October 20, 2021

Web Analytics: Think Big, Move Fast

web analyticsWeb Analytics platforms today have many features that allow your organization to gain insights. However, none of these features come with the out of the box implementation. Customization steps  must be employed to answer your business goals. These include customization of code, and customization of your reports and dashboards.

Web Analytics: Think Big
In today’s competitive environment we must think beyond Visits, Page Views, Time on Site and Pages Per Visit. Deployment of KPIs (Key Performance Indicators) that closely mimic sales is the goal. I fully agree that a visit, page view, is somewhat correlated to a sale. However, just because you visit widgets.com, does not mean you are going to buy a widget. [Read more…]

Google Analytics 201: Goals, Events, Segments Presentation

I created these slides as a theoretical approach to Google Analytics reports. These theories are there to help guide you towards better Google Analytics Goals, Events, and Segments.

Please keep in mind that Google Analytics consistently enhances their web analytics platform and some of the things may have changed since the release of this slide deck. For any questions, please contact us.

Analysis Types: Reporting, Hypothesis Testing, Data Mining

Analytics ReportingWith so much focus on Big Data, I think users forget the basics of different types of analysis. I want to discuss the differences between Data Mining, Hypothesis Testing, Ad-hoc Reporting, and Analytics Reporting. Understanding the differences between these will help marketers and executives communicate with Analysts.

Data Mining
Data mining used to be called Exploratory Data Analysis or EDA. It uses software packages such as SPSS, SAS, and R to do “detective work” on your data. In this practice you are running descriptive statistics, frequencies, and creating scatter plots on the data. The purpose of this exercise is to identify outliers and data clusters that will lead you to an appropriate algorithm to find relationships in the data. Depending on the size of your data set, and software package you use these practices can be quite complex and take some time. [Read more…]

What is The Difference Between Big Data and Small Data

Big Data is here. Before looking at the difference between Big Data vs. Small data, it is important to assess the data mining process for the data. This will allow you to see the steps necessary to arrive at valid data insights.


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Data Driven Digital Marketing Answers to Difficult Executive Questions

Imagine you are an digital marketer driving a campaign to your company website. Next month you are adding new media to your current marketing mix and your C-level executive asks “What kind of conversion rate should we expect with the addition of new media?’

Executive Focus
Executives only focus on Return on Investment (ROI), or Return on Marketing Investment (ROMI). These are the questions their bosses or shareholders will ask. They have no interest in details or understanding exactly what is happening online, it’s all about money.

Internet Marketer’s Focus
This person has been diligent about collecting data for the campaign and monitoring it for conversion. They have spent countless hours making sure that the media mix is converting the goals they have set out for the campaign. The marketer has done their job researching new opportunities to add to the media mix, but the question the C-level executive has posed is unfair.

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Statistics Provides Clarity in Web Marketing Analytics

Suppose you are running a campaign to your website landing page. Your team has just made a significant change to a landing page that is supposed to drastically improve your conversion rate. Everyone is wondering about the impact, and your statistical web marketing analyst has pulled some conversion rate data that visually looks like this:

Statistics provides clarity in web marketing analytics


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When to Switch Your Web Analytics Platform

I was browsing some web analyst job postings to see the current market demands. I saw job postings with requirement of the web analyst to have knowledge of Omniture, Google Analytics, Coremetrics, and WebTrends. Folks, it’s not possible for one analyst to have access to more than two platforms at once. No organization can afford that!

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What Web Analytics Platforms Cannot Do: Prove Causality

Web analytics platforms such as Omniture (Adobe Marketing Cloud), WebTrends and Google Analytics are amazing tools. They are trending tools of your websites actions and conversions. In a web analytics tool you can pull forward paths to conversion, pages users saw prior to conversion, and many other reports that allow you to assume conversion. However, you cannot assume causality with statistical significance.

In order to have a causal relationship, you need to have association. Once a strong association has been created, you can infer a causal relationship between two variables. In other words, Page A caused Conversion A. For this, you need raw data and access to IBM SPSS, SAS or R.

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Small Businesses Need Analytics Too

It is no secret that the biggest constraint of small businesses is time. There is no time to go into Google Analytics and create specific reports, read blogs about new and free services, and understand traffic sources. Most small business owners focus on one Key Performance Indicator, sales.

What most small business owners don’t realize is they have hidden opportunities that lie within their web analytics platform. There are keywords, traffic sources, and landing pages that provide them with the most lift and opportunity. Finding these opportunities takes a few minutes for a good analyst.

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Case Study: Ecommerce Store Optimization with Google Analytics Segmentation

TechKeysTechKeys, sells custom keys, artisan keys, keyboards and other items for keyboard enthusiasts. The site is an ecommerce store that connects to an offsite gateway where users can purchase items. Because of the offsite connection, there is a discontinuity in data in Google Analytics once users go offsite to purchase items.

Transaction Discontinuity Solution

The first data challenge we resolved is collecting goal data in Google Analytics on the amount of purchases that were happening on the website. This allowed us to see how close Google Analytics was to actual purchases that were in the clients payment gateway. Over time we worked to get this data to 97%-99% accuracy. Everyone should keep in mind that Google Analytics is a trending tool, and will not exactly match your actual purchases for various reasons.

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