Business evolution happens in experimentation sprints: Insights from André Morys, GO Group Digital

Business evolution happens in experimentation sprints: Insights from André Morys, GO Group Digital

Many executives are seeking a “digital transformation” as a lofty solution.

But transformative change really happens in sprints.

That’s because experimentation is the agile approach to business evolution.

When it comes to business evolution—you don’t know, what you don’t know. If you aren’t learning, your business is not evolving at the pace you need to surpass your competitors.

Let’s start with a story…

In the late 1990s, business leaders were hyped on the possibilities the internet offered. Countless start-ups sprouted up to get an early grasp on web business.

But by 2002, when the dot.com bubble burst, many companies struggled to survive. Companies like Pets.com, WorldCom, and WebVan failed completely, and other organizations experienced declining revenues after a period of optimistic growth.

WiderFunnel André Morys konversionsKRAFT
A young, confident André Morys with his business partner at the start of konversionsKRAFT in 1996. (Source: André Morys)

André Morys, Managing Partner of GO Group Digital and Co-Founder of konversionsKRAFT, lived through this experience. In the first five years, it seemed like he had hit pay dirt with his business. His team was confident about their future direction until 2002 when they struggled with declining revenue of 60% over three months.

But when André reflects back on his company’s history, this struggle provided an unprecedented opportunity to learn about leadership and finance, culture and motivation. The aftermath of the dot.com bubble accelerated his understanding of how to strategically evolve his business for future growth.

There are many parallels in today’s market. 52% of the Fortune 500 since 2000 don’t exist anymore. And business leaders are constantly battling this threat.

Today, many Executive teams are aspiring to the “digital transformation” solution because how can you keep pace with the market, with the rapid technological change?

WiderFunnel André Morys Unbounce CTA Conference Presentation GO Group Digital
André Morys, Managing Partner of GO Group Digital, presented “The Source of Disruption Is in the Mind of Your Customer” at Unbounce’s 2018 CTA conference.

In this post, you will learn key insights from André Morys, adapted from his presentation, “The Source of Disruption Is in the Mind of the Customer,” at Unbounce’s 2018 CTA conference.

These key insights include:

  • Why we should be focusing on velocity of learnings (not tests) by prioritizing impactful experiments
  • How to speak the same language as your Executive team by pinpointing their emotions and motivations
  • And why a valuable customer experience is at the heart of business evolution.

Scoping out the big picture: The Gartner Hype Cycle

Roy Amara, a researcher, scientist and futurist, claimed that we tend to overestimate the effects of technology in the short term and underestimate the effects in the long term. This phenomenon is called Amara’s Law.

When we think big picture about digital transformation, this forecast is true. We are hyped up on the new tools and technologies when we first adopt them, but once they present challenges, we can become discouraged. Because how can really leverage technology to solve our business problems?

WiderFunnel The Gartner Hype Cycle for Digital Transformation
The Gartner Hype Cycle is a framework for viewing the path from adoption to actually driving business decisions. (Source: Gartner )

The research firm, Gartner, furthered Amara’s Law by introducing the concept of a hype cycle. When it comes to experimentation, WiderFunnel traces the maturity of organizations through its five different stages, including:

  1. The Technology Trigger: You are excited at the possibilities of experimentation but business impact is yet to be proven at this initial stage.
  2. Peak of Inflated Expectations: Early adopters claim success with testing at this stage, but you might be failing to properly leverage experimentation as a strategy.
  3. The Trough of Disillusionment: Initial hype is tapering off. Internal ennthusiasm fades. You might recognize at this stage that experimentation must provide results to continue the investment.
  4. The Slope of Enlightenment: Experimentation is starting to show its possibilities as you understand how to better leverage testing to create business impact.
  5. And the Plateau of Productivity: With consistent bottom-line impacts, you can now start to leverage experimentation as an organizational strategy for business evolution.

Technology can be a solution to business evolution, but leaders need to strategize how to leverage technology to solve real business problems. André articulated that such challenges are actually what is driving your digital transformation.

WiderFunnel André Morys Digital Transformation
When you embrace the pain, you can start to understand the truth to make your business grow, according to André.

As you start to scale your experimentation program, these learnings make your team’s workflow more efficient and allow you to zero in on the hypotheses that can make the most impact.

The good news: You can accelerate your learnings for how to evolve your business through experimentation. Even if these seem small wins, compounded over time, you are truly driving your organization’s growth through digital technology. (For example, what is the calculated impact of a reported 2% lift over 50 experiments? It’s not 100%; it’s a compounded 264%!)

