Tag Archives: officer

Creating A UX Strategy

(This is a sponsored article.) As designers working primarily on screen, we often think of user experience design as being primarily a screen-focused activity. In fact, user experience affects the entirety of what we build and that often includes activities that are undertaken off-screen.

To design truly memorable experiences, we need to widen our frame of reference to include all of the brand touchpoints that our users come into contact with along their customer journey. Doing so has the potential to materially impact upon business outcomes, recognizing the role that design — and user experience — can play at the heart of a wider business strategy.

Whether you’re building a website or an application, at heart you are designing for users and, as such, it’s important to consider these users at the center of a customer-focused ecosystem. Great brands are more than just logos or marques, and websites or applications, they’re about the totality of the user experience, wherever a customer comes into contact with the brand.

This expanded design focus — considering touchpoints both on- and off-screen — becomes particularly important as our role as designers widens out to design the entirety of the experience considering multiple points of contact. It’s not uncommon for the websites and apps we build to be a part of a wider, design-focused ecosystem — and that’s where UX strategy comes in.

Over the last few years, we have seen designers move up the chain of command and, thankfully, we are starting to see designers occupy senior roles within organizations. The emergence of designers as part of the C-Suite in companies is a welcome development and, with it, we are seeing the emergence of CDOs, Chief Design Officers.

As James Pallister put it in “The Secrets of the Chief Design Officer,” an article exploring the CDO phenomenon written for the UK’s Design Council:

“As Apple’s valuation shot higher and higher in recent years, a flurry of major corporations — Philips, PepsiCo, Hyundai &mdahs; announced the appointments of Chief Design Officers to their boards.

This was no mere coincidence. Seeking to emulate the stellar success of design-led businesses like Apple, global companies are pouring investment into design.”

This investment in, and appreciation of, design has been long overdue and is beginning to impact upon our day-to-day role as designers.

Forward-thinking companies are elevating the role of designers within their hierarchies and, equally importantly, stressing the importance of design thinking as a core, strategic business driver. As a result, we are seeing design driving company-wide business innovation, creating better products and more engaged relationships with customers.

As this trend continues, giving designers a seat at the top table, it’s important to widen our scope and consider UX strategy in a holistic manner. In this article, the eighth in my ongoing series exploring user experience design, I’ll open the aperture a little to consider how design impacts beyond the world of screens as part of a wider strategy.

Considering Customer Journeys

Before users come into contact with a website or an app, they will likely have been in contact with a brand in other ways — often off-screen. When considering design in the widest sense, it’s important to focus on the entirety of the customer journey, designing every point of contact between a user and a brand.

Forrester, the market research company, defines the customer journey as follows:

“The customer journey spans a variety of touchpoints by which the customer moves from awareness to engagement and purchase. Successful brands focus on developing a seamless experience that ensures each touchpoint interconnects and contributes to the overall journey.”

This idea — of a seamless and well-designed experience and a journey through a brand — should lie at the heart of a considered UX strategy. To design truly memorable experiences, we need to focus not just on websites or apps, but on all of the touchpoints a user might come into contact with.

Consider the Apple Store and its role acting as a beacon for Apple and all of its products. The Apple Store is, of course, an offline destination, but that doesn’t mean that the user experience of the store hasn’t been designed down to the last detail. The store is just one part of Apple’s wider engagement strategy, driving awareness of the business.

The Apple Store is an entry point into Apple’s ecosystem and, as such, it’s important that it’s considered in a holistic manner: Every aspect of it is designed.

Jesse James Garrett, the founder of Adaptive Path which is an end-to-end experience design company, considers this all-embracing approach in an excellent article, “Six Design Lessons From the Apple Store,” identifying a series of lessons we can learn from and apply to our designs. As Garrett notes:

“Apple wants to sell products, but their first priority is to make you want the products. And that desire has to begin with your experience of the products in the store.”

Seen through this lens, it becomes clear that the products we design are often just one aspect of a larger system, every aspect of which needs to be designed. As our industry has matured, we’ve started to draw lessons from other disciplines, including service design, considering every point as part of a broader service journey, helping us to situate our products within a wider context.

If service design is new to you, Nielsen Norman Group (helpful as ever), have an excellent primer on the discipline named “Service Design 101” which is well worth reading to gain an understanding of how a focus on service design can map over to other disciplines.

When designing a website or an app, it’s important to consider the totality of the customer journey and focus on all of the touchpoints a user will come into contact with. Do so, and we can deliver better and more memorable user experiences.

Designing Touchpoints

As our industry has evolved, we’ve begun to see our products less as standalone experiences, but as part of a wider network of experiences comprised of ‘touchpoints’ — all of which need to be designed.

Touchpoints are all the points at which a user comes into contact with a brand. As designers, our role is expanding to encompass a consideration of these touchpoints, as a part of a broader, connected UX strategy.

With the emergence of smartphones, tablets, wearables and connected products our scope has expanded, widening out to consider multiple points at which users come into contact with the brands we are designing.

When considering a UX strategy, it helps to spend some time listing all of the points at which a user will come into contact with the brand. These include:

  • Websites,
  • Apps and mobile experiences,
  • Email,
  • Support services,
  • Social media.

In addition to these digital points of contact, it’s important to consider >non-digital points of contact, too. These off-screen points of contact include everything, from how someone answers the phone to the packaging of physical products.

To aid with this, it helps to develop a ‘touchpoints matrix’ — a visual framework that allows a designer to join the dots of the overall user experience. This matrix helps you to visually map out all of the different devices and contexts in which a user will come into contact with your brand.

The idea of a touchpoints matrix was conceived by Gianluca Brugnoli — a teacher at Politecnico di Milano and designer at Frog Design — as a tool that fuses customer journey mapping with system mapping, which can be used as the basis for considering how different user personas come into contact with and move through a brand.

Roberta Tassi, as part of her excellent website Service Design Tools — “an open collection of communication tools used in design processes that deal with complex systems” — provides an excellent primer on how a touchpoints matrix can be used as part of a holistic design strategy. Tassi provides a helpful overview, and I’d recommend bookmarking and exploring the website — it’s a comprehensive resource.

As she summarises:

“The matrix brings a deeper comprehension of interactions and facilitates further development of the opportunities given by the system — of the possible entry points and paths — shifting the focus of the design activities to connections.”

This shift — from stand-alone to connected experiences — is critically important in the development of a ‘joined up’ UX strategy.

When you embark upon developing and mapping a broader UX strategy, a touchpoints matrix helps you to see how the different nodes of a design join up to become part of an integrated and connected experience or an ‘ecosystem.’

Building Ecosystems

When we holistically consider our role as designers, we can start to explore the design of the whole experience: from initial contact with a brand offline, through engaging with that brand digitally. Collectively, these amount to designing a brand ecosystem.

Ecosystems aren’t just for big brands — like Facebook, Instagram or Twitter — they are increasingly for everything we design. In a world that is ever more connected, what we design doesn’t stand in isolation. As such, we need to consider both context and scope as part of an integrated strategy.

In addition to considering the design of products, we also need to consider the wider ecosystem that these products sit within. For example, when considering the design of applications — whether web-based or native — we also need to consider: the user’s first point of contact and how we drive discovery; the experience while using the application itself; and addressing wider issues (such as offering users support).

All of the aspects of an ecosystem need to be designed so that we deliver great user experiences at every point in the process. This includes:

  • The process of discovery, through social and other channels;
  • The design of a company or application’s website, so that the story that’s told is consistent and engaging;
  • The content of email campaigns to ensure they’re equally considered, especially if there are multiple email campaigns targeted at different audiences;
  • The packaging, when we’re designing physical, connected products; and
  • The support we offer, ensuring that customers are looked after at every point of the journey, especially when issues arise.

This list is just the tip of the proverbial iceberg, but it clearly shows that there are multiple points on a customer’s journey that need to be designed. A considered UX strategy helps us to deliver on all of these aspects of an ecosystem and become increasingly important as the ecosystems we design become richer and more complex.

