Demystified’s Data Governance Principles

In digital analytics, “Governance” is a term that is used casually to mean many different things. In our experience at Web Analytics Demystified, every organization inherently recognizes that governance is an important component of their data strategy, yet every company has a different interpretation of what it means to govern their data. In an effort to dispel the misconceptions surrounding what it means to truly steward digital data, Web Analytics Demystified has developed seven data governance principles that all organizations collecting and using digital data should adhere to. These principles constitute a thorough consideration of stewardship of digital data throughout its lifecycle. Organizations that adopt and apply Web Analytics Demystified’s Data Governance Principles can operate with the assurance that they have a solid program in place for managing digital data.

The following principles constitute a responsible data governance program:

1. Collection

– All organizations collecting data across digital platforms must be aware of exactly what data they are collecting and how they are attaining that information either directly, through user agents, or via third parties. Data collection methods should be cataloged and documented to identify any data that is extrapolated, passively collected, or explicitly collected on web pages, mobile sites, apps, and other owned digital media assets. Further, this documentation needs to include information specific to the technologies employed such as log file processors, web analytics tools, panel based trackers, tag management systems, and other solutions used to collect all types of digital data.

2. Quality

– Data quality is critically important when governing data for business use. The first component of ensuring data quality is to audit data collection agents to ensure that data collected is in fact what an organization believes that they are collecting. In our experience, we’ve recognized that most web analytics implementations devolve over time. This often leaves organizations with data elements that do not align with business requirements, do not function as designed, or those that have been obfuscated by technology without any clear indication of what the data represent. We advise companies verify data collection implementations and to regularly audit their data to ensure data collection tags (if used) are firing properly and that existing tags are not producing duplicative data. Further, we advocate for routine data quality checks to validate ongoing data collection and to alert organizations to potential data collection errors.

3. Access

– As companies implement data collection methods and provision access to employees and potentially contractors, agencies, and technology partners, access to an organization’s data becomes an increasing concern. The first line of defense for governing data access is to only provision access to email accounts of corporate employees or trusted agency partners (i.e., no personal or gmail accounts). This is an easily administered best practice that reduces the risk of a former employee gaining access to your businesses’ data. A more challenging aspect of data access that you need to govern is when technology partners share data with others. Often this is aggregated, non-identifiable data, yet organizations must be aware of instances of data sharing by third parties, data aggregators, ad servers, targeting technologies and other solutions that potentially compromise restricted access to your digital data.

4. Security

– Data security is often coupled with data access, but we at Web Analytics Demystified believe that data security goes beyond merely provisioning access to qualified analysts, but that it includes a business’ ability to safeguard it’s data stores. Most data collection solutions today are amassing large volumes of data and have opted for cloud-based storage solutions. While nearly all of these solutions fortify their security with multiple layers of redundant measures, the onus of understanding where and how any data is transferred from these solutions to other technologies falls upon the business. An area of concern is data “leakage” that could occur when data is inadvertently (or unknowingly) shared with external parties. Companies should minimize this risk by clearly understanding and documenting how their data is stored, shared, and secured across all data collection agents.

5. Privacy

– For any business that is collecting consumer data, privacy is a critical concern. It is the responsibility of the business to inform consumers what data is being collected and how that data will be used. Numerous best practices exist around divulging this information within published privacy policies, but the best guidance that we can offer is to deliver a clear and concise data usage/privacy policy that offers an opt-out for consumers who do not wish to be tracked. Businesses should also be aware of and classify data that is anonymous, segment identifiable, and personally identifiable and treat each independently. This classification element of data governance should be governed at the technology level because it extends beyond web analytics technology into business intelligence, enterprise marketing management, and customer relationship management solutions.

6. Integrity

– Governing data integrity is predicated on the fact that many data collection technologies today leverage processed data in their outputs. In web analytics tools, this might equate to a series of actions that constitute a “user session”, or a “path” the leads to a conversion event. In other technologies like IBM Tealeaf, multiple online activities can be associated with a single session that provides the necessary context for the data output. This data often requires that it is presented in processed form such that it reveals the true nature of what happened in a digital environment. Many businesses have the temptation to disaggregate processed data for inclusion in an enterprise data warehouse as “raw” data that can be analyzed at a later date. However, their are inherent risks in doing this because it could lead to inflated activity counts, incongruous data, or simply incomprehensible data. For these reasons, data integrity is an important principle of governance to ensure that data is utilized and analyzed in its intended form.

7. Presentation

– In digital analytics, there is an old adage that by “torturing” your data you can make it say anything you want. Responsible stewards of digital data are cognizant of this fact and strive to present data in proper context. While some organizations attempt to assign gatekeepers to assure data is presented in proper context, this becomes increasingly difficult as data sets accrue to petabytes in scale and access is granted to numerous individuals. Rather than restrict access to a responsible few, Web Analytics Demystified recommends companies to train their employees to recognize what data is being collected and what outputs are appropriate for specific data types. This level of education minimizes improper data interpretation and is the foundation for solid presentation and delivery of digital data assets.