WiderFunnel Digital Transformation throug Experimentation
Experimentation is the agile approach to digital transformation. It facilitates data-driven decision making. (Source: André Morys)

Once organizations introduce a defined process and protocol, have systems and procedures in place for prioritizing experiments by impact, they are able to scale their programs for long-term business evolution.

Relevant resource

Addressing your strategic blind spots: The Dunning-Kruger Effect

It is far more common for people to allow ego to stand in the way of learning.

If you are relying on the HiPPO’s strategy for business evolution, how confident are you in their abilities? And do you think they have the competence to judge their limitations?

When André reflects back on his first five years of business before the dot.com bubble burst, he sees how his confidence was an example of the Dunning-Kruger Effect.

The Dunning-Kruger Effect is a cognitive bias where an individual with low ability have mistaken confidence and believe they are more competent than they are.

And people with high competence often view themselves as having lower abilities than in actuality. As André states, “You don’t know, what you don’t know.

WiderFunnel The Dunning-Kruger Effect
The Dunning-Kruger demonstrates how people with low abilities can overestimate their competency, and people with high abilities can underestimate their competency. Hello imposter syndrome!

This cognitive bias has been explored in depth by psychologists David Dunning and Justin Kruger. Their research shows that people that suffer from the Dunning-Kruger Effect may resist constructive criticism if it doesn’t align with their own self-perception. They may question the evaluation and even deem the process as flawed.

Kruger and Dunning’s interpretation is that accurately assessing skill level relies on some of the same core abilities as actually performing that skill, so the least competent suffer a double deficit. Not only are they incompetent, but they lack the mental tools to judge their own incompetence.

When it comes to innovation, the Dunning-Kruger Effect creates a blind spot for threats and opportunities that can affect your business success. Instead, a business leader needs to always interrogate their perception of reality to get closer to the truth.

Truth―more precisely, an accurate understanding of reality―is the essential foundation for producing good outcomes.

André now sees how the growth and learning that came out of the dot.com bubble challenged his own self-perception. He began to understand the implications of his business decisions, and he became more in tune with the possibilities of the unknown.

WiderFunnel André Morys konversionsKraft
Today, konversionsKraft has grown to a team of 85. The downturn from the dot.com bubble lead to increased learnings in growth, management, culture, finance, leadership, motivation, and more. (Source: André Morys)

Applying his own professional and personal learnings from this experience to the world of experimentation, André sees a chasm between the manager’s aspirations of digital transformation and the optimizer’s experiments that lead to the desired data-driven decision making.

The problem is that [the Dunning-Kruger Effect] happens in organizations all the time,” explains André.

Many optimizers are very skilled in experimentation, they know everything about it: about A/B Testing, confidence levels and statistics, testing tools and psychology. Whereas management has no idea; they don’t get it. They are talking about digital transformation as a big project, while their optimization team is really doing the work that is needed.

What makes André’s argument that organizations need to focus on learnings so compelling is that a culture of experimentation—testing and learning—can really drive your business evolution and lead to the hockey-stick growth that you need to sustain your market.

But the Executive team and the tactical experts need to get on the same page, especially when it comes to successful business evolution.

As an Optimization Champion, you are the catalyst.

Change-agents build bridges between their peers, empowering them to accept change as it comes. They understand how to build and nurture relationships in order to find common ground with others. They are organized and understand how to speak to c-level executives clearly.

André’s comparison of Optimization Champions and Executive teams within an organization with the Dunning-Kruger effect is insightful.

He argued that Optimization Champions have a high-level of competence, but they don’t understand how they can position their work to gain Executive buy-in because they are too immersed into their specialization.

But he also emphasized that Optimization Champions truly are the catalysts of digital transformation. His advice is simple: Optimization Champions need to speak the same language as the Executive team.

Optimizers should stop talking about uplifts and statistics and A/B tests. They should talk about what A/B testing changes within an organization. They should report business impact, not statistical confidence levels, so they are compatible, so they are speaking at the manager’s level.

Experimentation drives the digital transformation at many successful organizations. Just look at Airbnb, or Uber, or Facebook. These organizations test and learn their way to business evolution.

André points out that experimentation facilitates an organization’s digital transformation, but many managers just don’t know it yet.

Your communication of experimentation’s value needs to be accessible to those who aren’t educated in the technical aspects.

