Wednesday, November 2, 2016

How to design more ethically engaging experiences

Hi. My name is Neil, and I’m an addict. I’ll admit it, I’m addicted to technology, and you know what, I suspect that you are too. We’re all addicts now aren’t we? We’ve all become addicted to a very modern drug called technology.

It’s not our fault that we’re addicted to technology, we’re only human after all. You see technology is just too damn addictive. And why is it so addictive? Because it’s been designed to be so by designers like you and me. It’s been designed to engage, to demand our attention, to draw us in and to slowly but surely get us hooked.

In this talk from UCD 2016, I’m going to argue the case for why we as designers should be helping to break this cycle of addiction. Why we should be focusing on making a positive impact on peoples’ lives, rather than chasing ever greater usage of our products and designs. I’m going to show you how to create products that are more ethically engaging; that let people get on with their lives without becoming a slave to the machine!

from UXM

5 ways to use personas in your projects

I wonder, now that teenagers spend more time staring at their glowing mobile and computer screens, than seemingly anything else in the world, is adorning your bedroom with posters still the done thing? It certainly was when I was growing up. Heck we even had shops on the highstreet (those from the UK might remember Athena) that pretty much just sold posters. I seem to remember Cameron Diaz rather awkwardly sharing wallspace with Robert De Niro from Taxi Driver on my bedroom wall. Quite the odd Hollywood couple.

I often think of posters when I think of personas. This is probably because posters seem to be the natural vehicle for sharing personas. The two go together as naturally as Strawberries and Cream, or Gin and Tonic. Teams spend a great deal of time and effort creating personas, and then stick up some persona posters in the office (like the excellent examples from MailChimp above) with the expectation that suddenly the whole organization will start talking about ‘the users’. I’m not saying that doing this isn’t a great idea, it usually is, but there’s so much more that you can do with your personas than just creating some nice posters to help brighten up the office. So with this in mind, here are 5 additional ways that you put your personas to work in your projects.

1. As a starting point for usage scenarios

I’ve written before about the importance of thinking about real world usage scenarios when you design a product or service (see The complete guide to scenarios). When you’re thinking about how a product has been used, or might be used, personas are a great starting point for your scenarios. For example, how will our persona ‘Ben’ use the product? How, when, where and why will Ben be using the product? Rather than picking scenarios out of thin air, personas provide a great starting point for your usage scenarios.

Chocolate tea pot

Sadly the design team’s teapot didn’t work quite so well in the real world

2. As extra context for agile user stories

If you’ve ever worked in an agile team then I’m sure that you’ll be familiar with the now ubiquitous user story format:

As a <type of user> I want to <some goal> so that <some reason>

An example user story might be something like:

“As an editor I want to edit a page so that I can add new content”

User stories are great because they help teams and stakeholders to think of features from a user perspective. However, user stories in this form are also very short on context. What sort of user is an editor? What sort content would an editor be adding? When would our mysterious editor be editing a page?

Personas are a great way to add some extra context to user stories. By adding references to personas within your user stories, stakeholders and teams can straight away get a much better idea of the context. You might use the following format:

As a <type of user> like <persona> I want to <some goal> so that <some reason>

An example user story might therefore be something like:

“As an editor like Edith I want to edit a page so that I can add new content”

Why liken the type of user to a persona, rather than simply replace them with a persona? Well because using a specific persona (i.e. As Edith I want to…) is too specific. Not only would you have to have personas for every type of user, but it is potentially misleading. After all, you’re not designing just for Edith, you’re designing with users like Edith in mind.

3. As characters for experience maps and storyboards

Experience maps (also known as user journey maps) show a user’s end to end journey and importantly their experience for a given goal. For example, an experience map for going on a holiday might cover everything from researching holidays, to actually going on the holiday and then writing a holiday review. Storyboards on the other hand graphically show a scenario in a comic book fashion. Both are a great way to show how a product or service is currently used, or might be used in the future. Both at their core are really just stories and of course we know that all good stories need good characters. Personas make for obvious characters for experience maps and storyboards. They provide a great starting point for thinking about the sorts of journeys and experiences to map out and the sort of stories to illustrate.


Personas make for excellent storyboard characters. This fine example of a storyboard is from Bonny Colville-Hyde

4. As a focal point for ideation sessions

Get a bunch of people in a room. Ask them to come up with ideas to tackle a particular design challenge and you’ll generally get lots and lots of ideas. Some will be good, some will be bad, most will be somewhere in-between. However, without a focal point this sort of an ideation session can feel a bit like shooting fish in a barrel. Sure you’re going to hit a fish every now and then, but you’re going to waste an awful lot of bullets doing so.

To get the most out of ideation sessions you want to provide some focus to help channel people’s thinking. Personas are a great way to do just that. If you have a design challenge that you want tackled you can ask everyone to focus on your personas. For example, what would our persona Sarah want to be able to do? What features would be important to her? What would Sarah really love? Personas can not only provide a focal point for ideation sessions but also help to ensure that everyone considers a problem from the user’s perspective.

Wall filled with post-it notes

Avoid scattergun ideation sessions by using personas to provide a focal point

5. As characters for scenario based usability reviews

Scenario based usability reviews provide a structured way to examine the usability of an existing design, or design idea, by evaluating it against a potential usage scenario. For example, would a user be able to find and buy the sort of camera he or she might be looking for given the current design? Scenario based usability reviews are a great way to critique a design from a usability perspective. They also help you to think about how a design might perform in the real world.

As the name suggests scenario based reviews require scenarios to walkthrough. You’ve already seen that personas can provide potential starting points and characters for usage scenarios and they are also an important ingredient for scenario based usability reviews. For more about usability reviews, including how to carry them out take a look at my guide to carrying out usability reviews.

Photo credits

from UXM

Personalization, Data and Trust: The Role of Brand in a Data-Driven, Personalized, Experience Economy

People experience the world differently. Authentic and differentiated experiences must be rooted in an understanding of this uniqueness, and should respond to an individual’s behaviors and the variability of life. Smartphones and smart connected products are making it easier for companies to offer tailored, personalized experiences that still embody the company’s brand values. The wide availability of many different types of personal data, coupled with the ability to store and analyze that data, allows firms to personalize consumer experience. With technologies such as smartphones, sensor networks and smart connected products, large global firms can interact with their consumers in personal ways not seen since we left behind the village store to enter the industrial age.