It’s important to note that these Data Governance Principles are merely a starting point for developing your own data governance program. In our extensive experience consulting with organizations of all sizes, each presents their own data governance challenges. By adopting these seven principles and institutionalizing a process around each, companies can operate in today’s increasingly digital world with the confidence that they are responsible stewards of digital data and that they have taken precaution to safeguard their data according to industry best practices. To learn more about any of Web Analytics Demystified’s Data Governance Principles, please reach out to us at , we’d love to help launch your data governance program or to learn how you’re currently governing your digital data.

Published on December 5, 2014 under General Web Analytics

Bulletproof Business Requirements

As a digital analytics professional, you’ve probably been tasked with collecting business requirements for measuring a new website/app/feature/etc. This seems like a task that’s easy enough, but all too often people get wrapped around the axle and fail to capture what’s truly important from a business users’ perspective. The result is typically a great deal of wasted time, frustrated business users, and a deep-seated distrust for analytics data. All of these problems can be avoided by following a few simple rules for collecting and validating business requirements.

Rule #1: Set Proper Expectations for What’s Really Worth Measuring

You’ve heard the saying from Albert Einstein…Not everything that can be counted counts, and not everything that counts can be counted. Well, Einstein was ahead of his time when it comes to digital analytics. There is an understandable tendency to measure everything, but this certainly doesn’t help when it comes to sifting through data to determine the effectiveness of your digital efforts. In many cases, less is more. Remember that collecting business requirements creates the foundation for developing KPIs to gauge effectiveness of your digital efforts. And, if you’re reading my colleague Tim Wilson’s blog, you know that the “K” in “KPI” is not for “1,000”!

So, the first rule in gathering effective business requirements is sitting down with your business user counterparts and explaining to them that their new digital asset should be measured on the merits of what it’s designed to accomplish with as few metrics as possible. In plain english, you should ask the question, What is this new thing of yours supposed to do? Once you have the answer to that question, you can start digging into the real meat of what’s needed in terms of measuring its performance. Most business users don’t want to spend hours analyzing and interpreting data, so this rule allows you to set the expectation that you can save them time and headaches by distilling the metrics down into the most salient measures.

In my experience I’ve found that asking your stakeholders to do a little homework prior to meeting will help these conversations go much more smoothly. By prompting them with probing questions about which elements of their digital asset are critical and setting expectations about what digital analytics can do well, you will have a much more productive requirements gathering session.

Rule #2: Break Requirements Down into Manageable Categories

When asked which specific things a business user wants to measure on their shiny new digital asset, the conversation usually goes something like this…

    Analyst: What data would you like to collect about your new website/app/feature/etc…?
    Business User: I don’t know, what do you have?
    Analyst: Well, we can collect anything you want, if you just tell me what it is that you want to know.
    Business User: Okay, I want to know everything…
    Analyst: So, everything is important?
    Business User: Yep.
    Analyst: Grrrrrr…
    Business User: WTH?

Asking business users what they want to measure — or what data they need — is truly a difficult question to answer. As an analyst, you have to put yourself in their shoes and lead them into data collection conversation with some guidance. I recommend the approach of breaking your measurement requirements down into categories that can be addressed one at a time. In many cases, there will be different stakeholders who want to know different things about their digital asset and the category approach helps you to generate a comprehensive list of requirements while considering everyone’s feedback. The table below illustrates a handful of requirement categories and corresponding questions that a business user might want to know.

The exact categories and business questions will vary based on the digital asset you’re measuring so be sure to customize the categories to take into consideration when you’re measuring a mobile app, checkout feature, or entire website.

Rule #3: Verify Requirements and Provide Example Reports

My third rule for verifying requirements is often overlooked by analysts because it is both time consuming and labor intensive. But, if you do take the time to do this, you’ll not only ensure that you have the right requirements, but that you may also save yourself a lot of work in the long run.

Once you’ve solicited requirements from all stakeholders, go through the exercise of prioritizing and de-duping your list so that you can identify what’s really important. Once you receive stakeholder approval for your list, you should then take the next step of providing an example of the reports that business users will receive once you’re live with data collection. This helps because while you may have a solid understanding of how the data will be represented, you’re typically working with users who aren’t equipped to visualize the output of your requirements. As such, providing a mock-up of an analytics report that shows the key data points you will collect helps to validate that you’ve got the right information. Use this process to also ask stakeholders if they will be able to make decisions about their digital asset given the reports you’re providing. If the answer is no, then you need to keep working on the requirements.

By taking this extra step, you’re not only ensuring that you understood the business requirements, but also providing the opportunity to refine your metrics to capture critical decision-making data. Not only will you impress your stakeholders with your proactive approach, but you’ll also avoid having to go back and implement tracking on something that they may have overlooked during your discovery process.

In summary, collecting business requirements for digital analytics is no easy task. It takes a process to illicit good information and it takes some analytical foresight to visualize the results. These are skills that take time to master, but once you get them right, you’ll be on your way to providing the most useful and pertinent data to your business colleagues.

