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January 30, 2013

Four Techniques to Improve AnalyticsBased on Customer Knowledge

Sponsored by:

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Daniel Burstein, Director of Editorial Content MECLABS/MarketingSherpa @DanielBurstein

Ben Fillip, Data Analyst MECLABS @benjamin_filip

Introductions

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If I only had __________, my marketing efforts would be substantially more effective

Chart 1.37 - Analytics needed to increase marketing effectiveness

Source: ©2012 MarketingSherpa Marketing Analytics Benchmark Survey Methodology: Fielded November 2012, N= 1,002

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Analytics should not just tell you what happened…

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…but help you identify what is going to happen

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So today we’ll help you communicate better with your data analysts…

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So today we’ll help you communicate better with your data analysts…

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• Correlation explains the dependence of two variables or data sets

• Allows you to see what affect changing one area of your site will have on the others

Technique #1: Correlation

Source: What is PageRank Good for Anyway? (Statistics Galore)

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• When overall site traffic increases we also see an increase in homepage, login, and location pages.

• Having traffic down the funnel rise with increased traffic to the landing page indicates we are sending motivated traffic to the site

Correlation Analysis ExampleTechnique #1: Correlation

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Correlation Analysis• White cells show little or no correlation and can be useful by letting you know that

you will not impact these pages by making changes in other areas• In this example the majority of cells are white which is beneficial because we know

changes to the upper funnel will not have a huge impact down the funnel path

Technique #1: Correlation

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Correlation Analysis• Negative correlations, in red, can indicate how certain metrics can be expected to

behave with changes elsewhere• Here we see that Pages/Visit and Avg. Visit Duration are negatively correlated with

overall visits• If we increase traffic to the homepage then we will see lower figures for these metrics

Technique #1: Correlation

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• Profit analysis allows you to know what gain is needed to generate ROI for a test

• It let’s you see the impact on the bottom line that lifts in certain areas will have

• You can identify the low-hanging fruit from the major impact areas

Technique #2: Profit Analysis

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Profit AnalysisTechnique #2: Profit Analysis

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Profit AnalysisTechnique #2: Profit Analysis

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Profit AnalysisTechnique #2: Profit Analysis

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• There are many types of regression models, and picking the right type for your data is key to getting any usable knowledge• Regression Limitations Exist

• The range of predictions do not extend past the range of the data the model is based on

• Outliers can influence any model and must be dealt with appropriately

• Lurking variables are those that could explain part of the change that aren’t considered or tracked in the model

• For linear regression, the residuals must be linear• Often, linear models are inappropriately used to explain data

that is non-linear• Linear models can only explain numeric data

Technique #3: Regression Modeling Types

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• Linear Regression – explores relationship between one dependent variable (y) and one or more explanatory variables (x)• One variable is simple, multiple

variables is multivariate linear• Ordinary Least Squares – used to

estimate unknown parameters of linear model• Often used in economics• Helpful when a key variable

does not have data available• Polynomial – used to estimate data

that does not follow a linear trend• Can be simple with one curve

or complex with many curves

Regression Modeling TechniquesTechnique #3: Regression Modeling

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• Generalized Linear Model – gives the ability to use linear regression techniques for random variables that are non-normal • Poisson Regression is an example that is used to

model count data• Assumes the response variable has a Poisson

distribution• Logistic Regression – used for predicting dependent

variables that are categorical based on predictor variables• Changes the dependent variable levels to a

probability in order to model using continuous independent variables

• Non-Linear Regression• Nonparametric• Analysis of Variance (ANOVA) – uses the observed

variance of a variable and breaks it into different sources• Effective for comparing multiple groups

Regression Modeling TechniquesTechnique #3: Regression Modeling

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This report includes:• More than 1,260 companies surveyed• 325 ready-to-use slides for powering your

next presentation, fueling a proposal or making a business case

• 426 charts with methodical commentary

The NEW 2013 Marketing Analytics Benchmark ReportSee how fellow marketers choose metrics, define ROI and turn marketing analytics into

actionable items.

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• Uses graphs that resemble trees to model what a particular decisions outcome could be

• Can predict event outcomes

• Used to help characterize a strategy that will help reach a goal in the optimal way

• Can predict spend amount vs ROI

• Can develop strategy on who to market to

Technique #4: Decision Trees

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• Company surveyed employees and asked for their 10 best and 10 worst customers they dealt with

• With that list, we created a database that had several common variables associated with their customers• Industry• Yearly Number of Transactions• Revenue• Spend• Number of Employees• Location

• Used those variables to find key factors that would predict the probability of a new customer being a best/worst customer before they even started negotiating services

Case Study: Predicting best and worst customersTechnique #4: Decision Tree

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Case Study: Predicting best and worst customersTechnique #4: Decision Tree

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• The first split looked at companies with less than $981,456 in revenue

o 91% probability of Best

• After that split, Number of Employees was used

o 99% probability of Best if less than 1,000 employees

• Non-intuitively, smaller companies with a lower revenue had a higher probability of being a best customer

Case Study: Predicting best and worst customersTechnique #4: Decision Tree

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• The left side of the tree split is companies with revenue greater than $981,456• 75% probability of being worst customer

• Second split is spend less than $10,564• 98% probability of being worst customer

• Third split on number of employees• Less than 200 employees has 88% chance of

being best customer• With more than 12 yearly transactions, chance goes up

to 94%

• More than 200 employees has 81% chance of being worst

• In certain industries chance goes up to 97%

Case Study: Predicting best and worst customersTechnique #4: Decision Tree

Even though certain factors indicate worst customers, some exceptions can actually still have a best tendency

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• Once we had an idea of certain characteristics we ran the model again to tease out info excluded from the first model• Right split looks similar with lower revenue

and less employees indicating a better chance of being a best customer

• Left split now shows that customers with large revenue in Asia, Australia, South and North America have a 91% chance of being a worst customer• In certain industries the probability

increases to 99%

• Left split also shows that companies in Europe and Africa have a 96% chance of being a best customer

Case Study: Predicting best and worst customersTechnique #4: Decision Tree

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• They can now market to the right types of companies to increase their customer base with best customers

• Does not mean other companies shouldn’t be considered

Case Study: Predicting best and worst customersTechnique #4: Decision Tree

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• Technique #1: Correlation

• Technique #2: Profit Analysis

• Technique #3: Regression Modeling Types

• Technique #4: Decision Trees

Today’s Summary

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• Daniel Burstein, Director of Editorial Content MECLABS/MarketingSherpa @DanielBurstein

• Ben Fillip, Data Analyst MECLABS @benjamin_filip

Introductions