A few months ago I had the opportunity to lecture in the content engineering class at the University of the Pacific (UOP), where I’m an executive-in-residence for digital marketing and media and where I have helped design the curriculum. For the last few years, I lecture every semester on the newest technology-drivers
What a difference a few years make and I am pleased to say that change is afoot at the university and across the U.S., as schools are now building programs geared to meet the critical requirements of business in some of the most sought-after areas of marketing: social media, content marketing, and content analytics, where clearly an imbalance exists between skills taught in classrooms and the skills sought in the marketplace. A few years ago many of the students questioned the value of analytics.
This time I presented my case study of a consumer packaged goods (CPG) company using cross-channel analytics, correlating online and retail behavior, utilizing mobile apps, iBeacons, and augmented reality. I’ve written about it here. In class we mapped out just where content was created, who created it, and where measurements are needed. We talked about CPG company goal of increasing lifetime value (LTV), greater customer engagement, and we designed calls to action and discussed and identified conversion events and stages. We talked about metrics for augmented reality, profile information, and purchase history and their relationship and how we look to normalize structured and unstructured information in analytic applications. We discussed what person in the process is responsible for what action. They get it.
Universities and their business partners are indeed looking for ways to leverage their current curriculum toward a higher technological quotient. They see the need to produce graduates with the skills geared for today’s competitive environment. Yet teaching “Internet marketing” and “how to” classes – most often a minor repackaging of traditional marketing – is only a quick fix when instead we need an earth-moving overhaul at a foundational level.
For the last four years, UOP has been creating a program that addresses the needs of graduates, one that leverages the university’s existing curriculum in business, liberal arts, and engineering, but also remains adaptable to train students in new competencies as the market changes dynamically. When we started the program, few students in the liberal arts school studied the scientific method of test and control or used analytic tools. Today, students in the content engineering course build websites and use analytic tools to test and optimize the results. Some sites have thousands of visitors a day. They are learning that creativity without conversion equals zero.
We need to create a stronger culture of measurement in higher education, one that is market-based and rewards innovation. Anthony P. Carnevale, director of The Center on Education and the Workforce at Georgetown University, has called for a fundamental shift in thinking about the way students are educated. He writes, “The old model, where you go to college and then go out and find a job, is largely outmoded. It needs to be replaced with a new model, in which college years are spent explicitly preparing for an occupation.”
U.S. higher education – long a source of pride and differentiation across the globe – is undergoing a true crisis of value and identity. Pundits wonder whether universities are the next “bubble” of the U.S. economy, and university students are questioning whether their high-priced education and gargantuan debt loads – up more than 50 percent after inflation from a dozen years ago – will position them for a college-worthy career.
Says Jim Sterne, chairman of the Digital Analytics Association, “The need for analysts and content engineers who can determine the value of content is so great that our association’s online courses have been steadily sold out since inception in 2006. When times are good, companies invest in tools and systems.”
The market needs well-trained content marketers who can create compelling content and measure and optimize that content using new analytics, predictive modeling, business intelligence (BI), marketing, and content management tools.
Marketing students should be given rigorous, cross-disciplinary training in writing, analytics, and technology; engineering students should be taught to create content; and English, journalism, and communications students should be taught about optimizing content for business value.
“I’m pioneering content engineering in our English department focusing on teaching the latest tools of analysis, analytics, and optimization alongside traditional writing and marketing techniques,” says Dr. Eric Sonstroem, the UOP English department chair. “I’m determined that my students really understand how content works on the Web, how it can be tested and measured, and how you can act on the data you get back.”
While classroom teaching is critical, UOP is also in the planning stage of a “hands on” content lab for its new campus in downtown San Francisco, opened last month near Twitter, Adobe, and in the city’s start-up SoMa district. It is envisioned that the lab will create, utilize and test software applications, conduct research, and educate students on content creation and analytic applications, and that students will intern within the tech community headquartered in the area.