If you’d like to learn more about gathering bulletproof business requirements, please send me an email. Or better yet, join me for a half-day workshop on Requirements Gathering the Demystified Way in Atlanta prior to Web Analytics Demystified’s ACCELERATE conference, where I will go into detail about what it takes to gather requirements and teach you all the tips and tricks of the trade.

Published on September 3, 2014 under General Web Analytics

Black Friday Analytics

So it’s that time of year again when commercialism runs rampant, people spend with reckless abandon, and at any moment there could be fisticuffs at your local Wal-Mart. But alas, this is Holiday Season in America, so be joyous about it!

I’ve been watching online spending trends for the past decade and most recently tying to discern what impact mobile and social media plays in all that glitters online. All signs indicate that 2013 is door-busting records with all time highs for online sales, yet depending on which data you believe in, there’s different stories to be told.

Two analytics leaders, IBM and Adobe routinely benchmark holiday shopping. And while their methodologies differ, so too does their data. Here’s a snapshot of some of their published findings thus far:

Show me the Money

IBM’s Digital Analytics Benchmark reports a +18.9% increase from 2012 in Black Friday sales during this year’s holiday season. Average Order Value (AOV) was $135 with on average 3.8 items per order.

Adobe’s Digital Index reported slightly higher profits with a 39% increase from 2012 for a whopping $1.93 Billion in online sales. Adobe reported a similar AOV at $139 and also revealed that the peak shopping time on Black Friday was between 11AM and noon ET, when retailers accrued $150 Million during this single profitable hour.

While both companies reported lift on 2013 online sales during these two days of shopping, each indicates substantial lift in Thanksgiving Day sales, which may have cannibalized some of Friday’s profits. And while Cyber Monday numbers are still being tallied, all signs point to the biggest online shopping day yet, which likely has retailers grinning from ear to ear early on in this short 2013 holiday shopping season.

Mobile Madness

Both indices show mobile as a significant driver in online sales. Adobe reported that on Thanksgiving and Black Friday, nearly one out of every four sales was made via mobile device. IOS devices and in particular, iPads were the device of choice in both company’s findings. Adobe reported that a total of $417 Million was recognized in just two days (Thanksgiving and Black Friday) via iPad sales by businesses within their index.

This should come as no surprise to those of us following the data, but mobile now represents nearly 40% of all Black Friday traffic. That’s a trend that retailers just cannot ignore. And as a consumer, you probably can’t ignore it either. Tactics reported by IBM indicate that retailers sent 37% more push notifications via alerts and popup messages on installed apps during these two heavy online shopping days.

Where in the World?

The biggest discrepancy between the two online shopping benchmarks comes from the geographic perspective. Keep in mind here, that IBM’s Digital Analytics Benchmark is comprised of data from 800 US Retail websites; and the Adobe Digital Index data represents a wholly different set of US retailers that accrued 3 billion online visits during the Thanksgiving to Cyber Monday shopping spree. (Note that exact comparable data isn’t provided in publicly available information.)

Yet, Adobe’s data reflects the majority of online shopping on Black Friday coming from 1) Vermont, 2) Wyoming, 3) South Dakota, 4) North Dakota, and 5) Alaska. They cite weather and rural locations as rationale for these states topping the list. IBM on the other hand, indicates that on Black Friday 2013, the highest spending states from their benchmark include: 1) New York, California, Texas, Florida, and Georgia. It’s not atypical to see variances in data sets, yet keep in mind when interpreting results for yourself, it’s all about the data collection method. Results will vary based on who is in your benchmark and how you’re slicing the data.

Social Influence

While IBM’s early data cited in an article by All Things Digital made the outlook for social appear dreary,
Adobe weighed in with a contradictory and uplifting perspective on social. IBM did not report on social sales for Black Friday in 2013 apparently because the findings weren’t “interesting”, but their report from 2012 showed that directly attributable revenue from social media (last click) was a dismal .34% of Black Friday sales. By my math that equates to a paltry $3.5 Million total online dollars via social media sales for Black Friday. The AllThingsD reporter managed to eek out of Jay Henderson, IBM’s Strategy Director, that social sales were flat again this year. Moreover, the article quotes Henderson as saying “I don’t think the implication is that social isn’t important, but so far it hasn’t proven effective to driving traffic to the site or directly causing people to convert.” Hmm…

However, this year Adobe is telling a slightly different story. According to their Cyber Monday blog post, social media has referred a whopping $150 million in sales in just five days from Thanksgiving to Cyber Monday. While, it’s not clear if they’re tracking using a last- or first-click perspective, this data indicates that social is pulling its share of the holiday sled this 2013 season. Well, at least social is pulling about 2% of the sled based on a total of $7.4 billion in total online sales from Thanksgiving through Cyber Monday.

Whichever metrics you choose to believe, counting dollars in social media ROI is never an easy task and it usually doesn’t lead to riches. I’m about to publish a white paper on this very topic, so if you’d like to learn more about quantifying the impact of social, email me for more info.