And that’s exactly what André recommends. Understand your experimentation program’s internal stakeholders—your Executive team. Understand their fears and anxieties, their emotions and motivations when communicating your experimentation program’s value.

WiderFunnel André Morys Evangelizing experimentation personas of internal stakeholders
André recommends creating personas for your internal stakeholders so you can communicate the value of your experimentation program in a way that they’ll appreciate. (Source: André Morys)

As a marketer, you are best prepared for this task as you can craft your internal stakeholder’s persona, so you can demonstrate—in their language—the impact of your program on the business.

Featured Resource

Build your internal stakeholder persona to get buy-in!

Dive into the emotions and motivations that will resonate with your internal stakeholders to start proving the value of your experimentation program.

In your experimentation sprints, prioritize business impact over speed.

If you’re not failing, you’re not pushing your limits, and if you’re not pushing your limits, you’re not maximizing your potential.

In the world of experimentation, there is an emphasis on experimentation velocity. It makes sense: the more tests that you are running, the more insights you can obtain about your business.

But the buzz around velocity has led many leaders to focus on speed, rather than the quality of insights and the business impact of experiments.

And if you aren’t focusing on business impact, you’ll never get on the same page as your Executive team.

If you decide to test many ideas, quickly, you are sacrificing your ability to really validate and leverage an idea. One winning A/B test may mean quick conversion rate lift, but it doesn’t mean you’ve explored the full potential of that idea.

Experimentation sprints are chock full of business insights and impact—exactly what organizations need to continuously evolve their businesses.

That’s why our emphasis as optimizers should be on velocity of learnings, not just experiments.

It’s an agile approach to business evolution. And that’s a sentiment that was echoed by Johnny Russo, in “The 5 Pillars of Digital Transformation Strategy at Mark’s.”

Because how do we process all this change and learning, without being efficient?” he described.

Those organizations first to adapt will be most prepared. And so I think the foundation has to be an agile methodology.

But optimizers need to ensure they are driving higher impact experiments and deeper learnings by implementing rigorous processes. A prioritization framework ensures you are launching experiments that have the highest potential for results, on the most important areas of your business, with the easiest implementation.

But to increase experiment velocity, you need a defined optimization process.

According to the “State of Experimentation Maturity 2018” original research report, experiment velocity is a focal point for most organizations.

WiderFunnel State of Experimentation Maturity Velocity Increase
The majority of both Small and Medium Enterprises (52%) and Large Enterprises (64%) plan to increase experiment velocity in the next 12 months.

52% of Small and Medium Enterprises and 64% of Large Enterprises in the survey indicated they plan to increase experiment velocity in the next year.

However, only 24% and 23% (respectively) of these organizations plan to increase budget, which can only add emphasis on workflow efficiency and prioritization so that you are not straining your resources.

One of the most common roadblocks to increasing velocity is workflow efficiency. Review and document your workflows from ideation to analysis to ensure seamless experiment execution,” explains Natasha Wahid in the research report.

Another common roadblock is a lack of resources, particularly in Design and Web Development. Ensure you have the right team in place to up your velocity, and plan for possible bottlenecks.

As André articulated, focusing on business impact instead of speed will ensure you are learning faster than your competition.

That’s because digital transformation is not about implementing technology, it’s about leveraging technology to accelerate your business.

How to use the PIE Prioritization Framework to identify the most impactful experiments.

You can’t test everywhere or everything at once. With limited time and resources and, most importantly, limited traffic to allocate to each test, prioritization is a key part of your experimentation plan.

Prioritizing where you invest energy will give you better returns by emphasizing pages that are more important to your business.

The PIE Framework is made up of the three criteria you should consider to prioritize which pages to test and in which order: Potential, Importance, and Ease.

WiderFunnel PIE Prioritization Framework
The PIE Prioritization framework allows you to zero in on those experiments that can drive the most business impact. That’s how you get executive-level buy-in!

Potential: How much improvement can be made on this page(s)? You should prioritize your worst performers. This should take into account your web analytics data, customer data, and expert heuristic analysis of user scenarios.

Importance: How valuable is the traffic to this page(s)? Your most important pages are those with the highest volume and the costliest traffic. You may have identified pages that perform terribly, but if they don’t have significant volume of costly traffic, they aren’t experimentation priorities.

Ease: How difficult will it be to implement an experiment on a page or template? The final consideration is the degree of difficulty of actually running a test on this page, which includes technical implementation, and organizational or political barriers.

