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With Google Tag Manager released a while ago and in it’s second version, hopefully you know about all about it, realize how amazing it can be and understand how it facilitates rapid tag deployment (and analytics measurement by association). Just in case you need a quick reminder, here is a quick top 10 reasons you should implement Google Tag Manager right now.
1. Easy One Time Install: Google Tag Manager lets you make a gradual switch and test as you go. In addition, GTM makes future site upgrades and enhancements much easier since modifications can be made directly via GTM, no longer requiring someone to modify each page of your website.
2. Speed: Since changes and new tags do not require code changes to the website, they can be made much faster.
3. Access: One of the greatest benefits of Google Tag Manager is that marketers (analysts) can test changes themselves before deployment and deploy once everything is tested all without involving a developer.
4. Resources: Since marketers can take a more active role in tagging and making quick tag changes, developers gain much needed time to focus on more important tasks. And when developer resources are needed with tagging, they will find a much more useful and robust tool in Google Tag Manager.
5. Lots of Free Debugging Options: Offering greater access to GTM is awesome, but that means users may no longer be trained developers…but thankfully, GTM addresses the issue. A key development technique is using safe test environments where users can test their work before publishing and going live. GTM has a built-in debug feature that allows users to test and debug each update within the browser before publishing changes.
6. Versioning: GTM saves all prior tag versions when a new version is created. In cases where the new version has tagging issues, this access to past versions provides the ability to rollback to prior working versions.
7. User Control: GTM allows administrators to set permissions for users. In addition, administrators can easily provide access to partners to edit their tags remotely.
8. Built-In Tags: GTM currently offers 20+ built in tags, such as Google Analytics, AdWords, doubleclick, Google Trusted Stores, comScore, etc. These allow users to customize the tags fairly easily without the complication of implementing difficult code.
9. Built-In Variables: There are also dozens of built-in variables available that make building contingency based tag triggers much easier. This is especially true for WordPress users who can use the built-in Click and Form variables to easily fire tags based on various click and form fill behavior.
10. Easy to Use Listeners: GTM removes the hassle of having to manually tag each link that you want to track since it provides the ability to links and by attributes that are already contained within the link (Elements, Classes, ID, Target, URL, and Test).
I attend a lots of events on marketing analytics and marketing technology. I love learning and talking about this stuff, so I usually arrive early and stay late to take advantage of the opportunity to learn, see what others are doing, discuss challenges and talk about solutions. The topic eventually comes around to what people and companies are doing relative to marketing analytics and technology, how come so few seem to be getting it right, and why. That’s usually when I reference this wonderful chart which Scott Brinker recently updated. It goes a long way toward answering why everyone is struggling to figure out their integrated marketing. I give you the the ultimate Marketing Technology LandScape Supergraphic.
Great marketing is tough and time consuming…always has been, always will be. In addition to all the things marketing has traditionally done, now they have all of these technology decisions to sort out. On this list are nearly 1,900 marketing technology vendors across 43 categories. There are so many categories, companies and things to do…it’s mind boggling. Imagine if you had to solicit RFPs, review and chose service providers in each of these categories. That alone could take the better part of a year. And then there’s the implementation which, I can tell you from experience, always takes way more time and resources that you originally thought possible. Now multiply that by ~43. It’s difficult to get you mind around it, especially when you think about implementing each one and then integrating it with your other marketing technology investments.
This post gives me somewhere to refer colleagues when they wonder aloud:
The answer is always the same…digital marketing has grown faster than most companies can keep up with. There is only so much time in a day, and so many well qualified (and budgeted) staff to do it. For the foreseeable future, marketing departments will be playing catch-up with marketing technology and all the cool, interesting and money making opportunities. Just be patient and we’ll all get there together. Over time, my guess is that marketing technology will go through a major consolidation so that we can find more services in one place, which should theoretically make things easier.
There is a metamorphosis taking place in corporate America with all departments trying to figure out how to mine and utilize growing mountains of data. My particular background has been heavily influenced by marketing research and marketing analytics, and leveraging them to optimize marketing decisions, so I tend look at things from that viewpoint. Marketing departments, after all, have a long history with marketing measurement and return on investment going back almost a century to when Nielsen started his infamous marketing measurement company. Not too long ago the majority of marketing measurements approved for corporate decision making came through traditional market research departments (a subject of upcoming article), but marketing effectiveness measurement has grown far beyond the reach of traditional marketing research departments and now comes from decentralized resources and functions across the company. This has complicated things significantly. Consider a list of marketing intelligence generators that initially included, Primary Market Research, Syndicated Research, Secondary Research and Competitive Intelligence, but now also include Web Traffic, Social Media (too many to list), Pay Per Click Data (keywords, banners, display), Email Marketing, Marketing Automation, Traditional Advertising (with links to landing pages), etc.. The truly interesting thing is the amount of analytics now available to marketing, how marketing analytics has evolved, as well as how the term analytics has evolved over the past several years.
