Business Analytics – An Industrial Perspective · Business Analytics – An Industrial Perspective Dr. Markus Ettl ... perception of value and brand ... Example: LG G PAD 7 BLACK

© 2015 IBM Corporation

Business Analytics – An Industrial Perspective

Dr. Markus EttlSenior Manager – Commerce Advanced AnalyticsIBM T.J. Watson Research Center


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IBM at a glanceWe create business value for enterprise clients through integrated solutions that leverage innovative IT and deep business insights

$92.8B2014 Revenue $20BOperating Income

Operations in over 170 countries

ServicesKey Business Segments

Software HardwareResearch Financing

A highly inclusive workforce

380,000 employees

50% with less than 5 years of service

40% working remotely


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© 2014 International Business Machines Corporation

Almaden

Brazil

T.J. Watson

Austin

Ireland

ZurichHaifa

AfricaIndia

ChinaTokyo

Australia

IBM Research: 3,000 global researchers … $6.3B R&D budget


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Research Organization

Science & Technology

Cognitive Computing Industries and Solutions

Computingas a Service

Transformation of IT through cloud

Transformation of business through data

Next-gen IBM Watson capabilities, information management & human-computer interaction

Fundamental science to advance the core technologies that will create the future of computing and enable the areas above


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Quiz – What does this look like?It’s a HARD DISK– part of the IBM 305 RAMAC Super Computer Launched in 1956It’s a HARD DISK– part of the IBM 305 RAMAC Super Computer Launched in 1956

Drivers for Analytics

1. Growth of data: The growth of computing power, storage, the web, mobile devices and penetration of digital technologies into various systems is creating has created huge volumes of data

2. Near real-time analytics: The combination of advances in analytics methodologies combined with the enhanced computational power make analytics applicable to a wide range of operational scenarios.

3. Enterprise level optimization: The increased need for improving efficiencies at an enterprise level is causing companies to better leverage their data.

Drivers for Analytics

1. Growth of data: The growth of computing power, storage, the web, mobile devices and penetration of digital technologies into various systems is creating has created huge volumes of data

2. Near real-time analytics: The combination of advances in analytics methodologies combined with the enhanced computational power make analytics applicable to a wide range of operational scenarios.

3. Enterprise level optimization: The increased need for improving efficiencies at an enterprise level is causing companies to better leverage their data.

102,400 times more storage than the RAMAC


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An explosion of data

1.3 Billion RFID tags in 200530 Billion RFID tags in 2010

Google processes

�24 Petabytes of data in a single day

Facebook processes over

500 Terabytes of data every day

Hadron Collider at CERN generates 40 Terabytesof data / sec

For every session, NY Stock Exchange captures 1 Terabyteof trade information

Twitter processes 7 Terabytes of data every day

7.3 Billion mobile phones worldwide

2 Billion Internet users in 2011In 2015, annual Internet traffic will reach 1 Zettabyte

6


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say they don’t trust the information they use to make decisions

International Travelers each month

10 millionCommercial Aircraft

Engine Data

10 TB/30 mins

say they use the wrong information at least once a week

Analyze customer data for increased sales and service

opportunities

Dig deep to discover customer sentiment intent

to purchase primary/secondary customer linkages

Discover equipment component usage and failure

patterns to predict optimal maintenance needs

Big Data presents a huge opportunity – if companies can harness it

Reservations, Bookings, Sentiment, Maintenance

1 in 3 business leaders don’t trust the information they use to make decisions

Establishing the Veracity of Big Data sources

Volume Velocity Variety

+


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Data: the next natural resources

2010 2020

Percentage of uncertain data

Sensors & Devices

VoIP

Enterprise Data

Social Media

40 Zettabytes

Systems ofengagement

Systems of record

You are here


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Continuous Dialog

Personalized Interactions

Precision Marketing

Predictive Insight

Integrated Information

Next Best ActionCapture and consolidate

customer data

Understand customer purchases, preferences,

motivations and interactions

Optimize messages and offers

Provide personalized recommendations

Deliver continuous communications

Customer Segmentation

Customer Lifetime Value

Cognitive Computing

Understanding natural language

Generating & evaluatinghypotheses

Adapting & learning

Enterprise

Data Warehouse Big Data

Consumers engage more directly with businesses through multiple channels including mobile and social media


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Challenges

� Retailers need to ensure that pricing and inventory decisions seamlessly follow a customer across channels, maximizing the purchase decision at every touch point.

