Four Techniques to Improve Analytics Based on Customer Knowledge
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Four Techniques to Improve Analytics Based on Customer Knowledge

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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 ...

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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  • Everyone should have a copy of the handbook – worksheets Certification at the end of the day – Q & A at the end of each section– EvaluationsInteractive session
  • Lead author - My expectation is that you will all leave here today with a revolutionized B2B Marketing plan that will generate highly qualified leads for your organizations, accelerate sales pipeline performance, and maximize Marketing’s impact on revenues. Kaci can share her expectations
  • Today we’re going to help you identify some advanced customer behavior analysis
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  • Same as Slide 15/16?
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  • ---Copy heavy---If we take this approach, we may lose them… Remember we have to give them what they have been promised, but there is also a slight expectation to remain entertained as well.
  • R^2 = .729
  • R^2 = .823
  • ---Graphic?---
  • Lead author - My expectation is that you will all leave here today with a revolutionized B2B Marketing plan that will generate highly qualified leads for your organizations, accelerate sales pipeline performance, and maximize Marketing’s impact on revenues. Kaci can share her expectations

Four Techniques to Improve Analytics Based on Customer Knowledge Presentation Transcript

  • 1. Join the conversation #SherpaWebinar #sherpawebinar
  • 2. Four Techniques to Improve Analytics Based on Customer Knowledge January 30, 2013Sponsored by:
  • 3. Introductions Daniel Burstein, Director of Editorial Content MECLABS/MarketingSherpa @DanielBurstein Ben Fillip, Data Analyst MECLABS @benjamin_filip #sherpawebinar
  • 4. Join the conversation #SherpaWebinar #sherpawebinar
  • 5. Chart 1.37 - Analytics needed to increase marketing effectivenessIf 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 #sherpawebinar
  • 6. Analytics should not just tell you what happened… #sherpawebinar
  • 7. …but help you identify what is going to happen #sherpawebinar
  • 8. So today we’ll help you communicate betterwith your data analysts… #sherpawebinar
  • 9. So today we’ll help you communicate betterwith your data analysts… #sherpawebinar
  • 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 othersSource: What is PageRank Good for Anyway? (Statistics Galore) #sherpawebinar
  • 11. Technique #1: CorrelationCorrelation 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 #sherpawebinar
  • 12. Technique #1: CorrelationCorrelation 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 #sherpawebinar
  • 13. Technique #1: CorrelationCorrelation 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 #sherpawebinar
  • 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 #sherpawebinar
  • 15. Technique #2: Profit AnalysisProfit Analysis #sherpawebinar
  • 16. Technique #2: Profit AnalysisProfit Analysis #sherpawebinar
  • 17. Technique #2: Profit AnalysisProfit Analysis #sherpawebinar
  • 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 #sherpawebinar
  • 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 #sherpawebinar
  • 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 #sherpawebinar
  • 21. #sherpawebinar
  • 22. The NEW 2013 Marketing Analytics Benchmark ReportSee 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 #sherpawebinar
  • 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 #sherpawebinar
  • 24. Technique #4: Decision TreeCase 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 #sherpawebinar
  • 25. Technique #4: Decision TreeCase Study: Predicting best and worst customers #sherpawebinar
  • 26. Technique #4: Decision TreeCase 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 #sherpawebinar
  • 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 #sherpawebinar
  • 28. Technique #4: Decision TreeCase 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 #sherpawebinar
  • 29. Technique #4: Decision TreeCase 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 #sherpawebinar
  • 30. Today’s Summary • Technique #1: Correlation • Technique #2: Profit Analysis • Technique #3: Regression Modeling Types • Technique #4: Decision Trees #sherpawebinar
  • 31. Introductions • Daniel Burstein, Director of Editorial Content MECLABS/MarketingSherpa @DanielBurstein • Ben Fillip, Data Analyst MECLABS @benjamin_filip #sherpawebinar