quaero’s guide to analyticschops. after listening to eric siegel, the founder of predictive...
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Quaero’s Guide to Analytics
Data Scientists and Analysts eat, sleep, and breathe advanced analytics. For the rest of us, it can be a struggle to grasp.
Quaero’s Guide to Analytics features a collection of articles written by our very own analysts. Our subject matter experts populated this guide to cover topics ranging from advanced modeling and segmentation techniques to remarketing through Google Analytics.
Whether you’re an expert or amateur, this guide has something for you. If you find that you reach the end and still have an unanswered question, please send it our way! We’ll give you the answer and update the guide for future readers.
About the Guide
Quaero’s Guide to Analytics
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K-Means Segmentation: Explained in Simple English
Table of Contents
Uplift Modeling: Don’t Market to Him, He’s Going to Buy Anyway
Quaero Analytics: Supercharging Predictions Part II
Quaero Analytics: Supercharging Predictions
Predictive Analytics & Speed Dating: Unlikely Bedfellows for Avoiding Sales Fatigue
Predictive Modeling: Back to the Basics
Response Attribution: Benefits, Challenges, and Implementation
Why Intelligent Cross Channel Response Attribution is the Way to Go
There’s Gold in Them Mountains, No Seriously, and You’ll Find it Using Text Mining!
Taking Online Analytics to the Next Level, Part 1: Google Analytics Incorporates Remarketing
Taking Online Analytics to the Next Level, Part 2: Building Your Online Ecosystem
Explanatory or Predictive Modeling?
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Quaero Analytics: Supercharging Predictions
© 2015 Copyright
Ensemble modeling is not new. Random forest modeling has long been a favorite methodology of Quaero consultants as the prediction method of choice. But as with anything, there is always room for improvement and ensemble modeling offers advantages over any single one model. Ensemble modeling is basically combining multiple modeling methodologies together. In fact, random forest models by definition are an ensemble of decision trees.
Companies are just now starting to realize the tremendous benefit of predictive analytics. They’re beginning to look into modeling and, more importantly, starting to figure out how best to model. Utilizing an ensemble type of methodology is as cutting-edge as it gets currently. While others are just beginning to use analytics, in this way Quaero is ahead of the game. By supercharging our predictions, we are setting a benchmark.
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Quaero Analytics: Supercharging Predictions Part II
What’s interesting about ensemble modeling is that it’s various models all combined together. Once each model is built, they all pull out different intricacies from the data. Some are better at non-linear trends while others perform well with linear-type trends. Let’s use football as an example. If you look at pro football web pages, that’s highly indicative of you signing up for fantasy football. Age, on the other hand, is not the best linear indicator. Those on the far ends of the curve, either very young or very old, are much less likely to sign up than age groups in between.
By utilizing different types of algorithms, we’re pulling out different types of complexities. By combining them, we make a more robust prediction that will be more accurate than any one single model.
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Explanatory or Predictive Modeling?
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Often, when we talk of statistical modeling, the terms explanatory modeling and predictive modeling get intermingled. I have often wondered before developing a model if the model will be used for explanatory purposes or as a predictive exercise. There is a thin line differentiating the terms and often we intermix the two when talking about them.
To put it into simple words, explanatory modeling refers to plotting and encapsulating the trends and the major factors which were instrumental in driving these trends. Explanatory modeling is helpful when we want to know which of the independent variables we can alter to get the desired outcome in the future. Predictive modeling generally refers to indicating trend patterns in advance, understanding about the future based on the current scenario.
Predictive Modeling: Back to the Basics
Last week my friend and colleague, Cory Hefner, wrote a very noteworthy article touching on a type of predictive modeling method called Ensemble Models. He concluded that when you combine the results of more than one predictive modeling method, it is better than any single method alone—just like two heads are better than one. As these methods gain more and more popularity within the analytics community, I thought I would touch on a very key point that should be taken into consideration before a single model is even built. Aside from using more accurate methods which may obtain better results the age old process of variable creation should not be ignored.
Building models is very much an art as it is a science. I believe one of the keys to building good models is how creative you can be in developing variables which truly capture’s your customer’s behaviors.
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Predictive Analytics & Speed Dating: Unlikely Bedfellows for Avoiding Sales Fatigue
© 2015 Copyright
Sales occur when consumer demand meets supply. In today’s competitive marketplace every organization is looking for the opportunity to spur further demand and many are quickly realizing that there isn’t a short-term or quick fix. It’s not enough to have the right product; marketers need to craft the right message, at the right time, and deliver it the right way to a targeted audience. By applying predictive analytics to influence consumer response to a sales offer, organizations can better understand a consumer’s preference for how the message is crafted, inclination towards a specific value proposition, and threshold for frequency of communications.
