Data Visions Big Data Visual Analytics Tool

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dataVISIONS is built with novel machine learning algorithms in combination with deep data mining by fraud concepts in response to a simple but profound question,"What should be the Pricing strategy to stop eCommerce fraud, improve Cyber-security, decrease Anti Money Laundry, Call center behavior analysis etc?" What segmentation techniques can be applied towards those goals?

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Data Visions Big Data Visual Analytics Tool

  1. 1. dataVISIONS: Big Data Visual Analytics Tool: VERSION 2: •Predictive analytics provides an entry into Retail Analytics for example, finding propensity of buying. •Very few variables get selected out of 500 or so, hence it NEEDS Holistic approach. Rank order doesn’t mean largest possible dollar made from historical buying patterns... •Propensity of buying needs to be looked through pricing strategy. Evaluate its effects through as many variables as possible because of correlation with hottest topic in retail or return rate analysis. dataVISIONS is cost effective compared to similar new SAS tool. •Furthermore, Big Data Retail Analytics needs Visual Analytics that is prospective, not retrospective. It also must unearths good questions, hypothesis and interpretation that one must be able to see!
  2. 2. Big Data Analytics Vision: Visualize or not IS the Question!•Customers are not comfortable with just numbers orinterpretation that differs by person-to-person.•Visualization tool needed in the company’s assets.•Helps with practical application of Statistics; stopspeeling layers of data forever!•Current visualization tools in market lackcombination of statistical methods to meet demandsfor Data Mining (segmentation appreciation). Severalexamples are provided below:-
  3. 3. Pricing Strategy: Gender Effects on eCommerce: New Pricing shows clearsegmentation: Spatial statistics in combination with Regression shows very strongprice sensitivity (very bottom right pane).
  4. 4. Pricing Strategy: Gender Effects on eCommerce: Same Method: Market need for crisp Segmentation Visualization: low, medium (center), high and very strong price sensitive customers (very bottom right pane).
  5. 5. What we learned so far…APPLICATION OF SPATIAL STATISTICS SIMULATION SHOWS MANYSELLER STORES ON ECOMMERCE WEBSITES THAT NEEDS TO BETURNED OFF WITH NEW PRICE.MAJORITY OF THEM ARE AT VERY LOW PRICE; LIKELY STOLENCONSUMER ELECTRONICS PRODUCTS SOLD TO MAKE A QUICK BUCKIN ECOMMERCE. WHAT WILL BE THE CONSUMER PROPENSITY TOBUY IN FUTURE FROM THIS ECOMMERCE WEBSITE ?COMPANY NEEDS TO INSTITUTE NEW PRICING POLICY BECAUSE THEWORKING HYPOTHESIS IS : “IN BOTH GENDERS, NEW PRICINGREMOVES SOME SELLER STORES THAT COSTS MONEY TO SERVE ANDADDS TO FRAUD REDUCTION/SECURITY”
  6. 6. eCommerce Actionable Findings Found •Old price was an easy entry to any seller. Buyer cloud from simulation for new price allow segmentation and identification of price sensitive and insensitive customers of various levels. •eCommerce Company needs to have a strategy that serves well to low price sellers and buyer aswell. New Pricing works with segmenting high value seeker as well; want to pay more.•More information through social media behavior is provided why this strategy will serve well…
  7. 7. Pricing Strategy VS Social Media (photo sharing= yes)behavior interaction simulation: old price, Gender=Male
  8. 8. Pricing Strategy VS Social Media (photo sharing= yes) behavior interaction simulation: new price, Gender=Male
  9. 9. Pricing Strategy VS Social Media (photo sharing= no) behavior interaction simulation: old price, Gender=Male
  10. 10. Pricing Strategy VS Social Media (photo sharing= no)behavior interaction simulation: New price, Gender=Male
  11. 11. Actionable Findings Learned •Photo-sharing is an important social mediabehavior; dataVISIONS methods remove clutter and finds price sensitive customers even for those who do not indulge in photo sharing behavior. • some seller stores drop out with new pricingsimulation; indicating some fraud reduction. This inturn improves buyer return to eCommerce website.•dataVISION removes clutter and shows this data iscoming from Very price sensitive customers (Y-axis). •A happy eCommerce customer will have low Recency (time taken to next purchase) and not ask for product return at the cost of company.