The less time and resources you need to invest for the same return, the better. This includes both technical and “political” ease. A page that would be technically easy to test on may have many stakeholders or vested interests that can cause barriers (like your homepage, for example).

You can quantify each of your potential opportunities based on these criteria to create your test priority list. We use the PIE Framework in a table to turn all of these data inputs into an objective number ranking.

Learn more about PIE

Seek the “truth” in a delightful customer experience.

Imagine your a taxi service. And you are seeing Uber’s market success as a threat to your livelihood.

What makes them more successful?

Get out the whiteboards and some might write: “We need an app!” or “We need to hire a data scientist!” Because on the superficial level, data and technology seem pivotal to a digital transformation.

But when you look deeper at Uber’s strategy, you will see that they focus on delighting their customers with their experience.

And because of Uber’s success, how people get rides has radically changed.

WiderFunnel Uber Growth Customer Experience
Look at the hockey-stick growth of Uber. It’s all attributed to a valuable customer experience. (Source: Spaceotechnologies.com )

But Uber is not the only example.

Many businesses have transformed the market through delightful customer experiences.

Amazon makes online ordering a breeze. No more long wait times, André joked that he has to slow down their service so that he will actually be there to accept the order.

And Airbnb—they’ve made vacationing a unique and desirable experience.

André, an expert in emotional marketing and the Limbic Model, emphasized the need to go beyond conversions and focus on a customer experience that delights.

Companies who fail to embrace CX as strategic path to growth won’t just be lagging, they’ll get left behind.

And that’s because you can drive experiments with the most business impact by honing in on your customer experience.

That means, you have to understand your customer—their fears and anxieties, their thoughts and desires—when designing an experience to meet their emotional needs and states.

The most successful organizations have honed in on what makes their experience delightful. In “Moving the needle: Strategic metric setting for your experimentation program,” I talked about creating a True North metric that align to your value proposition, as a way of creating internal focus on your customer experience.

Slack, a team collaboration program focuses on optimizing for teams that send over 2,000 messages.

LinkedIn focuses on quality sign-up, ensuring that new users are actively making connections on the social platform.

And Facebook optimizes for new users who make 10 friends in seven days.

See it’s not tools and technologies that evolve these businesses. It’s how they leverage digital technologies across the organization to solve real business problems.

And when you align your customer experience goals with your experimentation program, you are the competition.

You learn and adapt with each new experiment to what your customer wants and needs from your experience. That’s how you drive business impact. That’s how you get internal buy-in.

But what’s more—that’s how you stay relevant in the market.

And if you are experimenting, keep slogging through the “trough of disillusionment”.

André Morys knows from experience that business that do, can reach enlightenment.

What are your biggest challenges with experimentation that can be attributed to the “trough of disillusionment”? We’d love to hear your comments!

Author

Lindsay Kwan

Marketing Communications Specialist

In this roadmap for the executive, we explore the biggest challenges faced by enterprise organizations as they work to embed experimentation within their infrastructure – and how to surmount them.

Get your roadmap

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Ride-hailing firm Lyft launches IPO road show in Uber’s shadow

Ride-hailing firm Lyft launches IPO road show in Uber's shadow

(Reuters) – Lyft Inc kicked off the investor road show for its initial public offering on Monday, targeting a valuation of up to $23 billion and seeking to woo money managers before larger ride-hailing rival Uber Technologies Inc goes public in April.

An electric scooter from the ride sharing company Lyft is shown on a downtown sidewalk in San Diego, California, U.S., March 15, 2019. REUTERS/Mike Blake

The IPOs of Lyft and Uber represent a watershed for Silicon Valley’s technology unicorns, which for years have snubbed the stock market in favor of raising capital privately, with investors happy to back their frothy valuations.

The market rally of the last few years, however, coupled with the desire of some of the startups’ insiders to cash out, is leading many technology firms, including Airbnb Inc, Slack Technologies Inc and Stripe Inc, to plan market debuts.

Both Uber and Lyft are losing money, so like several unicorns before them, they will seek to tap investor anxiety about missing out on a red-hot technology IPO. Yet despite the hype, some investors and analysts say they will push the companies to outline a path to profitability, as well the prospects of eventually replacing some of their drivers with self-driving vehicles.

“They need to give us some good direction on when they expect to turn the corner and get to profitability, and let us know what a sustainable sales and marketing level is,” said Kathleen Smith, founding principal at Renaissance Capital, a research firm and manager of IPO-focused exchange-traded funds.

