Business Analytics Project

Part 1Use of Analytics in Retail Industry

Build Smarter Merchandising & Supply Networks

• Localized Assortment• Increase the precision of customer segmentation • Select the right merchandise for each channel and

fine-tune local assortment

• Inventory & Demand Replenishment• Build smarter supply chains and optimize

merchandising across a multi-channel retail operation

• Optimize inventory across multiple channels by using leading indicators such as customer sentiment and promotional buzz to anticipate future demand

Build Smarter Merchandising & Supply

Networks

Build Smarter Merchandising & Supply Networks

• Dynamic Pricing • Predict optimal pricing to maintain a price leadership position • Real-time price comparisons with top competitors• synchronize price changes with demand and deliver real-time offers

• Fleet & Logistics Optimization• Improve logistics by using real-time traffic, weather data and more

to re-route shipments and avoid costly delays

Build Smarter Merchandising & Supply Networks

• Space Optimization• Fine-tune store

planograms by analyzing customer buying patterns and purchasing trends

• Merchandise Selection• Identify emerging trends

by analyzing 360-degree view of each individual

Deliver a Smarter Shopping Experience

• Personalized Shopping Experience• Enrich the understanding of customers by integrating

multichannel data to develop a 360-degree view of each individual

• Predict consumer shopping behavior and offer relevant, enticing products to influence customers to expand their shopping list

Deliver a Smarter Shopping Experience

• Marketing Optimization• Optimize customer interactions by knowing

where a customer is and delivering relevant real-time offers based on that location

Drive Smarter OperationsRealize a variety of operational goal

• Improving labor utilization • Enhancing financial management

Labor Optimization• Optimize staffing

levels by predicting changes in customer demand

• Better match employee skills with retail store needs and create the right incentives to drive strong sales performance

Financial Management

• Facilitate better-informed financial decision making by drawing on complete, trustworthy and timely data from a wide array of sources

• Improve fraud detection by analyzing large volumes of transactions

Examples

Examples-Types of Analyses used

Cluster Analysis and Decision Trees

• E.g. Best Buy

Market Basket Analysis

• E.g. Walmart

Procurement and Spend Analytics

• E.g. Walmart

New Techniques

• Operational analytics• Text analytics

Examples1. Cluster Analysis & Decision Trees

• Identify the most profitable customers• E.g. When analytics told Best Buy that 7% of its

customers accounted for 43% of sales, the consumer electronics retailer reorganized its stores to address the needs of these high-value customers

• Understand customer behavior• Fosters cross–sell and up–sell opportunities

Examples2. Market Basket Analysis

• Uncover hidden buying trends • Products display together to increase

sales. • E.g. Walmart - exogenous demand models• Optimize pricing and discover up–selling

and cross-selling opportunities. E.g. Staples

Examples4. New techniques

• Operational analytics –• Re–ordering to drive better inventory management • Instantly offering promotions to customers based

on their purchases. • Text analytics –

• Determine consumer trends and perceptions of their products and services

• More quickly discover problems – comments on social media

Examples3. Procurement and spend analytics

• Data from suppliers• Identify savings across geographies, product

categories, business units and procurement organizations

• E.g. Walmart - inventory management system

• Help better manage their stocks

Part 2Business Experiments

Context of the Research• Stock market bubbles and economic meltdown resulted from:

• Systematically misleading and overly optimistic research reports by stock market analysts.

• Favorable analysis was traded for the promise of future investment banking business,

• Analysts were commonly compensated for their role in garnering investment banking business for their firms.

• Additionally, initial public offerings were allocated to corporate executives as:

• A quid pro quo for personal favors or the promise to direct future business back to the manager of the IPO.

• Auditors were supposed to be the watchdogs of the firms, but:• Incentives were skewed• Recent changes in business practice had made the consulting businesses of

these firms more lucrative than the auditing function.• For example, Enron’s (now-defunct) auditor Arthur Andersen earned more

money consulting for Enron than by auditing it; given Arthur Andersen’s incentive to protect its consulting profits.

Context of the Research• Dodd–Frank Wall Street Reform and Consumer

Protection Act

• Signed into federal law by President Barack Obama on July 21, 2010

• Passed in response to the Great Recession

• Significant changes to financial regulation in the United States

• Addressed such areas as:

• Wall Street transparency and accountability

• Settlement supervision

• Investor protection

• Anti-predatory lending

Test & Learn1. In at least 70% of the IPO violation cases, the

plaintiffs have also nominated investment banks as defendants.

2. The average number of underwriters sued in an IPO related case has increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011) due to “regulatory capture”.

