Analysis of. Quiz on Sales Force

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1 Analysis of Price Changes in B2B Markets ITIR KARAESMEN American University, Washington DC (joint with Wedad Elmaghraby, Wolfgang Jank, Shu Zhang) Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. Quiz on Sales Force 1. How many people are involved in full time sales in the US? a) less than 5 million b) 5 10 million c) million d) over 20 million 2. How much do US companies spend on sales force? a) less than $600B b) $600B $700B c) $700B $800B d) more than $800B 1

2 Quiz on Sales Force 1. How many people are involved in full time sales in the US? a) less than 5 million b) 5 10 million c) million d) over 20 million* 2. How much do US companies spend on sales force? a) less than $600B b) $600B $700B c) $700B $800B d) more than $800B* * Zoltners (2010) Quiz (cont d) 3. What % of articles published in J of Marketing, J of Marketing Research and Marketing Science during were sales force related? a) less than 4% b) 4 8% c) 8 12% d) more than 12% 4. What % of industrial organizations delegate pricing decisions to sales people? a) 10% b) 20% c) 33% d) 70% 2

3 Quiz (cont d) 3. What % of articles published in J of Marketing, J of Marketing Research and Marketing Science during were sales force related? a) less than 4% * b) 4 8% c) 8 12% d) more than 12% * Zoltners et al. (2008) 4. What % of industrial organizations delegate pricing decisions to sales people? a) 10% b) 20% c) 33% ** d) 70% *** **PPS and Zilliant Survey (2007), *** Stephenson et al. (1970); Hansen et al. (2008) White Collar Workers Hopp, Iravani, Liu POM 09 High degree of discretion in decision making Challenges: modeling and analysis of systems, difficulty in predicting behavior of individuals/system To improve the management of discretionary decision making perform empirical studies to determine how white collar workers actually make decisions Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. 3

4 Context and Research Context: Sales force in B2B pricing B2B and B2C contribute equally to US economy B2B sales Buyer contacts sales person Pricing flexibility; customized pricing Ad hoc negotiations Data from a Grocery Products Distributor (GPD) Research: Study sales force decisions in B2B pricing B2B Pricing Environment at GPD Sales Process Buyer (e.g. a cafeteria, restaurant, daycare) contacts sales person Ad hoc negotiations but no long term contracts Role of Sales People Manage accounts of multiple buyers; CRM Manage multiple products Prepare quotes (prices) Information Available Sales history Pricing and cost history of products Recommended prices (centralized system) 4

5 Outline Conceptualizing the decision process Statistical analysis and the decision model(s) Single vs. two stage models Sales person effects and nonlinearities Which predictors are influential? Observations and conclusions Preview of ongoing research (time permitting) Decision Process Decision processes and decision making Process tracing or interviews Challenging Decision processes of sales force Weitz (1978): sales routine (not pricing) No prior analysis or model for B2B pricing Open questions: Do sales people have common heuristics or biases? How do sales people use information? 5

6 Decision Process (at GPD) Several factors may influence pricing decisions Cost and economic factors Product related ltdfactors Current cost, cost history, Type, brand, shelf life, trend, quantity available, Customer related factors New / repeat, quantity Market related factors demanded, bargaining Competitors prices, power, wtp, pricing gp power, Decision support, internal Pricing guidelines, business rules, recommendations, Sl Sales person related ltdfactors Compensation, volume targets, experience, motivation, Decision Model Decisions are traced as triplets Salesrep X Customer X Product Decision is change in price for repeat customers General model dl Δ Price = Function of factors listed 6

7 Decision Model Literature on decision making (e.g. Payne, 1976) Linear decision models for multi attributes Multi stage screening given a set of alternatives Literature in marketing and operations (e.g. Gensch, 1987) Focus on B2C and consumer choice Two stage models of discrete choice Challenge: What if no distinct set of choices? Data Driven Approach to Model Decisions Data from GPD sales rep ID customer ID product category item ID commodity vs. non commodity (highly perishable vs. longer shelf life items) date of transaction transaction price unit cost quantity recommended price 7