The Bottom Line

This holiday season is shaping up to be the biggest yet for retailers of all sizes. Remember when just a few years ago people were afraid to buy ***anything*** online? Well, it certainly appears that those days are gone. So, as the days before Christmas (or whichever holiday you celebrate) wind down, and the free shipping deals get sweeter, and the door-busters swing closed until next year, take a close look at your data to see what the digital data trends leave for you.

Published on December 9, 2013 under Events, General Web Analytics

Will Chrome Solve our Multi-Device Problem?

Google recently launched a new television commercial that advertised their Chrome browser as a solution for your computer, tablet, and mobile device. For marketers and digital analytics pros of all types, this solution has real potential. Not because of the convenience of the solution, but because it potentially solves our problem of identifying visitors to our websites and mobile apps as they traverse from work computer, to mobile to tablet…throughout the day.

First check out the video:

Here’s why this solution has potential for consumers…

Errr…what’s my password again? In an increasingly password-protected web, users will find this unified browsing service valuable. How many times have you scratched your head and asked yourself…”What’s my password?” This unified browser resolves that issue with Chrome’s saved password feature. For those of you not using OS keychains or another solution for recalling your passwords, this is a sure-fire way to minimize the dreaded password reset.

Faster than a speeding search engine. Google’s search (while Bing is giving it a good run) is getting smarter. The Chrome “Omnibox” (you know it…it’s the address bar) will automatically predict what you’re typing (if you let it), which virtually tells you that Google is smarter than you are. Not only does this help get to the right stuff more quickly, but it also recalls where you’ve been previously. But if you’re not into that sort of thing, “Google only records a random two percent of this information received from all users and the information is anonymized within 24 hours. However, if you use Chrome Instant, your data can be kept up to two weeks before it’s deleted.”

Remember my Tabs? No, I’m not talking about the “Totally Artificial Beverage” soft drink (for those of us old enough to remember Tab cola), which was the predecessor to today’s ubiquitous Diet Coke. I’m talking about the tabbed browsing experience. Since most of us bounce between devices as a matter of habit, the ability to bookmark a tab on one device and pick up another to find the same page is becoming increasingly valuable. No more searching for that web page you found right before your boss walked into your cubicle. Simply tap the bookmark star and you’ve got it remembered on all of your Chrome-synched devices.

Here’s why this is a web analysts’ dream…

For us web analytics wonks, having Google Chrome Now Everywhere could help us solve the problem of identifying visitors across devices and sessions when they don’t log in. I cannot count the number of conferences, expert panels, and lobby bar conversations where I’ve heard the question asked: “How can we identify anonymous users across devices?” Well, Google could now potentially solve this problem for a subset of devoted Chrome users…if they choose to make this data available. That’s a big if…

Despite the fact that Google also announced Universal Analytics today, Google would have to make this cross-device data available to us #measure folks. Wouldn’t that be AWESOME? But who knows if they’ll open the kimono on this really valuable data? Perhaps, Google may be holistically trying to help marketers by someday tying products like Chrome and Google Analytics into a common perspective… But perhaps that’s just too progressive for the privacy pundits. I don’t know.

While no digital analytics solution is 100% accurate in its ability to understand user behaviors due to cookie deletion rates, missing data, and anonymous browsing. Chrome’s omni-device presence would certainly help identify with precision those users who opt in to use this solution because of the benefits that it offers. I’ve been saying this for years, but it’s all about the value exchange. And the value derived from having Chrome remember all of your passwords, favorite pages, and preferences is well worth it for many. Don’t be surprised if Safari, Firefox and others start riding GOOG’s coat tails on this one…

What about you? Do you think this will change #measure?

Published on March 22, 2013 under Uncategorized

Re-Examining Attribution

Attributing credit across a multitude of marketing efforts is one of those sticky problems in digital analytics that seems to generate a whole lot of controversy. This is a topic that comes up with nearly all of my clients and is one that both Eric T. Peterson and I have been researching and writing about for some time now. My latest findings on attribution will be published in a whitepaper sponsored by Teradata Aster, titled, Attribution Methods and Models: A Marketer’s Framework, but you can tune in to our webcast on January 16th, to get the high notes.

While some pundits will argue that attribution is not worth the trouble and that all attribution models are flawed, others contend that attribution simply requires a healthy dose of marketing science, which will enable marketer’s to reap benefits tenfold. At the risk of opening up a whole can of Marketing Attribution worms, I’ll offer my Marketer’s Framework for Attribution, which is a pragmatic approach to organizing, analyzing, and optimizing your marketing mix using data. But first, let’s define marketing attribution:

Web Analytics Demystified defines Marketing Attribution as:

The process of quantifying the impact of multiple marketing exposures and touchpoints preceding a desired outcome.

The first question that you need to ask yourself is whether or not you really even need to include attribution in your analytical mix of tools, tricks, and technologies. I offer this as a starting point because attribution isn’t easy and if you don’t really need it, then you can save yourself a whole lot of headaches by short-cutting the process and offering a data-informed validation of why you don’t want to mess with attribution.

