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: http://bit.ly/Attribution_litmus_test. 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.
The skeptics were quick to pounce on the paltry figure, with #WhoopDeeFrigginDo’s and “rounding error” rhetoric (see the Storify.com 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.
So, my prediction is that the movie Moneyball, set to release this Friday September 23rd, will add a level of awareness to Analytics that skyrockets our little cottage industry straight to household status.
For many of us in the analytics and optimization business, Michael Lewis’ book Moneyball is something of a bible. I know that when I first read it back in 2003, it made me want to become a web analyst. The book chronicles the unorthodox methods of one maverick baseball manager who was forced to break the traditional paradigm of scouting and recruiting big market baseball players to build a winning team that didn’t match his shoestring budget. The manager was Billy Beane, responsible for the 2002 Oakland A’s baseball club, who irrevocably changed the business of baseball using analytics.
Back in 2009, when Steven Soderberg was directing the film, the critics were calling this a niche movie with a purported $60M budget. But since then, with Bennett Miller taking the Director’s chair, this film is set to leap off movie screens across the country. This isn’t merely because they wrangled A-listers like Brad Pitt and Jonah Hill to star in the film, but because this movie has universal appeal. Baseball, business, and Brad Pitt.What brand doesn’t want to imagine themselves as the underdog who bucked the system and came out ahead of the game? Even the biggest brands will see the potential for doing more with less as depicted in the movie. And my guess is that many c-level executives will walk into their offices on Monday and ask who’s running their analytics. Brad Pitt is about to put the sexy into analytics. While, this parallels are somewhat different, I think that just like Pitt’s 1992 movie A River Runs Through It catapulted flyfishing to mainstream status, Moneyball will do the same thing for web analytics. While there may not be a flashmob at the next eMetrics event with newbies clamoring to become Certified Web Analysts, there will certainly be a widespread awakening to what we do.
The thing about Moneyball is that despite the fact that analytics enabled the team to recognize talent and even predict what/who was likely to be successful, it also reveals that running a business purely by the numbers doesn’t guarantee your win. This is akin to the debate ignited by my partner Eric T. Peterson about whether or not your business should be data-driven. While I agree with Eric’s argument on many levels, commentary from the other side of the argument penned by Brent Dykes makes a lot of sense too. I’ll go on record as saying that I do believe that both of these guys are trying to slice it too thin by getting into the semantics of analysis because they’re both right. What we do as analytics professionals requires a balance of data and experience. So the way I see it, both these guys are arguing for similar results. The Oakland A’s got the jump on most major league teams back in their day by using data for competitive advantage. But just like many of the stalwart directors and scouting veterans likely thought, it didn’t get them all the way to the world championship. In analytics too, we need to balance data with business acumen. Tipping the scales all the way toward managing by business experience and intuition won’t net big wins any more than managing purely by the numbers.
What we can take away from analytics and now thanks to the movie Moneyball is that data can gets us a whole lot closer to the answers. While Billy Beane’s character depicts a relentless pursuit of his goal using data, his visibly abrasive personality and callous nature of treating players reveals that balance is required. The fact is that analytics are everywhere in business today. In baseball, Billy Beane still works for the Oakland A’s and my beloved Redsox hired Bill James (another Sabermetrics guru), but many NBA basketball teams reveals that numerous big leaguers are employing interns, analysts and consultants to study the numbers. And of course, businesses too. For every digital proprietor, business-to-business operation, or consumer facing brand selling today; using data to understand customers and to improve digital marketing has undeniable allure. So, have we finally made it to the mainstream? Well, I think we’re close and that this movie will certainly help.
So the next time you’re explaining to your neighbor – or grandmother – what it is that you do for work … Don’t be surprised when they say “Oh, it’s like that movie Moneyball!” Just smile and say, “Yep, it’s something like that.”
There’s a great deal of fear, uncertainty, and doubt (FUD) in the hearts and minds of consumers regarding their privacy online. While not totally unmerited, this FUD is fueled by mainstream media sources like The Wall Street Journal and USA Today, that typically paint the issues with a stark black and white perspective. Unfortunately, this perspective corrals all advertisers, website operators, and would-be digital trackers into a single category of shameful voyeurs.
While some tracking practices may indeed be dubious, other allegations are accused of slander. Both scenarios are reason enough to give conscientious consumers pause, thereby placing your online business and the way you track customers in jeopardy. The root of the problem is a fundamental communication breakdown.
What’s Really Going on Behind the Privacy Curtain?
