Pricing DiscriminationDynamicPersonalization Real Time September 2013

Price Discrimination n The law of demand tells us that demanders are different, and so are willing to pay different amounts (elasticity of demand differs and values are different—so different willingness to pay) n What it means: –Charge different prices to different consumers in an effort to increase market and profits

It Is Done Everywhere It Is Done Everywhere (Especially to Foreigners)

Price & Profit increase

Where Does Pricing Fit? n Price is rarely the headline n Pricing is never about the number, it’s about the model n Part of the business model –How do we make money? How much? –Revenue/profit/shipment forecasts n Supports core value proposition –“Our product/service saves you $$$$…” –…and we want 20% of the savings n Often an obstacle to buying –Too complex –Much too high (sticker shock) –Much too low (desperate, unprofitable) –Free (no reason to trade up)

Pricing Objectives n FIRST… –Don’t make price the primary issue –Don’t over-complicate the sale –Don’t require customers to be smart –Don’t change prices too often n THEN… –Support the business model/plan –Reinforce benefit of products/services –Pick natural units –Make correct ordering easier

Price Models n Fixed pricing – one national price, everywhere, for everyone n Dynamic pricing – the price of a product is based a merchant’s understanding of how much value the customer attaches to the product and their own desire to make a sale – supply and demand n Trigger pricing – used in m-commerce applications, adjusts prices based on the location of the consumer n Utilization pricing – adjusts prices based on the utilization of the product n Personalization pricing – adjusts prices based on the merchant’s estimate of how much the customer truly values the product

Price Discrimination n First degree: willingness to pay (rare) n Second degree: artificial hurdles but open n Third degree: based on external factors  Geography (neighborhood, state)  Gender (women's clothing)  Age (senior/student discounts)  Profession/affiliation (small/large business, educational, medical…)

Dynamic Pricing: Is This Price Right? n New forms of dynamic pricing include:  Time-based dynamic pricing: Adjusts price to different points in the product life cycle  Peak-load dynamic pricing: Adjusts prices to times of day  Clearance dynamic pricing: Used when products lose value over time (plus, “perishables”): Produce, Airplane seats, Hotel Rooms, “old” technology n Dynamic pricing is opposed by some consumer groups and individuals  Amazon example  Some forms appear to be more acceptable than others

Internet Pricing Models Through the use of real-time pricing technology, e-tailers can change and post prices instantly at very little cost Real-time pricing the ability to change prices instantly to keep up with changes in the marketplace Secion 14-2 This gives them a competitive edge

Definition: Use knowledge about a customer to merchandise, present, modify and deliver products and services most appropriate to that individual at that time Real Life Example Giving a personal gift, comforting words to people in suffering,... Specific offer, pricing, … It must have Knowledge about the customer Knowledge about supply choices Business rules about what to offer Dynamic web delivery mechanism

Personalization for B2C Analyze who The User is Deliver Personalized Service Analyze what We have Match making 1. User Profiling2. Content Management 3. Marketing control 4. Dynamic Delivery Satisfied Customer

The Match Making Engine

A Personalized Shop Page

Example

Analyzing item price movements and its impact on: Basket size over a long duration (6-10yrs) will provide key insights into halo impact and affinity contribution for items Basket composition over a long duration (6- 10yrs) will provide key insights into price bands for items. Analyzing Affinity of items over a long duration (6-10 yrs) will provide key insights into running better promotions, planogram and price planning of around affinity items. Analyzing Affinity of items impact on basket composition Aster use case example

Business Questions: Analyzing item price movement and its impact on basket size and affinity of items over a long duration (6 yrs). Data Set (6 years): Transaction Data, Price data Aster Steps: 1. Use Aster Collaborative Filter function to create resulting correlation coefficient.  Query runtime: 48 minutes 2. Use Aster Correlation Stats function to discover relationship between items.  Query runtime: 48 minutes 3. Use BI tool like a Tableau to visualize results and drill into individual categories. Pricing Affinity

Aster Discovery Platform

Aster Discovery Platform New business insights from all kinds of data with all types of analytics for all types of enterprise users with rapid exploration Large Volumes Interaction Data Structured Unstructured Multi-structured Hadoop 1 Relational/SQL MapReduce Graph Statistics, R Pathing 2 Business Users Analysts Data Scientists 3 Fast Iterative Investigative Easy 4

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