3. The number of insider trading cases have increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).

Sample Size and Grouping

• Sampling method• Simple random sample for each year from 2010 - 2014

• Sample size• 150 data points • i.e. 30 data points for each year from 2010 – 2014

• Data Grouping for Analysis• 2010 + 2011• 2012 + 2013 + 2014

Variables

v1

false/misleading its business(prospect)/financial results/financial statementsInflate/overstate revenues/earnings/profit/assets/cash flows/operationsfailed to correct/disclose net income/revenue/assets/sales/negative trendfailed to disclose proper losses/expenses (undisclosed/underestimate)misrepresent/omitting material information about its sales/earning/revenue inventories/operations/financial results/performance (prospect)/customer/profitability/company conditionmisrepresents/omitting material fact about its product( prospects/strong demand) / quality control business (prospect)relation/billing practice/salesfailed to disclose operational problems/financial /business condition/customer service/product problem/divisionundisclosed adverse information/fail to disclose adverse material factpositive but false statement its product/business/earning growth/financial results/(prospect)

v2 Artificially inflate stock price, securities pricesv3 The lawsuit mentioned that the firm had engaged in IPO /SEO issuancev4 Insider trading, stock sale by managersv5 SEC 1934 Sections 10(b) and rule 10b-5v6 SEC 1933 Section 11v7 GAAP; improper accountingv8 SEC 1933 Sections 12(2) and/or 15

v9 Investment bankers (underwriters, merger advisors) also sued in the same filing

Data Collection

1st Test and Learn

• There is systemic violation of SEC security laws, especially those pertaining to IPOs, where IPO underwriters collaborate with issuing companies to misrepresent the vital business information to potential buyers of company stock. • So, in more than 70% of IPO cases underwriters are

nominated as defendants along with the issuing company.

Hypothesis Testing of Population Proportion

• H0: p ≤ 70% (Upper tail test for α=0.05)• Ha: p > 70% (Our claim)Population p 70%Total number of IPO/SEO Cases 80Number of times the investment banks were sued 63Sample p 78.75%Test Statistic, z 1.708p-value 0.044

• So, we reject H0, because p-value < α.• This means that in more than 70% of IPO cases

underwriters are nominated as defendants along with the issuing company.

Visual Insight into the Claim

2010 2011 2012 2013 20140%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

78.9%88.9%

70.0% 75.0%

50.0%

21.1%11.1%

30.0% 25.0%

50.0%

Investment Banks Sued Investment Banks Not Sued

2nd Test and Learn• Being sued in a securities case seldom has

any impact on the underwriter's reputation and it doesn't modify their behavior. That's why we assume that with every passing year after recession higher percentage of underwriters get sued.• The average number of underwriters sued in an

IPO related case has increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).

Time-Series Analysis

2008 2010 2012 2014 20160.0%

10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%

100.0%

f(x) = − 0.071783625731 x + 145.15432748538R² = 0.621660150625293

Percentage of Investment Banks Sued in IPO Cases

H0: β1 = 0 Ha: β1 ≠ 0 F-value: 4.929P-value: 0.113 > α = 0.05

We do not have enough evidence to reject H0. So, regression is not significant.

Compare-Means Analysis• H0: μ2012-2014 - μ2010-2011 ≤ 0• Ha: μ2012-2014 - μ2010-2011 > 0 (Our claim)

• Since p-value > α, we do not have enough evidence to reject H0.

• Hence, the average number of IPO cases in which the plaintiffs have named investment banks as defendants are significantly less or same in 2012-2014 as compared to similar cases in 2010-2011.

  2010-2011 2012-2014Mean 0.839 0.65Variance 0.005 0.018Observations 2 3Pooled Variance 0.013 Hypothesized Mean Difference 0 df 3 t Stat 1.796 P(T<=t) one-tail 0.085 P-value > α (0.05)t Critical one-tail 2.353

3rd Test and Learn

• The number of insider trading cases have increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).

Time-Series Analysis

H0: β1 = 0 Ha: β1 ≠ 0 F-value: 14.90P-value: 0.03 < α = 0.05

We can reject H0. So, regression is significant.

2008 2010 2012 2014 201605

101520253035 f(x) = − 6.3 x + 12698.4

R² = 0.832424496644284

Number of Insider Trading Cases

Compare-Means Analysis• H0: μ2012-2014 - μ2010-2011 ≤ 0• Ha: μ2012-2014 - μ2010-2011 > 0 (Our claim)

• Since p-value > α, we do not have enough evidence to reject H0.

• Hence, the average number of insider trading cases are significantly less or same in 2012-2014 as compared to similar cases in 2010-2011.

  2010-2011 2012-2014Mean 0.839 0.65Variance 0.005 0.018Observations 2 3Pooled Variance 0.013 Hypothesized Mean Difference 0 df 3 t Stat 1.796 P(T<=t) one-tail 0.085 P-value > α (0.05)t Critical one-tail 2.353

Managerial Implications – 1st Test and Learn

Security mispricing

•Negative underwriter image and reputation

Underwriter selection

•Managers avoid underwriters recently accused of mispricing.

Overpricing of securities at IPO

•Information asymmetry is created by the issuing firm and the underwriter, which is illegal.

