Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight (Piyanka Jain; Puneet Sharma, 2014)

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  • Analytics Methodology
    • Aggregate analysis - describe population, segment or compare segments - descriptive analysis, profiling, campaign analysis, winner-loser analysis
    • Correlation analysis - relationship between prospect - pre and post, test control, drivers, dashboard
    • Trends analysis - over time and period - trends of sales, revenues, breaks in trends and segments
    • Sizing/Estimation - near accurate guesstimate in absence of historical data - limited data or dependent on external data
    • -Stratification - dice the problem into smaller pieces and identify segments that behave differently
    • -Correlations and drivers - determine what metrics and factors could have any influence on the metric is that being sized
    • -Assumptions - what do we know about the various factors that make up the equation
    • -Computation - involves doing the math to get to the estimates for each segment
    • -Triangulation/orthogonal method - approach same estimates with different drivers, matching
    • Predictive Analytics/Time series - current and historical data - conversion, customer engagement, forecasting
    • Segmentation - grouping through customisation - targeting and customisation
    • Customer life cycle - determine its stage and how to move them up - purchase to use, sales funnel
  • BADIR
    • Business questions - what happened, why are you interested, what is the problem, when did it take place, where did it happen, who is impacted, what might have caused this, what do you think drive this
    • Analysis plan - analysis goal > hypotheses (criteria to prove or disprove) > methodology > data spec (determine level of granularity, assign unique ID, aggregate it) > project plan > kick-off meeting
    • Data collection - Data pull (define time period, shunting long tail: 80/20) > data cleansing and validation (univariate, cardinality, ratio and total, triangulation)
    • Insights - review pattern (is this a real problem, test vs control) > prove or disprove hypothesis (relationship between business question and hypothesis variable) > findings
    • Recommendations - actionable, based on key insights, supported by detailed findings
    • Predictive analytics - builds on insights from business analytics that can evaluate hundreds of metrics that can identify the most important parameters to drive business but it is time and resource intensive
  • Predictive techniques
    • Linear regression - customer lifetime value, cost of acquisition
    • Logistic regression - churn or attrition model, fraud detection model, response model
    • Decision tree - cross sell product prediction, customer segmentation
    • K-means clustering - unsupervised customer segmentation
    • Time series forecasting - sales over time, forecasting
    • Survival analysis - hitting credit limit, customer tenure
    • Neural networks - fraud detection, response model
  • Vision (define) > plan (scope, stakeholders, resources, timeline) > execute > learn (feed results in next projects)
  • Prioritise to keep focus, coordinate to save on redundancy and a harmonious organisation
  • Pitfalls: not measuring success, not knowing success driver, lacking decision-making, gut-based decision making, treat analytics as cost centre rather than a profit centre, not embed analytics to P&L, not understanding analytics, depend on others for insights, measure everything, not asking the correct questions, not prioritising, thought you have no clean data, expect data to tell the answer, too much buzzwords, not aligning business goals, reactive rather than a transformational approach, thinking small, no right team, not cultivating and motivating the team, using incorrect methodology, not customising presentation


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