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Too many businesses today are product focused. Many think, “If we have more products to sell, we’ll make more money!” That is true if, and only if, the people actually want what you’re selling. Instead of starting with a product and finding someone to sell it to, it is better to begin with a group of people, and then figure out what to sell them. What do they want or need? The best way to determine what your customers want is by studying their behavior. Yes, this even surpasses asking people what they want because people often don’t know what they want. For example, everyone says they WANT to eat healthier, but very few actually change their diets. You can gain a lot of insight about your customers by digging into your database(s). I’m not talking about mining the BIG DATA you hear about in every IBM commercial, but about the simple data you probably already have at your disposal. That data is like a diamond in the rough. Join us for a quick, 30-minute webinar – sponsored by Paramore | the digital agency. Daniel Burstein and Benjamin Filip will discuss how you can use the data you already have to: -Learn more about who your customers are and how to best interact with them -Identify areas of your website that may be hurting your conversion rate -Decide which changes will have the most profitable effect -Determine which type of modeling makes sense for what you’re trying to learn Want to learn more? Check out the FREE excerpt from the 2013 Marketing Analytics Benchmark Report here: http://www.slideshare.net/marketingsherpa/free-excerpt-from-the-2013-marketing-analytics-benchmark-report-16262381
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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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Join the conversation
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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.
Webinar Attendees Save $100 until Feb 18th
Discount Code: 419-BM-4011
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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