B2B Pricing, Information and Sales Person Decisions

1. B2B Pricing, Information and Sales Person Decisions Itır Karaesmen Wedad Elmaghraby Wolfgang Jank Shu Zhang American University, University of Maryland, Sentrana Inc. YAEM 2010 Sabancı University

2. State of B2B Pricing • Professional Pricing Society (PPS) survey in 2007 • High level of executive attention: 82% • Pricing processes very effective: 6% • Active price improvement initiative: 73% • Evaluating or deploying software: 48% • Pricing decisions delegated to sales people • 33% of companies in PPS survey (2007) • 70% of companies in Stephenson et al. (1979) • 70% of companies in Hansen et al. (2008)

3. B2B Pricing • Sales people perform multiple “functions” • Gathering information on customers’ needs • Gathering feedback on product/services sold • Following up on orders • Cross-selling • Pricing • Gathering information on competitors’ prices • Gauging customers’ maximum willingness-to-pay

4. Sales People and Pricing Information • Pricing authority with pricing information • Business rules • Market indicators, segment-level indicators • “Scientific” price recommendations Win-win or lose-lose? Each of the “top” market performers provided price elasticity information to their sales people (Alldredge et al., 2002)

5. Goal & Questions • Goal: To understand how sales people make B2B pricing decisions • Question: Can we build a “mental model” for a sales person in B2B sales? • What factors influence price adjustments? • What is the direction and magnitude of price adjustment given changes in other factors? • How (if at all) do price recommendations influence decision making?

6. Our Analysis& Data

7. Business and Pricing Process • A grocery products distributor in US • Multiple sales (geographic) divisions • Products distributed range from fresh vegetables to toothpicks • Customers: Restaurants, hospitals, schools,… • Pricing process • Sales people interact with customers • Price recommendations made to sales people weekly • Not all products have up-to-date price recommendations • Sales people can override the recommendations (approval to pricing decision needed in exceptional situations) • Sales people have information on costs and customer history • Incentives: Fixed salary vs. margin-based commission vs. “sales contests”

8. Mental Model of Sales People • Price adjustment may be influenced by • Observable factors: cost, cost change, price recommendations, target margin, last price,… • Unobservable factors: competitor’s price as observed by sales people, sales person’s individual “target,”…

9. Constructing the Mental Model • Interviews • Managers at the company • Sales people at the company • Pricing consultants and practitioners at other organizations • Academic literature • Transactional data

10. Based on interviews… • Sales people on their pricing decisions • “I know which item is critical for a customer and choose the price accordingly” • “The price of an item is not the same for two different customers” • “I try to maintain margins by not lowering prices” • “I give discounts to increase the volume if I want to win a sales contest” • “I lower the prices to match competitors’ prices” • Sales people and price recommendations • “I will not take the recommendation if it suggests a margin lower than what I usually get from a customer” • “Customers are not happy with too frequent price changes”

11. Based on interviews and research… • Managers, pricing consultants and academics on sales people • “Sales people take the customers’ side” • “Sales people give unnecessary discounts” • Stephenson et al. (1979) • Academics on price changes • Price asymmetry: “prices rise faster than they fall” |Price increase given a unit cost increase| > |Price decrease given a unit cost decrease| • Price rigidity: small cost changes not transferred to customers when price changes are costly (Zbaracki et al. 2004, Ray et al. 2006)

12. Constructing the Mental Model • Customer- and product-specific factors • Cost-related factors • Cost increase vs. decrease • Small vs. large cost change • Price recommendation • Sales contests and other sales incentives • Competitors’ prices • Sales person-specific factors

13. Data Set • Transaction level data • 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

14. 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: 132 • 99% 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.’08 2.1 million transactions

15. Preliminary Observations: Price change

16. Predicting Price Changes in One Step

17. Predicting Price Changes in Two Steps

18. Two-Stage Analysis Stage-1: What is the probability of price change? • What factors trigger a price change?

19. Two-Stage Analysis Stage-1: What is the probability of price change? • What factors trigger a price change? Stage-2: What influences the magnitude of a price change? • Cost-related factors ? • Size of cost change • Sign of cost change (0,+,-) • Relative magnitude of cost change (Small, Medium, Large) • Upward vs. downward trend • Product-related factors ? • Commodity vs. non-commodity • Customer-related factors ? • Number of transactions over time • Bundle size • Price recommendations ?