In an environment where there’s so much confusion around analytics, how can such a thing be investigated? It’s tough, but here is one approach that came to mind. The job aggregator Indeed.com offers something called “Trends,” which is a collection of data for all jobs posted over the past several years. The database can be scanned for general keyword occurrence, keywords in titles or company fields, etc. There are many inherent flaws with using Indeed, the two most obvious being reposting of the same jobs on multiple sites and the fact that job requisitions can be terribly written, but even so, job descriptions show the language and understanding at the time it was written and how that trends with time, so it is extremely useful. Consider this…
Indeed Keyword Search Parameters
Indeed Keyword Search Parameters
Indeed Keyword Search
The lexicology of analytics in the workplace is fascinating. For those outside of analytics functions, it’s somewhat understandable, but even analytics professionals themselves seem uncertain. As you will see exemplified in the charts below, I believe this comes from the recent staggering growth of descriptive analytics positions (mainly interactive marketing). These positions previously did not exist and the majority are being filled by a new crop of marketing professionals who don’t have a similar grounding in statistics as their marketing research predecessors did…who therefore be unaware of the underpinnings of the transition. A key point of confusion comes from the metamorphosis of longitudinal tracking of interactive data. Syndicated multi-client longitudinal tracking studies such as Nielsen’s TV ratings, Arbitron’s radio ratings, MRI for publications, IRI/Nielsen for shopper data (etc.) have been around for a long time, with analytics professionals spending their entire careers working with various syndicated data sources. These studies updated annually, semi-annually or monthly, but they update more frequently now. And then along came the explosion of digital tracking data available from all of the marketing channels mentioned above….and along with it came exponential growth in the volume and speed of marketing analytics data available for nearly instantaneous decision making. Another thing that changed is the analytics processes, because now one person is often responsible for an entire data stream, from setting up campaigns and tracking, to producing dashboards and funnels, to reporting final results daily. The game has definitely changed.
It’s generally accepted that around 80% of all analysis on Big Data is descriptive in nature, which simply means that data analysts are using simple statistics to describe what has happened as opposed to what will happen. With better and faster measurement systems for marketing performance evolving every day, especially within digital marketing, a significant paradigm shift has taken place that’s impacting everyone in the marketing department. It’s all about report usefulness, insights, performance measurement and improvement, with lots of descriptive analytics professionals trying to figuring out:
While much less complex for a variety of reasons, the descriptive marketing analytics process lines up closely with a big data analytics process, but it seems to be doing a bulk of the heavy lifting in the shadows while big data, predictive analytics and prescriptive analytics gets the media limelight.
Big Data is the catch phrase of the day. Everyone has heard of it, most have a vague idea what it means, some have a clear grasp of what it can really do, and few can execute it effectively. I’ve been doing a lot of reading on Big Data and there is certainly no lack of resources on the topic. As I read through many reports, white papers, press releases, magazine articles and company presentations (all available via a quick Google search) it occurred to me that visually communicating the value of Big Data is challenging because of the need to convey different concepts simultaneously. The most popular category by far are plot charts on an X-Y axis. These charts plot analytical complexity against some sort of business value measurement in a positive correlation that looks entertainingly similar to human evolution charts we’ve all seen, with man becoming more upright and intelligent with time.
Less popular, but also useful, are a bulls-eyes, Venn diagrams and an stacked area triangles. Regardless of graphic representation, they all follow the progression from What Happen (descriptive analytics), Why Did It Happen (correlation analytics), What Will Happen Next (predictive analytics), and What Should I Do About It (prescriptive analytics). Which one do you prefer?
Source: This graphic is from SAP, a leading provider of business intelligence and predictive analytics software.
Why We Like It: This chart is unique in that it goes all the way back to the beginning when data is first created and gathered in raw form. So much of the resources needed to develop prescriptive analytics takes place in the very early stages of the process, and it’s nice that this graphic gives it a mention. The overwhelming majority of data available for analysis does make it to the final predictive/prescriptive model. If each circle represented the amount of actual data at that stage, the raw data circle (and cleaned data circle) would dwarf all the others, so thank you SAP for giving data its due.
Source: This graphic is from Gartner Group, a leading information technology research and advisory company.
Why We Like It: It’s clean and simple and gets the job done, with a simple positive linear correlation between analytical difficulty and value. No longer a nice to have indeed…
Source: This graphic is from Strategy At Risk, a predictive analytics consultancy.