The shift to personalized experiences changes the relationship a brand has with its consumers. A consumer buying a smart connected product is no longer taking the last step in the journey that begins with awareness to consideration and ends with preference and selection, but is instead taking the first step in a relationship with a firm. Take something as simple as a thermostat; 10 years ago, a buyer would have done some research — perhaps by reading product reviews or talking to a sales associate at a hardware store — and then bought the product. Today, if you buy a smart connected thermostat, such as Google’s Nest or Honeywell’s Lyric, buying and installing the product is just the start of the relationship with the brand. The thermostat learns the preferences of a household, and the comings and goings of those in the house. Nest sends a monthly email, detailing the household energy consumption compared to others in the neighborhood. In this way, personalized experiences evolve the relationship between the brand and buyer from consumption to participation.

The Role of Personal Data in Brand Perception

Brands need to gather various types of personal data, from self-reported information to digital exhaust (the information that users generate as they go about their daily digital lives), to power these experiences.

Beyond the legal requirements to meet privacy regulations, how can firms gather these data streams in a way that builds consumer trust while meeting the business needs of the organization? What are the implications for brands?

Four important implications

In our work, helping brands large and small define experience strategies in a world of smart connected products; and in our research on people’s attitudes towards personal data, we find four important challenges for chief marketing officers (CMOs).

1.     The value exchange — Are you giving consumers a compelling reason to share their data with you?

2.     Designing a brand trust framework — Have you designed important trust-building moments — moments of trust — within the customer journey?

3.     Aligning privacy behavior with brand values — Do your brand actions around privacy align with the brand values?

4.     Building trusted ecosystems — Are you thinking about ways to maintain consumer trust while increasingly sharing consumer data and digital exhaust with other firms?

The value exchange

Are you giving consumers a compelling reason to share their data with you?

Targeted advertising is not seen as a value exchange by most consumers. Witness the success of ad-blocking software on web browsers and smartphones; most consumers would rather avoid advertising messages if they can, even if the message is tailored to their interests. Moreover, targeted advertising is simply an expression of a brand through creative communications, rather than a move towards expressing a brand through experience. Which is not to say that cross-selling and up-selling services based on personal data are inherently a non-starter for a successful value exchange with consumers. Rather, firms need to ensure that they are giving consumers enough value for the exchange to be worthwhile.

There are many examples of firms successfully using consumer data to deliver on-brand experiences in the digital world, which result in greater consumer spending with a brand. Amazon tailors recommendations based on your search and purchase history, and has become so good at predicting what we might want that they patented ‘anticipatory’ shipping in 2014. Amazon’s promise is the wide selection and availability of goods, coupled with convenient and rapid shipping. Making use of consumers’ data and analytics to better deliver on that promise encapsulates their brand promise in the experience of the service.

Moving from pure digital to blended physical and digital experiences, the Vail Resorts Management Company has launched the EpicMix app for smartphones, linked to skiers’ season pass or ticket. Skiers have their pass scanned each time they ride a chair-lift, leaving a trail of digital exhaust as they make their way through a ski resort. The app makes this information interesting and useful to skiers by calculating vertical feet descended each day, the number of ski days for the season, and so on. EpicMix adds some interest and fun to the skiing experience with challenges, contests and awards. The resorts employ professional photographers who take pictures of skiers and snowboarders, and these images are linked to a skier’s profile and app. The Vail Resorts Company’s stated mission is to provide ‘the experience of a lifetime’. More practically, their value to skiers is to enhance the enjoyment of the great outdoors and skiing, something the EpicMix app does well.

Using consumer data to power tailored experiences is not simply restricted to the consumer realm. GE Energy provides software tools to its power generation customers to manage power plants. Power plants are very complex systems, with many thousands of parts that need to be maintained and must be replaced on different time scales. In some ways, power stations behave more like living organisms than machines, in that the state of the power station is not simply ‘on’ or ‘off’ but rather it exists in various degrees of health. It takes power to make power, so unplanned downtime caused by some component failing is very costly. GE’s MyFleet product offers a way to oversee and manage the health of a power station by tracking and monitoring many elements of the system, and using that data to minimize downtime and optimize the health of the power station. The experience of the MyFleet software system is a living instantiation of GE’s brand promise that the industrial Internet will bring together brilliant machines, advanced analytics and people at work to deliver ‘never-seen-before performance levels’. The experience is tailored to meet the specific needs of a customer’s power station and to various operator roles in maintaining that power station.

These examples seem to suggest that the only way to offer a brand experience is through product or service design. Is there any role for marketing to play? We firmly believe there is. For example, frog recently worked with a chain of fitness clubs that offer a wide variety of services, including personal training, fitness classes, spa sessions and expert content. Looking to create a deeper connection with current and prospective members, we created a recommendation algorithm that matches members with relevant services and content, strategically tailored to fit their fitness personality type. The tool generates a ‘type’ that members reference in conversation, similar to a Myers-Briggs personality type. From onboarding to post-workout meals, the algorithm cross-cuts all of the brand’s touch points, helping to shape hyper-relevant, memorable experiences.

Designing a brand trust framework

Have you designed important trust-building moments within the customer journey?

To deliver value in exchange for personal data, brand managers need to consider the entirety of a customer journey and design the touch points across that journey to reinforce trust for a brand. It is not sufficient to optimize the mobile experience, or physical experience, or customer service center experience in isolation; they all need to reinforce each other for the experience to be successful. CMOs need to build a framework that enhances overall trust for a brand and optimize experiences to build that trust.

We recommend augmenting moments of truth with moments of trust. Google introduced the notion of the Zero Moment of Truth (ZMOT), the moment where shoppers conduct research, look for coupons or comparison shop. The ZMOT can be designed to add trust — creating the Zero Moment of Truth and Trust (ZMOTT) — with confidence-building measures, such as offering online reviews from other customers popularized by services such as A more dramatic example is Progressive Insurance, a firm that offers comparison shopping quotes from competitor insurers through their Progressive Direct web quoting system.