In Closing

The opportunities ahead are fantastic for designers working in this industry. The landscape we are designing for is evolving rapidly and, if we’re to stay ahead of the game, it’s important that we turn our attention towards the design of systems in addition to products. This involves an understanding of UX strategy in the broadest sense.

When embarking upon the design of a new website or product, or undertaking a redesign, it’s important to widen the frame of reference. Taking a step back and considering the entirety of the user experience leads to better and more memorable experiences.

By considering the entirety of the customer journey and all the touchpoints along the way we can create more robust, connected experiences. By focusing on the design of holistic experiences, we can delight users, ensuring they’re happy with the entire experience we have crafted.

This article is part of the UX design series sponsored by Adobe. Adobe XD is made for a fast and fluid UX design process, as it lets you go from idea to prototype faster. Design, prototype, and share — all in one app. You can check out more inspiring projects created with Adobe XD on Behance, and also sign up for the Adobe experience design newsletter to stay updated and informed on the latest trends and insights for UX/UI design.

Smashing Editorial
(ra, yk, il)

See original – 

Creating A UX Strategy

How Quora Can Help You Drive Massive Traffic and Conversions to Your Website

Many of you are probably familiar with Quora. But not everyone knows that Quora can be turned into a perfect marketing tool that will drive high quality targeted traffic and conversions to your website. I made the most out of Quora and continue to use it as one of my major marketing tools for increasing conversions. How, you ask? I’m one of the most viewed writers on many popular topics including videos, online videos, video production, and explainer videos. My answers gained 1.2 million+ views during the last 10 months. Want to know how to make a go of Quora…

The post How Quora Can Help You Drive Massive Traffic and Conversions to Your Website appeared first on The Daily Egg.

Original source:

How Quora Can Help You Drive Massive Traffic and Conversions to Your Website

How To Protect Your Users With The Privacy By Design Framework

In these politically uncertain times, developers can help to defend their users’ personal privacy by adopting the Privacy by Design (PbD) framework. These common-sense steps will become a requirement under the EU’s imminent data protection overhaul, but the benefits of the framework go far beyond legal compliance.
Note: This article is not legal advice and should not be construed as such.
Meet Privacy By Design Let’s give credit where credit is due.

Source:  

How To Protect Your Users With The Privacy By Design Framework

Thumbnail

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?

Hollywood paints a grim picture of a future populated by intelligent machines. Terminator, 2001: A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and — if given the opportunity — they will not hesitate to overthrow the human race.

Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.

machine-learning-and-marketing-featured-650
The machines are coming. But fear not — they could help you become a better marketer. Image via Shutterstock.

Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.

For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.

Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?

These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.

What is Machine Learning?

Machine learning is a complicated subject, involving advanced math, code and overwhelming amounts of data. Luckily, Tommy Levi, Director of Data Science at Unbounce, has a PhD in Theoretical Physics. He distills machine learning down to its simplest definition:

You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.

You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.

Other examples of machine learning include spell check, spam filtering… even internet dating — yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.


Machine learning may be helpful in getting products or services in front of the right prospects.
Click To Tweet


How Machine Learning Works

While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.

Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.

So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.

Supervised learning

Supervised learning systems rely upon humans to label the incoming data — at least to begin with — in order for the systems to better predict how to classify future input data.

Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.

Unsupervised learning

Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback. Instead, data scientists will often use a reward/punishment system to indicate success or failure.

According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right — like it makes a good prediction — you kind of give it a little pat on the back and you say good job.”

Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.

The Power of Machine Learning

A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever.

Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.

How do your landing page conversion rates compare against your industry competitors?

We analyzed the behavior of 74,551,421 visitors to 64,284 lead generation landing pages. Now we want to share average industry conversion rates with you in the Unbounce Conversion Benchmark Report.
By entering your email you’ll receive other resources to help you improve your conversion rates.

The advantage is not just speed, it’s also retention and pattern recognition. Tommy explains:

To go through that many pages and see those patterns and hold it all in memory and be able to balance that — that’s where the power is.

For some marketers, this raises a troubling question: If machine learning systems solve problems by finding patterns that we can’t see, does this mean that marketers should be worried about job security?

The answer is more nuanced than a simple yes or no.

Machine Learning and the Digital Marketer

As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data.

This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.

As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.


Marketers that don’t embrace data will fumble. Those that do will grow — ML can help.
Click To Tweet


That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard… or maybe you can’t even drive at all.

Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points.

So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many niches in marketing that are becoming more automated. Here are a few that stand out.

Lead scoring and machine learning

Lead scoring is a system that allows marketers to gauge whether a prospect is a qualified lead and thus worth pursuing. Once marketing and sales teams agree on the definition of a “qualified lead,” they can begin assigning values to different qualified lead indicators, such as job title, company size and even interaction with specific content.

These indicators paint a more holistic picture of a lead’s level of interest, beyond just a form submission typically associated with lead generation content like ebooks. And automating lead scoring takes the pressure off marketers having to qualify prospects via long forms, freeing them up to work on other marketing initiatives.

Once the leads have reached the “qualified” threshold, sales associates can then focus their efforts on those prospects — ultimately spending their time and money where it matters most.

Content marketing and copywriting

Machine learning models can analyze data points beyond just numbers — including words on your website, landing page or PPC ads. Machine learning systems can find patterns in language and detect words that elicit the most clicks or engagement.

Is emotional copywriting on your landing page effective in your industry?

We used machine learning to help create the Unbounce Conversion Benchmark Report, which shares insights on how different aspects of page copy correspond to conversion rates across 10 industries.
By entering your email you’ll receive other resources to help you improve your conversion rates.

But can a machine write persuasive copy? Maybe, actually.

A New York-based startup called Persado offers a “cognitive content platform” that uses math, data, natural language processing, emotional language data and machine learning systems to serve the best copy and images to spur prospects into action. It does this by analyzing all the language data each client has ever interacted with and serving future prospects with the best possible words or phrases. An A/B test could never achieve this at the same scale.

Think this is a joke? With over $65 million in venture capital and a reported average conversion rate uplift of 49.5% across 4,000 campaigns, Persado’s business model is no laughing matter.

Still, there is no replacement for a supremely personalized piece of content delivered straight to your client’s inbox — an honest call to action from one human to another.

Recently Unbounce’s Director of Campaign Strategy, Corey Dilley, sent an email to our customers. It had no sales pitch, no call to action button. It was just Corey reaching out and saying, “Hey.”

corey-dilley-marketing-email-1

Corey’s email had an open rate of 41.42%, and he received around 80 personal responses. Not bad for an email written by a human!

Sometimes it’s actions — like clicks and conversions — you want to elicit from customers. Other times the goal is to build rapport. In some cases we should let the machines do the work, but it’s up to the humans to keep the content, well, human.


There is no replacement for personalized content and an honest ask from one human to another.
Click To Tweet


Machine learning for churn prediction

In the SaaS industry, churn is a measure of the percentage of customers who cancel their recurring revenue subscriptions. According to Tommy, churn tells a story about “how your customers behave and feel. It’s giving a voice to the customers that we don’t have time or the ability to talk to.”

Self-reporting methods such as polls and surveys are another good way to give a voice to these customers. But they’re not always scalable — large data sets can be hard for humans to analyze and derive meaning from.

Self-reporting methods can also skew your results. Tommy explains:

The problem with things like surveys and popups is that they’re only going to tell you what you’ve asked about, and the type of people that answer surveys are already a biased set.

Machine learning systems, on the other hand, can digest a larger number of data points, and with far less bias. Ideally the data is going to reveal what marketing efforts are working, thus leading to reduced churn and helping to move customers down the funnel.

This is highly relevant for SaaS companies, whose customers often sign up for trials before purchasing the product. Once someone starts a trial, the marketing department will start sending them content in order to nurture them into adopting the service and become engaged.

Churn models can help a marketing team determine which pieces of content lead to negative or positive encounters — information that can inform and guide the optimization process.