The majority of first-party digital measurement (“first-party” data is obtained by the entity that owns and controls the domain) is designed to improve the user experience online by making processes easier, enabling faster access to relevant goods and services, as well as offering time-saving conveniences for everyday users. These practices have been going on since the dawn of consumerism, and for the most part are tolerated and even appreciated by consumers as long as they adhere to some semblance of consumers’ rights. However, consumers must retain the right to shop, browse, and otherwise interact online in an anonymous manner if they choose to do so. Thus, the opt-out policy. But technologies today have inadvertently enabled ways to circumvent the opt-out by regenerating cookies (dubbed “zombie cookies”) or embedding locally stored objects into users’ machines. These practices are wrong and deftly explained and criticized in Eric T. Peterson’s whitepaper, “Flash LSO’s: Is Your Privacy at Risk?” (registration required).
The flip-side to first-party tracking is third-party tracking, (“third-party” data is obtained from the first party and typically not reasonably known to the end user). This data is often employed by ad-serving technologies as a method for targeting consumers. The primary objection to third-party data is that it can be used to track visitors across multiple domains (“history sniffing” or “daisy-chaining”), thereby creating a history of multi-site browsing behavior that reveals aggregate details on consumer actions unbeknownst to the user.
Most third-party data sources still don’t know names, nor do they profit from selling any personally identifiable information. Instead, anonymous user data is brokered to a slew of third-party advertisers, ad exchanges, ad networks, ad platforms, data aggregators/exchanges, and market research companies who work to serve up relevant content based on the websites users visited. I hate to break it to folks, but that’s how most content websites work. Visitors get free content, hosts deliver ads. It’s a trade-off that most of us are willing to accept. It’s also this trade-off that’s sucking any remnants of serendipity out of the Internet, because things just don’t happen by coincidence today; they happen by marketing.
If They Want Out, Show Them the Door!
The fact is that if consumers don’t want to be tracked, then you must offer them a simple and permanent way out for the wary. Of course, browsers can do this today and consumers can take proactive steps to delete cookies, but it’s still the responsibility of the business to offer choice. Your primary responsibility as a vendor or business is to educate your users through effective communication. This is where most of the confusion festers because vendors don’t provide easy-to-understand guidelines about how their technologies are designed to be used; and businesses often don’t educate their customers about how they treat personal data. As a result, technologies are used inappropriately and consumers feel violated by targeted content and there’s typically a whole lot of fingerpointing going on to pass the blame.
If you’re a business, it’s your responsibility to understand how the technologies you use for digital tracking work, but also to give consumers a choice regarding their ability to remain anonymous and to opt out of all types of tracking. For first-party data collectors, this should be a relatively straightforward exercise; don’t retain customer information if they don’t want you to. If you need more guidance on the right thing to do as a practitioner or data collector, visit the Web Analytics Association’s (WAA) Code of Ethics that outlines the core tenets of ethical first-party, data-handing practices.
For third-party data collection, organizations like the Network Advertising Initiative (NAI) or the Digital Advertising Alliance (DAA) offer third-party opt-out choices for consumers. Consider joining one of these coalitions to join the ranks of the self-regulated. Alternatively, you can brush up on third-party data collection guidelines issued by organizations like TRUSTe, who act in the best interests of consumers by offering guidance on what to do and what not to do regarding digital data collection.
Create an Action Plan for Maintaining White-Hat Digital Tracking Practices
Finally, the best thing that you can do as a vendor, a marketer, or a business is to operate above the fray of privacy pundits by following a few key principles. Take these steps to use digital tracking in the way in which it was designed and to deliver value for your customers and your business:
1. Understand the technologies. While this sounds relatively basic, you must know what the technologies you build or deploy are capable of doing. While getting inside the minds of the devious shouldn’t consume all your time, vendors should issue guidance for utilization as well as educate constituents about how technologies function.
2. Keep PII safe, secure, and private. It should go without saying that keeping customer data safe and private is a top priority, but go beyond offering lip service and spell it out for consumers. Demonstrate how you protect and secure data by communicating to your audience about the measures you take to do so and instill confidence by provisioning multiple safeguards.
3. Divulge data usage practices. If your business is collecting and utilizing first- or third-party data, make it known by divulging your practices in clear and readable language. This requires keeping the legalese to a minimum and offering consumer-friendly policies and explanations for what you’re trying to accomplish. Transparency is the best practice here, so explain what you’re doing and how visitors benefit.