Under pricing of securities

•banks intentionally under-price securities to gain larger profits at IPO

Managerial Implications – 2nd Test and Learn•  Test and learn and regression shows that in the

later years (2011-2014) the average number of IPO cases suing investment banks are almost same or less.• This means that there are signs of behavior

modification and once sued, a bank generally avoids getting sued again.

• Also, monetary penalties help in curbing securities violations e.g.• Morgan Stanley paid $5 Million Fine Over Facebook

IPO in 2012

• Citigroup was slapped with a $2 million fine in 2012

• Citigroup fined $15M by FINRA for mishandling of non-public information in two IPO roadshows

Managerial Implications – 3rd Test and Learn

• Issuing firms, their auditors, and underwriters can avoid insider trading through:• Stringent data security• Regular inside audits of data systems• Monitoring data transfers through flash drive and

emails• Keep track of lost or stolen devices• Deter any unauthorized access to the company’s

data• Very recent popular insider trading scandal was in

2013 at KPMG, causing it to resign as auditor at two companies (Herbalife Ltd. and Skechers USA)

Part 3The Design of an Analytics Organization

in a Retail Industry

Analytics OrganizationRight Product

in the Right Placeat the Right Timefor the Right Price

Inventory planning

Accurate, available data

Supply-chain speed

Forecasting

Analytics Organization• Marketing:

• Sales Forecasting, Advertising, Promotions, Pricing, Consumer Insights

• Finance:• Reporting, Profitability, Pricing, Marketing Support, etc.

• Supply Chain:• Sourcing & Procurement – Purchasing Agreements with vendors,

inventory planning, order forecasting• Distribution & Logistics – Warehousing & Transportation• Demand Planning – Forecast Accuracy, Trend Analysis, Statistical

Modeling

Analytics OrganizationDescription Avg. Cost/

Employee# of

EmployeesTotal

Amount

Labor Costs:• Marketing $100k 1k $100M• Finance $100k 1k $100M• Supply Chain $100k 1k $100M

Total Labor Costs: $300M

Hardware & Software Costs:• Laptops, Monitors,

etc.$2k 3k $6M

• Server $1k 3k $3M• Microsoft Sharepoint $100 3k $300k• Statistical Software

(SPSS)$8k 3k $24M

• Other/Miscellaneous $16.7MTotal Hardware & Software Costs:

$50M

Total Costs: $350M

Thank You!Any Questions?

References• Fisher, M., Raman, A., & McClelland, A. (2000). Rocket Science Retailing is Almost Here. Are you Ready? 115-

124. • Forbes (2014).Morgan Stanley Hit With $5 Million Fine Over Facebook IPO - Forbes. [ONLINE] Available at:http

://www.forbes.com/sites/steveschaefer/2012/12/17/morgan-stanley-hit-with-5-million-fine-over-facebook-ipo-by-massachusetts/. [Accessed 02 December 2014].

• IBM (2014).Big Data in Retail - Examples in Action (n.d.). Retrieved November 28, 2014, from http://www.slideshare.net/IBMBDA/big-data-in-retail-examples-in-action?related=2

• IBM (2014). Capitalizing on the power of big data for retail. [ONLINE] Available at: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=WH&infotype=SA&appname=SWGE_IM_DM_USEN&htmlfid=IMW14679USEN&attachment=IMW14679USEN.PDF. [Accessed 23 October 2014].

• IBM (2014).Harness the Power of Data for Improved Business Outcomes in Retail . [ONLINE] Available at: https://www-950.ibm.com/events/wwe/grp/grp006.nsf/vLookupPDFs/Session%203%20-%20Selling_Big_Data_in_Retail%20-%20N.Katsan%20/$file/Session%203%20-%20Selling_Big_Data_in_Retail%20-%20N.Katsan%20.pdf

• Marks, G. (2013, April 29). Do You Replace Your Server Or Go To The Cloud? The Answer May Surprise You. Retrieved November 21, 2014, from http://www.forbes.com/sites/quickerbettertech/2013/04/29/do-you-replace-your-server-or-go-to-the-cloud-the-answer-may-surprise-you/

• NASDAQ.com. 2014. Citigroup (C) Fined $15M by FINRA for Negligence - Analyst Blog - NASDAQ.com. [ONLINE] Available at:http://www.nasdaq.com/article/citigroup-c-fined-15m-by-finra-for-negligence-analyst-blog-cm417412. [Accessed 02 December 2014].

• Passport Advantage Express. (n.d.). Retrieved November 21, 2014, from https://www-112.ibm.com/software/howtobuy/buyingtools/paexpress/Express?P0=E1&part_number=D0EJNLL,D0EEELL,D0EJJLL,D0ED4LL&catalogLocale=en_US&locale=en_US&country=USA&PT=html

• (n.d.). Retrieved November 21, 2014, from www.dell.com• State of the Industry Research Series : The Future of Retail Analytics. (2013, January 1). Retrieved November

21, 2014, from http://www.sas.com/content/dam/SAS/en_us/doc/research2/ekn-report-future-retail-analytics-106717.pdf