248 Change in CP -1.3 -4.2 1.0 0.3 18.2 -70 -0.4 0.4 -1.1 -0.3 Pred. change CP -1.0 -2.1 0.6 0.8 -1.1 31.4 -0.6 0.3 -1.8 -0.3 Premium orders 4.4 5.5 2.9 1.4 3.8 0.2 2.8 0.4 0.6 0.00 Returns ratio 0.14 0.13 0.47 0.20 0.05 0.28 0.24 1.16 0.44 0.93 Categories 10.3 9.6 9.2 10.7 10.0 9.7 8.4 6.5 6.1 6.5

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5 DISCUSSION

In this chapter we first present our main findings, in which we discuss contributions and implications for theory. We then discuss managerial implications, and how our research contributes to marketing practice. Next, we discuss limitations of our study, and provide suggestions for future research. The chapter concludes with a final conclusion for our research.

5.1 GENERAL DISCUSSION

We posited that future CP is driven by past customer behavior, customer characteristics, and both past and current firm actions. We found considerable evidence that past behavior is a strong predictor of future behavior, which confirms existing theory (Blattberg et al., 2009; Reinartz et al., 2005). Recency (negative) and the number of orders in the previous period (positive) show the strongest effects on purchase propensity. The strongest predictor of the number of orders/visits is the cumulative average of visits up till the previous period. We found a diminishing effect above a certain point, which indicates that there is an ideal number of purchases to optimize returns (Niraj et al., 2011). The number of visits in both the current and the past period are shown to be two of the strongest predictors of current spending levels. Returns have the strongest, negative effect on spending levels. However, we did find that a higher CP is related to a lower returns ratio, and vice versa.

We also found that the number of categories purchased had a significant effect on purchase incidence, which confirms the theory of cross-buying having a positive effect (W. Reinartz et al., 2005; W. J. Reinartz & Kumar, 2003; Rust, Kumar, & Venkatesan, 2011).

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For population density we did not find any significant effects, which confirms the theory of Reinartz and Kumar (2003) that the effect of population density is not significant within a B2B context. We did not investigate customer size. However, the fact that we found significant effects for customer group for our visits component, and individual fixed effects for our gross margins component, we confirm our theory that there is significant heterogeneity between customers.

Based on our previous discussion of our model’s results, we conclude that we have confirmed our hypotheses that past customer behavior, customer characteristics, and firm actions are significant drivers of CP.

We hypothesized that a model that accounts for changes in revenues over time would result in a better performance than a model based on past average contributions. We have modelled CP with separate zero-hurdle components for costs based on the number of visits, and gross margins, and found that our model does not significantly outperforms a simple model that uses the average past contribution to predict future CP. We therefore conclude that our hypothesis on an improved model performance when modeling changes in revenues over time is not confirmed. Therefore, we could not contradict the findings of Donkers, Verhoef, and De Jong (2008), who found that simple models to predict CP often perform just as good as more sophisticated models.

Also, we did not find evidence to confirm our hypothesis that a model that attributes customer costs on the individual customer-level outperforms a model without a separate cost component.

5.2 MANAGERIAL IMPLICATIONS

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resources and model performance need to be made, a simple model offers more advantages compared to a more sophisticated model.

If a manager whishes to determine drivers of customer profitability, our model does provide value, especially in distinguishing between the most and the least profitable customers. It can also be used as a tool to segment and compare customers based on their relative CP Thus, we can conclude that for diagnostic and descriptive purposes, our model could be used as a management tool. For predicting changes in CP over time, our model could provide guidelines, but we recommend not to trust it as a normative tool, as these changes show high predictive errors, with both a large amount of under- and overpredictions.

5.3 LIMITATIONS

One major limitation of our research is that the company that was under investigation filed for bankruptcy after our observation period. This bankruptcy was not suddenly: it had been struggling for quite some time. We cannot determine whether this might have biased our predictions. For example, right before the bankruptcy, several customers ended their relationship with the firm because the company was not able to restitute payments for returned products. Also, in the last six months of our observations, the company launched a new product group that was not present during our estimation period, and we can therefore not determine whether this had influenced our predictions. Especially since a very large amount of these products were eventually returned, there is a serious possibility that this made our predictions less accurate.

Although we attributed costs on the individual level based on the average order handling costs, we needed to make several assumptions regarding cost allocation. For example, the sales force did not record hours spend on each customer, and it is possible that visits took place without a purchase, which would not have been captured by our model.

PREDICTING THE UNPREDICTABLE:

PREDICTING THE UNPREDICTABLE:

Investigating Customer Profitability over Time

author

Jantien Dekker

PREDICTING THE UNPREDICTABLE:

Investigating Customer Profitability over Time

SUMMARY

PREFACE

CONTENTS

1 INTRODUCTION

2 CUSTOMER PROFITABILITY

3 MODEL

4 RESULTS

5 DISCUSSION