Measuring the Effect of Waiting Time on Customer Purchases Andrés Musalem Duke University

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Agenda Background My research Measuring the effect of waiting time on customer purchases

Background: Santiago, Chile Ph.D., Wharton Ind. Engineering MBA, U. of Chile

Teaching Interests: Market Research (U. Chile) Pricing (Wharton) Marketing Management (WEMBA, CCMBA, MEM) GATE: Global academic travel experience (Daytime MBA) – South America Product Management (WEMBA, CCMBA) Marketing Practicum (Daytime MBA):

My research: Quantitative Marketing Mathematical models to study: – How consumers react to coupon promotions? Implications for targeting – How consumers react to out of stocks? Implications for inventory planning – How consumers react to waiting time? Implications for customer service – How to estimate demand for products not yet introduced in a market? Implications for assortment/product line decisions – How should firms make efforts to attract or retain customers? – How should firms manage customer expectations? underpromise and overdeliver? Data driven Game Theory

Measuring the Effect of Waiting Time on Customer Purchases Andrés Musalem Duke University Joint work with Marcelo Olivares, Yina Lu (Decisions Risk and Operations, Columbia Business School), and Ariel Schilkrut (SCOPIX).

RETAIL DECISIONS & INFORMATION  Point of Sales Data  Loyalty Card / Customer Panel Data  Competitive Information (IRI, Nielsen)  Cost data (wholesale prices, accounting) Customer Experience, Service Assortment Pricing Promotions  Lack of objective data  Surveys:  Subjective measures  Sample selection 8

Operations Management Literature Research usually focuses on managing resources to attain a customer service level – Staff required so that 90% of the customers wait less than 1 minute – Number of cashiers open so that less than 4 customers are waiting in line. – Inventory needed to attain a 95% demand fill rate. How would you choose an appropriate level of service? – Trade-off: operating costs vs service levels – Link between service levels and customer purchase behavior 9 Research Goal

Real-Time Store Operational Data: Number of Customers in Line Snapshots every 30 minutes (6 months) Image recognition to identify:  number of people waiting  number of servers + Loyalty card data  UPCs purchased  prices paid  Time stamp 10

Modeling Customer Choice 11 Require waiting (W) No waiting

Modeling Customer Choice 12 Require waiting (W) No waiting Waiting cost for products in W Consumption rate & inventoryPrice sensitivity consumer product visit Seasonality

Matching Operational Data with Customer Transactions Issue: do not know what the queue looked like (Q,E) when a customer visited the deli section Use marketing and operations management tools to model the evolution of the queue between snapshots (e.g., 4:45 and 5:15) : – Choice Models: how likely is a customer to join the line if Q customers are waiting? – Queuing theory: how many customers will remain in the queue by the time a new customer arrives? 13 4:154:455:155:45 ts: cashier time stamp Q L2(t ), E L2(t ) Q L(t ), E L(t ) Q F(t ), E F(t ) ts Queue length Number of employees

RESULTS 14

Results: What drives purchases? Customer behavior is better predicted by queue length (Q) than expected waiting time (W, which is proportional to Q/E) 15

Question: Consider two hypothetical scenarios: – What if we double the number of employees behind the counter? – What if the length of the line is reduced from 10 to 5 customers? Both half the expected waiting time, but which one would have a stronger impact on customer purchase behavior? What’s the implication? 16

17 > Single line checkout for faster shopping

Managerial Implications: Combine or Split Queues? Pooled system: single queue with c servers Split system: c parallel single server queues, customers join the shortest queue (JSQ) 18

Managerial Implications: Combine or Split Queues? Pooled system: single queue with c servers Split system: c parallel single server queues, customers join the shortest queue (JSQ) 19

20 – Pooled system is more efficient in terms of average waiting time – In split system, individual queues are shorter => If customers react to length of queue, this can help to reduce lost sales (by as much as 30%) Managerial Implications: Combine or Split Queues? congestion

Estimated Parameters 21 Effect is non-linear Increase from Q=5 to 10 customers in line => equivalent to 1.7% price increase Increase from Q=10 to 15 customers in line => equivalent to 5.5% price increase Negative correlation between price & waiting sensitivity Pre-packaged products don’t help much. Attract only 7% of deli lost sales when Q=5 -> Q=10 Effect is non-linear Increase from Q=5 to 10 customers in line => equivalent to 1.7% price increase Increase from Q=10 to 15 customers in line => equivalent to 5.5% price increase Negative correlation between price & waiting sensitivity Pre-packaged products don’t help much. Attract only 7% of deli lost sales when Q=5 -> Q=10

Waiting & Price Sensitivity 22

Waiting & Price Sensitivity 23

Managerial Implications: Category Pricing Example: – Two products H and L with different qualities and prices: p H > p L – Customers sensitive to price are insensitive to waiting and vice versa. – What if we offer a discount on the price of the L product? 24

Congestion & Demand Externalities 25 $$$ $ $ $$ $$$ $ Price Discount on Product L $

Managerial Implications: Category Pricing Example: – Two products H and L with different prices: p H > p L – Customers sensitive to price are insensitive to waiting and vice versa. – What if we offer a discount on the price of the L product? – If price and waiting sensitivity are negatively correlated, a significant fraction of H customers may decide not to purchase 26 Correlation between price and waiting sensitivity Waiting None Sensitivity Medium Heterogeneity High Cross-price elasticity of demand: % change in demand of H product after 1% price reduction on L product

Conclusions New technology enables us to better understand the link between service performance and customer behavior Estimation challenge: limited information about the queue – Combine choice models with queuing theory Results & implications: – Consumers act as if they consider queue length, but not speed of service > Consider splitting lines or making speed more salient – Price sensitivity negatively correlated with waiting sensitivity > Price reductions on low priced products may generate negative demand externalities on higher price products 27

QUESTIONS? 28

Queues and Traffic: Congestion Effects 29 Queue length and transaction volume are positively correlated due to congestion

Summary Statistics 30

Model Estimation Details 1.Customer arrival rate ( ¸ ): store traffic data 2.Service rate ( ¹ ): given ¸ and an initial guess of utility model we estimate ¹ by matching the observed distribution of queue lengths with that implied by the Erlang model. 3.Queue length: Given ¹ and ¸, and the initial guess of utility model we estimate the queue length that customers faced (integrating the uncertainty about the time when they visited the deli). 4.The estimated queue lengths is used to estimate the probability of a customer joining the queue. 5.The process can be repeated until utility converges. 31

Empirical vs Theoretical Queue distributions: 32

Marketing and other disciplines Marketing Economics Psychology Engineering Sociology Statistics Ethnography competition sales force allocation consumer decisions demand forecast in-depth consumer research word of mouth

34 Help Vinay & Sameer Marketing Management

35 3C’s STP+4P’s Angiomax: What price would you charge? Why Teams Vinay and Sameer’s social media approach was successful? Would you improve Starbucks’ service? Unilever: Should Unilever introduce a new product in Brazil? Hulu: Ads vs No Ads? How would you promote the Ford Ka? Molson: Why the social media campaign was not successful?

Purchase probability versus queue length and number of employees 36