The approach I offer is shamelessly ripped-off from Derek Tangren of Adobe, who blogged; Do we really need an advanced attribution marketing model? Derek encourages his readers to answer this question by looking at their existing data to determine what percentage of orders occur on a user’s first visit to your website vs. those that occur on multiple visits. I bastardized Derek’s idea and applied it to help marketers understand how many visits typically precede a conversion event. While Derek offers a way to do this using Adobe Omniture, I’ve created a custom report within Google Analytics that does virtually the same thing. I call it the Attribution Litmus Test.

My version is a quick sanity check for those of you running Google Analytics to determine the number of conversions that occur on the first visit versus those that occur on subsequent visits. To use this, you must have your conversion events tagged as Goals within Google Analytics (which you should be doing anyway!). If you’d like to run the Attribution Litmus Test on your own data within Google Analytics, you can add the Custom Report to your GA account by following this link: Remember that you must have goals set up in Google Analytics for this report to generate properly.

So now that you’ve determined that Attribution is a worthwhile endeavor to pursue for your organization, let’s dive into the Framework. According to a study conducted by eConsultancy, only 19% of Marketers have a framework for analyzing the customer journey across online and offline touch points. Yet, the reality of consumer behavior today illustrates that multi-channel marketing exposures and multiple digital touch points are commonplace. As such, Marketers need a method for understanding their cross-channel customers in a systematic and reproducible way.

Step 1: Identify Your Data Sources

The first step in utilizing an Attribution Framework is to identify and input your data sources. Because advanced attribution requires understanding marketing effectiveness across all channels, it means that you must acquire data from each channel that potentially impacts the customer path to purchase. Typical digital channels may include: display advertising, search, email, affiliates, social media, and website activity.

Step 2: Sequence Your Time Frame

All attribution models must consider time to understand which marketing exposures occurred first, and also to discern the latent impact of exposure across channels. This requires that organizations sequence their data. While numerous data formats will likely go into the model, we’ve seen the greatest success when attribution data is stored and aggregated within a relational database.

Step 3: Apply Attribution Models

The actual attribution models will determine how you look at your data and make determinations about which marketing channels, campaigns, and touch points are effective in the context of your entire marketing mix. There are five models that are commonly used in the attribution world: First Click, Last Click, Uniform, Weighted, Exponential. To learn more about these models, tune into the webcast where I explain each in more detail.

Step 4: Conduct Statistical Analysis

After the data has been prepped, sequenced, and cleansed; this is typically where Data Scientists conduct general queries, apply business logic, and run what-if analyses against the model. At agencies that specialize in attribution modeling like Razorfish, they have an advanced analytics team comprised of data scientists that attack the data. They’re looking for correlations to identify if users are exposed to marketing assets A>B>C, are they likely to take action D?

Step 5: Optimize Marketing Mix

Of course, the ultimate goal in utilizing an attribution framework is to make decisions that impact your marketing efforts. These decisions can be strategic such as: deciding to invest in a new social media channel; discontinuing use of a non-performing affiliate partner; or reallocating budget to highly successful channels. But an attribution model can also play a major role in making daily life marketing decisions such as: which keywords to bid on during a specific campaign; who should receive an email promotion; or where to place that out of home billboard to attract the most attention.

In conclusion, Marketing Attribution continues to be an Achilles’ heel to many marketers. But, the good news is that approaching attribution with the right toolset and a framework for solving the attribution riddle is definitely the way to go. Throughout my latest research, I talked with companies like Barnes & Noble, LinkedIn, and the Gilt Groupe to learn how they’re using and applying Marketing Attribution models. I’ve also had the good fortune to demo some of the latest attribution tools from industry leading vendors like Teradata Aster and Visual IQ. Through this research, I learned that there is some truly innovative work going on with regard to attribution, but there is no single best way to do it. I’d love to hear how you’re solving for attribution. Please shoot me a note, tune into our webcast, or comment on how you’re re-examining attribution.

Published on January 7, 2013 under Analytics Culture, General Web Analytics

Balancing the Quantitative with Qualitative

This was originally published as the President’s Message in the August DAA Newsletter.

I’ve been spending a lot of time recently working with data. For some clients I’m helping to assemble data from multiple sources across their enterprise to answer business questions like how does clickstream behavior impact revenue. For other clients, I’m strategizing about using aggregate data to create new opportunities that provide added insights and actionable steps toward increasing profitability. And for fun, I’m slicing through data to gain greater understanding of events I’ve missed or simply things that I’m curious about.

This last effort is what got me typing today. As I sorted through Tweets and scoured the web for information about the recent DAA Symposium in San Francisco, I was heads down looking at data. I wanted to accomplish two very specific objectives: 1) to validate a new calculated metric that I’m working on, and 2) to simply find out how the event was and what type of knowledge was being shared.

So I turned to five different tools to try to find the answers that would satisfy my curiosity.

