Author Archive

02
Apr

The Internet of Things Starts With Agility Everywhere

Written by Rand Schulman. Posted in Internet

analytics-shutterstock-139983946-185x114The Internet of Things is all the rage with marketers today, and for good reason. Connecting to customers everywhere lets us marketers deliver more consistent and meaningful experiences. But behind the scenes, mobile apps, the lifeblood of these devices, operate at a fundamentally different pace than the traditional Web.

With the Web, tag management systems have become de-facto tools for enabling marketing agility and eliminating dependencies on IT cycles. But for many brands, marketers find themselves bound to development cycles all over again in the world of apps. By definition, apps are “compiled,” meaning they are hard-coded just like websites of the old days. When marketers want to extend technologies on mobile apps, they must typically rely on app developers to hard code each technology’s software development kit (SDK). With this very manual SDK approach, marketers have to wait for app developers to compile the app, wait for the app store to accept the new or revised app, and then wait for consumers to download an app update. Unlike today’s Web, marketing through the Internet of Things is hardly agile.

The Internet of Things represents an additional challenge as each “thing,” or device, is both a consumer of information and an originator. Our phones act more like devices and our devices act more like phones. Devices range from TV sets to mobile phone and computers; from clothing or wearables like Fitbit or Google Glass, to something movable – your car, plane, bike, tennis racket, and toothbrush that all report collected data from the device. So how can a marketer leverage this data for better marketing everywhere, and deliver more meaningful experiences on these “things” in the first place?

While I’ve noted the aggressive move to mobile in the last 12 months by many leading companies, some companies are technically making this all work, and they’ve been able to remain agile even in compiled mobile app environments. Companies with a forward-leaning omnichannel vision, such as Getty, Vivint, and United Airlines, to name a few are taking a “real-time” and a “no-SDK” approach to their apps and delivering excellent omnichannel experiences for their users. In other words, their apps are making a tangible impact on customer conversion, engagement, and loyalty — and they’re not slowed down by old-fashioned app development processes. So, in the static world of apps, how are they able to succeed where others struggle?

Ensighten chief executive (CEO) Josh Manion notes that delivering optimized omnichannel experiences is at the heart of today’s marketing. Marketers must insist on agility in any environment, including native mobile app environments. “For a marketer to settle for anything less does a disservice to both marketers and their customers. The ‘Internet of Things’ opens worlds of possibilities, and marketers should not be held back,” Manion says.

Indeed, many enterprise companies doing a killer job with their apps have adopted Ensighten’s no-SDK approach to omnichannel marketing.

Gamers Drove Mobile App Development

So how did the Internet of Things evolve, and agile app marketing become a requirement for marketers? Mobile game developers drove the development, starting with mobile analytics to drive quality gaming experience. According to a recent Wall Street Journal report, Zynga really considers itself an analytics companies only “masquerading” as game company. Their key: Analytics drives game content around conversion over time. In other words, they are leveraging data to drive better in-app experiences.

The early use of these app tags was built into the game platforms along with analytic reporting tools – the platform or walled garden included content creation, data collection, analysis, and reporting in one place. It was a closed ecosystem that gave marketers what they needed to create better gaming experiences; but the ecosystem was still confined to the hard-coding process and all that waiting we talked about earlier.

Companies like Mixpannel, Segment.io, and Kissmetrics have built tools to optimize mobile apps, and are creating new metrics for mobile developers that are becoming as common as any page metric for the Web developer utilizing user location, trends, demographics, and a host of personal information as we have “opted in” to the device and it is co-located with all of us all of the time.

It is the use of this data, based on “the law of large numbers,” combined with personal information and aggregated across user cohorts – age, location, time of day – that yields powerful trending information giving developers keen insights into the use of their product in very short timeframes – hours and days instead of weeks and months. Metrics like Daily and Monthly Active Uniques (DAU and MAUs), and the tracking of user engagement within the app by cohort, now drive app developer decisions about features and the monetization of the application.

Yet, these new measures create new challenges, says Andrew Edwards, managing partner at Efectyv Digital. “If it were hard enough to compare page views to page views across one brand and another, it’s harder still to determine rough equivalents in mobile apps. Organizations will have to pay even closer attention to their KPIs in mobile, because there are no standards.”

But again, deploying technologies with the app in an agile fashion –with no hard coding, or waiting — remains a major obstacle. Yes, deploying analytics within an app is important. With a rigid SDK approach, all that analytics data must be pre-planned so it can be hard-coded in the first place. In contrast, Ensighten’s agile approach to mobile app marketing doesn’t require pre-planning and lets marketers change their mind, test new tools, and live the agile lifestyle they are used to.

Best of Breed Mobile Innovators

In the last quarter I’ve spent time meeting with and listening to some of the largest brands in the world talk about their direction and key use of mobile as part of their omnichannel strategy. What they have in common are the following:

  • They are rapidly adding mobile applications
  • They are becoming data-driven organizations and are creating data-driven processes
  • They are transforming and re-aligning their organizations around data-driven, omnichannel demands by adding the right people and processes
  • They are staying as open and agile as possible as the rate of change is monumental and unpredictable

Two of the brands I mentioned earlier – Getty and Vivint – are leveraging a no-SDK approach to mobile app marketing, and this has helped propel them as leaders in the space of omnichannel marketing. As mobile app game developers drove games, Vivint is driving the Internet of Things, starting with the connected home, and is a great example of thought leadership and mobile app vision. Vivint is serving hundreds of thousands of customers with smart technology for home security and management by bringing together omnichannel data to drive interconnected experiences. They have a three-fold mobile app strategy that to drives their vision of a connected smart home:

  • Start slow and learn with a few basic mobile applications. Keep customer satisfaction high and measure with NPS and in-app metrics.
  • Understand, test, and optimize customer engagement metrics within the app experiences and build KPIs with users and applications using analytics
  • Broaden app offerings to other devices in the home, while keeping agile and responsive to customer needs. Let data drive their mobile app experience decisions.

Similarly, Getty is going “all in” with the agile, no SDK approach to mobile app marketing. Having moved from Web-based marketing, Getty is now creating a suite of mobile applications with a goal toward increasing customer engagement and loyalty. To do that, Getty is rapidly creating applications that engage their loyal visitors with their stunning images. More visits/time on the app means more customer loyalty for Getty. And more revenue. They are re-crafting their organization to support their mobile efforts.

In this new era of the Internet of Things, the age-old struggle for marketing agility presents fresh challenges. Understanding how to keep an agile pace, innovate on traditionally constrictive platforms, and deliver the right experience across a range of devices is key to today’s marketing mix.

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01
Mar

Creativity Without Conversion Equals Zero

Written by Rand Schulman. Posted in Analytics

conversion funnel strategiesA 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.

16
Apr

Your Small Data Just Sucks

Written by Rand Schulman. Posted in Convergence Analytics

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
people-search-shutterstock-128521253While 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.

Conclusion
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.

money-falling-shutterstock-98093063We 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.

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