The First Moment of Truth (FMOT), the moment when consumers are confronted with a product or service, is also an opportunity for trust building to become the First Moment of Truth and Trust (FMOTT). U.S. telecom carriers AT&T, Sprint and Verizon have all recently redesigned their FMOT to add trust. Physical stores play an important role in the overall telecom customer journey, acting as the decision point to buy a new device or service plan. All three carriers designed their store experience to reinforce trust during this critical journey moment by creating face-to-face, semi-private conversation nooks or tables. When signing up for a new service plan, consumers share a great deal of personal information, including drivers’ license, credit score and payment terms. Moving away from over-the-counter transactions to a seated, semi-private conversation with visual access to all the information the carrier is collecting reinforces perceptions of brand trust.

Uber is an example of a firm that has successfully turned their Second Moment of Truth — the moment when a customer has bought a product and is using it — into a Second Moment of Truth and Trust (SMOTT). They do this in a number of ways. First, drivers and passengers know exactly who is on either side of the ride experience, building trust in a very human way. Second, Uber maintains GPS data tracking for every trip, so they always know where the car is at all times. More recently, Uber added a family tracking feature that allows account holders to track a family member’s trip in real time. Let us say you wanted to send an elderly parent to a medical center for a check-up. You can order an Uber for your parent and watch the trip unfold to ensure that the person got to the medical center as planned. Although significant incidents have occurred with Uber riders that raise questions around how fool-proof their system is, the company has structured a clear trust framework, reinforcing Uber’s brand promise of being ‘everyone’s private driver’.

The Walt Disney Company, which scored 12th on the Forbes 2015 most trusted brands rankings, is a good example of a firm that carefully calibrates each step on its customer journey to build and reinforce trust, making guests happy to share their personal data with the firm. Guests to Disney World are issued MagicBands, a passive radio frequency identification (RFID) wrist band that arrives at your home several weeks before your visit. This wrist band becomes your ticket into the park, your fast pass to rides that you can pre-book through the Disney website or mobile app, your hotel key for properties in the resort, your wallet for payments at kiosks throughout the parks and resort hotels, and your identity bracelet by which you can tag the professional photographs taken of you and your party by the park photographers. The current MagicBands have elements that make the experience of being in the park more magical than before. For example, while waiting in line for the Test Track ride in Epcot, guests can design their own car. During the ride, the guest’s design is scored on various car performance metrics, and after the ride is over, the guest can create a commercial for their car. In this way, the firm is using consumer data to enhance the user experience, reinforcing trust throughout the customer journey, and encapsulating the brand principles by making the guest experience magical.

It is this higher trust that allows firms to offer services and products that leverage personal data. In 2014, frog conducted a study in five countries to understand consumer attitudes towards sharing personal data with multiple brands. We found that consumers are most willing to share their self-reported data with companies, even if they only get small value in return. Demographic information, as well as taste and preferences, are readily shared by most consumers, especially if a company uses that information to enhance the experience of a service or product. For example, users of streaming music service Pandora readily share their favorite artists with the firm when they create playlists and stations. Consumers are slightly more sensitive about digital exhaust — the personal data we create through using smart connected products and digital services. Staying with the Pandora example, this is the personal data we create when we play some stations more than others, or skip specific songs. The company gets to know us better with each interaction and piece of personal data, but, again, if the information is used to enhance a service experience, most consumers consider it to be a fair exchange. What is more sensitive is if a firm uses personal data to market to consumers, or if they sell the data to third parties.

Aside from the data type and use is the question of whether consumers trust a brand with their personal data. frog’s 2014 study and subsequent studies from other organizations show that consumers have some understanding of whether they can trust a brand with their personal data. Social networks such as LinkedIn or Facebook in the United States, and Chinese equivalents such as Renren and Weibo, consistently garner lower trust from their users than mobile carriers and banks. Payments and credit card firms, as well as e-commerce firms, tend to score the highest on trust with personal data. Even if they cannot fully articulate data privacy policies and protections, consumers have internalized the fact that some brands and business models rely on sharing and selling personal data, whereas other firms only use personal data to enhance the experience of a service of the product. As we point out in a paper summarizing frog’s 2014 personal data study, Customer Data, Designing for Transparency and Trust, companies that are trusted have an advantage because they can design and launch personal data-intensive services with more speculative value and still expect consumers to adopt them, compared to less trustworthy brands.

Aligning privacy behavior with brand values

Do your brand actions around privacy align with the brand values?

Although direct marketers may have been working with consumer data for many decades, what is new is the availability and depth of data offered by smart connected products, and the powerful use cases for that data. As the field evolves, a line of business executives, IT teams, marketers, product and service designers, and corporate lawyers continue to grapple with new questions.

Lorrie Faith Cranor famously estimated in 2012 that it would take 244 hours to read the privacy policies of the websites and apps people typically visit in a year. Add in the end user licensing agreements (EULAs) and terms of service, and it is clear that very few people have the time to understand the implications of the digital exhaust they are creating, let alone manage it. Consumers cannot make an assessment of a firm’s data policies and its trustworthiness with personal data on a case-by-case basis, given the effort involved. In the absence of a rational assessment of a firm’s data policies, we need a short-cut — an emotional decision-making mechanism that stands for a firm’s data privacy policies.

Privacy policies need to become part of an organization’s brand image. Brand curators, marketers and experienced designers need to wrest control of data and privacy policies from the lawyers. This is already happening. In 2014, Apple started to make strong declarations about consumer data and privacy. It launched ever more sophisticated encryption on iPhones and iPads. In early 2016, it resisted efforts by the U.S. Department of Justice to force the company to build a backdoor into the iPhone, which would allow police and security services access to phones of criminals and terrorists. Whatever you think of the politics of the stance, Apple is sending a clear message that it stands for privacy and protection of its customers’ data.

On the flip side, if a firm loses control of consumer data, it has a direct impact on the brand’s image. Although several major retailers have suffered data breaches, Target’s fall 2013 breach was one of the most public, impacting 70 mn individuals. According to their U.S. Securities and Exchange Commission (SEC) filings, as of January 2015 they had incurred US$252 million in data-breach-related expenses. Beyond the direct cost, however, is the reputational harm and brand damage caused by the loss of trust. Something as arcane as consumer data threatened to undo years of brand building. Given the data breaches at several other competitors, consumers may forgive Target, but it will take them longer to forget. With brand managers at the table, or better yet at the helm, firms are better able to align privacy policies with brand values.