  12. 12. Actionable Findings Learned•Product recommendation on social media is a way that Retailer can count on return purchase. Higherrecommendation could mean higher Net Promoter Score (NPS).•Next two slides show value of Net Promoter Score in eCommerce. NPS is the difference between Promoters and Detractors, same number can be arrived in multiple ways (Detractors>Promoters=<0). Hence predictive analytics will NEVER use it!•dataVISION removes clutter and shows this data is coming from Very price sensitive customers.
  13. 13. Male: Net Promoter Score <=0 (more buyer gave bad review of product), old price simulation
  14. 14. Male: Net Promoter Score <=0, New price simulation: atleast some bad reviews by buyers are gone from very price sensitive customers; could become return purchaser.
  15. 15. CYBER SECURITY AND RETAILVARIABLES INTERACTION • Cyber security is an important concern that permeates every aspect of US corporate system: •http://www.businessweek.com/articles/2012-08-02/the-cost-of-cyber-crime •Billions of $ being poured in unsuccessfully! •As consumers switch to mobile apps; there will be phenomenal growth in fraudulent bills paid from apps hacking because it is very easy to push a button on mobile inadvertently. Plus the global busy life makes it so much easier… •Retailers need to pay lot more attention to prevent it prospectively and grab the market share NOW!
  16. 16. Cyber security: Algorithm encourages to form spikes but 7 observations from right refuses to do so; just in time price or product will sell very quickly due to “right sizing”. Seller’s Original price; new pricing strategy unable to stop this. Low r-squaresfraud patterns: male gender, Net Promoter Score= <0; 4 weeks of a month (same data source as above: Male Gender)
  17. 17. How does pricing strategy interacts with cyber security? One seller fromabove slide occurred twice in male gender. there are only three spikes out of 20 observations below. R-square pattern shows fraud hits in the data. This seller’s pattern below for 8 months: Male Gender, Net Promoter Score= <0.
  18. 18. Cyber security VS Online Pricing Strategy: The method is designed to smooth out large pricing spikes; complicated, Aberrant Online Selling Pattern (AOSP) on the same data above shows exact same patterns. Hypothesis in slide 5 is rejected!
  19. 19. Female Gender is not the victim here. New Pricing helps with female gender only.Hypothesis: “female gender has better awareness of a consumer electronics product such as phones in ecommerce space”. Net Promoter score =<0 and only one time seller were found here. Low NPS is not from fraud, perhaps over advertisement is culprit here… product return rate should be lower than male gender
  20. 20. How does Pricing strategy interacts with Net Promoter Score (3 & 4) and Recency (500 or50% of time likely to visit this ecommerce website) of buyer (male) for cyber security? New pricing strategy kicks at observation #7, all sellers participated only once: No difference between old and new price (A/B Testing)…. Explains buyer Recency is only 50%
  21. 21. How does Seasonality interacts with Online Fraud? Mock up data of holiday sale: Onlyvisualization from combination methods shows behavior of fraud (right); one product reallyhigh price , one low price and two at expected price. This way no one suspects of anythingwrong; explains the change in statistics from left (no fraud) to right at that time (malegender). Program this and find thousands like it in database; saving millions $ prospectively! Mean normalized + moving average Mean normalized + moving average
  22. 22. One known online fraudster’s sale was added: Highest variations explained differs by20% in right pane (R-sq: fraud). Finding more patterns like this in database will make Millions $ for company. But this data mining pattern came from question thatbecame obvious only after visualization of spike pattern in the center of right pane. Large price spike normalized: Large price spike normalized: how pattern change: No fraud pattern changes!