San Francisco-based Lyft said in a regulatory filing on Monday that it plans to sell a little more than 30 million class A shares, which have fewer voting rights than class B shares, at between $62 and $68 per share.

It aims to raise up to $2 billion in its IPO at a fully diluted valuation of as much as $23 billion, which includes restricted stock. The company would fetch a public market capitalization, which counts only shares listed, of just over $19 billion at the top end of the indicated price range.

Lyft is on track to be the biggest U.S. technology IPO since Snap Inc in 2017, and would be the tenth-largest technology or internet IPO of all time in the United States, according to data provider Dealogic.

Lyft begins its IPO road show in New York on Monday and Tuesday. The company will have meetings in Boston and New York later this week between investors and co-founders Logan Green and John Zimmer, as well as Chief Financial Officer Brian Roberts and Catherine Buan, vice president of investor relations.

The road show will move to the U.S. Midwest and West Coast next week. The company is scheduled to debut on Nasdaq on March 29 under the symbol “LYFT.”

SIMPLICITY VERSUS DIVERSIFICATION

Uber hopes for a valuation of as much as $120 billion, according to sources, although some analysts have pegged it closer to $100 billion based on selected financial figures it has disclosed. Uber is planning to kick off its IPO in April, Reuters has reported.

Uber, which promotes itself as a global logistics and transportation company, is much larger and more diverse than Lyft, whose core focus remains ride-hailing.

(Uber and Lyft, side-by-side: tmsnrt.rs/2VAzDBQ)

Lyft will pitch investors on the simplicity of its business, while Uber is expected to play up its more diversified strategy, according to people familiar with the matter.

After Lyft’s IPO, Green, the CEO, and Zimmer, the firm’s president, will collectively have just shy of 50 percent of the company’s voting rights. Their stakes would be worth $569.4 million and $392.7 million respectively, if the IPO prices at the top its target range.

Their firm grip on the company has been criticized by some investors focused on corporate governance.

“Lyft’s dual-class share structure leaves investors virtually powerless. This is highly risky for long-horizon investors and for the integrity of the capital markets,” Ken Bertsch, executive director of the Council of Institutional Investors, said in a statement earlier this month.

Lyft has nearly 40 percent of the U.S. ride-sharing market, but has warned that further growth could come at the expense of more losses for a company already deep in the red.

Lyft’s revenue was $2.16 billion for 2018, double the previous year’s and far higher than $343 million in 2016. It posted a loss of $911 million in 2018 versus $688 million in 2017.

J.P. Morgan, Credit Suisse and Jefferies are among the lead bookrunners for the listing.

Reporting by Joshua Franklin in New York and Diptendu Lahiri in Bengaluru; Additional reporting by Greg Roumeliotis and Carl O’Donnell in New York; Editing by James Emmanuel, Jeffrey Benkoe and Dan Grebler

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Back to Basics: How every marketer can tame the analytics beast

Back to Basics: How every marketer can tame the analytics beast

For most marketers, analytics exists in a magic Pandora’s box, encompassing everything from CPCs to CTRs, from algorithms to artificial intelligence, from machine learning to quantum computing — with a bit of blockchain sprinkled in for good measure.

Buzzwords aside, the barriers to incorporating analytics into your life aren’t as high as analytics behemoths may make it seem. To the contrary, once you clarify a few misconceptions, you can make this seemingly enigmatic field not only relevant but also remarkably useful.

You don’t need an Excalibur

Cost is an often-cited obstacle to starting a data journey. Despite the shiny advertisements, you may see for Adobe’s Marketing Cloud (which costs upwards of $100,000 a year) and the dozens of LinkedIn messages you get from martech salespeople; you don’t need Fortune 500 money to take a stab at unlocking analytics. Google Analytics, Google Search Trends, Hotjar and HubSpot are just a few examples of industry-standard platforms that can dramatically improve your decision-making capabilities for free.

Even better, these platforms are made for data amateurs. Their interfaces are straightforward, and if you get lost, there are countless tutorials, help forums, boot camps and even classes to help you. Google also offers a certification program for Google Analytics, complete with videos and walkthroughs. It’s perfect for anyone who needs a place to start.

Don’t let the tool guide be the craftsman

Marketers often forget that data is merely a tool. Expecting a Google Analytics tag to fix your website is like throwing a hammer at your newly opened IKEA purchase and expecting a sofa to emerge.