Building trusted ecosystems

Are you thinking about ways to maintain consumer trust while increasingly sharing consumer data and digital exhaust across organization boundaries?

The earlier examples of successful brand experiences that are mediated by consumer data and digital exhaust predominate in controlled environments such as resorts, parks and campuses, where a single organization owns all of the touch points. The EpicMix app, for example, only works within a ski resort owned and operated by the Vail Resorts Company. As our cities, cars and homes become smarter and increasingly connected, however, we will see more tailored, designed experiences. We will see these experiences cross multiple brands, firms and organizations, arranged around specific customer journeys. This raises new challenges: How does a brand express itself within a wider customer journey when there are multiple brands involved? How can brands maintain consumer trust while increasingly sharing consumer data and digital exhaust across organizational boundaries?

Again, we see Uber as an example of this strategy in action. The service began as a way to call a car within a proprietary mobile application. Uber, however, evolved to offer its service as a capability within other companies’ applications. For example, within the United Airlines mobile app, after checking in for a flight there is a button to order an Uber to take you to the airport. As an experience, this flows nicely along a customer’s typical journey map. Similarly, it is possible to order an Uber from within Facebook Messenger without ever leaving Messenger. Consumers may be comfortable with information flowing between the two firms, given the convenience of ordering a car without opening a new app.

Car makers designing in-car experiences for connected cars or self-driving cars are in a similar situation. Most carmakers offer third-party experiences today, such as SiriusXM or Android Auto. Others are building their own proprietary experiences. We believe that blended experiences are more likely to succeed in the market, but CMOs need to thoughtfully curate a trusted ecosystem and partner with brands that enhance their trust. Low-trust brands become the weakest link in an ecosystem, dragging down the higher trust brands to the lowest common denominator.

The rise of smart connected products and services, powered by consumer data, is not something that can be left to engineering, IT and legal teams to figure out alone. CMOs and brand marketers need to be front and center in defining on-brand experiences for consumers. Marketing teams need to make sure that the firm is giving consumers a compelling value proposition in exchange for personal data.

As consumers are more willing to share personal data with firms they trust, CMOs need to push their organizations to add trust-building moments into customer journeys. And while legal teams have legitimate reasons to push privacy policies and licensing agreements that defend the firm, CMOs need to ensure that the policies and actions the firm takes with regard to consumer data reinforce the brand promise. Data breaches, and the resulting loss of trust, may begin as an IT operational failure but have dramatic consequences for brand perception and value. Personal data and consumer trust are the building blocks of the personalized experience economy. They must be earned by firms, and willingly given by consumers. It is the new face of brand building, and very much the domain of marketers.

Originally published in the Journal of Brand Strategy, Summer 2016

from frog

The 2017 Content Marketing Framework: 5 Building Blocks for Profitable, Scalable Operations


At Content Marketing World this year, I met the CMO of a mid-sized B2B company. During our discussion about the event (and how great it was), he said, “Robert, you know the thing that I’m missing is how we’re ever going to draw a line from content marketing to top-line revenue. If I can’t do that,” he said, “then I’m not sure we actually should do content marketing.”

My response was that it’s absolutely possible to draw a line to revenue. However, if your only goal is to increase top-line revenue more efficiently with content marketing (i.e., a cheaper investment) than through traditional advertising, you’re missing out on the greater benefits content marketing can offer.

We broke content by being good at it

These days, when I get to have deep conversations about our industry with folks like my CMO friend, I typically find that they have created a near-Faustian “bargain business case” for content. The deal typically goes like this:

Give us permission and budget to create content, and we’ll produce awesome stuff that will be more successful than advertising at driving top-of-the-funnel results.

Interestingly, these promises typically work. Sort of. In fact, even CMI’s new 2017 B2B Benchmarks, Budgets, and Trends research points to things looking up for content marketing: 62% of marketers surveyed say that they are more successful now than they were last year.

62% of marketers say that they are more successful now than they were last year via @cmicontent. #research
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But gains made in the early days of these programs can be deceptive because, in many cases, they mark the first time the company has delivered valuable content to its easiest-to-reach audiences. But soon those audiences will become harder to reach and more demanding (and discerning) when it comes to content they consider valuable. As the content marketing operation matures, it becomes more challenging to find new ways to amp up the impact while keeping up with the increasing demand for higher-quality content. Unfortunately, most such programs eventually reach their breaking point – if they haven’t already.

To compensate for flagging results, many companies seem to feel obliged to publish content more rapidly, at higher volumes, and on more and more digital channels all at once. Unfortunately, organizations that set their sights on pumping out “more-more-faster-faster” rarely put systems and strategies in place to enable all those pieces of content to function as strategic business assets.

We’ve been too successful in simply making the case for content. Now we need to make the case for slowing ourselves down so we can get better at it.

We need to make the case for slowing ourselves down so we can get better at content via @Robert_Rose
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Let’s take a look at how to do that.

Step 1: Adjust your view of content’s value potential

Two common misconceptions typically are to blame when companies struggle with content marketing:

  • The value of content is defined in terms of the assets themselves: Here, the business views content marketing solely as the practice of producing a different kind of sales/marketing collateral, which it can use to fuel its direct-marketing campaigns. Thus, the business assumes that “doing content marketing correctly” means hiring some content creators who deliver materials to brand managers, demand generation teams, or sales teams, who then use it as a new way to get “attention” from prospective consumers.This speaks to the heart of the Faustian bargain and the ever-increasing pressure to meet the demand for content. And while most do, indeed, become moderately successful at pumping out good content, they fail to invest in building anything of lasting value. They simply put in more effort without enabling the returns from those efforts to scale in tandem. Eventually when the well runs dry, they get stuck in an even deeper hole that they’ve dug for themselves, with no plan for how to climb out. At that point, when the business rightly asks, “Why should we invest in more depth? Can’t everyone just start doing more of this kind of content?” or “Can’t we simply outsource this need?” the only solution is to just keep on digging.
  • Content is solely viewed as a more efficient means to a sale: Here, the business believes that the value of content marketing lies in how much more efficient (or effective, in some cases) it is in turning a prospect into a lead or sales opportunity. This is the straight line to revenue that the above-mentioned CMO was so desperately in search of. But here’s the thing: Content may, in fact, be more efficient or effective at closing or attracting new leads. But then again, it might not. The scary truth is that content marketing done well can turn out to be more expensive than advertising or less efficient than a cold call; it can even slow progress through the stages of the brand-consumer relationship. Put simply: Content marketing is often no faster, cheaper, or more effective at moving customers down the funnel than other marketing techniques. However, its greater power lies in its ability to produce a better customer, a more loyal customer, or a customer more willing to share his or her story with others – which compounds the value he or she provides to the business. But when we simply stop the business case at how much more can we squeeze out of the marketing process, we overlook this important potential.