The process of building a predictive model is analogous to speed dating. Participants often have limited information on the person that the dating service has provided – perhaps just biographic background or details on common likes or dislikes.
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K-Means Segmentation: Explained inSimple English
As advanced analytic techniques in marketing applications become more prevalent, non-technical marketers must have a general understanding of how these techniques work to prevent them from appearing like a mysterious black box. One of the most popular is K-Means Segmentation. Let’s break it down into plain English.
If you do a quick Google search on k-means segmentation, you’ll find no shortage of technical explanations that accurately depict how it works. For the busy marketing professional, however, these explanations are so technical that the topic becomes either too difficult, or sometimes, too boring to understand.
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Response Attribution: Benefits, Challenges,and Implementation
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With the availability of data today, response attribution has tremendous benefits as well as certain challenges. Understanding the strengths and weaknesses or RA is especially helpful when it comes to implementation.
BenefitsThe benefit of response attribution is the simple fact that a company can see which marketing channels are most influential in driving a sale. Companies can gain a lot of insight to see which channels are working and which are not. They will also have the benefit of seeing which channels work well together and which are not commonly used together. Building the RA solution will allow a company to drill down and see what a customer’s digital journey looks like on his way to making a purchase.
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Uplift Modeling: Don’t Market to Him, He’s Going to Buy Anyway
At Quaero, we like to stay on the cutting edge, so we’re always looking for ways to strengthen our analytic chops. After listening to Eric Siegel, the founder of Predictive Analytics World, I decided to take a closer look at uplift modeling, a newer technique that has a great deal to offer within the realm of analytics.
When you see ads, receive emails, or open junk mail, believe it or not, companies expect you to respond in some way. The company communicating with you desires something specific. In some cases, they want you to purchase a product. In others, they want to help you better understand and recognize their brand. Sometimes, organizations want you to actively participate in an activity like the recent viral social media campaign, the ALS Ice Bucket Challenge. Whatever the purpose of the communication, organizations will find that some recipients will respond positively regardless of the marketing campaign...
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Why Intelligent Cross Channel Response Attribution is the Way to Go
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Response attribution, while challenging, provides tremendous benefits. Not only does it provide full visibility into channel performance, it may be used as an input to optimize long term media spend as well.
The key benefit for cross channel attribution is to provide visibility into which marketing channels are driving sales well and which are performing poorly. This will allow you to adjust your marketing tactics accordingly and track performance over time.
In many environments several challenges exist when implementing a cross-channel response attribution model...
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There’s Gold in Them Mountains, No Seriously, and You’ll Find it Using Text Mining!
Forrest Fenn, an art collector from Santa Fe, New Mexico, has placed a 40+ pound bronze antique lockbox, filled with gold and other treasures, ‘in the mountains north of Santa Fe’ for you to find. The lockbox, and all its contents, have been estimated to be worth up to $2 million. This treasure hunt has been going on for the past 2+ years, but recently has been given a publicity boost on the Today Show. So, if you are interested in hunting for $2 million, I’d suggest you start now. Where to start?
First, start with the poem (at the end of this blog). Forrest says that this poem provides 9 clues to the location of the treasure. Forrest also wrote a book called The Thrill of the Chase, which is a collection of memoirs that will help in your hunt.
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Google Analytics has been a tremendous asset for marketers. It tells them where your site visitors come from, what they look at, and what they’re most interested in. Google Analytics pairs seamlessly with AdWords and the two can be used to create highly targeted campaigns. But Google is taking it one step further and has recently released Remarketing with Google Analytics. Remarketing was available in the past but it required custom code and the retargeting criteria was limited. Now that Remarketing is tied to Google Analytics the process is more seamless and your targeting options are much greater.
As the word implies, remarketing is simply marketing to those who have already been on your site...
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In part 1 of this series I provided an overview of Google Analytics with Remarketing, a valuable tool set for online marketers that would like to target customers (both known and anonymous) online based on interests. But what if you want to integrate all that rich online data within other sources to build a complete view of the customer and apply those insights in all available channels? This is currently not possible using Google Analytics as data access is limited. What are your options? Below are a few tips to get you started...
Quaero’s Guide to Analytics
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Taking Online Analytics to the Next Level, Part 1: Google Analytics Incorporates Remarketing
Taking Online Analytics to the Next Level, Part 2: Building Your Online Ecosystem
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Quaero’s Guide to Analytics