Upcoming LunaMetrics Seminars
Washington DC, Dec 1-5 Los Angeles - Anaheim, Dec 8-12 Pittsburgh, Jan 12-16 Boston, Jan 12-16

Extracting Schema & Metadata With Google Tag Manager

gtm-schema

If you’re evaluating the performance of your site content, it can help tremendously to segment that content into a variety of cohorts. Unfortunately, many website owners have trouble getting enough information about their content into Google Analytics to help them with their analysis.

Some information may already be available on your website, like information about your page or extra information that gives context to the page.

Ultimately we want to bring these additional dimensions about your content into Google Analytics to help with your analysis. One way to do this is by leveraging Schema and Google Tag Manager.

What’s Schema

If you’re still unaware of Schema, it’s a way of marking up your content so that it is recognized by Google and other search providers. This helps search engines to better understand your content, and hopefully deliver it in a more relevant way to people searching on their systems.

Ultimately, it’s about driving more organic visitors to your website. Read More…

Easy Upload for Google Tag Manager’s Lookup Table Version 2

blog-gtm-lookuptable

In October 2014, the Google Tag Manager team announced a new version of their popular tool, complete with easier workflows, a brighter design, and many other wonderful features. Most things work in a familiar fashion, with a few name changes.

Macros are now called Variables, and the Lookup Table Variable works exactly as we would expect it to. Sadly, there is still no support for CSV upload, so there still exists a need for a tool that people can use to quickly copy and paste from Excel or Google Drive.

I created a clunky workaround for Version 1, and at the request of many, I’ve now created an updated version that works with the new interface. As the GTM team continues to improve the design and functionality of Version 2, this tool could possibly stop working, and could hopefully become unnecessary.

Read More…

4 AdWords Geo-Targeting Tactics to Optimize Across Multiple Locations

blog-adwords-geo

Tell me if this scenario sounds familiar. You want to build Google AdWords campaigns focused on your core audience and their associated geographic location. ROAS is of high importance and you want to focus on conversion optimization as a result.

You also know that Google AdWords plays a large role in generating demand for your products, so brand awareness and overall ad reach are both important to you as well.

Expectations are high. You’re feeling overwhelmed. You’re not sure where to start.

Relax. Take a deep breath. You’re not the only one. Plus, you’ve come to the right place.
Read More…

Reminder: Include Macro Goals in Your Social Strategy

blog-macro-social-goals

The topic of social media is becoming increasingly popular, as shown in the Google Trends chart below. From the outside looking in you might say that social media is the “cool guy” at the digital marketing party. I have chosen to pick on social media strategists for this very reason. However, my point is relevant to all marketers. Read More…

Form Engagement Tracking with Google Tag Manager

blog-form-engagement

Do you know how people are completing forms on your site? Are there certain fields that get skipped frequently or that cause users to drop off?

Almost two years ago, I wrote a post showing how to use a simple script to track form abandonment in Google Analytics with event tracking. I’ve gotten a lot of great user feedback (and requests) about that script, and wanted to share an updated version that is a little more elegant.

This new version more effectively handles fields that are completed or skipped. I’ve also modified this script and included instructions for how to add it to your site through Google Tag Manager.

Use this script to see which fields get the most completions, but also use it to compare to the amount of forms that get submitted successfully. If you find that people are starting to complete the form but failing to submit it, you may need to look into ways to improve the user experience.
Read More…

10 Years of Digital Marketing Trends in 10 Graphs

blog-digital-marketing-trends

Each year about this time, digital marketers are bombarded by reviews of industry trends and projections on what’s to come. It’s all 5 Content Marketing Lessons from 2014 and Secret Strategies for SEO Success in 2015.

This is not one of those posts.

Leave your marketing plans in the drawer and forget your keyword research docs. This won’t help you with them. Think of it more like hump day material to take a break from the inbox and reflect on how much our industry has changed over 10 years.