Ethical Implications of Machine Learning in Marketing

We hinted at the ethical implications of machine learning in marketing, but it deserves its own discussion (heck, it deserves its own book). The truth is, machine learning systems have the potential to cause legitimate harm.

According to Carl Schmidt, Co-Founder and Chief Technology Officer at Unbounce:

Where we are really going to run into ethical issues is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.

This could mean targeting someone in rehab with alcohol ads, or someone with a gambling problem with a trip to Las Vegas. The machine learning system will make the correlation, based on the person’s internet activity, and it’s going to exploit that.

Another dilemma we run into is with marketing aimed at affecting people’s emotions. Sure copywriters often tap into emotions in order to get a desired response, but there’s a fine line between making people feel things and emotional manipulation, as Facebook discovered in an infamous experiment.

If you aren’t familiar with the experiment, here’s the abridged version: Facebook researchers adapted word count software to manipulate the News Feeds of 689,003 users to determine whether their emotional state could be altered if they saw fewer positive posts or fewer negative posts in their feeds.

Posts were deemed either positive or negative if they contained at least one positive or negative word. Because researchers never saw the status updates (the machine learning system did the filtering) technically it fell within Facebook’s Data Use Policy.

However, public reaction to the Facebook experiment was generally pretty scathing. While some came to the defense of Facebook, many criticized the company for breaching ethical guidelines for informed consent.

In the end, Facebook admitted they could have done better. And one good thing did come out of the experiment: It now serves as a benchmark for when machine learning goes too far, and as a reminder for marketers to continually gut-check themselves.

For Carl, it comes down to intent:

If I’m Facebook, I might be worried that if we don’t do anything about the pacing and style of content, and we’re inadvertently presenting content that could be reacted to negatively, especially to vulnerable people, then we would want to actively understand that mechanism and do something about it.

While we may not yet have a concrete code of conduct around machine learning, moving forward with good intentions and a commitment to do no harm is a good place to start.

The Human Side of Machine Learning

Ethical issues aside, the rise of machines often implies the fall of humans. But it doesn’t have to be one or the other.

“You want machines to do the mundane stuff and the humans to do the creative stuff,” Carl says. He continues:

Computers are still not creative. They can’t think on their own, and they generally can’t delight you very much. We are going to get to a point where you could probably generate highly personal onboarding content by a machine. But it [will have] no soul.

That’s where the human aspect comes in. With creativity and wordsmithing. With live customer support. Heck, it takes some pretty creative data people to come up with an algorithm that recognizes faces with 98% accuracy.

Imagine a world where rather than getting 15 spam emails a day, you get just one with exactly the content you would otherwise be searching for — content written by a human, but served to you by a machine learning system.

While pop culture may say otherwise, the future of marketing isn’t about humans (or rather, marketers) versus machines. It’s about marketers using machines to get amazing results — for their customers and their company.

Machine learning systems may have an edge when it comes to data sorting, but they’re missing many of the things that make exceptional marketing experiences: empathy, compassion and a true understanding of the human experience.

Editor’s note: This article originally appeared in The Split, a digital magazine by Unbounce.

See original article – 

Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?

How To Set Up An Automated Testing System Using Android Phones (A Case Study)

Regression testing is one of the most time-consuming tasks when developing a mobile Android app. Using myMail as a case study, I’d like to share my experience and advice on how to build a flexible and extensible automated testing system for Android smartphones — from scratch.

How To Set Up An Automated Testing System Using Android Phones (A Case Study)

The team at myMail currently uses about 60 devices for regression testing. On average, we test roughly 20 builds daily. Approximately 600 UI tests and more than 3,500 unit tests are run on each build.

The post How To Set Up An Automated Testing System Using Android Phones (A Case Study) appeared first on Smashing Magazine.

More: 

How To Set Up An Automated Testing System Using Android Phones (A Case Study)

5 Conversion Rate Optimization Challenges For Enterprises To Solve

Although the interest in conversion rate optimization is increasing over time, organizations are unable to adopt it fully. To ensure its smooth adoption and implementation, certain challenges and misconceptions need to be addressed.

interest in conversion rate optimization google trends
Google Trends

In this post, we will talk about 5 such conversion optimization challenges that enterprises face and ways to overcome them.

Challenge 1. Politics and People—A Cultural Challenge

An organization’s culture is made of 2 core components—people (skill and mindset) and their interpersonal relationships (power to influence and politics ). Creating a conversion optimization culture becomes challenging when either people lack the understanding and skill or when influential people in the organization want their opinions to be valued more than what data and facts indicate.

Political

Brian Massey, Founder, Conversion Sciences shares his view on the political challenge as follows:

Brian Massey

Why has Donald Trump’s top-down, opinion-driven leadership style been accepted by the white-collar working public in the US? Because enterprise businesses have trained us that this is how leadership works. We have a name for this leadership style: “HiPPO,” or Highest Paid Person’s Opinion. Joel Harvey calls it Helicopter Management. This is the management style of charismatic or autocratic leaders who drive action in their organizations by helicoptering in, expressing a lightly-informed opinion, and enforcing their opinion in one of the following two ways:

* They bestow budget upon the loyal.

* They threaten the jobs of the disloyal.

So marketing teams can grab the budget and buy the latest tools. But they then struggle to find the man-hours necessary to make the tools effective.

Like all big business problems, it’s a cultural issue.”

James Spittal, Chief Executive Officer, Web Marketing ROI also talks about the HiPPO effect and the political challenge that obstructs a culture of conversion rate optimization.

James Spittal

Only a small portion of changes are A/B tested, kind of like the “HiPPO” effect. The typically small and under-resourced internal CRO team madly tries to work with an agency to get as many A/B tests launched as possible and keeps up their A/B test velocity while talking to everyone about CRO. Meanwhile, a C-level executive asks for a change to be pushed straight into the source code base without it being tested, costing the organization potentially millions of dollars and because they don’t know any better.

Keith Hagen, VP & Director of Conversion Services at Inflow views politics as an obstacle in the implementation of quality insights for any CRO program.

Keith Hagen

Not all insights are equal. One insight can be worth millions; the other may not move the needle at all while the enterprise pays its employees to test and implement that insight as well.

Terming what an insight actually is, is important as well. Insights come from customers and identify a customer obstacle or opportunity.  If you are not making something better for the customer or capitalizing better on what you have, it should not be worked on. Enterprise organizations have a lot of voices, and the higher paid voices tend to influence what optimizations are made to a site.

The solution he proposes—Score Insights Based on Their Potential.

Every insight should be scored on its potential and shared across the organization. Whether the insight is about an obstacle to a purchase or an opportunity to sell more, the potential should be assigned a dollar value so that it is clear what NOT working on the insight will cost.

People

James Spittal, Chief Executive Officer, Web Marketing ROI attributes the lack of skill—technical or development—with regard to why people in an organization pose a challenge to creating a culture of CRO.

James Spittal

This challenge simply occurs because of people in an enterprise not having the knowledge, talent, or skills. Often, we see people with a graphic design, pure web design, pure analytics, or pure UX background become the “de facto” CRO team. But they struggle because it’s unlikely that they have the technical skills or development skills to be able to implement advanced A/B test ideas (major layout changes, modals, segmentation, changing cart flows, doing tests on pricing, etc.). Often, they also struggle to get resources internally or externally and build a strong business case to increase the CRO budget.

Johann Van Tonder, COO, AWA Digital, shares similar views regarding people and the lack of talent to implement conversion optimization.

Johann Van Tonder

The challenge is to find good optimization talent. While there is no shortage of people marketing themselves as CRO practitioners, only a small percentage of the candidates we screen make it into our organization. This is the same pool that enterprises are recruiting from.  

A good optimizer is both analytical and creative, with a solid grasp of disciplines as diverse as psychology, copywriting, marketing, and statistics. They are brilliant communicators with an entrepreneurial drive and at least basic coding skills. Finding them is not easy.

Solution

The first step of creating a culture of data-driven conversion optimization in any organization is to educate the people about its benefits. Any enterprise planning to implement such a shift—moving from random A/B testing to scientific conversion optimization—must first understand the “why” behind it. That’s why we have 15 conversion rate experts share why they feel it is important to step up from A/B testing to conversion optimization.