4. Empower consumers to opt out. This one bears repeating…give consumers a way out. And for crying out loud, don’t opt them back in if they don’t request it. This is potentially the biggest threat to online privacy today and as more and more organizations abide by consumer preferences, the ones who don’t will be outed and ultimately tarnish their reputations.
5. Spread the word. The Internet offers many incredible opportunities for networking, commerce, education, and entertainment, but collectively we must act as stewards of consumer data. Perhaps I’m naïve, but I believe that most data collectors are ethical and simply need to do a better job of describing what they’re up to and where the value exchange exists for consumers.
I personally applaud researchers like Ashkan Solanti and Jonathan Mayer for the work they do and for keeping vendors honest about the realities of their digital tracking applications. We need more education and we desperately need to voice the digital measurement side of the argument to crystallize the validity of what we do as analytics professionals.
The online privacy discussion won’t dissipate anytime soon, so the best we can do is communicate effectively, demonstrate value, and offer choice. Do you agree?
The following originally posted in Exact Target’s 10 Ideas To Turn Into Results report. It’s part of their Letters to the C-Suite Series and this is my letter…
To The Executive Team:
Do you even know who your customers are anymore? Chances are, you probably don’t. You
catch fleeting glimpses of them as they open your emails or pop onto your website for a quick
visit. You might even momentarily engage with them when they drop into your store to browse
around or see your products firsthand. Or maybe you meet them ever so briefly as they feign
interest in your brand by “liking” something you posted on Facebook.
If you’re doing it right, your business is collecting feedback across many customer
But you only really hear them when they shout from the rooftops, irate and full of vim. That’s
probably where you begin to learn what’s on their minds. But do you even know that it’s the
same person who was showing you all that love during your last promotion? Probably not.
In actuality, few companies really know their customers. Whether your customers are end
users or other businesses, how they interact with your brand, where they discover new
information, and how they communicate is changing at an astounding rate. Customers
are increasingly unaffected by traditional marketing conventions, and their tolerance for
redundant messaging, static content, and conflicting brand information is nonexistent. They
don’t see your organization like you do—in departmentalized silos of categories, products,
business units, and operating divisions. To them, you’re just that brand they either love, hate,
or treat with ambivalence. That is, until you knock their socks off by impressing them with your
service, support, and relevance. Yet, to really deliver value to your customers, you need to get
to know them. This starts by remembering the interactions you have with them and building
off of these activities.
Digital communication is the new reality, and treating customers through digital channels is
synonymous with how you’d treat someone you meet in person. Listen to what they’re saying
and respond with appropriate dialog. But most importantly, remember these things (because
upon your next conversation, your customer might just remember you):
• Your memory of customers exists at the database level.
• By maintaining customer profiles and appending them with attributes that contain history,
activity, and propensity (among other things), you can truly begin to have meaningful
• To do this effectively, the database must contain information from all your touch points.
This includes transactional systems, web analytics, call centers, mobile devices, social
media, ATMs, stores, email systems, and whatever else you’re using to reach out.
Bringing your data together through integrations enables you to achieve a holistic picture of
your customers. A little scared by this? Well, you should be. Customer behaviors are going to
fundamentally change the way you engage with your audience. If you’re not equipped, they’re
going to take their conversations (and their wallets) elsewhere. By integrating your data, you
open opportunities for new customer dialogs.
Okay…I’ve been quiet about the Coremetrics acquisition by IBM for long enough now. While the dust still won’t settle until sometime in Q3’10, when this deal passes FTC scrutiny, I’m compelled to weigh in and offer my $.02 USD mainly because there’s been some good dialog in the blogosphere from people I respect like: Eric, Joe Stanhope, Akin and more recently Brian Clifton.
I’ll take a slightly different approach and use the acquisition to talk about the state of the web analytics marketplace. For starters, let me just say that this acquisition was inevitable. So too will Webtrends be acquired by some player looking to incorporate metrics into their overarching set of technology capabilities. And as I blogged earlier this spring, yet another even bigger fish will eat the existing big fish and we’ll utter oooh’s and ahhh’s as the analytics technology market evolves into a vital organ for all businesses with a heartbeat. While not immune to arrhythmia, this course of events shouldn’t really take anyone by surprise. I’ve been saying this for a while now and even penned “Web Analytics is Destined to Become an Integrated Service” back in May 2009 when I wrote the Forrester US Web Analytics Forecast 2008-2014 (subscription required). I’ve been advocating web analytics as a function within the marketing organization, which seems to be a logical orientation. However, it’s interesting that the consumption of analytical technologies has come from a smattering of different perspectives.