Here are 10 graphs from Google Trends (focused on the US for consistency and accuracy) that illustrate how we have evolved and why we will all have “hacker” or “influencer” in our job title some day. Read More…

Using Visual Website Optimizer with Google Tag Manager

blog-vwo-gtm

Recently, I was working with a client to integrate Visual Website Optimizer (VWO) with Google Tag Manager. I started by following the integration guide on VWO’s support pages, but ran into a few issues that required a creative workaround.

Not only did the timing of the VWO loading present issues, but I found that the specific data that is supposed to made available on the dataLayer wasn’t being made available.

Follow the instructions below to fix both of the problems! Read More…

Taking Advantage of Semantic Search NOW: Understanding Semiotics, Signs, & Schema

blog-semantics1

Semantic Search. I imagine saying it five times into a mirror conjures an effect similar to horror classic Candyman. It’s all anyone in the Search world is talking about on blogs, at conferences, and in hushed whispers in the break rooms of agencies.

Yes, the future is coming, and it is semantic. Some of it is already here. Let’s take advantage of it! Many posts just like this one focus solely on the how, but today I’m going to switch it up and give you the why.

Google’s Hummingbird release, as documented by our own Andrew Garberson, changed the search game in a major way. Not only did (not provided) significantly alter data available to search marketers, Hummingbird signaled a major learning leap on the part of Google.

No longer confined to a toddler-level reading ability wherein a term is just a term unto itself and needs endless repetition (read: keyword stuffing), it signals a shift towards a first-grade reading level by the search engines to place words in context and take educated guesses at synonyms, meanings and full language understanding.

Example: “hot dog” and “hotdog” meant different things to pre-Hummingbird search, but could easily be synonyms to the current technology.

It’s clear that the concept of a singular keyword is dying if not dead. Read More…

Google Tag Manager Basics: Links and Clicks

blog-gtm-basics

While Google Tag Manager touts itself as a code free alternative to website development, sometimes a knowledge of basic web mechanics (and a little bit of code!) can help make your setup go much easier!

Whether or not you’ve started using the new version of GTM, this post will help explain how to target clicks on specific html elements like links, images, or buttons. Read More…

Using Latent Dirichlet Allocation to Brainstorm New Content

blog-LDA

I recently had a problem with my client – I ran out of things to write about. The client, a chimney sweep, has been with our company for 3 years and in that time we have written every article under the sun informing people about chimneys, the issues they cause, potential hazards, and optimal solutions. All of that writing has worked and worked well. We have seen over 100% traffic increases YoY. The challenge now is to keep that momentum.

Brainstorming sessions weren’t working. They looked more like a list of accomplishments than of new ideas. Each new idea seemed like we were slightly changing an already successful article written in the past. I wanted something new and I wanted to make sure it was tied to a strategy. Tell me if this sounds familiar!

So I internalized the problem. I let it smolder and waited for the answer. Then while reflecting on the effects of website architecture and content consolidation, topic modeling popped into my head. If I could scrape the content we’ve already written and throw it into an Latent Dirichlet Allocation (LDA) model I could let the algorithm do the brainstorming for me.

For those of you unfamiliar with Latent Dirichlet Allocation  it is:

“a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word’s creation is attributable to one of the document’s topics.” -Wikipedia

All that basically say is that there are a lot of articles on a website, each of those articles is related to a topic of some sort, by using LDA we can programmatically determine what the main topics of a website are. (If you want to see a great visualization of LDA at work on 100,000 Wikipedia articles, check this out.)

So, by applying LDA to our previously written articles, we can hopefully find areas to write about that will help my client be seen as more authoritative in certain topics.

So I got to researching. The two tools I found which allowed me to quickly test this idea were a content scraper by Kimono and a Topic Modeling Tool I found on code.google.com.

Scrape Content With Kimono

Kimono has an easy to use web application that uses a Chrome extension to train the scraper to pull certain types of data from a page. You are then able to give Kimono a list of URLs that have similar content and have it return a CSV of all the information you need.