Any cultural change requires the complete support of the top management. That’s why it is all the more important to convince it about conversion optimization. Here’s how you can use data to convince your top management about why they need conversion optimization:

  • Highlight improved user experience as a double win.
  • Present a competitive analysis.
  • Stress the gaps in your current approach.
  • Show the money.
  • Show the data.

Challenge 2. No Defined Structure that Supports CRO

It’s a huge challenge for enterprises to put together a structure that supports conversion optimization effectively. There are a number of questions that arise when addressing this challenge. Would it be beneficial to hire a dedicated conversion optimization team, or would it mean only additional expenditure? Who is responsible for conversion optimization?

With regard to this challenge, some interesting observations were listed by ConversionXL’s report on State of Conversion Optimization 2016. One of the findings quoted in the report mentions, “…only 29% of people said that there’s a single dedicated person who does optimization. 30% more said there’s a team in charge of optimization, but 41% of respondents had no one in particular that was accountable for optimization efforts.”

Some companies have internal conversion optimization teams that comprise an analyst, designer, marketer, and project manager. However, should these people invest all of their time on conversion optimization? One way of dealing with this is to have all team members allocate time between core job functions and conversion optimization.

Another challenge related to the lack of structured process to conversion optimization, as explained by Tim Ash, CEO of SiteTuners, and a digital marketing keynote speaker, is the isolation of the CRO team from the rest of the teams.

Tim Ash

The biggest problem that an enterprise CRO faces is the siloing emblematic of big companies. All job functions and even departments are compartmentalized and do not communicate well with each other. So even though a CRO group or team exists within the company, it is only able to focus on limited tactical objectives and simple split testing. Typically, CRO initiatives pass through compliance and approval reviews, get watered down by the branding gatekeepers, and then languish in the IT development queue to get implemented.

At SiteTuners, we have developed our Conversion Maturity Model to grade organizations on key aspects of their optimization effectiveness. Dimensions include culture and processes, organizational structure and skill set, measurement and accountability, the marketing technology stack, and of course the user experience across all channels.

One of the biggest determiners of success is whether there is active and consistent support for CRO from high-ranking executives. If there is political air-cover and the CRO team reports high up in the company, this team can work across the silos to tackle fundamental business issues involving products and services, the business model, back-end operational efficiencies, and fundamental user experience redesigns.

Solution

Lay down a clear process for conversion optimization that needs to be followed by everyone in the organization. Create a dashboard or platform where all the conversion optimization activities are planned, updated, and reported. Share this platform with everyone in the organization. Encourage a culture where everyone contributes to conversion optimization. However, make decisions based only on data. For example, while deciding what to test and optimize, follow a scientific hypotheses prioritization framework. The benefit—though everyone gets to share their observations and hypotheses—is that only the most relevant of those are tested.

Challenge 3. Inefficient Methodology to Implementing Conversion Optimization

Paul Rouke, Founder and CEO, PRWD points out that lack of user research is one problem in the current conversion optimization methodology followed by most enterprises.

Paul Rouke

Among enterprises, a lack of an intelligent and robust optimization methodology is a major barrier to them making experimentation a trusted and valued part of their growth strategy. Lack of user research in developing test hypotheses, alongside lack of innovative and strategic testing, instead a focus on simple A/B testing, are some of the biggest barriers which prevent enterprises from harnessing the potential strategic impact conversion optimization could have for their business.

As shown below, the interest in A/B testing is far more widespread than in conversion optimization.

interest in a/b testing vs. interest in conversion optimization - google trends
Google Trends

It is important to understand that testing random ideas based on opinions is not a smart way of testing. You may get a winning variation even by testing “ideas,” but this will not help solve the real pain points that users face. The challenge, therefore, is to eliminate guesswork; and the solution is to focus on data instead.

Here’s what Brian Massey has to say regarding eliminating guess work and relying on a behavioral data-based methodology.

Brian Massey

Enterprises are missing out on an area, that is, following Moore’s Law in terms of increasing capability and decreasing costs. Behavioral data collection is dropping precipitously in price, and new capabilities are coming online weekly. Just as Microsoft didn’t realize that mobile phone market would follow Moore’s Law, enterprises run the risk of missing the growth in Behavioral Science, a discipline designed to eliminate guessing from business strategy and tactics.

Mathilde Boyer, Head of CXO, House of Kaizen and Peter Figueredo, Founding Partner, House of Kaizen also talk about what is inefficient about the current conversion optimization methodology, as followed by some enterprises.

Mathilde Boyer

Opinion-based A/B testing is the gangrene of CRO programs. It hinders the process of objective creation and prioritization of test hypothesis. This tendency can lead to situations where a high level of resources are invested in low-impact optimization activities. Generation and prioritization of test hypothesis needs to be data-driven, systematic, repeatable, and teachable to allow for expansion of optimization activities across a business.

Peter Figueredo

Companies who invest in CRO typically rush to get testing started and overlook the importance of conducting research. Without proper research for informed testing, the design process CXO has lower chances of success. If your doctors do not know the root cause of your ailment, then they are likely only treating the symptoms but not curing the disease. Research should never be ignored and should be a critical component of House of Kaizen’s CXO success.

Solution

Data-driven optimization is focused on identifying friction, understanding the why behind user behavior, and testing hypotheses based on that data/information. Here’s what a formalized conversion optimization methodology would comprise:

  1. Researching into the existing data
  2. Finding gaps in the conversion funnel
  3. Planning and developing testable hypotheses
  4. Creating test variations and executing those tests
  5. Analyzing the tests and using the analysis in subsequent tests

You can read more about the scientific methodology for conversion optimization in this post.

Andre Morys, CEO of Web Arts,  in one of his interviews, talks about what’s wrong with the methodology. According to him, 80–90% of big companies do not aim for bigger goals, which could be change in the growth rate. This is another methodology-related drawback, as the goals being set do not take the profitability into account. Andre’s interview answers many other questions related to business growth.

Challenge 4. Choosing the Right Tool to Meet the Business Goals

The decision-makers in an organization have a variety of tools to choose from for meeting their business goals.  For example, when deciding on an A/B testing tool, they have to make a choice between a:

  • Frequentist-based statistical engine
  • Bayesian statistical engine

Moreover, there are multiple tools that help accomplish specific objectives. Enterprises might use hotjar for heatmap reports, a/b testing from VWO, and some other tool for on-page surveys. Reporting becomes a pain when instead of using one connected platform, enterprises use multiple tools to execute their conversion optimization program. If enterprises instead switch to a single connected platform, they can save a lot of time and resources.

Another problem with not using a single tool for testing and optimization is that it becomes difficult to explain instances of success and failure to the top management. This could be confusing for managers who are not in touch with day-to-day implementation of the conversion optimization program.

Solution

For selecting the correct tool, decision-makers need to weigh the pros and cons of their actions. They need to evaluate the tool based on how effectively and efficiently it can solve their specific business problems. For enterprises looking to invest in a tool for business growth, here’s a post on what decision-makers need to know before investing in CRO or A/B testing software.

Challenge 5. Insufficient and Incorrect Budget Allocation

Back in 2013, most companies spent less than 5% on conversion optimization from their total marketing budget.

budget for conversion optimization - graph

Moving on to 2014, a report from Adobe says that top-converting companies spend more than 5% of their budgets on optimization. Per the conversion optimization report 2016 by ConversionXL, businesses have increased their spend on optimization. The problem, however, lies in correct allocation.

Paul Rouke talks about inefficient budget allocation as follows:

Paul Rouke

Budgets for conversion optimization within enterprises are continuing to increase, but typically in the wrong direction. Enterprises focus far too much of their marketing investment in enterprise technology. As a result, there’s little investment in people and their skills to actually harness the technology—whether building their in-house team or harnessing specialist agencies.

Enterprises which invest in Human Intelligence (HI), above and beyond technology, and AI are the ones who are positioning themselves for significant and sustainable growth. Growth is about people.