Step 2: Invest in building a more strategic asset

Over six years of deep explorations into our industry’s evolving landscape, CMI has found that the greatest potential for content marketing success lies in viewing content as a strategic business activity that just happens to be performed by marketers, rather than as a marketing and advertising tactic that gets applied for the express purpose of reproducing incremental wins or amplifying upper-funnel marketing results.

As we’ve said for years, content marketing isn’t a replacement for other forms of marketing – it just makes those forms work better. How? Content marketing adds value to the business by building a critical strategic asset: a subscribed audience.

#Contentmarketing adds value to business by building a strategic asset: a subscribed audience via @Robert_Rose
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Put plainly – content marketing is a different kind of investment for marketing, as it offers the potential to provide multiple lines of business value simultaneously. Strategic content creation helps build an engaged audience of people who exhibit specific, desirable behaviors – like greater willingness to share personal data, greater interest in upselling opportunities, and greater brand loyalty and evangelism. When your content compels your audience to adopt these behaviors, not only does it become easier for your business to achieve its long-term marketing goals, it can also open up new business opportunities – and even new revenue streams.

It’s not so much that top-of-the-funnel activities should alter the purpose of content, but rather that content should be strategically designed to add a valuable dimension to all your marketing initiatives by contributing a new form and functionality.

Step 3: Follow our framework to put all the pieces in place

It’s been three years since we unveiled the Content Marketing Framework. At the time, its purpose was to serve as a high-level view of the principles that govern the world of brand storytelling.

Since then, CMI has worked with more than 100 brands, helping them put these core principles into practice. Those partnerships taught us a lot about which parts of the framework worked, which didn’t, and where we still needed to provide greater clarity and transparency.

To better reflect the insights I discussed above – as well as the many shifts that have occurred across the digital ecosystem – we’ve streamlined our original discussion and have added a distinct new process model to each node.

Allow us to introduce the redesigned Content Marketing Framework for 2017. Think of it as a syllabus of sorts, covering the five core elements necessary for running successful, scalable, and highly strategic content marketing operations within an organization:

  • Purpose and goals: Why you are creating content and what value it will provide
  • Audience:  For whom you are creating content and how they will benefit
  • Story: What specific, unique, and valuable ideas you will build your content assets around
  • Process: How you will structure and manage your operations to activate your plans
  • Measurement: How you will gauge performance and continually optimize your efforts

#ContentMarketing Framework is a syllabus for running strategic #contentmarketing operations via @Robert_Rose
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Taken as one cohesive unit, this framework unifies the methodology we teach at CMI University. It is our hope that each of the five nodes serves as a trigger point that helps you understand how to grow stronger, more agile, and more innovative in your approach to creating content that builds value for your customers, as well as for your business.

Instead of continuing to pursue your path of potentially diminishing returns, why not take a step back, rebuild the case for content marketing in your enterprise, and then move forward with a much stronger potential to reach your business goals through content?

Are you ready? Then let’s get started by checking out the 2017 Content Marketing Framework.

Want more on content strategy for marketers? Sign up for our Content Strategy for Marketers weekly email newsletter, which features exclusive insights from CMI Chief Content Adviser Robert Rose. If you’re like many other marketers we meet, you’ll come to look forward to his thoughts every Saturday.

Cover image by Joseph Kalinowski/Content Marketing Institute

The post The 2017 Content Marketing Framework: 5 Building Blocks for Profitable, Scalable Operations appeared first on Content Marketing Institute.

from Content Marketing Institute

Digital Attribution's Ladder of Awesomeness: Nine Critical Steps

Purple StrokesCulture is a stronger determinant of success with data than anything else. Including data.

[People + Process + Structure] > [Data + Technology]

It seems hard to believe. Yet, it is so fantastically true. At least for now. At least until AGI takes over.

Why is this formula material?

The first part of the equation, for better or for worse, improves in an evolutionary manner. The second part of the equation most frequently improves in a revolutionary manner.

The challenge for Senior Leaders is that revolutions seem a lot more attractive and hence they charge full speed ahead. This results in frustration, derailed careers and a massive amount of money flushed down sad places.

Revolutions in our context, almost always fail. Evolution works. Hence, it is dangerous to overlook the super critical importance of P+P+S.

You want to win big with data, with marketing, with transformative digital yada yada and blah blah, evolve. Do so at the fastest pace you can put in place for transformation of the left-side of the above equation, and use the same pace to evolve the right-side of the above equation.

This will ensure that the people, process and structure will be smart enough to take advantage of the smart and wizbang tech.

Maybe this metaphor will help make this real.

You can't give a toddler a Harley Davidson motor cycle. The moment your start the motorcycle, the toddler is going to start crying. It is not the mistake of the toddler, she is just a toddler after all. It is not the mistake of the Harley, it is a very cool motorcycle. The mistake is yours.

The toddler needs something to steady her, something she can push, something to exercise her legs to make them stronger. At some point, she would love a Harley (as her father that might freak me out, but I digress).

This post is organized into the following structure:

I'll say this again at the very end… As a Marketer or an Analyst, there is nothing you'll attempt that will be more complex and challenging than what you are about to read in this post. The spectrum of upgrades you have to make to your tools and data along with your people, process and structure, are likely to be unmatched.

That is why this is so much fun. I have a huge smile on my face as I'm typing this sentence, I get so excited about this stuff. If you follow the advice outlined, the most likely outcome is an increase in the slope of your career's graph as it heads up and to the right! :)

Let's go.