Here’s how the post-acquisition landscape looks:
Adobe’s acquisition of Omniture undoubtedly took many by surprise (myself included – although you’re never allowed to admit surprise as an analyst). The promise Adobe made to investors was that they would incorporate the market leading web analytics technology into the creative life-cycle by enabling measurement at the point of content creation. Perhaps that’s not exactly how they positioned it, but that was my impression and they’re now executing on that promise. Say what you want about acquisitions and the slow moving integration process, but Creative Suite 5 debuted in April just six short months after the deal closed, with measurement hooks from FlashPro and Dreamweaver into both SiteCatalyst and Test & Target. They’ve also accomplished this remarkable feat using a visual interface allowing content editors and non power-users the ability to begin measuring their digital assets. This utilization of analytics places measurement at the operational level, yet by and large it’s still within the marketing group.
The Marketer’s Toolbox…
Enter Unica with their rebranded Marketing Innovation product suite where NetInsight (formerly Sane Solutions) web analytics sits at the core. While both Omniture and Coremetrics made pre-acquisition strides to amass a truly effective online marketing suite, they were merely playing second fiddle to Unica Campaign, Interact and Marketing Platform solutions. Unica is widely acclaimed as a leading Campaign Management tool and sits proudly in the marketing departments across many an enterprise business. They’ve worked web analytics into the DNA of their overall marketing perspective and use it to power the automation and decisioning that many organizations strive for with lust and admiration. Their utilization of analytics really does empower analytics as a lynchpin for integrated marketing.
With speculation still swirling about the how’s and why’s of IBM’s intended use of Coremetrics, it’s tough to ignore Coremetrics’ strength in the retail vertical. While Coremetrics has an impressive client based outside of retail, including publishers and financial institutions among others, they’ve clearly got some good mojo going with their triple-A retail clients. Just thinking of how Big Blue will assimilate the nimble teams of relentless Coremetrics marketers in San Mateo and Texas makes me slightly nervous. Not for any loss of focus by the Coremetrics team on their dedication to client support or from their delivery of leading analytical capabilities that they offer – rather – where will this newly acquired asset live within the IBM estate? The way I see it, two possible scenarios can play out here:
1. First is the scenario that Akin speculates upon whereby IBM is folded into the Websphere group and serves to illuminate the value of customer interactions within website platforms across IBM’s customer base. This would greatly benefit Websphere customers although it would narrowly define a finite application of a technology that is so much bigger than just online commerce.
2. The scenario that Eric envisions (and one that I believe would benefit our industry exponentially) is the one where IBM becomes the “business analytics” juggernaut in the enterprise. If this were to occur, IBM would need to integrate its SPSS and Cognos acquisitions to get really crafty about delivering extremely high value digital insights.
These are two very different outcomes and both speculatory, but I’m rooting for the latter simply because it has the potential to push analytics so much further along. My sources tell me that some long-time IBM’ers feel this way too. One confidant with access to IBM brass even shared with me that internally the acquisition will be deemed a failure by some at IBM if Coremetrics isn’t integrated with SPSS and Cognos. That’s great news, because wholesale failure of business analytics isn’t an option.
So here we have Webtrends as the only standalone web analytics player remaining from the set of truly original US-based technologies. They’re doing a good job of playing the part of Switzerland as they not-so-quietly establish a platform of Open Analytics whereby data flows in -and- out of the interface fueling other operations around the business. While this is not the same as an integrated approach, Webtrends is taking a strong stance on have-it-your-way analytics. Their open APIs and REST URLs make it easy to leverage their data collection and pump data to any application within the enterprise. Thus, they too offer an integrated approach yet do so by maintaining a position that supports rather than delivers the adjacent marketing functions.
The Low End Theory…
Any post about the state of the analytics marketplace would be remiss if Google Analytics wasn’t included in the conversation. I include the Big Googley in the Low End Theory – not because they’re trailing – but because they’re sneaky smart. Just in case you haven’t been watching, since Google acquired Urchin Software, GA has been quietly amassing millions of installations across businesses large and small adding to the democratization of web analytics. I’d argue that they’re not doing this in a concerted enterprise-wide way, but they are probably gaining the most ground across the enterprise by sheer adoption and hands-on utilization. What this means is that pockets of users are deploying Google Analytics for very focused use of the data and the organization is becoming more accustomed to seeing GA data and using it to make key decisions in their day-to-day operations.