In other words: Collecting data is the easy part. Understanding what to do with all this info is where the magic happens.

So, spend a few weeks studying how to interpret data. Bootcamps and classes are always helpful, but the secret that every engineer already knows is that Youtube and Google are your best friend. Dig out your notes from that statistics class in college and learn how to run a simple correlation in Excel. An investment of your time today learning how to interpret data will pay dividends for the rest of our career.

Keep perspective

There are no sure things in marketing. Even scientists (and yes, I mean the ones in lab coats) often need years of data collection, rigorous modeling and endless testing to prove a hypothesis. And that’s in a lab. Imagine what happens in the real world, where things are constantly changing and driven by deadlines.

In this chaos, it’s no surprise that data rarely provides a bullet-proof answer. Sure, you can add more expensive technology, but it’s important to remember that, as marketers, we’re dealing in the realm of probability, not exact certitude.

What’s more, it’s okay to be wrong. Take every failure as a badge of honor; minimizing risk does not mean avoiding it entirely. A 95 percent chance of sunshine tomorrow still means that rain is a possibility, but also, your decision to not bring an umbrella isn’t necessarily incorrect. Make peace with the risk as long as you separate logic from emotion. In the long run, your data-driven approach will result in far more wins than losses.

You’re a solver of problems, not a creator of reports

All too often, people associate analytics with reporting. While reporting is critical, it is merely a means to an end. No business has ever been transformed by a single report.

Data is meant to be used as an unbiased means to test something. Nowhere in that definition does it stipulate that you must create daily, weekly or even monthly reports.

As we’ve seen, data takes time to collect. And while you should consistently check your data, it’s up to you to find the reporting cadence that works best for your team.

Then, instead of focusing on frequency, you can focus on presentation quality. Data is like a foreign language; it’s only useful if someone else understands what you’re saying. So, make sure your reports are thoroughly readable. Be concise, use visuals and err on the side of plain language. Above all, always return to the core business problem you’re trying to solve.

Next steps in your journey

Contrary to conventional wisdom, analytics isn’t shorthand for building sophisticated statistical models. Properly understood, analytics is a philosophy that embodies something much simpler: applying the scientific method to test your educated guesses. Whether you’re running a simple paid Facebook campaign or trying to get into shape for that Bahamas cruise this summer, you can leverage data to make more targeted, meaningful choices.

The reason you’ve read this far is that we agree on a key point: every marketer needs to integrate analytics to succeed in this digital world. In an age where it’s hard to keep up with the jargon, I fully empathize with those who view “analytics” as some enormous, mystical beast. On the contrary, understand that analytics is much more like a puppy; managing your data may be a little unruly at first, but with enough consistent training and respect, the lessons you learn will last you a lifetime.

A data journey can start tomorrow with nothing but a problem to solve or a hypothesis to prove (and a laptop with an internet connection).

So tell me, what are you waiting for?


Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.


About The Author

Jason Baik is the VP of Analytics at Hot Paper Lantern where he leads a team that uses data to minimize risk and maximize chances of success across all initiatives. Jason applies a blend of the scientific and Socratic methods to identify industry inefficiencies and provide unbiased, unorthodox business solutions.

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Building an attribution strategy – Search Engine Land

Building an attribution strategy - Search Engine Land

Customer journeys are complex.

Gaining an accurate and complete picture of your marketing attribution—which channels are producing leads and conversions—is crucial to getting the biggest return on your marketing dollars.

In this guide, CallTrackingMetrics presents a variety of channel attribution strategies, and describe how CallTrackingMetrics can help you finally gain a complete picture of the marketing journey.

Visit Digital Marketing Depot to download “Finding Success With Attribution and Call Tracking.”

About The Author

Digital Marketing Depot is a resource center for digital marketing strategies and tactics. We feature hosted white papers and E-Books, original research, and webcasts on digital marketing topics — from advertising to analytics, SEO and PPC campaign management tools to social media management software, e-commerce to e-mail marketing, and much more about internet marketing. Digital Marketing Depot is a division of Third Door Media, publisher of Search Engine Land and Marketing Land, and producer of the conference series Search Marketing Expo and MarTech. Visit us at http://digitalmarketingdepot.com.