Ladders of Awesomeness #wth

When it comes to your Digital Marketing and Digital Analytics practice, I've advocated slow and steady evolution.

The problem is sometimes you might not know what that path looks like, what the steps are. To address that, on this blog I've shared something I call the ladders of awesomeness – my view of what the entire evolutionary path looks like.

As an example, here's the Digital Marketing ladder of awesomeness:

digital marketing ladder of awesomeness

Very cool, right?

It is not easy to linearize it all, the world is rarely that clean. But, you have an overall structure that can guide your strategy.

My recommendation… Partake in honest self-reflection, let that help you identify where on this ladder today, then, rather than shooting for the moon, figure out how to get to the next step. In taking that step, you should not just implement cool technology and do cool marketing, you should also invest in growing the skills, experience of your people and invest in putting scalable structures and processes to take advantage of this next cool thing. Win that, then go to the next step. Win that, then… well, you get it.

Cool technology plus savvy people to take advantage of the new possibilities plus processes to execute at scale set in the best-fit structure equals winning big.

My second ladder of awesomeness was very exciting as well. It lays out an evolutionary path for the key performance indicators you should use to drive digital sophistication inside your company. You'll find it here: Digital Metrics Ladder of Awesomeness .

It tells you not to go after Customer Lifetime Value right away. That is a insufficiently prudent use of Earth's oxygen. The metrics ladder lays out a path that will get you there, step by step while ensure your org is coming along with you.

Digital Attribution's Ladder of Awesomeness.

The other day, I had the amazing privilege of delivering a keynote with my point of view on attribution. The CMO expressed a desire for the audience to learn about advanced attribution strategies.

It is a topic I love and adore, but it is also a topic way more complicated than anyone is willing to admit.

Rather than simply give them all the advanced attribution modeling techniques, I took the opportunity to create a ladder of awesomeness for digital attribution. I did not want them to make the mistake of trying to achieve revolution at the end of the keynote, rather I wanted to give them a path to achieving a global maxima. One step at a time.

Here's the ladder I drew at the end of my keynote summarizing my worldview…

digital attribution ladder of awesomeness

The overall execution I recommended was the same as in the case of my other two ladders of awesomeness :

1. Figure out what step you are at.

2. Check to make sure that your organization (people, process, structure) has maxed out the benefits of that step.

3. When confident that people, process, structures are helping you max out the complete value of that current step, go to the next step. Don't jump two steps! Just one step forward.

4. Buy new technology, if needed, invest in implementing it and using it, start to focus on getting your people, process, structure to evolve to take max advantage of this next step.

It is a mistake to believe that each step is the same "size" / requires the same effort or skills.

To illustrate this, in the space I had available on the slide I was projecting, I shared some sense of effort/skills/time that might be required to take one step up…

digital attribution ladder of awesomeness reality version

You can see that the initial elements are pretty small, then things get complicated, but it is not an even distribution. If you do Step 4, Step 5 might actually take less time. It is also clear that things get insanely hard as you get towards the end. Insanely hard is putting it mildly.

I am sure you are very curious, what each of these elements entail!

It is very hard to capture an entire keynote, and a life-time of bruises that the wisdom above reflects, in a simple blog post. The keynote contained solutions for each step, it would take too long. Let me give you a brief sense for each element, that should give you enough to explore in a much more focused manner.

But, before that…

Wait, Wait, What the Heck is Attribution?

: )

I'm sure it is clear to most of you, but for some of our new peers let me quickly explain, and then we'll explore all the elements in the digital attribution ladder of awesomeness.

Here's the simplest way to think about it. Most of us make decisions about the effectiveness of our digital marketing initiatives, owned, earned or paid, as if the real world looks like this…

conversions path google analytics

Irritatingly we believe this because Google Analytics, Adobe, IBM and all other digital analytics tools tell us to believe that. They base all computations in their standard reports on an awfully silly thing called last-click.

Why do I say irritating?

Because the above picture actually looks like this…

actual conversions path google analytics

Suddenly most of your standard Adobe and Analytics reports are more than lying to you about the effectiveness of your marketing investments.

The art and science of allocating optimal amounts of credit to each marketing channel, based on the activity it created, is called attribution analysis. The end goal is to recommend an optimal mix for your marketing budget.

Take a look at the first row above. Attribution analysis will help you understand how to value Social Network AND the Direct channel AND Organic Search.

Smarter attribution of the outcome, smarter marketing decisions.

Digital Attribution's Ladder: Step Details.

Getting back to our story.

My core recommendation is that rather than jumping directly to attribution modeling or media mix modeling, that you build a strong, step-by-step, foundation of people, process structure along with data/tools sophistication. Let's look at each step in the evolutionary journey.

Step 1: Optimal Metrics.

If your company's dashboard is full of Visits, Time on Site, Impressions, % Exits, basic activity metrics then your company is not ready for attribution anything. You would think if you throw in Conversion Rate in there and you are ready. Nyet.

The most primitive thing you can do to have a very strong people, process, structure foundation is to pick great metrics to measure. Tough metrics. Smart metrics. Metrics that actually tell you if the business is doing well.

There are many ways to pick really good metrics. For example, checkout my list here: Best Web Metrics / KPIs for a Small, Medium or Large Sized Business.

Or, if you have a savvy digital strategy powered by my ultra-awesome See-Think-Do-Care business framework, you can use my recommendations in the framework to judge how optimal your current metrics strategy is…

best web metrics see think do care

Using these metrics, vs. the basic activity metrics like Visits and Time, is hard, taking advantage of them requires smarter people. Additinoally, actioning the powerful insights you get from the above list requires smart processes and smart structure.

See what I mean when I say optimal metrics create the cultural and thinking sophistication required to do harder things? If you don't have this. Don't move forward.

Step 2: Macro and Micro-Outcomes.

A typical macro-outcome is an ecommerce order, a lead submitted for a B2B company, a new profile opened by a visitor to a content site, a donation on a non-profit website. So on, and so forth.

Most of you already measure the heck out of this. (If you don't, go back one step.)

Only a handful of you measure micro-outcomes.