Solution

Before deciding the amount that enterprises should spend on conversion optimization, they should think about the return on investment from CRO. Organizations need to budget for the conversion optimization tool while analyzing their goals and actual gains. To read more on how to budget for conversion optimization, read this post by Formstack.

Summary 

Although the interest in conversion optimization is growing, due to certain challenges, it is not being adopted fully by enterprises. Some of the drawbacks that this post talks about are related to organizational culture, structure, methods and processes, tools for conversion optimization, and budget. These challenges are either related to adoption of conversion optimization or its smooth implementation. Solving these can help enterprises deploy conversion optimization efficiently and effectively to achieve growth and success.

Hope you found this post insightful. We’d love to hear your thoughts on challenges that enterprises face when implementing conversion optimization. Send in your feedback and views in the comments section below.

Approach_Increasing_Conversion_Rates_Free_Trial


The post 5 Conversion Rate Optimization Challenges For Enterprises To Solve appeared first on VWO Blog.

Read original article: 

5 Conversion Rate Optimization Challenges For Enterprises To Solve

The Realities Of User Experience Design Within The Luxury Industry

For luxury companies and upscale lifestyle service providers, excellence in experience is an essential component of the value delivered. Conceptually different from the mass market, the luxury domain relies not only on offering the highest differentiated products and services, but on delivering experiential value.

The Realities Of UX Design In The Luxury Industry

Adopting technology and embracing a digital presence through platforms and initiatives, the luxury industry today is tackling the challenge of designing an unparalleled user experience (UX) online. In this article, we’ll present a case study and share observations on the peculiarities of the UX design of a luxury lifestyle service platform and its mobile apps.

The post The Realities Of User Experience Design Within The Luxury Industry appeared first on Smashing Magazine.

Link to original:  

The Realities Of User Experience Design Within The Luxury Industry

Going Omnichannel | A Robust Framework for eCommerce Enterprises

Consumers in the digital age want an integrated shopping experience. They might browse an eCommerce website on mobile but ultimately make a purchase from desktop. Or they might pay online, but pick up the purchased item from the store.

Such user behavior has been highlighted by a 2014 GfK study: “With people constantly moving between devices, it is important for marketers to reach their audience across all platforms. Brand experiences should be consistent, allowing for people to begin an activity on one device and finish on another.”

In this post, we discuss a robust omnichannel strategy that can help eCommerce enterprises create such integrated experiences across devices. The strategy includes:

  • Understanding cross-device user behavior
  • Crafting smooth shopping experiences across channels
  • Forming organizational structures that support omnichannel

But before we begin, let’s see how an ideal omnichannel experience for a consumer, say “Sarah” would look like:

Sarah is checking Instagram from her mobile and likes a dress her friend is flaunting. She visits the retailer’s website on mobile. She adds the product to her “wishlist” on mobile. Later during the day, she accesses her wishlist on the desktop, with the decision to make a buy. She chooses the option “inform when available in my size” and 3 days later, gets an email notifying her about the availability of the dress. It also informs her that “click and collect” is available on the product. She decides to pick up the dress from a physical store.

So how do eCommerce enterprises go omnichannel successfully? Let’s talk about the three steps.

Tracking Cross-Device User Behavior

The fact that people toggle among multiple devices throughout the day makes understanding the cross-device user behavior an absolute essential for eCommerce enterprises. Traditional analytics tracking tools such as Google Analytics do not offer the scope for establishing a connect between users and their disparate gadgets. Cross-device tracking removes this barrier for eCommerce enterprises and enables them to understand their users’ behavior across all touchpoints.

Cross-device tracking allows enterprises to understand whether a person browsing a website from smartphone X is the same person who made the purchase from laptop Z. Such information is important to rectify conversion credit allocated disproportionately to the last device of purchase. So if the use of mobile devices leads to desktop purchases, eCommerce enterprises might want to spend more on mobile ads and mobile website optimization.

cross device user tracking
A simple representation of cross-device usage

There are two main methods to track cross-device user behaviordeterministic and probabilistic.

Deterministic Device Matching

This methodology makes use of user’s signin information. As users are required to sign in to the website on each device they use, enterprises can track their behavior across all touchpoints. User Authentication is a type of deterministic device matching. It uses specific identifiers such as a customer ID, signin information, and so on to study and form a link between user behavior across devices.

Probabilistic Device Matching

Unlike deterministic device matching, this cross-device tracking technique does not rely solely on the user’s signin information. As the name indicates, this method computes the probability that various devices belong to or have been used by the same individual. An example of how probabilistic device matching works is extrapolation. For example, if a mobile and a tablet use the same Internet connection, it can be extrapolated that they belong to the same household. Device Fingerprinting is another famous probabilistic cross-device tracking technique. It combines device settings and browser options with some other attributes such as WiFi info, IP address, and more to identify users.

Build Smooth Shopping Experiences Across Channels

The next step, after tracking and understanding user behavior across devices, is to create seamless experiences for your users.

Walmart CEO, Doug McMillon shares his thoughts on a seamless customer experience:

“Ultimately, customers don’t care about what channel they’re shopping in or about how we deliver them a product or service. They simply know they’re shopping with Walmart.”

For Walmart, no matter what channel their customers buy from, it is important that they recognize the brand and get the same shopping experience throughout. Creating cohesive, consistent brand voice/experience can help eCommerce enterprises pave trust and encourage strong engagement, and, therefore, improve sales.

Other than brand consistency, a smooth and seamless shopping experience also constitutes customer experience. Hubspot talks about Oasis, a UK fashion retailer, in their seven inspiring examples of omnichannel user experience. On entering one of their stores, you’ll find sales associates walk you through all the product-related information using iPads. So, just in case something  is out of stock, the staff places an online order for the customer and the item  is shipped directly to customer’s home.

Here’s how Oasis uses iPads in-store to assist customers:

Omnichannel Strategy Oasis
Source

eCommerce enterprises should focus on the following points for providing a superior omnichannel shopping experience:

  • Providing relevant local information
  • Ensuring faster, safer payment solutions
  • Providing personalization
  • Making use of advanced technologies

Providing relevant local information

 A post on Think with Google reports that 75 percent of the shoppers who find local retail info in search results helpful are more likely to visit stores. For eCommerce enterprises, this data opens up a number of opportunities. For example, eCommerce enterprises can  inform online customers looking for a particular item online about its availability at a nearby store. To make this activity more effective, they can use geo-targeting to drive more in-store purchases from people  from the local vicinity who have an intent to buy.  Moreover they can also provide information such as local store hours, directions to the local store, or any discounts running in the store. Providing local relevant information online can also help convert more of those shoppers who view shopping as an experience and not just a purchase activity. Retailers, on the other hand, can benefit from the impulse buying tendency of people who exhibit a search online, shop local behavior.

Ensuring faster, safer payment solutions

 A Search Engine Journal post lists 10 popular online payment solutions such as Amazon Payments and Google Wallet. As these options are trustworthy and secure, these will encourage users to pay from any channel that they use.

Deploying these payment solutions is a win-win for both the parties, because these solutions are  convenient, quick, and trustworthy.

Providing Personalization

The interconnected and digitally empowered consumer demands relevant and personalized experience. For an omnichannel player, this would mean understanding which devices are used by the consumers and how. For example, Evergage talks about how eBay creates omnichannel personalization for its users. The eBay mobile app allows users to enable push notifications, which informs them about the start or end of any auction. The desktop site, on the other hand, is designed for easy search and window shopping.

omnichannel strategy - ebay personalized push notification
Source

Advanced Technologies

Innovation and technology enhance the omnichannel experience both for buyers and eCommerce enterprises. Using virtual reality, for example, can help eCommerce players make use of virtual environments that are otherwise difficult to create inside a store. For the user, these technologies can address buyer’s uncertainty.

For example, before making a decision to buy a hat, a person would like to know which hat type, color, width, and so on would suit him the best. Without physically trying a number of different hats, he can use such technologies to find out what looks best on him. For the eCommerce enterprise, this means being able to provide their users with better services and experience even if all the types of hats are not physically in store.