Many other analytics programs are delivering similar value to business users, yet in an extremely isolated manner with tools like KissMetrics, Twitalyzer, Visible Measures and Radian6 just to name a few. This is truly the low end theory because the data is rarely seen by anyone outside the marketing group, but it’s driving key activity around specific marketing functions without the larger business really taking note. Think grassroots baby – under the radar – with potential super smartie effectiveness.
Can Marketing Come from the Heart?
By now you should be asking yourself; So where’s this all going? Despite how each of the companies I described above fit into the overall aspect of a company’s business, I think that we can all agree that analytics is about understanding business performance. Here is where Eric’s vision of the Coming Revolution in Web Analytics fits into the story and the quietly powerful behemoth that’s already penetrated the enterprise garden sits in wait down in Cary, North Carolina. Whether it’s SAS, another player, or an amalgamation of services from multiple players – analytics needs to be at the heart of the organization. Here’s where my analogy pays off…because if this is to happen, then data becomes the lifeblood of the enterprise and analytics allows companies to relate to their customers and offer more tuned in and relevant products and services. Marketing should control this blood flow but use it to power the brain and the working limbs of the organization. While this may start to look like Business Intelligence, I believe it’s different because it requires real-time information, automated decisioning and ultimately creativity. These are qualities that I have yet to see from a BI tool. But maybe I’m naive.
Before this diatribe gets any longer, and you dear reader need resuscitation I’ll call it quits. But I’ll offer fair warning that this is just the beginning of my thoughts on the matter and there’s more to follow. I’d also love to hear what you think.
Building a Culture of Measurement is the title of the Keynote “Sprint” I’ll be delivering at Webtrends Engage next week in New Orleans. Like the other distinguished speakers during the keynote, I’ve only got 10 minutes to deliver my message and then get off the stage. Ten minutes isn’t nearly enough, so I thought that I’d elaborate here on my blog to hammer out the concepts behind my presentation.
Let me put it right out there and state that culture isn’t built overnight. And changing culture takes even longer. So save your get-rich-quick schemes for some other ponzi project. There is no quick fix for business culture because it exists in the ethos of your organization, not in the conference rooms, offices and cubicles. Culture consists of values, beliefs, legends, taboos and rituals that all companies develop over time. Attempting to force culture will most likely result in failed efforts and an ingenious solution. Instead, organizations that don’t have an inherent culture of measuring their marketing efforts must ingrain some key measurement enablers into the system.
Know your surroundings – know your audience. Start by understanding what you’re working with by taking a realistic assessment of your organization’s culture. This may be easier for an outsider to gauge who can spot promise and dysfunction much more quickly than the tenured veteran who is so ingrained within the culture that it’s a part of their daily routine. In either case, taking a realistic assessment of how the company utilizes data and reacts to data-driven ideas is the launching point.
Find levers that trigger change. Once you’ve assessed the situation and gained your bearings, then you need to find out what motivates individuals and business units within the organization. Measurement is largely about producing results, so if there are decisions being made in absence of data, perhaps digging up some examples of bad decisions with proof from historic data might offer some subtle hints about operating differently. Yet, all companies operate differently, so if yours is one that wouldn’t react well to this tactic, then figure our how to push the buttons (whether positive or negative) that will affect change.
Always ask why. Not so much in the way that a three year old persistently asks…why? Why? WHY? But more so to determine if data requests and new projects have a well thought out plan with measurable goals. So much of what we do in analytics is founded on having clearly defined objectives and goals that it is imperative for web analysts to enforce their clarity by insisting that data has a purpose.
Once you’ve established what you’re working with, the next step is to develop a measurement strategy that meshes with your culture. I advise my clients to create a “Waterfall Strategy”. I introduced my concept of the Waterfall Strategy in my manifesto, so I won’t attempt to recreate it – here it is:
Strategy Credo #8: Establish a waterfall strategy. By this I mean strategy should flow from the headwaters of the organization and align with the corporate goals set forth by the executive team. Once your measurement team is clear and united on the goals, then identify objectives as the next tier in your waterfall that supports the corporate goals (these are your business promises). The base of your waterfall strategy consists of the tactics. Tactics are the actual campaigns and programs that emerge from your marketing machine (your creative promises). Each tier within the waterfall has specific metrics that indicate success. These metrics must be clearly defined and baked into the system at all levels to ensure proper measurement. It’s also critical to recognize that neither you nor an external consultant is likely to change your corporate goals, but you can refine the way in which you get there.