My research quickly yielded data that showed how many Tweets with @DAAorg and #SanFranDAA were flying; who the top contributors were; and in some cases how many impressions were created by these messages across the Web. As I researched more, I became more and more focused on the numbers and sought to find the story within the data that would tell me more. As I dug deeper, my tracking spreadsheet started to grow and I began to see that across the five tools, each had significant gaps in the data that they provided. While most were able to reveal the total volume of mentions for my specific keywords, there was a great deal of variation in what they found. Further, the data produced by these tools was often lacking metrics that I wanted to perform my calculations. But what really struck me was the fact that amid all this data I was looking at, very few of these tools told me anything about the content of what was being said. Sure, I could scroll through the individual Tweets and see the content, there were also lists of top keywords showing me what was mentioned most, and even in a few cases there were word clouds that highlighted commonly mentioned terms and their relationship to my search query. But through all of this data I still didn’t know what really happened at the DAA Symposium in San Francisco. I needed someone who was there to fill in this essential piece of information.

But I was still determined to produce something from my exercise in curiosity, so I sent out a Tweet with a quantitative perspective on what I had discovered. Almost immediately, I received a response that asked… “@johnlovett @DAAorg so what’s the qualitative story?” I too had this question in my mind and with the help of this one innocuous Tweet; I realized that every data exercise can benefit from both the quantitative and qualitative sides of the story. Either one alone is woefully insufficient. By digging into the data, there were things that I could see that helped me to understand what happened at the event, and I was even able to gain a better understanding of the awareness created by the event using my calculated metric. However, what I failed to capture in looking solely at the data alone was the qualitative message. Through all the Tweets and data I analyzed, I learned some very interesting things, but the results of my analysis were hollow without a first hand narrative to accompany them.

While this may be painfully obvious to many, all too often I see organizations lose sight of this fact. They expect digital analysts to amass data and crunch numbers to uncover revelations about the business. But in many cases, these analysts don’t have the benefit of understanding the strategy behind the numbers or the context of a story that they data can support. This makes their jobs incredibly more difficult and ultimately it leaves their analysis with a hollow void that is begging for a narrative. In my experience, I’ve found that this narrative comes from collaboration between analysts and business stakeholders who take both sides (the quantitative and the qualitative) to showcase results in a manner that is not only meaningful, but also leaves a lasting impression.

So the next time you’re itching to deliver that beautiful analysis you just created…or if you’re listening to an eager analyst share new data…ask yourself if the perspective you’re hearing considers both the quantitative and qualitative sides of the story. If not, ask for more.

What do you think?

John Lovett DAA President

PS! Here’s links to blog posts from Krista Seiden’s (BloggerChica) and @AllaedinEzzedin’s Thanks!

Published on September 8, 2012 under Uncategorized

Fortune 500 CEOs Aren’t Social — Ummm…Thank You!

A new report debuted last week on from the creators of DOMO, which citied findings about the social participation of Fortune 500 CEO’s. The report showcased the fact that only 7.6% of big company CEO’s are on Facebook; only 1.8% actually use Twitter; and that 70% of global CEO’s have no social media presence at all. To these numbers, I say…FANTASTIC!

Now, don’t get me wrong…I’m a huge proponent of social media and of measuring it methodically…I even wrote a book on this topic. Further, I corroborate the statements that social media has become a transformative force that’s changed the way individuals and businesses communicate. Of course, without a doubt! Yet, when CEO’s are called to task for not individually participating in social channels…well I for one think that they should be spending their time focusing on fiscal responsibility, shareholder value, and customer satisfaction with their products and services. These CEO’s should be lauded for focusing on what matters and for delegating a social presence to others within their organizations who are hired to interact with consumers and to keep touch with the pulse of their marketplace.

The downloadable report is accompanied by a slick video and jazzy infographic…that basically tell us that most CEO’s aren’t Twittering all day (Ummm…that’s good, right?).

While this report certainly doesn’t shed light on what CEO’s actually do spend their days doing, it proves that they aren’t looking to social media as an output channel. And thank goodness for that. While social media is undeniably valuable for communicating to consumers, marketing to them, and interacting in meaningful ways…last time I checked, that’s not the job of an officer in chief. Do they need to be aware of it…? ABSOLUTELY! Do they need to be open to consumer and employee interactions? Why Yes! But do they need to be a first-line responder? I think not. There are lots of ways for executives to stay informed and to communicate. Yet, bolstering a social media presence only to abandon it shortly thereafter, or allow it to die a slow unused death doesn’t help anyone’s credibility.

Maybe I’m alone, but in my opinion the underlying premise of this research missed the mark by a long-shot. Fortune 100 CEO’s shouldn’t be pandering to consumers on social media. Let’s allow the executives in chief the opportunity to focus on business and save the Twittering and Facebooking for the marketers.

Published on July 16, 2012 under Social Media Measurement

eMetrics Chicago – Wrapup

Before too much time passes during these dog days of summer, I thought that I’d offer a recap of the eMetrics Marketing Optimization Summit that took place in Chicago recently. First of all, Chicago really digs analytics. Despite a smallish eMetrics crowd of around ~100 or so people, there was lots of energy, young talent and academic interest.