20. Stage-2: Regression Analysis • Linear Regression • Response variable: Price Change ($) • Models with single predictor

21. Stage-2: Model Fit

22. Stage-2: Combined Regression Model • Model M6: RPC, CC$, Sign of CC, Size of CC, Product Type, TREND • All predictors except TREND are statistically significant • All interaction terms (except the ones with TREND) are significant • Effect of RPC depends on cost-specific factors and product-specific factors • Effect of cost change depends on Sign of CC$, Size of CC$, product type and RPC -> “Price Asymmetry” and “Product Asymmetry”

23. Stage-2: Key Observations Price change ($) Cost change ($)

24. Stage-2: Key Observations • “Reverse” Price Asymmetry: Prices fall faster than they increase  From Stage-1, we know sales people are less likely to change prices when costs decrease • But when they decide to change the price in the direction of the cost change, then |Price increase given a unit cost increase| < |Price decrease given a unit cost decrease|  Why?

25. Key Takeaways • Price adjustments made by sales people can be predicted by “observable” factors • Sign of cost change, size of cost change, cost trends, number of transactions, type of product, price recommendation • A two-stage mental model for sales people • Price recommendations are powerful predictors of transaction prices in the absence of medium and big cost changes • Decision making • Decisions are made in two stages • Hierarchy in processing information

26. Thank you! karaesme@american.edu

27. Ongoing and Future Research • Comparison of results to data where there is no price recommendation • Looking at the effect of sales regions • Analysis for new customers • No prior price information • A second data set yet to be analyzed • Currently no information on sales reps or supply • Info on gender, age, tenure, wage structure… • Sales person characteristics and mental model • Sales person characteristics and attitude towards RPC • Supply issues and pricing decisions

28. Literature & Contributions • Research literature • Pricing optimization: OR models, econ models characterize “optimal” analytical solutions, • Reference price effects to model demand • Price asymmetry: economics and marketing • Asymmetry established for product categories at an aggregate level • Human intervention in decision making disregarded • Psychology of pricing: (Un)fairness, dual entitlement, … • Our contribution • Understands what contributes and/or affects price changes • Take an operational perspective to improve pricing process

29. Profits by Product Category

30. Percentage cost change

31. % recommended margin

32. Percentage cost change

33. Percentage Price Change

34. Recommended Markup • To see if they pass on price decreases ???

35. Commodities vs Noncommodities • Cost distributions • Price distributions

36. Logistic regression

37. Stage-2 Regression All other variables and interaction terms are not significant at 5% level

38. Data without Price Recommendations • Cell-1, cell-2, Cell-4 percentages in Stage-1? • Cost and price distributions?? • Other summary statistics??

39. Further Analysis • Effect of Size of Cost Change • No cost change different than non-zero cost change, but is the effect of small vs. large cost changes the same? • Cluster the transactions based on size of cost change • Clustering based on distribution of price change in each category and total number of transactions

40. Price Change 7.33 6.05 5.35 -10 Cost Change 10 -7.40 -8.09 -9.38 Price Asymmetry? Non-Commodity --- : big --- : medium--- : small

41. Value of Providing Recommendations ?

42. Logistic Regression • Predictors ordered by BIC score and other measures

43. Stage-1: Key Observations • Effect of RPC depends on Cost Change and Product Type • When RPC = 0 and Cost Change =0 (≠ 0), chances of price change for non-commodities is ≈1.5 (≈1) times that of commodities. • When Cost Change = 0, • As the magnitude of recommended mark up increases, chances of price change go down. • As the magnitude of recommended mark down increases, chances of price change go up. (regardless of T and regardless of product type) • Sales people disregard recommended price increases but accept recommended price decreases when cost does not change • Effect of T depends on Product Type • As T , P(price change ≠ 0)  (regardless of Cost Change, RPC and regardless of product type) • Decrease in P(price change ≠ 0) higher for non-commodities • Chance of a price change for an item purchased more often is lower

44. Stage-1: Summary Statistics

45. Percentage cost change

46. Price ($/unit)

47. Ongoing and Future Research • Comparison of results to data where there is no price recommendation • Looking at the effect of sales regions • Analysis for new customers • No prior price information • A second data set yet to be analyzed • Currently no information on sales reps or supply • Info on gender, age, tenure, wage structure… • Sales person characteristics and mental model • Sales person characteristics and attitude towards RPC • Supply issues and pricing decisions

48. Literature & Contributions • Research literature • Pricing optimization: OR models, econ models characterize “optimal” analytical solutions, • Reference price effects to model demand • Price asymmetry: economics and marketing • Asymmetry established for product categories at an aggregate level • Human intervention in decision making disregarded • Psychology of pricing: (Un)fairness, dual entitlement, … • Our contribution • Understands what contributes and/or affects price changes • Take an operational perspective to improve pricing process

49. Profits by Product Category

50. Percentage cost change