The third effort that you must undertake when attempting to build a culture of measurement is to make your data sing. And no I don’t mean going on American Idol or belting out karaoke at your next company function. Here I’m talking about the ability to tell a story with your data. Think about culture for a minute here…it’s built on stories. You need to become a story-teller within your organization and find the narrative within the data. Communicate to your constituents not with numbers and spreadsheets, but with examples of how their efforts and activities contributed to the success of the organization. In doing this, you will create heroes and legends within your organization who earned their status through data. The next thing you know, others will be knocking at your door and asking for metrics and measures to show the brilliance and success of their projects. You’ll inherit a whole new set of problems when this starts to happen, but we can tackle that at another time.
So that’s my story and I’m sticking to it. Please let me know your thoughts and how you’ve built a culture of measurement at your organization.
Oh yeah, if you’re going to be at Webtrends Engage next week please seek me out and let’s talk about building a culture of measurement; my concept of the waterfall strategy; or simply share a story over a cup of coffee. If you haven’t seen it yet, Webtrends has built out an awesome site for networking with fellow Engage attendees, so let’s meet.
See you in NOLA!
**Update** Here’s the video courtesy of Webtrends of my official presentation.
One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being “Amazon-ed,” which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on Amazon.com. This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping.
One of the most valuable ways to be sure your recommendations are heard is to forecast the impact of your proposal. Consider what is more likely to be heard: "I think we should do X ..." vs "I think we should do X, and with a 2% increase in conversion, that would drive a $1MM increase in revenue ..."
I am delighted to share the news that our 2014 “Advanced Analytics Education” classes have been posted and are available for registration. We expanded our offering this year and will be offering four concurrent analytics and optimization training sessions from all of the Web Analytics Demystified Partners and Senior Partners on September 16th and 17th at the Cobb Gallaria in Atlanta, Georgia.
In working with a client recently, an interesting question arose around cart additions. This client wanted to know the order in which visitors were adding products to the shopping cart. Which products tended to be added first, second third, etc.? They also wanted to know which products were added after a specific product was added to the cart (i.e. if a visitor adds product A, what is the next product they tend to add?). Finally, they wondered which cart add product combinations most often lead to orders.
As an analyst, your value is not just in the data you deliver, but in the insight and recommendations you can provide. But what is an analyst to do when those recommendations seem to fall on deaf ears?
If I could give one piece of advice to an aspiring analyst, it would be this: Stop showing your "math". A tendency towards "TMI deliverables" is common, especially in newer analysts. However, while analysts typically do this in an attempt to demonstrate credibility ("See? I used all the right data and methods!") they do so at the expense of actually being heard.
I'm always amazed (read: dismayed) when I see the results of an analysis presented with a key set of the results delivered as a raw table of numbers. It is impossible to instantly comprehend a data table that has more than 3 or 4 rows and 3 or 4 columns. And, "instant comprehension" should be the goal of any presentation of information — it's the hook that gets your audience's brain wrapped around the material and ready to ponder it more deeply.
This post (the download, really — it’s not much of a post) is about dealing with exports from Facebook Insights. If that's not something you do, skip it. Go back to Facebook and watch some cat videos. If you are in a situation where you get data about your Facebook page by exporting .csv or .xls files from the Facebook Insights web interface, then you probably sometimes think you need a 52" monitor to manage the horizontal scrolling.
Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries.
Funnels, as a concept, make some sense (although someone once made a good argument that they make no sense, since, when the concept is applied by marketers, the funnel is really more a "very, very leaky funnel," which would be a worthless funnel — real-world funnels get all of a liquid from a wide opening through a smaller spout; but, let’s not quibble).
Those of you who have read my blog posts (and book) over the years, know that I have lots of opinions when it comes to web analytics, web analytics implementations and especially those using Adobe Analytics. Whenever possible, I try to impart lessons I have learned during my web analytics career so you can improve things at your organization.
I am excited to announce that registration for ACCELERATE 2014 on September 18th in Atlanta, Georgia is now open. You can learn more about the event and our unique "Ten Tips in Twenty Minutes" format on our ACCELERATE mini-site, and we plan to have registration open for our Advanced Analytics Education pre-ACCELERATE training sessions in the coming weeks.
I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.
Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I’ve found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.
If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.
In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.
I had the pleasure last week of visiting with one of Web Analytics Demystified’s longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.
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 ...
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?" ...
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."