� Transparent competitor prices affect consumer purchasing behavior

� Consumer’s willingness to buy depends on the type of item, strength of competition, perception of value and brand loyalty.

Omni-channel retailing is concentrated on a seamless customer experience across all possible touch points, including stores, online, mobile, and social media


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Sell out in period 3

No Markdowns

Deep Markdowns

BrickBrick

OnlineOnline

Pri

ce\

Inve

nto

ryP

rice

BrickBrick

OnlineOnline

Pri

ce\

Inve

nto

ryP

rice

More effective markdowns

avoid margin erosion

Sell out in period 4 at

higher margin

Inventory partition for Online Orders

Markdown pricing Business problem Proposed solution


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Geo-spatial market size and channel shares Example: Tablets – 45 UPCs over life of the items


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Demand model

Demand model can be adapted to incorporate competitor prices as attributes

Remarks:

� O(J*M) parameters for estimations

� Desirable properties for optimization

� Lost sales is unknown; estimation methods like EM or 2-step

(attraction demand model)

Market size at location j and

time t

Market share at channel m, location j

and time t

*

where = attraction to channel at location and time

Example:

(Multinomial logit or MNL)


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Percentage decrease in channel sales for each 1% decrease in price

Channel Price Competitor Price

Store

price

.com

price

Amazon

price

Store sales -0.6% 0.8% 0.8%

.com sales 2.9% -4.9% 1.7%

Impact of channel prices on retailer’s store and online sales Example: Tablets

If Amazon lowers prices by 1%, retailer’s store sales drop by 0.8% and .com sales drop by 1.7%.

If Amazon lowers prices by 1%, retailer’s store sales drop by 0.8% and .com sales drop by 1.7%.

*Average elasticity to final prices across entire selling season


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Model ValidationCompare model predicted demand/revenue against realized

16

Accounting for location-specific cross-channel impact yielded a 30-point improvement in predicting online sales and 3-point improvement in

predicting brick sales

Accounting for location-specific cross-channel impact yielded a 30-point improvement in predicting online sales and 3-point improvement in

predicting brick sales


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Omni-channel pricing and inventory optimization problem

17

Revenue (includes salvage)

Cost: Shipping, transportation

Online sales less than demand

Online sales less than inventory + SFS

Ship-from-store variables

Pick-up in store sales

Brick sales less than demand and inventory - SFS

Markdown prices and business rules


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Solution characteristics and tractability

Prices are fixed

� Easy network flow problem

� Optimal inventory ratio between brick and online at a zone depends on– Relative profitability of the two channel at the optimized prices

– Net-inventory flow in/out of a zone

Prices are decision variables

� Multi-location problems are hard problem (reduction to assortment optimization)

� Tractable MIP-based reformulation that exploits structure of attraction

demand models – Extends to any number of channels and can incorporate all business rules

– Grows linearly in the number of prices


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Experiments with data from a large retailer

� Product category: Tablets

– Focused on top 45 UPCs sold in both channels

that saw markdowns

– Contributed 14% of revenue of UPCs sold in

both channels

– Online channel generates 9.5% of category

sales and 85% of this is satisfied using SFS

� Weekly sales data with other attributes

– 12 months

– Attributes: prices, promotions, ads,

seasonality, holiday

– Retail store locations: 50 price zones

� Competitive prices (online only)

– Different competitors for different UPCs

– Up to 5 competitors per UPC


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Optimal channel pricesExample: LG G PAD 7 BLACK

Raise brick prices:

Optimized partition and lower elasticity

Reduce online prices:

Increase sales because of higher elasticity

Markdown Revenue

Actual: $202,818

Optimized: $214,626


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Channel level predicted sales and inventory positionsExample: LG G PAD 7 BLACK

Markdown Revenue

Optimized: $214,626

Actual: $202,818

Unsold Inventory

(OPT)

Unsold Inventory

(OPT)

Unsold Inventory (ACTUAL)

Unsold Inventory (ACTUAL)

Optimized inventory partition


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Optimal prices and partitions avoid margin erosion by delayed and/or shallower store markdowns synchronized with increased online sales

� 10% markdown revenue lift ~$9M p.a.