UOP is certainly not the only institution of higher learning to address the data-driven marketplace needs, but the one I’m most familiar with, and other schools are today launching their own programs. Innovation is part of the U.S. DNA, and has been for centuries. Our institutions of higher education have been moving at a glacial pace but it’s beginning to thaw as the heat of market demand for digital sophisticates is “melting” the slow rate of change in higher education. We need to turn up the heat.
A recent article in VentureBeat said that marketing automation tools had only a 3 percent penetration rate at non-tech companies. Meanwhile, marketers are clamoring for ways to act upon data.
More or less, the weakest link in the chain of digital analytics has been the “make necessary changes” part. It’s now been several years since marketers began to understand that having the information alone really didn’t help the business. Recommendations became important. And after recommendations, then action.
Action is messy. It hasn’t had much to do, until recently, with automation. It required getting marketers, developers, creatives and business owners to agree on what changes were needed based on the data. And then the often too-laborious process of actually implementing the changes and trying to tell if there was a meaningful difference in the before and after states. Too often these efforts fell apart in partisan bickering between teams and refusal of many to take risks.
When we talk about marketing automation today, we are referring to SaaS offerings like Eloqua, Hubspot, Leadsius, Act-on and others that build a form of call-and-response matrix into marketing efforts. The easiest way to understand this is to compare it to what used to happen if you were reading a comic book when you were a kid, and saw an ad to “send away” for something either free or cheap. You would do that, and then you’d get more offers from the same company in the mail, as they hoped you’d soon spend more.
Much more dimensional and sophisticated versions of this are being played out by marketing automation tools, and according the the VentureBeat article, there’s plenty of room to grow.
A recent example of how one company is addressing a call for marketing automation is Tealium’s AudienceStream. Tealium already has a key foothold in the tag management industry, and that puts it at an important juncture of data collection. AudienceStream links the collected data from many sources (legacy of Tealium’s TMS) and allows the marketer to quickly set rules, thresholds and triggers that communicate via new APIs to marketing-action software already in the market. In other words, an AudienceStream powers an Eloqua. Once the rules are set, AudienceStream can communicate with a tool like Eloqua and help determine what message goes out to what user without continuing human intervention.
We’re not at the stage yet where entire site pages and app screens are being re-made on the spot based on very fresh data. We are at a stage where certain updatable modules on sites, and certain marketing messages can be automated and substituted based on data. The reason why this market sector has such growth potential is that it actually fixes a real problem.
While we’ve had lots of time to gnaw on old chestnuts like page views and unique visitors, we’ve hardly gotten to a point where we can say we’ve got organized, incremental methods that improve marketing velocity. And we know that most of the friction comes from friction between different teams with different agendas.
Marketing automation has no agenda except to respond to data and seek a return on marketing content. It frees up humans to do more strategic work. It may have only a small percentage of the market today, but as marketers get more and more familiar with successes based on these tools, that percentage is likely to begin growing rapidly in the near future.
Think of 2014 as the year when marketing automation finally got some of the recognition it deserves.
I was sitting in my office last week working on a targeted email when I realized something so fundamental – it’s a bit embarrassing to admit. As a data-driven marketing guy, you’d think I’d realize the most fundamental building block of any conversion starts with accurate “top of the funnel” CRM contact data. With garbage in you only get garbage out.
There’s a lot of talk about convergence of all things. There’s the convergence of all the systems we use, and the convergence of new roles – especially those of marketing and sales and building a culture of measurement.
As marketers struggle with all of the new tools, we need to review the most fundamental component of marketing, yet one of the most overlooked – quality information, our small data. Without good contact information these systems are just plain dumb, and they cost us more than they help. According to Gartner, contact information ages up to 50 percent in any year, becoming inaccurate and out of date, only serving to compound the issue. The top of the funnel data just has to be solid. And we have to be agile and act on it quickly.
A Typical Day
At our consulting company, Efectyv Digital, we use a number of tools to help us target and engage our current customers, and find new ones. We use a marketing automation system, in our case HubSpot, and a bunch of Google tools, SEO, pay-per-click (PPC), analytics, and Viralheat for social analytics. We also use various email products so we can test and optimize the send and open rates with tuned messages.