Micro-outcomes for an ecommerce website would include store lookups, coupon downloads, new accounts, reports their users can download, email signups, reviews submitted, product amplification, videos watched, charitable efforts, blog subscribers, community celebrations, etc. etc. And, all of these are for just one brand's website. They make a few things like tooth-paste which are sold online, but the primary channel of distribution is offline stores. It is impressive to think that that aforementioned list are all the things they do online! We bring immensely smart nonline decision making for this client by optimizing for their macro-outcome (orders) and all these micro-outcomes.

Can you see how savvy the company's people, process and structure would have to become to allow optimization of a portfolio of outcomes, rather than just one (conversion rate)?

It is hard to do this. It is hard to compute the economic value of all these outcomes. It is hard to optimize for the entire portfolio.

That is how you get ready to do sophisticated things like attribution modeling.

Step 3: Assisted Conversions.

Can you smell attribution? Close, but one more step before we get to it. First, let's get your org ready to use the metric that truly is the precursor for sophisticated attribution modeling.

In Google Analytics go to Conversions tab, then Multi-Channel Funnels and finally click on Assisted Conversions.

I love this report.

It is your org's introduction to moving beyond the awful last-click conversion obsession. In this report you'll see a more complete view of your marketing performance…

assisted conversions report

You are going to have a lot of arguments about which department (and people!) should get more credit, how to value the budget now that you have these Assisted Conversion numbers, why did Display go from $121 to $6 (!), so on and so forth.

As you resolve these issues, and start to take action by changing how much budget you spend on the channels above, you are collecting the elements required to be successful with online and offline attribution modeling.

You jump directly to attribution anything, a cold, hard wall is waiting for you to run into it.

Step 4: Standard Attribution Models.

Congratulations, it took you 18 months, :), but you are ready to do attribution modeling.

It is very easy to start. In Google Analytics, including the free version, go to Conversions, then Attribution, and then Model Comparison Tool.

You'll see Last-Interaction listed already. Next to it you'll see vs. select model. Click.

You'll see seven default models listed. Most of these models are for esoteric needs, or are flat out wrong. Take the First-Interaction model as an example. Choosing this model is like you giving all the credit to your first girl-friend for you marrying your wife. The definition of insanity.

There is just one model that passes all the smell tests, Time Decay. It provides reduced credit to marketing touch-points that are future back in the customer journey. Simple.

Use Time Decay for your first step into attribution modeling.

time decay attribution modeling

The red and green arrows to your right are helping guide your decisions related to the shifts in budget that you should consider in order to optimize your marketing and advertising to get the best possible results from your budget.

At this point, you'll be delighted that you listened to me and did Step 3 resulting in increased savvy in your people, process and structure. If you'd skipped that, at this stage all you would have is a clever report that has zero impact on your company!

Even if you did this as Step 4, you'll still require incremental investment in getting your org to understand the data above, you'll have to invent a new cultural norm of taking the red and green arrows above and creating tests from the recommendations, putting the tests into market and create a feedback loop of lessons that your org structure can learn from and improve future strategy.

It is a lot of work. Totally worth it because of the impact on cost-savings and increased profit.

Step 5: Custom Attribution Modeling.

Having completed all that hard work, and now that the org is making incrementally smarter decisions, you are ready to take advantage of your unique knowledge you have about your business, your customer behavior, and your strategy.

Custom attribution models allow you to take a base layer of smarts from Google Analytics, and add in yours.

For a client I've spent a lot of time with, here's the custom attribution model…

avinash custom attribution model

The reason for the choices above is business knowledge, customer behavior and business strategy. As you make the seven choices required above, you'll lean on those three elements – and lots of conversations with key business leaders.

The actionable steps you'll take from application of your model will be similar to the ones outlined in the step above.

Customer attribution modeling is incrementally better than standard attribution modeling. In doing this step successfully, you are strengthening leadership connections, and more buy-in from multiple departments (finance, sales, support etc.). It is not hard to imagine how critical that is to achieving success with data.

Step 6: Data-driven Attribution Modeling.

One of the painful things you'll run into while creating your custom attribution model is that persistent pain in the rear-end… Opinions.

Person x will say no, Avinash is wrong, we should not favor Clicks, my ads have no clicks, they only have impressions, change Avinash's model to over value Interaction Type Impressions . This person is wrong, and I am right. :) But, sadly you can't pull me out of your pocket so that I can tell them how wrong they are!

I kid only slightly. You are going to run into a lot of this. And, for some of these opinions you'll never have definitive data to prove the opinion right or wrong.

Where humans fail you, let Machine Learning come to the rescue.

Google Analytics looks at all of *your* data, all of the click-paths of your actual visitors, how each marketing channel delivers value to you (based on a success criteria *you* define) and helps create an attribution model that reflects your reality. This attribution model is called a data-driven attribution model. Opinions can now go live in a very dark place, while Machine Learning illuminates the world.

You click on the Model Explorer in the Attribution folder to see your data-drive model…

data driven attribution model google analyitcs

As an Analyst, I have to admit I get a special sense of pride when I see the shades of blue above. There is no way that a human can get to this level of insight, at this scale, or so frequently (your model is refreshed all the time with new data/behavior). I should probably be scared that these machines are making me redundant. For now, I am simply amazed.

You can see why Google wants you to pay for this feature (among many other great things in GA 360). It is smart, it is computationally intensive, and a competitive advantage for you.

Your data-driven model eliminates opinions/feelings/politics from the process of getting to the best model for you, and it is exquisitely yours.

Actions you'll take, changes you'll drive to your marketing budgets, will follow the patterns set in Stage 4 and 5 (which is why it is still important to go through the pain and build the right foundational P-P-S upgrades).

Bonus : If you want to learn a lot more about each attribution model in Analytics, pros-cons, how to use them efficiently, this post has a deep dive you'll love: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models.

The next three steps in the ladder of awesomeness are complex and advanced. They apply to perhaps only the largest companies on the planet. Let me cover them here briefly, just so that you'll have a sense for them if you are in a large company or on your way to becoming one!

Step 7: Pan-Existence Modeling.