Tommy Hilfiger also provides a fantastic in-store VR experience. As a result, shoppers can view virtual catwalks and shop the season’s runway styles.If you are looking for more on the who and how of virtual and augmented reality in retail and eCommerce, here’s a Forbes post to read.

The following image shows customers experiencing Tommy Hilfiger VR:

Virtual Reality in Tommy Hilfiger Omnichannel Strategy
Source

Forming an Organizational Structure that Supports Omnichannel

Customer experience might suffer if an eCommerce enterprise is not structured to meet the requirements of omnichannel retail. When departments operate in silos, the problem of sales attribution often arises. Such conflicts are unhealthy, as they can jeopardize the enterprise’s ability to deliver a smooth omnichannel experience.

An organizational structure that is better aligned for omnichannel, requires various departments within an organization to work together and be accountable to each other. Macy’s, for example, has also completely restructured their merchandising and marketing functions. They have also created chief omnichannel officer positions in their organization.

Keith Anderson, SVP Strategy & Insight, Profitero,  suggests the following when it comes to creating supportive organizational structures for omnichannel.

“Here is the approach I suggest:

  • Top-down commitment and support are essential. In the absence of the same, many organizations fail to prioritize or align on how to implement and execute on omnichannel.
  • Key functions should be responsible, but the whole organization is accountable. Certain teams or titles should be primarily responsible for doing the work of marketing and selling through all channels. But the entire business should be accountable. There is a risk in simply appointing a “head of omnichannel,” without anticipating the impacts on other functions such as customer service, finance, and logistics. Digital and omnichannel competency is necessary for all company functions and disciplines, not just an isolated, specialist team.
  • Definitions of success and incentives matter. Many companies that try to embrace omnichannel discover internal conflicts driven by misaligned incentives. For example, who gets the credit for an online sale fulfilled and collected in-store? How are inventory and labor costs allocated?

Ultimately, KPIs and incentives need to balance near-term and long-term goals such as maximizing profitability in the short-term versus growing market share. Also, enterprise success must always be prioritized over success in an isolated channel.”

Conclusion

While creating  customer-centric experiences is the key to succeeding with omnichannel, it begins with understanding user behavior and extends to framing the right kind of organizational structures. There is a huge scope for eCommerce enterprises to adopt and excel at an omnichannel level, given that they make use of user information, technology, customer service, and their internal structures efficiently.

Over to You

Have feedback on how eCommerce enterprises can develop a robust omnichannel strategy? Please leave a comment.

0

0 ratings

How will you rate this content?

Please choose a rating

The post Going Omnichannel | A Robust Framework for eCommerce Enterprises appeared first on VWO Blog.

Continue reading: 

Going Omnichannel | A Robust Framework for eCommerce Enterprises

How to navigate the murky waters of marketing ROI

Reading Time: 6 minutes

“What are the best marketing channels to invest in for my business?”

As a marketer, this is a question you’ve probably mulled over, over and over again.

And it all comes down to Return on Investment (ROI). You should spend your marketing dollars on the strategy or strategies that you can prove will get you the biggest bang for your buck.

Yes, yesterday’s CMO was about communications, branding, and advertising. Today, the CMO is a strategic partner to the CEO, someone expected to understand the business landscape well enough to articulate and predict which markets, products, services, or execution strategies will deliver the most profitable growth.

The days of gut-feeling marketing are long past; today, being able to track and prove the validity of your efforts is vital.

But determining the ROI of a particular marketing strategy can be difficult.

Your customer experiences your brand and your website in numerous ways―they are coming in from so many varied touchpoints, from email, to Snapchat, to Bing Ads, to whatever the shiny, new marketing tactic is this week.

It’s unlikely that one interaction is responsible for capturing them, making it difficult to untangle and measure one marketing channel against another. While there are many different strategies and workarounds for attributing marketing ROI, it’s easy to get overwhelmed.

In a sea of murky ROI estimations, how can you best determine where to invest your marketing dollars?

First things first: How do I measure ROI?

The simplest way to calculate ROI from a marketing strategy is to take the sales growth from your product or business, subtract the marketing cost, and divide by the marketing cost:

Simple Marketing ROI:
ROI equation

This is an oversimplified equation, of course, as it is rare that a single factor is influencing your sales growth at any given time. However, you can use it to get a general idea of ROI for a particular strategy.

How-we-calculate-ROI_ROIGraph

For example, let’s say you invest $1,000 in an ad campaign that runs for one month, and you see sales growth of $2,000. Your simple ROI is 100%: (($2,000-$1,000) / $1000). That’s pretty great!

But this equation assumes that none of that observed sales growth is organic…which most likely isn’t the case.

Note: In the aforementioned equation, I use “sales growth”, but there are other values you can use that may make more sense for your business. Read more here.

Ok, so how do I account for organic growth?

To predict organic sales growth, examine your monthly sales from the previous year and calculate the average organic growth per month. You can use this average organic growth rate to estimate where your sales might have been without your marketing campaign activity, and adjust your original ROI calculation accordingly.

ROI_organic_growth

If your business has an average organic growth of 5% over the period of a year, your calculation would look like: (($2,000 – $1,000)/$1,000) – 5 = 95%

This slightly more sophisticated equation indicates that ROI for this campaign is actually 95%, a substantial difference.

How can I predict what strategy will have the biggest ROI for my business?

Your industry, location, pricing, and even brand equity can dramatically affect ROI, which is why relying on average benchmarks can be dangerous. However, you can leverage information like the following studies (published by Nielsen) to get an idea of where other companies are spending their marketing dollars:

Source: Nielsen.
Source: Nielsen.

According to this research, Online Ads/Digital Marketing Investments have a higher ROI across all industries, but “online ads/digital marketing investments” is a pretty big bucket.

Source: Nielsen
Source: Nielsen

With Instagram’s algorithm-based feed, Snapchat’s in-app ad growth, and even Reddit getting in on the “promoted post” action recently, there are tons of options for advertising online. You must take into account the strengths and weaknesses of each channel when you’re thinking about where to invest. Banner ads, for example, are a popular channel but 54% of online banner ads are never seen!

Using the aforementioned formulas, you can measure the ROI of your social media and email marketing campaigns using conversions from specific landing pages. (Simply replace “sales growth” with “funnel conversions”). You can also track conversions with UTM parameters in Google Analytics. This allows you to track your visitors from the source through your funnel to prove the results you’re driving.

For a strategy like SEO, you can track ROI using your Google Analytics: segment by organic, non-branded traffic (to gauge how you’re ranking for non-branded keywords) and track conversions. Because GA tracks multi-channel attribution, you should be able to determine whether or not a customer clicked on an ad, then came to your site through search, or vice versa. It’s not always black and white, but you can get a good idea of ROI on your SEO.

ROI_where_do_I_spend
The ultimate question: Where should you spend my marketing dollars?

Each marketing strategy you invest in will take a certain amount of time to reach your target market and begin generating ROI, so it’s important to prioritize for maximize impact. Vanity metrics ― like number of social media followers ― can be helpful in terms of gauging your brand awareness, but they shouldn’t be your main concern. You want to keep track of which strategies are actually generating sales and revenue, now and in the long run.

When you’re thinking about average ROI benchmarks for digital marketing strategies, be wary of conversions versus revenue. For example, a Fortune 500 company might be able to generate the same sort of revenue with a less than 2% increase in conversions as a smaller company with low traffic might be able to generate with a 40% increase in conversions.

Calculating the ROI of conversion optimization

Conversion rate optimization (CRO), more simply known as conversion optimization, is the science and art of getting a higher percentage of your web visitors to take action to become a lead or customer through testing.

Testing, measuring, and proving are built into conversion optimization, making ROI refreshingly easy to calculate: it’s unique in that you can see the return with each test that you run. CRO has become a de facto strategy because each of your marketing channels becomes more effective when your site is optimized.