8 Data Set # of sales reps: 1184 # of customers: 14,401 # of (sales rep, customer, item) triplets : 264,123 Each triplet has at least 10 transactions # of product categories: % of profits generated by 88 categories # of items: 43,857 Commodities (vs. non commodities ) 33.31% of the transactions 25% of all product categories 22.54% of all items 45% of profits Date range: Jan. 07 Aug million transactions Preliminary Observations Price stickiness: prices do not change in 64.71% of the transactions. Asymmetry: Price increases 76% of the time when cost increases but price decreases 57% of the time when cost decreases. Cost is not the only reason for price adjustments 8

9 Preliminary Observations Reverse asymmetry: Average decrease in price in response to a cost decrease is higher compared to an average increase in price in response to a cost in increase Variables Used Cost and economic factors Cost change ($) Sign of cost change (+,,0) Relative size of cost change Trend Market related factors Competitors prices, pricing power, Product related factors Type (commodity) Customer related factors Bundle size No. of repeat transactions Sales person related factors Fixed effects (individual) Decision support, internal Recommended price change Sign of recommended change 9

10 Statistical Models Single Stage Two Stages MARK UP/DOWN BY $... SAME PRICE!? NEW PRICE! MARK UP/DOWN BY $... Single Stage Predictions 10

11 Single Stage Predictions Predicted price change Two Stage Predictions Actual 2-Stage 11

12 Two Stage Decisions Stage 1: Whether or not to change the price Logitmodel to predict probability of price change Ex: Logit model that is additive ( linear ) Stage 2: Magnitude of price change Regression models use the same variables Four Models for Each Stage I. Base Model predictors II. Nonlinear model: Linear model + 2 way interactions III. Linear model with sales force fixed effects IV. Nonlinear model with sales force fixed effects Determine which model is best for each stage 12

13 Statistical Challenge Thousands of binary variables for fixed effects of sales people Solution: Monte Carlo sampling Randomly choose 5% of the sales people Create a data set with all triplets of the (chosen) sales people Record model dlfit and ranking (out of 4 models) Repeat Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. Comparison of Four Models N: Linear model with no sales person effects I: Model with interaction terms S: Model with sales person effects Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. 13

14 Observations (so far) Two stage model better predicts price changes Stage 1: Likelihood of price changes is best predicted by a LINEAR model dl Change of price change proportional to predictors Individual sales people effects visible Stage 2: Magnitude of price change is best predicted by a NONLINEAR model A complicated decision i model dl? Individual sales people effects not significant Stage 1: Do we need all the predictors? Stage 1 analysis: One predictor models Cost related factors most influential but not the actual $ change Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. 14

15 Stage 1: Do we need all the predictors? Stage 1 analysis: Model selection Need to factor in: Cost increase Cost decrease Large cost change Only binary variables Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. Stage 2: Do we need all the predictors? Stage 2 analysis: One predictor models Price recommendation most influential Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. 15

16 Stage 1: Do we need all the predictors? Stage 2 analysis: Model selection Need to factor in: RPC Cost change ($) Sign of cost change Continuous and binary variables Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. Key Takeaways B2B pricing decisions of sales people are predicted by observable factors Decisions are made in two stages Stage 1: linear rely more on coarser (binary) information only cost related information sales people may be heterogeneous Stage 2: nonlinear richer information sales people are homogeneous recommended price change and cost related factors influential 16

17 Ongoing Work Investigate sales force closely Are sales people heterogeneous in Stage 1? Are there differences across sales people in terms of how they use information? Thank you Paper appeared in Journal of Revenue Management and Pricing Karaesmen, Elmaghraby, Jank, Zhang. WINFORMS March 12, Analysis of Price Changes in B2B Markets. 17

18 Preview on Ongoing Research Distribution of coefficients of salesrep variables in linear model of Stage-1 Preview on Ongoing Research Sales people are heterogeneous in terms of whether or not they change the prices Distribution of coefficients of salesrep variables in linear model of Stage-1 18

19 Preview on Ongoing Research Sales people are homogeneous on how they change a price once they decide to change it Distribution of coefficient of salesrep variables in nonlinear model Clustering Sales People Given the transactions carried out by a sales person Frequency of price change (Stage 1) Magnitude of price change (Stage 2) Use the k means algorithm 4 distinct t clusters number of clusters 19