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

1. Join the conversation
#SherpaWebinar
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2. Four Techniques to Improve Analytics
Based on Customer Knowledge
January 30, 2013
Sponsored by:

3. Introductions
Daniel Burstein, Director of Editorial Content
MECLABS/MarketingSherpa
@DanielBurstein
Ben Fillip, Data Analyst
MECLABS
@benjamin_filip
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4. Join the conversation
#SherpaWebinar
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5. Chart 1.37 - Analytics needed to increase marketing effectiveness
If I only had __________, my marketing efforts would be substantially more effective
Source: ©2012 MarketingSherpa Marketing Analytics Benchmark Survey
Methodology: Fielded November 2012, N= 1,002
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6. Analytics should not just tell you what happened…
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7. …but help you identify what is going to happen
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8. So today we’ll help you communicate better
with your data analysts…
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9. So today we’ll help you communicate better
with your data analysts…
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10. Technique #1: Correlation
• 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
Source: What is PageRank Good for Anyway? (Statistics Galore)
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11. Technique #1: Correlation
Correlation Analysis Example
• 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
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12. Technique #1: Correlation
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
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13. Technique #1: Correlation
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
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14. Technique #2: Profit Analysis
• 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
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15. Technique #2: Profit Analysis
Profit Analysis
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16. Technique #2: Profit Analysis
Profit Analysis
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17. Technique #2: Profit Analysis
Profit Analysis
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18. Technique #3: Regression Modeling Types
• 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
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19. Technique #3: Regression Modeling
Regression Modeling Techniques
• 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
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20. Technique #3: Regression Modeling
Regression Modeling Techniques
• 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
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21. #sherpawebinar

22. The NEW 2013 Marketing Analytics Benchmark Report
See how fellow marketers choose metrics, define ROI and turn marketing analytics into actionable
items.
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
Webinar Attendees Save $100 until Feb 18th
Discount Code: 419-BM-4011
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23. Technique #4: Decision Trees
• 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
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24. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
• 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
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25. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
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26. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
• 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
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27. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
• 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 Even though certain factors indicate worst
• In certain industries chance goes up to 97%
customers, some exceptions can actually still
have a best tendency
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28. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
• 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
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29. Technique #4: Decision Tree
Case Study: Predicting best and worst customers
• 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
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30. Today’s Summary
• Technique #1: Correlation
• Technique #2: Profit Analysis
• Technique #3: Regression Modeling Types
• Technique #4: Decision Trees
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31. Introductions
• Daniel Burstein, Director of Editorial Content
MECLABS/MarketingSherpa
@DanielBurstein
• Ben Fillip, Data Analyst
MECLABS
@benjamin_filip
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