I had the privilege of sharing a few minutes of the opening keynote with Jim Sterne where I made a few announcements about the newly rebranded DAA (Digital Analytics Association). I proudly announced that we transitioned 25% of our Board of Directors by adding new members Eric Feinberg, Peter Fader and Terry Cohen to our diverse assembly of directors. I also took the stage in my new role as President of the DAA and shared my thoughts about the epic journey we’ve collectively embarked on in this industry that we call digital analytics. This is a theme that I reiterated during my closing presentation on The Evolution of Analytics, whereby I concluded, that the future state of evolution is up to each of us to determine.   
But speaking of future success, I commend the local DAA Chicago Chapter for the great strides they’ve made in not only organizing our open industry meeting, but also in championing the cause for digital analytics in the windy city. The DAA has much better brand recognition and awareness in Chicago than I thought. But I suppose I shouldn’t be too surprised because after all, according to the DAA Compensation scan, Chicago is the second best place to live if your seeking a job in analytics.

Moving onto more details about the conference, Jim Sterne always encourages attendees to measure the value of eMetrics not just in the content, but also in the hallway conversations and the key tibits that you take back to your desk when all the sessions and lobby bar fun is over. In Chicago, for me the hallway conversations focused on several hot topics in analytics including: tag management, privacy and of course, the perennial analytics issues of people, process and technology.
On the privacy front, the controversial WSJ article about Orbitz’ targeting was a hot topic of conversation for me (and Scot Wheeler) during the conference. Despite the fact that the WSJ got the headline wrong…it reiterated the fact of how very little the average consumer knows about what we all do… 
I also learned (privately) that Amazon is doing some crazy brilliant stuff, but it’s so good that they can’t even talk about it. The senior brass at the really good companies are very protective, but web analysts can still be plied (at least a little) with alcohol at a Web Analytics Wednesday
And finally, people who do know what we do are struggling to pull together the pieces for making an analytics program work…finding staff, selecting tools, building process. These are perennial issues in digital analytics and why we’ve built our consulting practice here at Web Analytics Demystified to help solve these problems.

But as always at eMetrics, I was invigorated to speak with new entrants to digital analytics and the usual suspects as well. For me, I’ll be taking from this eMetrics something back to my desk and to my clients…and that is a fresh perspective.

Anyone who has been in this game for any length of time should recognize that it’s easy to become steeped in your own myopic view of digital analytics and continue to rehash the same perennial issues with the same examples over and over again. Yet, any good analysis – or method of teaching – needs to evolve to remain relevant. And thus, for me this eMetrics taught me that experience needs to be tempered with the fresh eyes of unbridled passion and enthusiasm. While we may hold the frameworks and fundamentals, it is they who hold the spark. I for one appreciate what the next generation of digital analyst is bringing to this industry and hope to learn as much from them as I can offer.

What do you think?

Published on July 12, 2012 under Events, General Web Analytics

ACCELERATE Chicago Debrief

I’m on the plane returning home from the second ever Web Analytics Demystified ACCELERATE Conference and I can’t help but smile as I think about what an incredible event this was. For starters, demand for this event maxed out the ~200 person capacity of our Chicago venue at the Gleacher Center, but we managed to comfortably squeeze in all of our registered guests as well as everyone who showed up on the waiting list into the room. Of course, Chicago was well represented but there was also a preponderance of Ohio Analysts in the house as well. The OHiO solidarity was reiterated with incessant demands for a Columbus, ACCELERATE sometime in the not too distant future…to which we say, Anything’s possible ;-)

Once we kicked off, the room was electrified by Eric Peterson’s inspiring opening comments and you could definitely feel the energy in the air. We promised our attendees a fire hose of content and delivered by honing our “10 Tips in 20 Minutes” format to keep things going at a frenetic but well managed pace. Based on comments and feedback we received, I think it’s safe to say that anyone who was there will tell you that we over-delivered. You can check out the recent Tweets on #ACCELERATE yourself, but I’ll offer up a few notable comments:

medmonds: Very impressed with the #ACCELERATE conference – insightful tips & strategies for optimizing digital channels from industry leaders #MEASURE

Jonghee: Completely satisfied with #ACCELERATE. It’s quality is better than some of the expensive ones. Great job @erictpeterson and the team!

Ableds2: Few industries/professions strive for excellence like this group. I am honored to be surrounded by amazing people #ACCELERATE #measure

#ACCELERATE by the Numbers (April 4, 2012)

One of my responsibilities during ACCELERATE, beyond delivering my 10 Tips on Using a Social Media Measurement Framework was to track the Twitter stream to see what was coming in throughout the day of the conference and who the BIG Tweeters were. I thank TweetReach for providing access to their monitoring tool, which allowed me to conduct my analysis in near-real time as Tweets tagged with #ACCELERATE were flying across the Interwebs.

***Note: My TweetReach Tracker is set up for East Coast time, so this reflects a -1hr Time Zone delay.***

Exposure: (measured in Top Contributors by impressions) We did a pretty good job overall of sharing the love emanating from ACCELERATE on Twitter with 3.23 million impressions reaching an estimated 240k people on April 4, 2012. The 6 top contributors delivered 69% of the total impressions and they included: @EricTPeterson, @EndressAnalytic, @johnlovett, @jennyweigle, @monishd, and @MicheleJKiss (who wasn’t even there!). If you’re looking for folks to get the word out on Twitter, consider this your shortlist.

