The Real World View Of Analytics-How To Use Data To Drive Business Impact


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A real world view on analytics and how to use data to drive business

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The Real World View Of Analytics-How To Use Data To Drive Business Impact

  1. 1. The Real World View of Analytics Impactful Analytics How To Use Data To Drive Business Impact
  2. 2. Fast Analytics What will be covered: • The 6 steps to a successful analytic • The 4 key elements that drive strong, impactful analytics
  3. 3. Wikipedia: Business analytics make extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. Investigation of past business performance to quickly gain insight and drive business decisions Drive Fast Business Decisions
  4. 4. Successful Analytics Begin with Understanding How and Where the Results will be Used Many people begin an analytic with the data… … looking for gold in all the noise
  5. 5. Step 1: Define the business problem your analytics are to solve • Who are my best customers? • What offers should I make and when should I make them? • Where are my next best markets? • Why are my customers leaving? • Who is influencing my customers? • Who’s a fraudster, etc.? Once the business problem is defined, the next step is to consider the touch points where those analytics will have the greatest impact.
  6. 6. Step 2: Understand your touch points External Touch Points • On-line advertising • Sales teams • Customer Service call centers • Print and broadcast media • Point of sale • Mail Internal Touch Points • Annual budget • Capital investment • Strategic decisions • Vendor selection All touch points have constraints and challenges; understand the constraints before starting the analytics .
  7. 7. Step 3: Within the targeted touch points, understand the constraints Constraints include: • System Limitations • Regulatory • Reputational • Contractual • Timing • Access to data • Resources Design your analytic understanding these constraints and knowing which are worth challenging. Avoid developing analytics that will require months or years of effort to implement.
  8. 8. Step 1: Define the business problem your analytics are to solve • Who are my best customers? • What is the next best offer I should make to them? • Solution: Analyze historical and daily sales activities to identify best customers and to develop next best offer Step 2: What touch points can be employed to influence the customer? • Field sales staff making home delivery/sales rounds and • On-line Step 3: Understand the constraints. Sale staff need the promotional recommendations provided to the them in the field daily based on their customer routes • This information has to be easily accessed and available on their mobile computers Example: A home food delivery company with a sales staff of more than 3,000 in the field wants to increase its revenue.
  9. 9. Example: Bust-Out Fraud Losses are the Ultimate Disappearing Act • 80% of Bust-Out Fraud Cases had more than 6 Months of tenure; furthermore, 30% of Bust-Outs had more than 5 years of tenure. • Bust-Out trade lines ramp balances quickly and charge off at well over the credit limit. • Here the customer uses credit with intent to max out the Line of Credit before disappearing. 100% 200% Balance Customer spends normally and pays regularly (6-12mo) LineofCredit Line increase requested and granted Customer spends rapidly towards new credit limit Customer makes payment with check within cycle, increasing Open to Buy Increased spending on fencible merchandise, convenience checks or balance transfers No payment or payment bounces; Cardholder disappears Traditional Bust -Out Triggers 0%
  10. 10. Step 4: Select data for the analytic that allows you to solve the business problems but that also allows for fast and efficient implementation Considerations when deciding what data to use: • Timeliness and accuracy of the data • Completeness and access of the data • Relevancy to the business problem • Regulatory or reputational concerns • Cost • Ability to get the data to the appropriate touch points
  11. 11. Data that can be exploited for an analytic moves progressively from structured to semi-structured to unstructured data, and with that shift comes increased complexity. UnstructuredStructured StaticReal-Time Database Websites Audio Video Sensors Social Media Database data includes: • Historical sales • Credit • Employment and wealth • Address and identity
  12. 12. Example: The first card program in the country • Developed a new behavior score • A New model incorporated legal data element Tenure with Ward • Used a new behavior score to reduce lines on high risk accounts Issue: • Many older, long-tenured Wards customers where receiving Adverse Action letters stating, “Your credit line has been reduced…” and the top reason was “…have only been with Wards for 20 years.” • A TV reporter interviewed a grandmother in front of a Ward store. Having received such a letter, she complained about bad treatment by Ward and the bank and said she uses the card only to buy her grandchildren Christmas presents.
  13. 13. Issue: • Informing a women you know she is pregnant can be a sensitive subject. • An angry man entered a store outside of Minneapolis demand- ing to speak with the store manager. “My daughter got this catalog in the mail. She’s in high school, and you’re sending her coupons for baby cloths and cribs. Are you trying to encourage her to get pregnant?” • Must consider how the solution will be viewed by your customers. Example: Target’s Pregnancy Score • Birth records are usually public; therefore, couples with new babies are instantaneously barraged with offers and incentives. • Target notices certain buying habits of women who are pregnant including:  Increased intake of supplements such as calcium, magnesium and zinc  Buying scent-free soaps, hand sanitizer, cotton balls wash cloths • The score predicts Probability of Pregnancy and estimated due date.
  14. 14. Example: Give to Get • On a plane seated next to a Google Executive. • He was talking about a new Beta app. • Having dinner with friends but he needed to pick up his daughter from LaGuardia Airport. • While he was seated at dinner, the app sent him a notification that his daughter’s plane was late. It also told him, based on his current location and current traffic, when he should leave the restaurant to meet his daughter and the best route to take. • In order for the app to work, it needed access to much of his personal information
  15. 15. Step 5: Design the analytic Make sure your analytic design: • Meets the business needs that have been defined • Takes into account the touch points and constraints • Takes into account the data being used to solve the business needs • Uses analytical techniques that are appropriate for the industry and the business objective Don’t over engineer the design, use the 80/20 rule. Examples: Many different analytical techniques • Regression – logistic, linear, Poisson, gamma • Artificial Intelligence such as neural networks • Linear and quadratic programming • Machine learning • Hybrid solutions such as combinations of regression and neural networks • Multiple fusion techniques • Decision trees Choose the analytical technique most comfortable for your team so that the analytics can be completed quickly and are easy to implement.
  16. 16. Step 6: Analytics will often answer some questions, but good analytics raise new questions. • After completing your analytics, you may have the answers you were seeking. But it’s not unusual to derive results requiring further analytics. • Incorporate these results to refine your analytics.
  17. 17. Six steps to Successful Analytics 1. Define the business problem. 2. Determine the touch points where the analytics will have the most impact. 3. Understand the constraints and challenges for taking actions based on the results of those analytics at each touch point. 4. Understand the data that is available at these touch points given the constraints. 5. Design the analytics based on the data and implementation constraints you are not willing to change or do not have time to change. 6. Conduct the analytics, and based on the results, either refine the analytic or implement the recommendations.
  18. 18. Analytics That Drives Business Impacts Boils Down To Four Basic Elements Data Industry Expertise Analytical Design and Implementation Analytical Talent Easy and efficient access to relevant data. Industry knowledge to understand the customer problems to be solved and the data and analytical techniques to be used. Otherwise, there is a steep and costly learning curve. The ability to bring solutions to market and to make these solutions reliable and easy to use and to deliver them to the right place at the right time. The ability to attract and retain talent with deep analytical expertise in math, statistics and operations research.
  19. 19. A recent study by McKinsey indicated analytic talent will be in short supply. Therefore, companies must focus on attracting and retaining talent with the right skills across several types of analytics. Having a pool of experienced analytical talent will be a very important and valuable asset