Conversion optimization gives immediate results and that’s a great feeling. Particularly with e-commerce, if you have an idea, you test it, and you know you’re about to see what that idea is worth in monetary value.

– Jose Uzcategui, Global Analytics and Ecommerce Conversion Lead, ASICS

In an optimization experiment, your original page serves as the experimental control and benchmark for ROI. The challenger page (variation A) is tested against the original, showing the difference in conversion rates and projected revenue between the two. Marketing, promotions, and seasonality are all constant between the two pages, because they exist simultaneously.

The formula for calculating the ROI of CRO looks like this:
ROI (3)

Revenue from the Challenger or Original can be calculated from: (number of visitors x conversion rate x goal value).

For example, let’s say you run a test for one month. You spend $2,000 on designing the challenger page and it generates $5,000 in revenue from conversions. Meanwhile, your original page generates $2,000 in revenue from conversions.

The calculation would look like this:

($5,000-$2,000-$2,000)/$2,000=50%.

Related: Try our free ROI calculator to discover your company’s potential return on testing.

50% ROI! Not bad for a month-long test. However, unlike “pay once, benefit once” marketing tactics, the benefits of optimization are compounded and long-term. If this variation continues to perform at the new (increased) conversion rate for 12 months, the ROI is actually 600%: 12*(($5,000-$2,000-$2,000)/$2,000).

Additionally, as your other marketing streams (SEO, PPC, Content) funnel visitors to your website, the increased conversion rate from your conversion optimization efforts will help increase the ROI for those marketing streams as well.

Jamie Elgie | weBoost

Reading Time: 1 minutes

WiderFunnel delivers a cadence and quality of A/B testing that is game-changing for our brand. Direct sales increases are enabling us to increase our spend on other advertising because of the known performance return. That in turn is driving our overall brand awareness. Put simply, WiderFunnel does not just help us sell directly; it is rocket fuel for our entire cross-channel marketing program.

Jamie Elgie

Jamie Elgie
Chief Marketing Officer, weBoost

What does the return on testing look like over the long term?

It’s important to re-validate the results of your conversion optimization strategy every few months, to ensure that that 600% ROI prediction is actually something you can take to the bank.

Here’s an example of a re-validation test we ran for one WiderFunnel client. After two years of optimization, we had seen a calculated conversion rate lift of 259.8% compared to their original page, as shown by the dark blue vertical bars:

24 months.

This calculated, cumulative conversion rate lift had resulted in solid revenue increases. But we wanted to make sure that the calculated lift reflected the actual lift.

To do this, we ran a simple A/B test, pitting the client’s original page against the most recent variation. Not only did we validate the calculated conversion rate lift, we found that the actual lift was 282.2%!

24 months verified

This rigorous verification proves that the results from conversion optimization are not temporary. In fact, the results are, so far, permanent.

Find out your potential optimization ROI:

The ROI of conversion optimization is tangible and easy to prove because it’s baked into the strategy itself. With most other marketing strategies, you’re left guesstimating ROI; with optimization, each experiment paints a clear picture of your return on testing.

If you’re curious about your potential ROI from CRO, you should try out our ROI Calculator!

The post How to navigate the murky waters of marketing ROI appeared first on WiderFunnel Conversion Optimization.

Jump to original: 

How to navigate the murky waters of marketing ROI

Thumbnail

Using Motion For User Experience On Apps And Websites

Digital experiences are emulating real life more and more every day. This may seem counterintuitive, considering the hate that rains down on skeuomorphic visual design, but there’s a lot more to emulating real life than aesthetics. Interface designers can emulate real-life physics and movement on a digital screen. This type of motion is becoming more common, which is why it’s becoming easier for people to understand computers. We’re not getting better, the interfaces are!

A quick and common example is how iOS opens and closes apps. The transitions are very subtle, but they’re realistic. Tapping an app icon doesn’t just snap a new app on to the screen. Instead, users see the app physically grow out of the icon. In reverse, pressing the home key shrinks the app back into the icon.

Those interactions are based on properties of the real world. The app appears to come from somewhere physical and disappear back to that place. The high quality and realistic transitions here go a long way toward helping the user understand what’s happening and why.

Opening an iOS app without a transition vs. with the transition.

In this article, I’ll cover a little bit of the history of motion on the web, why that’s important, and what the future of motion on the web will look like. (Hint: motion is really important for usability, and it’s changing everything.) Then I’ll explain the CSS behind motion and how to use motion well.

The History Of Motion On The Web

It was only 2011 when all major browsers officially recognized CSS animation, and even now it requires browser prefixes to work everywhere. In large part, the push for CSS-driven animation was sparked by the death of Flash, where “movement was common” is an understatement.

In the days of Flash, some websites were basically movies. There was a lot of movement and animation, but most of it was unnecessary to navigate and absorb the content. It was for wow effect at best.

Flash was eventually forced out of the picture, but designers and developers were left without any really good tools for movement and animation on the web.

JavaScript and jQuery became really popular, and they were huge leaps forward, but there are all kinds of reasons not to rely on JavaScript for your site to function. Plus, JavaScript animation was, and in some ways still is, taxing for browsers. Some motion was possible, but it needed to be used sparingly.

It wasn’t long before the CSS3 animation and transitions specs were accepted and implemented by modern browsers.

Designers now have the ability to take advantage of hardware acceleration and can control movement with their style sheets, further separating content and visual markup. In addition, today’s average computers are more than capable of rendering complex animations, and even phones are powerful enough to process an impressive amount of movement.

The Future Of Motion On The Web

The combination of capable machines and evolving CSS specs means things are going to change in interface design. Websites and apps are going to start taking advantage of motion and what it can do for usability. It’s already happening in a lot of ways, but here are some examples to look out for.

Layers

Layers are everywhere in modern web and app interfaces. Apple really pushed the concept of layers with iOS7. An example is the Control Center, which slides up from the bottom as a new layer that partially covers whatever’s on the screen.

The iOS Control Center slides in over the current screen as a new layer.

Although layers aren’t movement in themselves, they go hand in hand because they work best when they animate in and animate out.

Layers are important because designers can keep information hidden on another layer until it’s called on, instead of refreshing the entire page to display large amounts of new information. This allows users to think less and understand more. It gives them context, which is the next thing you’ll start to see a lot of with motion.

Context

Context is a broad term. For this discussion, I use it to refer to elements and pages that don’t just snap from one state to another without showing where they came from and why. Context helps us remove the digital mystery and therefore it helps users’ brains focus less on interpreting the interface and more on the content and their goals.

To illustrate how transitions can convey context, take a look at the Instacart iOS app. Tapping on an item to see more detail about it doesn’t just snap open a new view with the item details.

While that would likely be understood by most users, take a look at the GIF below to see what happens instead. We see the item’s picture move from its current position to a new position above the details view. We completely understand what happened and how it relates to the previous view. In fact, this doesn’t even feel like we’re switching from one view to another. This seems much more natural than that.

The transition into the detail view in the Instacart app helps to give the user context.

The effect is subtle, but it has huge usability implications. Another example is the newly popular drawer menu, where clicking a hamburger icon reveals a full menu.

If the user taps the icon and their entire screen is instantly replaced by the menu, they have no context as to where that menu came from and why. It won’t completely derail anyone, but it’s not a good user experience.

All it needs is to slide in from the left and suddenly the user has context for what’s happening: “Oh, the menu was just sitting offscreen, waiting to be called.”

You can see a drawer menu example in almost every popular app these days and on most mobile versions of websites. The GMail and Facebook apps are both excellent examples of this concept.

The Single Page Application

The next trend we’ll see are single page applications (SPAs). As we add motion and transitions to parts of our user interfaces, we’ll start to want more control of the interface as a whole (not the interface within each page). Websites can now handle all kinds of transitions from state to state within a page, but what about the transition from page to page? Any small gap when the screen goes white or shifts content around hurts usability.

That explains the rising popularity of the single page application. Right now, there are a lot of popular frameworks to build SPAs, and they’re more like native mobile applications than webpages (at least in some ways).