Why We Like It: This graphic shows something many others do not, which is “what is happening now.” They refer to is as monitoring or “dashboard/scorecards.” One of the things Big Data proponents frequently overlook is the business value of filtered real time data. Yes, large scale cross-functional company-wide prescriptive analytics projects can have significant impacts on the bottom line, but marketing departments make daily decisions based on the real time data coming from sources such as the websites, social media, digital advertising, apps, videos, etc. Big Data is a significant strategic weapon but real time monitoring has become the tactical weapon of choice and should not be minimized. This chart does a nice job of blending it all together.
Source: This graphic is from IBM.
Why We Like It: It’s simple and different.
Source: This graphic is from The Ironside Group, a Massachusetts company that provides business intelligence, data warehousing, predictive analytics, and enterprise planning.
Why We Like It: It is a simple and original use of a Venn Diagram that’s quickly discernible. This chart is an quick way of showing how Big Data ROI increases as a company progresses from descriptive analytics to predictive analytics. I believe some might dispute the quantifiable ROI percentages (predictive analytics being too low), but it’s nice to see that they assigned a quantifiable ROI to descriptive analytics. Industry estimates are that 80% of all company-wide analytics are descriptive. With a quantifiable ROI of 80% that’s a lot of business value being created with descriptive analytics alone. When combined with prescriptive analytics, it’s an impressive 1-2 punch to the competition.
Source: Applying Advanced Analytics to HR Management Decisions: Methods for Selection, Developing Incentives, and Improving Collaboration by By James C. Sesil
Why We Like It: The first thing to like about this graphic is that it’s extremely simple. But the thing we really like is that it’s a little more accurate representation of how often each type of analytics is done by most companies. It shows that descriptive analytics is not only at the base of the analytics food chain, but also represents the lion’s share of analytics applied by companies today.
Over the past year or so, I have been getting an incredible amount of impromptu calls from folks asking the same question. “Do you know of any open research analyst positions?” My response is always the same. “What kind of research analyst are you?” Sometimes there is a long pause, and often, perhaps out of discomfort, the caller starts providing all the career highlights they can think of. The answer to my question helps quickly profile someone’s experience, proficiencies and interests so that I can better guide them relative to their competencies and interests. Equally important, it also shows how well they understand the function of a research analyst, how many types of research analysts there are, the difference between the types and how they all contribute to the biggest picture of all…better informed decision making by management. Most seasoned researchers gain experience analyzing multiple data sources over time and almost all start by specializing in just one of the data sources below. That experience becomes a proficiency they rely upon as a career asset. Once researchers have built up several years of experience with a particular data stream or within a vertical industry, they stay there for a very long time…so it’s an important thing to think about at the research analyst level. So the answer to the question is…what generally determines the kind of analyst you are (or want to be) is the type of data you spend the majority of your time analyzing.
Data Sources Include:
I searched the web looking for a great explanation of career tracks based on the data sources above. I consider myself an expert user of Google, Boolean logic and advanced search commands, and yet I could not find anything that wrapped it all up nicely. So I decided to take a different approach based on my most recent experience.
I have to admit it, I love job descriptions. They give so much information about people, positions, companies and industries. Regardless of the title, once you read the job description, you usually know exactly what that person does (and will do). Additionally, if read between the lines, they usually show a given company’s approach to their decision support function, what they think is important, what they are currently focusing on, and what talents and skills they believe are needed to compete and succeed.
To help research analysts trying to make sense of it all, I decided to do a comprehensive title search to exemplify career paths/titles a research analyst might consider. Simply Hired is one of the largest job aggregators there are and a great resource, so I decided use it as my analysis tool. Within a few minutes of searching, I was able to pull together a list of relevant and most commonly advertised analyst job titles that are relevant to the 5 data sources above.
This approach is not without challenges though, for there is insufficient title conformity from company-to-company or industry-to-industry, making it difficult to determine which data source(s) will be analyzed based on title alone. Additionally, there’s a continuing trend toward combining the analysis of multiple data sources into one position, although one data source usually predominates. I took a crack at sorting the below list of 30+ analyst titles by data source. The list of titles is by no means comprehensive, but it hits most of the major ones. If a particular title was vague or I was unable to make a decent guess as to the data source, I categorized the data source as unspecific, which means that analyst title is used for at least 2 data sources. Studying the descriptions will have a two-fold benefit.
The amount of data available for executive decision making support is growing at an increasing rate. Your value as an analyst, and as you grow into roles of more responsibility, will be based upon your ability to impact decisions. Having a better understanding of the data sources available will be a key to your success. The more you master any one data source, or multiple data sources better still, the more valuable you’ll be and the more job security you’ll have… and the more you will be compensated. Take a look at the quick list of titles below. Let me know if you have any questions or if there are other titles that should be added.
Click on any of the links below to see a listing of all positions currently posted for that title on Simply Hired in real time.