Training Kimono is easy; data selection works similar to the magnifying glass feature of many web dev tools. For my purposes I was only interested in the header tag text and body content. (Kimono does much more than this, I recommend you check them out). Kimono’s video about extracting data will give you a better idea of how easy this is. When it’s done Kimono gives you a CSV file you can use in the topic modeling tool.

Compile a Lists of URLs with Screaming Frog

Next I needed a list of URLs for Kimono to scrape. Screaming Frog was the easy solution for this. I had Screaming Frog pull a list of articles from the clients blog, then I plugged those into Kimono. You could also use the page path report from Google Analytics.

Here is what that process looks like:

Map Topics With This GUI Topic Modeling Tool

Many of the topic modeling tools out there require some coding knowledge. However, I was able to find this Topic Modeling Tool housed on code.google.com. The development of this program was funded by the Institute of Museum and Library Services to Yale University, the University of Michigan, and the University of California, Irvine.

The institute’s mission is to create strong libraries and museums that connect people to information and ideas. My mission is to understand how strong my clients content library is and how I can connect them with more people. Perfect match.

Download the program, then:
1. Upload the CSV file from Kimono into the ‘Select Input File or Dir’ field.
2. Select your output directory.
3. Pick the number of topics you would like to have it produce. 10-20 should be fine.
4. If you’re feeling like a badass you can change the advanced settings. More on that below.
5. Click Learn Topics.

topic-modeling-program
Main Topic Modeling Interface
topic-modeling-program-advanced
Advanced Settings Interface

 

Advanced Options
Besides the basic options provided in the first window, there are more advanced parameters that can be set by clicking the Advanced button.
badass_neil-degrasse-tyson

Remove stopwords – If checked, remove a list of “stop words” from the text.

Stopword file – Read “stop words” from a file, one per line. Default is Mallet’s list of standard English stopwords.

Preserve case – If checked, do not force all strings to lowercase.

No. of iterations – The number of iterations of Gibbs sampling to run.
Default is:
– For T500 default iterations = 1000
– Else default iterations = 2*T
Suggestion: Feel free to use the default setting for number of iterations. If you run for more iterations, the topic coherence *may* improve.

No. of topic words printed – The number of most probable words to print for each topic after model estimation. Default is print top-10 words. Typical range is top-10 to top-20 words.

Topic proportion threshold – Do not print topics with proportions less than this threshold value. Good suggested value is 5%. You may want to increase this threshold for shorter documents.

Analyze The Output

The output of this raw data is a list of keywords organized into rows, each row representing a topic. To make analysis easier I transposed these rows into columns. Now I put my marketer hat on and manually highlighted every word in these topics that directly related to services, products, or the industry. That looks something like this:

topic-modeling-spreadsheet

main-topics-topic-modelingOnce I identified the keywords that most closely related to the client’s industry and offering, I eyeballed several themes that theses keywords could fall under. I found themes related to Repair, Fire, Safety, Building, Home, Environmental, and Cleaning.

Once I had this list, I looked back through each topic column and added the themes I felt best matched the words above each LDA topic. That gave me a range at the top of my LDA topics which I could sum using a countif function in Excel. The result is something to the right.

Obviously this last part is far from scientific. The only thing remotely scientific about this is using Latent Dirichlet Allocation to organize words into topics. However it does provide value. This is a real model rooted in math; I used actual blog content not a list of keywords that came from a brainstorming session and Ubersuggest, and with a little intuition I got an idea of the strengths and weaknesses of my clients blog content.

Cleaning is a very important part of what my client does, yet it does not have much of a presence in this analysis. I have my next blog topic!

Something To Consider

LDA and topic modeling have been around for 11 years now and most search related articles about the topic appear between 2010 and 2012. I am unsure why that is as all of my efforts have been put toward testing the model. Moving forward I will be digging a little deeper to make sure this is something worth perusing. If it is, you can expect me to report on a more scientific application, along with results, in the future.