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Pricing Optimization: Close-out, Online and Renewal strategies, Data Reply

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“Product close-out strategy” by Ilaria Gianoli, Data Scientist, Data Reply

Abstract:

How to deal with products in their decline phase? Ilaria will share her experience in optimizing the close-out strategy for a multinational retail leader, with a particular focus on the price optimization.

Bio:

Ilaria is a Data Scientist at Data Reply, where she works as a consultant across different industries, in particular in the Retail. She uses her mathematical, statistical and machine learning background to turning data into business opportunities. She also works closely to the business to provide quantitative support for decision making, adapting the complexity of the mathematical models to customer needs.
She holds a MSc in Applied Statistics - Mathematical Engineering from Politecnico di Milano.

“Online pricing: from theory to application” by Giovanni Corradini, Data Scientist, Data Reply

Abstract:

Multi-Armed Bandit algorithms are populating the world of e-commerce. How do they work?
Giovanni will share the basic of this field and an application of a state-of-the-art algorithm on real world simulation of the ticket industry.

Bio: Giovanni is a Data Scientist at Data Reply.
He holds a MSc in Applied Statistics - Mathematical Engineering from Politecnico di Milano.
He has a background in statistics, machine learning and data mining and he provides decision making support to industries in many different fields.

“Renewal Price Optimization for Subscription products” by Riccardo Lorenzon, Data Scientist, Data Reply

Abstract:

We are observing a huge shift in modern economy from a pay-per-product model to a subscription-based model. When it comes to pricing strategies, it is important both to close the single deal and monetize long-term relationships with the customer. Riccardo will present an application of subscription renewal pricing optimization models for a company belonging to the publishing industry.

Bio:
Riccardo holds a MSc in Mathematical Models for Decision Making from Politecnico di Milano.
He developed hands-on experience on end-to-end data projects across multiple industries. His proactive creativity helps him be very effective in the business case design and early stages of projects.

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Pricing Optimization: Close-out, Online and Renewal strategies, Data Reply