  23. 23. RETAIL BANKING RISK ANALYTICS:ANTI MONEY LAUNDRY (AML) •Retail Banking is superpower of US economy; needless to write billions spend to bail out this sector to stabilize the US Economy (2007-10). •Banks provide loans to retail customers and make money based on loan origination rate and interest rate etc... •Risk/retail banking paradigm is shifting; pricing needs to be looked through the prism of Online and Social Media Behavior. •Need to find profitable customers, has working life left and will go through some more life changing events, hence creating retail demand. These customers must Never churn from your business!! •Customer segmentation here are 1.female gender, 2.online Ads imp=0, 3.TV Ads imp=0, 4.online photo sharing=0, 5.leader in providing mortgages and home equity lines of credit to consumers= 0. The segmentation below show the followings from Data Mining:-
  24. 24. Pattern 1: Business Question: are the AML customers have churned or still with the bank: no de-differencing and r-square is same for linear and quadratic equations (Loan Amount is Y- axis).
  25. 25. Pattern 2: differencing and samepattern as above (same Y-axis)….
  26. 26. Pattern 3: Genetic Algorithm also hasno effects in changing the coefficients!
  27. 27. Pattern 4: First three patterns above do not change. One expectsthese customers have churned. It is nice to confirm the interpretationVISUALLY! Anti mutation rate when brings shrinkage in coefficients,confirms continuation of same pattern as above; keeps oversegmentation rate low. Customer Churn is not just inevitable, but havedone so! Catching them for AML will be difficult.
  28. 28. CALL CENTER ANALYTICS:FRAUD and WASTEA) THE VALUE PROPOSITION (VPE) OF CALL CENTER IS “LOAD BALANCING” ORROUTE MAJORITY OF CALLS TO MOST PRODUCTIVE CALL CENTER SALES AGENTS.B) THAT MEANS AGENTS WITH HIGHEST SALES CONVERSION RATE (SRC).C) SALES AGENT CAN EASILY TAKE A SALES CALL AND INPUT IN SYSTEM AS NONSALE CALL IF SELLING DID NOT MATERIALIZE TO KEEP SRC HIGH. THIS ISFRAUDULENT ACTIVITY WHICH BEATS THE LOAD BALANCING CONCEPT.D) THEN THERE ARE CALL CENTER AGENTS WHO TAKE VERY LONG SALESCONVERSION TIME. THIS IS WASTE BECAUSE CUSTOMER USUALLY DO NOT WAITFOR 30 MINUTES ON PHONE TO BUY A PRODUCT IN NEXT 30 MINUTES.E) SALES AGENTS WITH SIMILAR TIMES OF WAIT AND CALL TIME IS VERYSUSPICIOUS BECAUSE CALL SALE TIME> WAIT RESULTS INTO REFERRED FORTRAINING. THIS SALES AGENT IS DOING BEST TO AVOID NEGATIVE EFFECTS ONPERFORMANCE. PLUS MORE TIME MEANS ADDITIONAL PRAISE FOR PRODUCTTHAT MAY NOT LIVE UP TO; TRIGGERING RETURN AND LOSS OF WARRANTY $.
  29. 29. CALL CENTER ANALYTICS: looking for condition E because it is fraud andwaste as well as company may lose warranty $ and could end up paying for return shipping $. No_seasonal r-sq coefficients are lower than season (good) because sell occur in Christmas. Bad news is that seasonality Linear and Quadratic coefficients are similar!
  30. 30. Similar Linear and Quadratic R-sq means the call center agent is avoiding training referral and company could end up incurring additional $ for thissales later . Mathematical Equation developed catches the agent in action;very low coefficient pattern means review all sales made by this agent in call center after sending for training.