Velocity: (measured in ReTweets and total impressions) Overall the most re-Tweeted tweet for the 24-hr period was by Erica Chain, who garnered 10 RT’s on her 140 character missive about Joan King’s Crate & Barrel presentation. Note to the velocity Tweeters: pictures get more RT’s! I had a chance to talk with Erica and learned of her amazing story which was an added bonus. But, Monish Datta won our cash money prize for the most Retweeted Tweet as of 3PM. He attained 7 RT’s and over 16k impressions. Monish and team from Victoria’s Secret were well represented at ACCELERATE and they all added great value and velocity to the Tweet stream.

Penetration: (measured as the percentage of #Measure Tweets containing the #ACCELERATE hashtag) Over the course of the day, #ACCELERATE occupied 71.2% of all Tweets on the #Measure. Since we were delivering a fire hose of information during ACCELERATE, we encouraged attendees to Tweet out over our hashtag as well as the #Measure hashtag throughout the day. Apparently they listened because we dominated #Measure by sharing the free content delivered at ACCELERATE with anyone who cared to listen in, one tip at a time. One UK onlooker even commented that either it was lunchtime or Twitter had crashed as our activity came to an abrupt slowdown during our noshing hour.

Impact: (measured as the perceived value generated by ACCELERATE) The true impact of this event is best measured by the actions that attendees will take when they arrive back at their desks and apply their newfound insights into their daily work. While this is a real tough one to quantify, measuring impact on these types of things always is. For me and my Partners at Demystified, we gauge our success by the speaker feedback we receive, the generous donations to our Analysis Exchange scholarship fund, and through the comments that we get from individual attendees. By all measures, this was a smashing success.

In closing, I’d like to issue one last word of thanks to our generous sponsors: Ensighten, ObservePoint, OpinionLab and Tealeaf who made this event possible. And if you missed ACCELERATE Chicago, try to make it to Boston. We’ll be doing it again on October 24th, and we hope to see you there.

Published on April 6, 2012 under Events

Counting ROI in Pennies with Social Media

“Goddam money. It always ends up making you blue as hell.” ~ Holden Caufield, The Catcher in the Rye

That is…if you let it.

During our webinar yesterday Activating Your Socially Connected Business, Lee Isensee (@OMLee) and I caused a minor flurry on Twitter when I Tweeted about the results Lee showed from the IBM/comScore social sales data from Cyber Monday. The findings revealed that $7 million dollars captured on Cyber Monday 2011 in online sales was directly attributable to social media. This makes up 0.56% of all online sales on Cyber Monday 2011.

The skeptics were quick to pounce on the paltry figure, with #WhoopDeeFrigginDo’s and “rounding error” rhetoric (see the synopsis). And I agree, that half a percentage point, by anyone’s count isn’t a whole lot of impact. Even when it equates to $7 million bucks in a $1.25 billion dollar day of digital shopping. However folks, remember that all online sales last year represented just 7.2% of holiday cha-chingle in retailers’ pockets. According to comScore’s numbers that’s $32.6B in digital business over the 2010 holiday shopping season. Yet, how many of the total $453B in last year’s holiday sales…or this year’s forecasted $469B in holiday sales…were/will be ***influenced*** by online channels? The answer is a lot.

According to research firm NPD, 30% of all holiday shoppers plan to buy online this year, with the numbers even larger for high income households. Further, a full 50% of shoppers will turn to the Internet to research products prior to buying this year. And this that doesn’t include another 20% that will rely on consumer reviews and 4% who will turn to social media for their pre-buying intel. As we know, many of these shoppers will hit the stores with smartphones in hand, ready to get info or tap into their social networks as necessary.

My point is that if you’re so narrowly focused on social media that the only reason you’re in it is for the money…then you’re missing the point. Social media is today – and will be tomorrow – an enabler. It’s a method to engage with people on a meaningful level and to allow them to engage with one another. As a brand, if you can’t see this then you’re totally missing the point. It’s not all about the Benjamin’s. Social media ROI is important, but trying to pin everything down to bottom line metrics will have you “blue as hell” when it comes time to tally the numbers.

Instead, work to identify other Outcomes for your social media objectives that ***don’t have*** direct financial implications, but that ***do have*** business value. Demonstrating that your social channels reduce call center costs, elevate customer satisfaction, or simply drive awareness of your in-store promotions will deliver value deep within the business.

I’m all for generating ROI from social media activities and making direct revenue correlations when they exist. Yet, in today’s world, social media isn’t just about the bucks. It’s a means to deliver better experiences for the many people who turn to that channel.

If you’re interested in learning more about Activaing Your Socially Connected Business, download Chapter 3 from Social Media Metrics Secrets, courtesy of IBM.

Published on December 16, 2011 under Analytics Culture, Social Media Measurement


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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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Eric T.








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You can contact Web Analytics Demystified day or night via email or by reaching out to one of our Partners directly.

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Web Analytics Demystified, Inc.
P.O. Box 13303
Portland, OR 97213
(503) 282-2601

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