  1. 1. PRICING OPTIMIZATION IN DATA REPLY Ilaria Gianoli | Data Scientist Giovanni Corradini | Data Scientist Riccardo Lorenzon | Senior Data Scientist
  2. 2. OPTIMAL DISCOUNT STRATEGY FOR PRODUCTS IN CLOSE-OUT PHASE Ilaria Gianoli | Data Scientist
  3. 3.  Customer: Retail/GDO Leader  Description: Identify the optimal discount strategy for products in their close-out phase, as a trade-off between margin loss and inventory cost THE PROBLEM
  4. 4. PROJECT BACKGROUNDInfo • The purchasing department establishes when a product goes out of range • In the close-out phase, the stores have limited time to get rid of the leftover stock, otherwise the have to pay a penalty • Each store chooses autonomously the discount to apply Alert • The product prices do not change often, except during the marketing campaign • The wide product range requires the elasticity model to be product- specific
  5. 5. SOLUTION OVERVIEW DATA TECH • Elasticity model: linear regression • Clustering model: DBscan • Time series forecast: arima + FFNN + seasonal rescaling ALGOS Sales Prices Product info Stock Promotions
  6. 6. SOLUTION DETAIL STEP 1: SALES FORECASTING STEP 2: PRICE ELASTICITY STEP 3: DISCOUNT OPTIMIZATION • Use all the information about sellout, stock, prices, calendar, marketing and discount campaigns to create a clean time series; • Create a forecast model on a weekly basis according to the nature of the product (new, young, historical). The forecast is at a product-store level;
  7. 7. SOLUTION DETAIL • Use specific department weights to remove seasonality from the time series; • Create an elasticity model at product level; • Clusterize the products according to their sales potential and price range; • Create a hierarchical elasticity model STEP 1: SALES FORECASTING STEP 2: PRICE ELASTICITY STEP 3: DISCOUNT OPTIMIZATION
  8. 8. SOLUTION DETAIL • Use the forecast model to estimate T*, i.e. the time in which the stock is exhausted; • Use the price elasticity coefficent to compute the Δp for the desired Δq; • Choose the optimal discount as ത𝑇 = 𝑎𝑟𝑔 max 𝑡 ∈[𝑇,𝑇∗] (𝑚𝑎𝑟𝑔𝑖𝑛 𝑝 𝑡 − 𝑓𝑒𝑒 𝑡 ) with stock(t) = 0 STEP 1: SALES FORECASTING STEP 2: PRICE ELASTICITY STEP 3: DISCOUNT OPTIMIZATION
  9. 9. BUSINESS BENEFITS Quantitative • Revenue improvement • Reduction in penalties • Reduction in inventory costs Qualitative • More visibility to new products • Better space allocation in stores • Homogeneity among stores
  10. 10. ONLINE PRICING: FROM THEORY TO APPLICATION Giovanni Corradini| Data Scientist
  11. 11. THE PROBLEM  Customer: Ticket selling company  Problem: Choose the price that maximize the total revenue through application of Contextual Multi-Armed Bandit algorithm
  12. 12. THE TRADE-OFF  Exploration  Find the best price by proposing different prices  Many mistakes  Exploitation  Propose current best price to make money  Maybe not the optimal one
  13. 13. A/B TESTING VS MULTI-ARMED BANDIT  A/B Testing  Multi-Armed Bandit
  14. 14. THEORETICAL BACKGROUND History of contracts  Bandit setting:  Consider n rounds  At every round we receive a request and propose a price (arm)  We receive a reward from the environment (the customers)  The price proposed if the customer bought the ticket  0 otherwise  Goal: maximize the sum of the rewards (minimize the “regret”)
  15. 15. BASIC ALGORITHMS History of contracts  ε-greedy  UCB
  16. 16. BASIC ALGORITHMS  ε-greedy  UCB Round t Random arm Current best arm ε 1 − ε
  17. 17. BASIC ALGORITHMS History of contracts  𝜺-greedy  UCB Compute UCB for the mean of each arm Round t Play arm with highest UCB UCB1, for arm j ො𝑥 𝑗 + 2log 𝑡 𝑡𝑗
  18. 18. HYPOTHESIS AND LIMITATIONS  Stationarity  People can change behaviour over time  No context  Additional information can be used to take a better decision Sliding windows Contextual MAB
  19. 19. PROJECT BACKGROUND  A part of the revenue of the provider comes from Metasearch engines  Providers have access only to the context x and not to the user directly User MS Provider R(x) x p(x)p(x)
  20. 20. SOLUTION OVERVIEW DATA TECH • UCB1 • ORAT (Online Risk Averse Tree)ALGOS Simulated requests from users
  21. 21. ALGORITHM DETAIL  Contextual Multi-Armed Bandit  Decision tree to partition the space of contexts  Splits are made with confidence level  A different policy in each partition based on UCB1  Non-stationary environment  Sliding window for UCB1  Batch phase to change the partition of the decision tree RM-LON LON-PAR 3 21 ! RM-LON ! LON-PAR
  22. 22. BUSINESS BENEFITS Quantitative • Increase of revenues • Decrease of cost of maintenance Qualitative • Increase in customer satisfaction • Increase in analytic know-how of the process • Improvement in testing process
  23. 23. RENEWAL PRICE OPTIMIZATION FOR SUBSCRIPTION PRODUCTS Riccardo Lorenzon | Senior Data Scientist
  24. 24. THE PROJECT  Customer: Publishing Leader  Description: Let an algorithm decide the optimal prices for renewal to subscription products, given some boundaries and objectives input by the customer
  25. 25. SUBSCRIPTION ECONOMY In the Subscription Economy, every company must better manage a direct, complex, responsive, multi-channel relationship with its customers. Customers are absolutely key in this relationship and rather than putting the focus of the business on the “product” or the “transaction,” subscription economy companies live and die by their ability to focus on the customer. Now, the formula for growth is focused on monetizing long-term relationships rather than shipping products. Tien Tzuo – CEO, Zuora
  26. 26. SOLUTION OVERVIEW DATA TECH • Elastic Net Regression • Simplex Optimization Method + Euristics ALGOS History of subscriptions Refused price proposals Promotions Customer Service contacts
  27. 27. SOLUTION OVERVIEW STEP 1: DATA MODEL PREPARATION STEP 2: ELASTICITY CURVES STEP 3: PRICE OPTIMIZATION
  28. 28. DATA PREPARATION History of subscriptions Refused price proposals Promotions Customer Service contacts Renewal Price Features 1 50 ... 0 58 ... ... ... ...
  29. 29. ELASTICITY CURVES 𝑙𝑜𝑔𝑖𝑡(𝑝) = 𝑓 𝑋 + 𝑔(𝑌) 𝑙𝑜𝑔𝑖𝑡(ෝ𝑝𝑖) = 𝛼𝑖 ∗ 𝑥 + 𝛽𝑖 ෝ𝑝𝑖 = 1 1 + 𝑒−(𝛼 𝑖∗𝑥+𝛽𝑖) X: actionable variables Y: non-actionable variables p: renewal probability x: price
  30. 30. ELASTICITY CURVES We need to estimate a whole probability distribution What is a good metric?
  31. 31. ELASTICITY CURVES renewal probability vs. price expected margin vs. price
  32. 32. PRICE OPTIMIZATION Let the user input: • Target KPIs • Business specific onstraints Get a global proposal of optimal price for each contract
  33. 33. BUSINESS BENEFITS Quantitative • Increase of Revenues • Increase of Margins • Increase of Sales Volume • Increase of Renewal Rate • Increase of Campaigns success rate • Process Costs reductions Qualitative • Increase Marketing analytic know-how of customers
  34. 34. THANK YOU i.gianoli@reply.it g.corradini@reply.it r.lorenzon@reply.it

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