  31. 31. BUSINESS VISION APPENDIXFRAUD, WASTE AND ABUSE HAS CAUGHT UP WITH RETAIL. IT IS WITHECOMMERCE, BRICK AND MOTOR STORE AND EVERYWHERE.PROPENSITY OF BUYING IS CORNERSTONE OF RETAIL PREDICTIVE ANALYTICS.EVEN EXCRUCIATING ANALYSIS OF TOP 2% DECILE RESULTS IN VARIANCE WITHPREDICTED VS OBSERVED PURCHASE $. MUST REVIEW PRICING STRATEGY!RETAILERS HATE TO SEE PRODUCTS RETURNED DUE TO POOR SHAPE OFSTOLEN PRODUCT OR EXAGGERATED ADVERTISEMENT. SMALL COMPANY INBAY AREA WILLING TO FORK OUT MILLIONS OF $ FOR PRICY SAS TOOL.SOCIAL MEDIA REVOLUTION IS SUCH THAT ONE NEGATIVE COMMENT EQUALSTO WASHING OF THOUSANDS OF $ IN ADVERTISEMENT SPEND AND GOODWILL.AML HAS ORIGIN IN INSURANCE AND REQUIRES COMPETENCY IN BANKINGLOAN ORIGINATION DATA, HEALTHCARE AND CAR INSURANCE DATA. THAT’SWHY EVEN TOP 5 CONSULTING COMPANY HAS LOWER PRESENCE IN IT; HARDTO FIND SME IN ALL THESE THREE AREAS.
  32. 32. DOUBLE CHECK CONSULTING: dataVISIONSPRATIBHA SINHA: MS PHYSICS, BIHAR UNIVERSITY, MBA IN INTERNATIONALMARKETING FROM IGNU IN PATNA.CORPORATE HIGHLIGHT IS RECOGNIZED ECOMMERCE EXPERT, EXPERIENCE WITHDIGITAL RIVER, SYMANTEC (NORTON PRODUCT) AND PACIFIC GAS AND ELECTRICCOMPANY IN BAY AREA. AFTER CORPORATE WORK, SHE ENJOYS EXPERIMENTINGWITH INDIAN AND CHINESE SPICES.NAVIN SINHA HAS MS IN AGRICULTURAL STATISTICS, STATISTICAL GENETICS,DECISION SCIENCES (MBA). HE IS AUTHOR OF 12 PEER PAPERS AND ONE USPATENT.CORPORATE HIGHLIGHT IS EXPERIENCE FROM SEVERAL BILLION $ COMPANY SUCHAS DSM FOOD SPECIALTY (6TH LARGEST EUROPEAN COMPANY) , BEST BUY, WIPRO,UNITEDHEALTH GROUP AND VERISK HEALTH. NAVIN IS RESPECTED FRAUD ANDDATA MINING EXPERT IN INSURANCE AND SIMILAR VERTICALS. NAVIN ENJOYSAPPLYING MATHEMATICAL GENETICS CONCEPTS TO BREED NEW VARIETIES OFTOMATO WHEN NOT WORKING ON CORPORATE PROJECTS.
  33. 33. CONCLUSIONS•dataVISIONS Big Data Visual Analytics Tool was built on mocking up Retail andBanking data from Navin and Pratibha Sinha’s corporate experiences.•Invited speaker by American Statistical Association for Cancer Data Mining(YouTube:2009). Pratibha Sinha is an eCommerce Expert.•The tool achieves its objectives: Unearth hypothesis, unexpected Data Miningpatterns in various dynamic US Corporate system. Like to know a Tool that does allthis???•Flexible to share growing pains to help build customized Visualization tool for acompany; learning and collaboration will only improve dataVISIONS!•Navin Sinha is an award wining poet from Utah State University (1998); took thatlevel of creativity and imagination to come up with dataVISIONS big data visualanalytics tool. The material presented here is a Very Small Sample of Methods.•Disclaimer: According to CA Laws, Propriety Technical Marketing Material ofNavin Sinha and Pratibha Sinha (952-905-6636). They are not liable forunauthorized use.•VPE: “Something for the money, and-more for the satisfaction!”

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