The sign-in and sign-up process for Dance It Yourself (an SPA I’m currently building) illustrates this well. If you go to http://app.danceityourself.com1, you’ll see the page initially loads like a normal website, but if you tap the Sign Up button, instead of refreshing the page, the content either slides up from the bottom (on smaller screens) or in from the left (larger screens). The technique uses JavaScript to add a class to the Sign Up page, which triggers a CSS transition.

The result is a smooth, fast and logical transition from one screen to another. Once you sign in to the app, the entire experience is treated the same way. All the movement and transitions are driven by logic and context, and they make this web application feel more like a native application than a website.

How To Do CSS Motion

Single page applications present a good opportunity to take advantage of CSS motion, but there are plenty of other places to use it, including potentially every element on every website you make from now on. But how do we actually do it? What does the CSS look like?

To understand the basics of CSS motion, it’s important to start simple. What follows are explanations with examples, but they’re definitely minimum viable examples. Follow some of the links to learn much more about the in-depth aspects of each type of CSS motion.

CSS Transitions

There are many times when a little transition can go a long way. Instead of changing properties of an element in a split second, a transition gives the user some real context and a visual clue as to what’s happening and why.

This helps usability because it removes the mystery behind digital state change. In real life, based on physics, there is always a transition from any one thing to another. The human brain understands this, so it’s important to translate that visual information into our interfaces.

To start explaining CSS transitions, let’s first look at a state change without any transition.

button 
   margin-left:0;


button:hover 
   margin-left:10px;

When the user hovers over the button, it jumps 10 pixels to the right. Check out the demo to see it in action2.

Now let’s add the most basic form of a transition. I’ve left out browser prefixes, but they’re in the demos, because we still need to use them in production code.

button 
   margin-left:0;
   transition: margin-left 1s;


button:hover 
   margin-left:10px;

That code will animate the margin-left CSS property when a user hovers over the button. It will animate from 0 to 10px in 1 second.

Here’s a demo for that3. Notice how unnatural it looks, though.

Next, we’ll make the motion look a little more realistic with just a small adjustment.

button 
   margin-left:0;
   transition: margin-left .25s ease-out;


button:hover 
   margin-left:10px;

Here’s that demo4. This example looks nice and natural. There’s probably little reason to animate the margin-left property of a button. You can imagine how this can apply to many different circumstances.

The last important thing to know about CSS transitions (and CSS animation for that matter), is that we can’t animate every CSS property. As time goes on, we’ll be able to animate more and more, but for now, we need to stick to a select few. Here’s a list of all the properties that will animate using the CSS transition property5.

When we talk about the hover state, it’s easy to see how CSS transitions apply, but also consider triggering transitions by adding an additional class to an element. This trick will come in handy. How the class gets added has to do with your implementation, but any time a class is added or removed, it will trigger the CSS transition.

button 
   margin-left:0;
   transition: margin-left .25s ease-out;


button.moveRight 
   margin-left:10px;

CSS Animations

The basic CSS for an animation is a little more complicated, but it’s similar to CSS transitions in a lot of ways.

The reasons to use CSS animations are also similar to transitions, but there are some different applications. We want to emulate real life as much as possible so that human brains can do less work to understand what’s going on. Unlike transitions, however, animations can be looped and can move independently of user input. Therefore, we can use animation to draw attention to elements on a page. Or we can add subtle movement to illustrations or background elements to give our interfaces some life.

Animation benefits may seem less tangible, but they’re equally as important. It pays to add some fun to our interfaces. Users should love to use our products, and animation can have a big impact on the overall user experience.

Here’s a shorthand example of a CSS animation. We use a block of CSS keyframes and give it a name, and we assign that keyframe animation to an element. Again, since browser prefixes add a lot of code, I didn’t include them. I did, however, include them in the demo, because, unfortunately, we still need to include all browser prefixes in the real world.

div.circle 
   background:#000;
   border-radius:50%;
   animation:circleGrow 800ms ease-in-out infinite alternate both;


@keyframes circleGrow 
   0% 
      height:2px;
      width:2px;
   
   50% 
      height:40px;
      width:40px;
   
   100% 
      height:34px;
      width:34px;
   
}

Here’s the animation demo6.

To break it down, there are really only two things going on here.

First, there’s the animation property itself. It’s very much like the transition property, but it has a lot more that we can control. I used the shorthand version in my example, but just like the transition property, each part can be controlled as a separate CSS property (you probably do this with background all the time).

The shorthand animation property breaks down like this:

animation: [animation name (from keyframe block)] [duration] [timing function] [delay] [number of times the animation repeats] [animation direction] [fill mode]

Here’s a more thorough explanation of all the different CSS animation properties7.

The second thing going on is the keyframes block. At a very basic level, this is self explanatory. Set any number of percentages from 0–100 to represent how far through the animation we are from start (0%) to finish (100%). Then add any styles for that stage of the animation. When the animation runs, all styles will animate between the values you specify at each percentage number.

Again, not all properties animate, but as times goes on, we’ll be able to do more and more.

How To Do CSS Motion Well

Now that you know how to write the CSS for motion, it’s time to think about using motion well. All of the concepts here will fail if executed improperly. Transition and animation need to feel real. If they don’t, they’ll be surprisingly distracting, and the distraction will actually hurt usability.

The trick to making motion look natural is two-fold: easing and object weight.

Easing

You may have noticed the easing part in the code examples. In real life, objects start moving gradually and slow to a stop. Things don’t just start moving at 100% speed. That’s where the third property for the transition style comes in from the examples: ease-out or ease-in. Sometimes, your best bet is actually ease-in-out (here’s a list of all the possible easing (timing) functions8).

Weight

Weight, on the other hand, is not a specific property of the transition or animation style. Weight mainly affects motion speed, and the basic concept is that smaller objects would have less physical weight in real life, so they’d move faster than larger objects. That’s why we increased the transition speed on the button from the second to the third example above. A small button seems really slow when it takes 1 second to move 10 pixels. A quarter of a second seems much more natural. (You can also use milliseconds, as in the example below.)

transition: margin-left 250ms ease-out;

A Tip If You’re Just Getting Started

This all may seem like a lot. If you’re new to CSS transitions and animation, I’d recommend one important thing. Build in steps. If you write an entire, complex keyframes block in one shot and then add timing, easing and looping into the animation property, you’ll find out very quickly that you’re confused. It will be hard to tweak and edit that animation. Start simple, and build the animation up by testing and iterating.

Coming Full Circle

When you’re up and running and using CSS motion, you’ll start to notice all kinds of different uses for these techniques. In most cases, it’s much more than a bell and whistle or a superfluous add-on. Movement is a tool, and it conveys context, meaning, importance and more. It can be just as important as any other usability technique that we use today.

As interface designers take advantage of motion, and as interfaces start to behave more like objects and environments in the real world, usability and user experience will improve as well. Humans will have to think less about computer interfaces and therefore the interfaces will be easier to learn and easier to use. Users may feel like they’re getting smarter or more tech savvy, but really, the interfaces are just conforming more to the ideas and concepts they’re already familiar with in real life.

So take advantage of CSS motion as a usability tool. Help your users by giving them realism and context. The world on the small screen doesn’t have to be so different from the real world around us, and the more similar it is, the easier it is for users to understand it.

(da, ml, og, il)

Footnotes

  1. 1 http://app.danceityourself.com
  2. 2 http://codepen.io/drewbrolik/pen/opskq
  3. 3 http://codepen.io/drewbrolik/pen/ivzfK
  4. 4 http://codepen.io/drewbrolik/pen/LFijf
  5. 5 http://css3.bradshawenterprises.com/transitions/#animatable
  6. 6 http://codepen.io/drewbrolik/pen/mJgqb
  7. 7 http://www.css3files.com/animation/
  8. 8 http://css3.bradshawenterprises.com/transitions/#differentTiming

The post Using Motion For User Experience On Apps And Websites appeared first on Smashing Magazine.

Source – 

Using Motion For User Experience On Apps And Websites