Like many of our clients, we are a B2B firm; we build marketing lists and segment and send targeted emails about our services to specific personas from those lists. The messages vary by role, industry, and need. They contain calls to action and other things you’d recognize as conversion events. We track our funnel and outbound conversions – and let me say we could do much better. Our list bounce rates are high and our open rates are low. We’ve hired an outbound lead generation person, and we’ve seen similar results.
We wondered if the issue was with our contact data or perhaps it was our offers and messages or timing (we just started using alerts)? To test, we decided to start one step at a time, and look specifically at the contact information contained within a few of the popular lead-generation tools to see why our conversion rates were so low.
It didn’t take us long to confirm, as we suspected, that our data just sucked and we needed to start making it better. Here’s our analysis. At least step one. We’ll always work on our messages.
The Simple Test
While there are scores of products on the market, including LinkedIn, Zoom, and One Source, and some great new start-ups that have various degrees of content mash-ups like Tempo and Refresh, we chose to test three of the more popular systems that come integrated with CRM systems, including D&B 360, which has contact and company information – mostly generated manually; Data.com, the roots of which are crowdsourced with Jigsaw data acquired by Salesforce; and InsideView, which claims to rely on technology to deliver results. The levels of integration vary, depending on the CRM system, Dynamics, Oracle, Sugar, or Salesforce.
We used a real person and a real institution, in this case Krystin Mitchell, senior vice president of human resources at 7-Eleven Inc. Since 7-Eleven’s revenue is in excess of $80 billion and they’re public, we thought they might be a good test to see how we can find her in our test systems.
The results. So, where is Krystin? According to their current company Web page, she is indeed at 7-Eleven, but according to Data.com Krystin Mitchell is not included in a “Find Contacts” 7-Eleven search results. When we broadened it, we found there were 16 wrong results with her name, company, and email address. That’s crazy and not acceptable. I can see why our emails bounce.
We then tested the trusty old saw, D&B 360. Since much commerce is based on its data, it has to yield accurate results, right? D&B is the gold standard of contact data, the truth, built with human editorial control so we thought we’d get correct results. But, even with D&B, Krystin Mitchell is not included in “Build a List” of custom search results…although this time 65 wrong contacts came up instead.
To find her we needed to do a “general people in search,” but like in Data.com it yielded multiple/duplicate results, and different types of wrong contact info that tends to defeat the whole purpose of a contact tool. Interesting and again not acceptable. More bounces and wrong numbers.
We then moved to our free version of InsideView, which works with Salesforce.com. They are one of the companies that include data from multiple sources (thousands), and then validate it through a technology they call “entity triangulation.” Using analytics, this process is designed to determine the relative “truth” about people, content, and key event information. For our test about Krystin they got it right, listing her correct title and correct company and contact information, which match the company website and public disclosure. It was CRM Intelligence and we now use their Target product to build our lists and are getting much better results.
All in all we’ve analyzed vendor results many times, testing with different contacts, and companies and a great percent of the time fundamental results were different between vendors. I admit it’s hard to do, to really know where someone is, but that’s what we need and I am optimistic that there are new tools just coming to market, like marketing automation company Autopilot, releasing a new prospecting tool for sales and marketing that they claim is generating up to 42 percent reply rates on cold emails.
So, while this column is about conversion marketing and analytics, and I usually write about more meta subjects, I thought I’d share some personal real world issues that impact marketing, and ultimately sales. In this case our sales. We expect to at least double our conversation rate by spending more time creating quality data and lists.
We are drowning in data. It is no simple feat to filter this sea. But, it seems to me that we need to get the basics right about “small data” before we talk about optimizing big data, real-time data, and the impact of attribution models. B2B or B2C, quality contact information is fundamental. It’s best to walk before we run and finally sprint to the holy grail of real-time conversions, and revenue falling from the trees.
Images via Shutterstock.