Unless you do something extraordinarily unique in your analytics execution, almost everything you do above won't be tied to a single person's behavior. It will be tied to cookies, it might be fractured by devices, browsers, and other things that make tracking a single individual difficult. It goes without saying that some of this might also be due to compliance to local laws (which I deeply stress you should read up and be familiar with for your local legal entity).

It is absolutely imperative to stress that even with all the limitations I've just mentioned, you are still better off taking the journey outlined in the above six steps when compared to being stuck in the awfulness that is last-click reporting.

Understanding how an individual human behaves does take you to a whole new level. Imagine data-driven attribution modeling that understands one individual's behavior across mobile and desktop! Now throw in the ability to tie that behavior to their activity inside your store or call-center (sales or support). #mindblowing

Imagine this stitched together view, across devices…

user explorer report google analytics detail

[More on the above report here: Develop a Smarter Understanding of Your Audiences]

What do you need to get to pan-existence attribution modeling?

Some way of identifying, incentivizing and tracking a person. The technology to do it exists. In the Google Analytics world, implement the Universal Analytics User-ID Override feature.

You can extend this to also tie to a CRM record you have for the person off a loyalty card or whatever global identifier your company has developed to identify its customers uniquely.

Everything in your analytics tool of choice becomes more powerful instantly, including attribution modeling.

This is hard to do, ask for help, there are tons of authorized consultants who can speed up your time to market/victory.

Step 8: Nonline Controlled Experiments.

I'm sure the above step planted in your head the thought of how you can attribute the online campaigns the impact that happens offline (remember 80%+ commerce in the US continues to be offline, even with Amazon becoming amazonish!).

There are two strategies to keep in mind.

First. You can attribute credit more directly (without Step 7) by leveraging the power of controlled experiments.

Here's a simple example. You already know that that non-brand PPC campaigns drive a ton of last-click and assisted conversion. But, you also know that online campaigns drive offline impact. But, how can you prove it?

Run a controlled experiment.

In this example we ran a four week test across a total of 11 test markets (covering 128 stores) and 39 control markets (covering 621 stores). A little picture for you to show distribution and the design for experiments savvy that you'll bring…

nonline controlled experiment

At the end of the test we proved that every $1 spent on non-brand paid search marketing drove $15 in store sales.

If you do this well, you can be even smarter. In this case we were able to identify that the $15 in sales was at a 22% contribution margin (an unheard of accomplishment in retail). Oh, and we were not done. We could also identify that the sales lift for product category X was 3.5% and for product category Y it was 2.31%.


Think of how incredibly powerful this can be if it is a part of your standard operating procedure on the web. Attribution of the effectiveness of your online advertising to drive multichannel results.

You can leverage the smarts of controlled experiments without any of the seven steps above, but it is easy to see how much measurement, analysis, marketing, people, process and structure savvy you need to pull this off, hence it is Step 8.

If you actually complete Step 7 successfully, you can take your controlled experiments up several notches, including tying online behavior to longer-term outcomes tied to a single human. Then, you can go back and customize your overall marketing portfolio to micro-segments of individuals with shared attributes. This is very much in the holy grail region.

Step 9: Advanced Controlled Experiments.

We eschewed attribution modeling above. We go back to modeling, but of a different type.

The most common implementation in Step 9 is media-mix modeling (or as some like saying, marketing mix modeling). Boiled down to its most essential it is the creation of a multivariate equation that when solved through the application of some delicious statistical regression, helps identify the optimal mix of your marketing portfolio.

Almost always, media-mix models include all your marketing – TV, radio, digital, etc. This allows them to be the go to source for CMOs choosing to drive unified strategic conversations.

There is plenty of art involved in creating media-mix models, and in the hands of an organization, or Agency, without the optimal people, process and structure, the results are no less garbagy then other opinion based strategies.

I believe that the best way to eliminate biases (or more usually opinions), I recommend a heavy use of complex controlled experiments, varying multiple elements (unlike just one above), as the optimal source of inputs required by the multivariate equation.

My biggest complaint about media-mix models, even the most sophisticated ones, is that, if you are executing See-Think-Care intent strategies, the thing digital is really, really, really good at (and traditional media mostly completely incapable of), media-mix models have a very hard time identifying value from those super valuable activities. They are biased towards short-term commercial results. Basically Do intent strategies.

Hence, a biblical belief that media-mix models are the word of God when it comes to optimal marketing investment is incredibly flawed. You will undervalue See-Think-Care business strategies, which in turn will mean you will not use digital to do what it is exquisitely qualified to do (ex: with See and Care help you build owned audiences!).

And, all because some Agencies and Companies believe in judging a fish by its ability to climb a tree . CMOs and Analysts at these Agencies are actively plotting against allowing the company's marketing to evolve to where the present is, and where the future will be.

With that little concern expressed, hopefully squarely lodged into your mind, I still recommend media-mix modeling powered by inputs from controlled experiments. The reason is simple. We all have to make money for our companies. And, media-mix modeling is an incredibly valuable tool in that quest. Just remember, it simply solves for the now and not the next or the long.

Bonus: If you would like a more complex view into the three strategic ways to frame the attribution opportunity, check out this post: Multi-Channel Attribution: Definitions, Models and a Reality Check .

Closing Thoughts.

That's your evolutionary ladder when it comes to solving one of the most complicated challenge you are likely to face as a Marketer or an Analyst. The spectrum of upgrades you have to make to your tools and data along with your people, process and structure, are likely to be unmatched by any other challenge in front of you.

That is what makes this so much fun, so satisfying as a career choice and so rewarding from a compensation perspective. There is literally no harder thing you can do. I hope that, when offered, you'll choose to accept the ring. :)

Good luck!

As always, it is your turn now.

Does your company/agency's macro approach to achieving the optimal marketing portfolio reflect a revolution or an evolution? If you've completed all the steps, which step was the hardest? What is the most difficult facet of attribution modeling to explain to your senior executives? Does your company prioritize evolution of people, process, structure as it drives new contracts and expenditure on tools/data? If you had to give our readers one advice from your attribution journey, what would you say?

Please share your feedback, critique, praise, wisdom and best practices via comments.

Thank you.

Digital Attribution's Ladder of Awesomeness: Nine Critical Steps is a post from: Occam's Razor by Avinash Kaushik

from Occam's Razor by Avinash Kaushik