� 31% drop is unsold units

� 43% drop in brick lost sales


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Offer recommendations based on live customer profiles

•Low miles balance•Low qualifying miles balance

Blue

•Medium miles balance

•Medium qualifying miles balance

Blue •Medium miles balance

•Low qualifying miles balance

Silver

•High miles balance

•High qualifying miles balance

Gold

Looking for 2 Economy Saver tickets, which depart in 3 months. Likely a leisure trip

: Offer price discount

Looking for 2 Economy Saver tickets, which depart in 3 months. Likely a leisure trip

: Offer price discount

Looking for a connecting long-haul Economy ticket. May need lounge access during connecting time

: Upsell with Free Lounge access

Looking for a connecting long-haul Economy ticket. May need lounge access during connecting time

: Upsell with Free Lounge access

Looking for Economy Saver fare. Mileage bonus may give enough miles for Award redemption

: Upsell with Bonus Miles

Looking for Economy Saver fare. Mileage bonus may give enough miles for Award redemption

: Upsell with Bonus Miles

Looking for Economy Discount fare. Additional miles may qualify for Silver level

: Offer Bonus Tier miles

Looking for Economy Discount fare. Additional miles may qualify for Silver level

: Offer Bonus Tier miles

Looking for 1 Economy ticket, which departs in 10 days. Likely a business trip

: Upsell to Economy Premium

Looking for 1 Economy ticket, which departs in 10 days. Likely a business trip

: Upsell to Economy Premium

For the same customer, provide different offers

depending on the journey

Provide different offers for the same journey based on live

profile of the customer


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Machine learning techniques discover the right personalized offers that increase conversions and drive incremental revenue

Multi-Armed Bandit (MAB)

� Name is derived from a gambler trying to figure out which slot machine (aka bandit) will pay out

the most money.

� The gambler has no initial knowledge about the machines.

� The tradeoff the gambler faces at each trial is between "exploitation" of the machine that has

the highest expected payoff and "exploration" to get more information about the expected

payoffs of the other machines (the trade-off between exploration and exploitation is also faced

in reinforcement learning).

MAB for customer-centric pricing and promotions

� Compute personalized estimate of the probability that a customer will accept an offer

� Online learning models discover which customer features are most important for success

Our approach discovers these connections quickly while simultaneously limiting wasted effort on poor performing offers

Our approach discovers these connections quickly while simultaneously limiting wasted effort on poor performing offers


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Multi-Armed Bandit Model: Personalized Version

Each customer k is represented by a vector of features xk

� Information xk includes basic, historical, and flight features of customer k

� rk is the revenue of the ticket customer k is considering

If customer k accepts promotion i, we receive a reward of Rik, and 0 if rejected

� Reward formula: RRRRikikikik = = = = rrrrkkkk –––– cccci i i i rrrrkkkk –––– ddddi

� ci and di are the variable and fixed costs of the promotion i

The probability customer k accepts promotion i is Pik.

� Probability model (logit): �� =

�� (��)

� The ��s are unknown and are estimated using logistic regression with data observed so far

� Let �� and �� denote the estimated probabilities and the std. dev. of our estimate

Personalized Upper Confidence Bound (P-UCB) technique

� Estimate �� and �� using logistic regression techniques

� Compute RRRRikikikik and �� using the formulas above.

� Score for each promotion i: �� + � �� !��


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Simulation results – Does personalization affect revenue ?

Experimental setup

� We simulated 500,000 website searches and customer responses (purchase/no purchase) based on actual .combooking data

� Recorded revenue lift over as-is scenario (no offers)

Observations

� Optimized promotions without personalization yield modest revenue improvements (1-2%)

� Personalization leads to a much higher gross revenue lift (5-6%) driven by higher conversions

Experimental setup

� We simulated 500,000 website searches and customer responses (purchase/no purchase) based on actual .combooking data

� Recorded revenue lift over as-is scenario (no offers)

Observations

� Optimized promotions without personalization yield modest revenue improvements (1-2%)

� Personalization leads to a much higher gross revenue lift (5-6%) driven by higher conversions

No personalization

Personalization

No personalization

Personalization

Our simulations showed 2-3% net revenue lift (revenue increase - cost of offers)


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Business Analytics – An Industrial Perspective · Business Analytics – An Industrial Perspective Dr. Markus Ettl ... perception of value and brand ... Example: LG G PAD 7 BLACK