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Equity funds see biggest weekly inflows in a year: BAML

BlueMountain names slate for PG&E board

LONDON (Reuters) – Investors plowed $14.2 billion into global equity funds this week, the largest amount in a year as investors jumped on to 2019’s stock market rally, Bank of America Merrill Lynch said on Friday, citing flow data provider EPFR.

An index of global stocks is up more than 16 percent since the end of 2018 as falling market volatility and a renewed dovishness from global central banks, led by the U.S. Federal Reserve has boosted risk appetite across the board.

BAML said most of the inflows went into exchange traded funds while mutual funds saw net outflows.

U.S. equity funds were the biggest beneficiaries with net inflows of $25.5 billion while emerging markets saw net outflows.

European funds also saw $4.6 billion of outflows after the European Central Bank slashed its growth forecasts and signaled a cautious economic outlook at its latest policy meeting.

The appetite for risk spilled over into bond markets as well with investment grade debt notching up the eighth consecutive week of inflows.

Reporting by Saikat Chatterjee; Editing by Tommy Wilkes

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Effortless 404 and site migration redirects Search Engine Watch

Effortless 404 and site migration redirects with Fuzzy Lookup

Effortless 404 and site migration redirects with Fuzzy Lookup

In recent years, the nature of SEO has become more and more data-driven, paving the way for innovative trends such as AI or natural language processing.

This has also created opportunities for smart marketers, keen to use everyday tools such as Google Sheets or Excel to automate time-consuming tasks such as redirect mapping.

Thanks to the contribution of Liam White, an SEO colleague of mine always keen on improving efficiency through automation, I started testing and experimenting with the clever Fuzzy Lookup add-in for Excel.

The tool, which allows fuzzy matching of pretty much any set of data, represents a flexible solution for cutting down manual redirects for 404 not-found pages and website migrations.

In this post, we’ll go over the setup instructions and hands-on applications to make the most of the Excel Fuzzy Lookup for SEO.

1. Setting up Excel Fuzzy Lookup

Getting started with Fuzzy Lookup couldn’t be easier — just visit the Fuzzy Lookup download page and install the add-in onto your machine. System requirements are quite basic. However, the tool is specifically designed for Windows users — so no Mac support for the moment.

Unlike the not-exact match with Vlookup (which matches a set of data with the first result), Fuzzy Lookup operates in a more comprehensive way, scanning all the data first, and then providing a fuzzy matching based on a similarity score.

The score itself is easy to grasp, with a score of one being a perfect match, for instance. This score then decreases with the matching accuracy down to a score of zero where there is no match. Regarding this, it’s advisable not to venture below the 0.5 to 0.6 similarity threshold in the settings, as the results are not consistent enough for a site migration or 404 redirects purpose below that limit.

Example of accuracy score

For greater accuracy, it’s also desirable to trim the domain (or staging site equivalent) from the URLs, making sure that the similarity score is not altered by too many commonalities. For more information about the setup, you can also refer to this Fuzzy Lookup guide.

2. Redirect mapping automation and its benefits

Considering the time necessary to familiarize with the site, categories and products/services, it’s safe to assume that a person would manually match two URLs roughly every thirty seconds. If that doesn’t sound too bad, consider that it would take between five to eight hours for a website of 1,000 URLs. This would make it quite a tedious and time-consuming task.

Bearing in mind that Fuzzy Lookup can provide nearly immediate results with a reliable fuzzy matching for at least 30 to 40 percent of the URLs, then this approach starts to appear interesting. If we consider the savings in terms of time as well, this would translate to about three hours for a small site or over ten hours for large ecommerce site.

3. Dealing with site migration redirects

If you are changing the structure of a site, consolidating more domains into one, or simply switching to a new platform, then redirect mapping for a website migration is definitely a priority task on your list. Assuming that you already have a list of existing pages plus the new site URLs, then you are all set to go with Fuzzy Lookup for site migrations.

Once you have set up the two URL lists in two separate tables, you can fire up the Fuzzy Lookup and order the matched URLs by the similarity score. In my tests, this has proven to be an effective, time-saving solution, helping in cutting down the manual work by several hours.

As displayed in the screenshot below, the fuzzy matching excelled with product codes and services/goods (such as 20600 and corner-sofas, for example). This allows the matching of IDs with IDs, and the URL with the parent category, in the case where an identical ID is not available.

