• Why Analytics?• Business Problems that can be addressed with analytics• Analytic approaches to solving business problems• Introduction to the two examples
Data Information Knowledge IntelligenceHindsight Insight Foresight
ETL OLAP Advanced Analytics Sums and Means Drilldown Statistical Predictions
Operational Decisions
Volumes of Data – How to Extract Maximum Utility
Exponential growth of corporate data and computing power in the past two decades• ETL with sums and means provides hindsight from corporate measurements
• OLAP with drilldown provides insight from the ETL data warehouse
• Only advanced analytics with statistical predictions provides foresight from the ETL data warehouse
Data Availability + Computing Power + Advanced Analytics → Competitive Advantage and Best Decisions
Interpreting the Variability of a Population Means are useful. Understanding the distribution around the
mean and what contributes to that distribution is essential to compare populations and make predictions
Statistical techniques “predict” the future by apportioning variance in the population to explanatory variables
As sales change over time in a well defined pattern, future sales can be predicted
If the likelihood of buying a product is associated with demographic characteristics, then we can predict how likely a particular individual is to buy that product
With a goal of maximum profits and knowing constraints within which a company operates, we can solve a series of linear (or non-linear) equations to obtain an optimal solution
Railroad must have efficient schedules to move freight• Before computers, colored strings on a bulletin board were used – time on the
X-axis and distance on the Y-axis
• Constraints included no crossing of trains except at sidings and stations
With computers, the business analyst could manipulate the trains and visualize on the screen• However, there was no guarantee of a “best” decision that produced optimal
usage of the tracks to move the most freight in the minimum amount of time
With analytics, one takes the problem and goal as stated above • One has constraints of the trains such as:
Minimum and Maximum departure and arrival timesMinimum and Maximum SpeedsDeparture and Arrival StationsAvailable routes
• The goal is solved for using an OR algorithmic approach with PROC NETFLOW and visually represented on a screen
• Interaction is provided to the user to modify the analytic result as desired
Problem Defines Solution – Example 2 Herbicide producer wants to deliver time sensitive herbicide to
farmers immediately prior to the planting of the corn• Chemical company uses hindsight as to when the farmers planted the corn in
previous years• Business experts also have a “sense” for whether the planting will be earlier or
later than previous years
Since the problem is to know beforehand when the farmers will plant their corn → Go visit the farmers!• Farmer walks out of house in the morning and sticks wet finger in air to gauge
temperature, kicks dirt to gauge moisture, and looks over horizon to see if neighbors are planting their corn.
With analytics, one takes the problem and understands process • Using a linear regression approach in each of 98 agricultural districts with
the following inputs:− Daily temperatures combined as necessary in day groups− Precipitation amounts grouped as appropriate− Records of previous years plantings
• Each year and each district provide a regression equation• Using a model selection approach provided a limited set of predictive
equations for the current year resulting in forecasts being within 2-3 days for 95 out of the 98 districts
Marketers have tried – for years – to understand and predict the ROI on promotions, advertising and other mass marketing tactics• How much does each marketing
tactic contribute?
• What is the effect of events and activities I cannot control?
• What is the “right” level of spend? Overall? By tactic?
• How do seasonality and geography affect results?
“The transformation of TPM [Trade Promotion Modeling], in conjunction with MMM [Market Mix Modeling], from a tactical to a more overarching and encompassing strategic function is well on the way.
At this very moment…the question of full functionality is less of an ‘if’ , but ‘when.’”
-- Michael Forhez and Charlie Chase, in ‘Consumer Goods Technology’, March 2005.
EDA (continued) Observe similar distribution of comments in voluntary
attritor, nonattritor comments
Since distribution of comments and “Direct Mail” is similar, we will assume that these two kinds of comments may be removed without affecting the analysis so that other comments may “speak”
Perform “optimal binning” of interval variables with respect to target variable to change them into ordinal variables• Represent continuous variable as set of ordered indicator
variables to better concentrate target variable into small number of bins
• Variables Age_Yrs, Cust_Tenure_Mo, N_Phone_Calls were transformed
− For example, Age_Yrs was binned into following intervals