Example of site migration redirects

4. 404 error redirects

Pages with a 404 status code are part of the web and no website is immune, hosting at least a few of them. Having said that, 404 errors have the potential of creating problems, hurting the user experience and SEO. Fuzzy Lookup can help with that, requiring just one simple addition a recent crawl of your site to extract the list of live pages, like the example below:

Example of 404 redirects

The fuzzy matching works pretty well in this instance too, matching IDs with IDs, and leaving the match to the most relevant category if a similar product/service is not live on the site. As per the site migrations, the manual work is not completely wiped out, but it’s made a whole lot easier than before.

5. Bonus: Finding gap/similarities in the blog

Another interesting application for Excel Fuzzy Lookup can be found in analyzing the blog section. Why? Simply because if you’re not in charge of the blog then you are not likely to be aware of what’s in it now, and what has been written in the past.

This solution works in two ways as well, because if a similarity is found, then you have the confirmation that the topic has been already covered. If not, this means that there’s still room for creating relevant content that can be linked to the service/product category to improve organic reach as well.

Example of finding gaps and similarities in the blog

Wrapping up

Time is money, and when it comes to dealing with large numbers of URLs that need to be redirected, a solution like Fuzzy Lookup can help you in cutting down the tedious manual redirect mapping. Thus, why not embrace fuzzy automation and save time for more exciting SEO tasks?

Marco Bonomo is an SEO & CRO Expert at MediaCom London. He can be found on Twitter .

Related reading

marketing automation for SEOs, five time-saving strategies
A primer to forecasting the value of SEO
How can brands utilize SEO to capture new users and markets
A quick guide to SEO in 2019

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Ways to Boost Your B2B Marketing Content Readership

How to Use Customer Testimonials in B2B Marketing

Forestville: March 7, 2019


A father bear, mother bear, and their baby bear arrived home yesterday afternoon to find that a young girl had broken into their home and was sleeping in the baby bear’s bed. Investigators said the girl, whose name they disclosed is Goldilocks, was last seen running from the site of the break-in after jumping out a bedroom window after having been awakened by the bears. Prior to falling asleep, Goldilocks ate all of the baby bear’s porridge and broke his chair, authorities alleged.

An elective course Allen took in college was an introduction to news journalism. Among the assignments in courses like this is to write a lead paragraph (the “lead” or “lede”) using a well-known children’s tale as the news item. Lead paragraphs are written to provide the reader a preview of the story to come, summarizing it with only basic facts—the “who, what, when, and where.” The objective of the lead is to prompt readers to continue on to get the details.

We led this article with that thought to make the point that if you want your business-to-business (B2B) marketing content to gain readership, the first thing you must do is think like a news journalist.


In this age of information overload, it’s critical to be able to quickly grab your reader’s attention with the key points you want them to take away. That way, even if they read only the first few lines of your marketing message, they’ll immediately grasp the most critical things you want to communicate. And, hopefully, if you’ve done a good job setting the stage, they’ll continue reading to pick up more of the specifics in your marketing content.

Content marketing is used by over 91% of B2B marketers; but only 37% of marketing organizations have a documented content marketing strategy and only 20% describe their approach to content marketing as “very successful,” according to a survey by MarketingProfs and Content Marketing Institute.

That’s because, as the survey noted, over 83% of those on the receiving end of online marketing messages reported being overwhelmed by both the amount and the length of communications. They want the content shorter, to the point, and prescriptive—as in “just give me a solution.”

Solution in mind, and aside from thinking like a news journalist as you take on marketing content initiatives, what are four other surefire ways to optimize your efforts?

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Investor group calls on Lyft to scrap dual-class share structure plan: FT

Investor group calls on Lyft to scrap dual-class share structure plan: FT

An electric scooter from the ride sharing company Lyft is shown on a downtown sidewalk in San Diego, California, U.S., March 15, 2019. REUTERS/Mike Blake

(Reuters) – A group of investors has called on Lyft Inc’s board to scrap a proposed dual-class share structure, as the ride hailing company pitches its initial public offering to investors next week, the Financial Times reported on Saturday.

San Francisco-based Lyft’s planned IPO includes a dual-class stock structure, with one class of shareholders getting 20 votes per share and another getting one vote per share.

The investor group, in a letter addressed to the company’s directors, said it should stick with its single class of shares with one vote each, the report said.

If the company’s board fails to resolve the issue, it should adopt a “sunset” provision to phase out the extra voting rights within seven years, the letter said, according to the newspaper.

The letter was signed by investors from Britain’s Local Authority Pension Fund Forum, BNP Paribas Asset Management, pension funds representing public employees in New York, Los Angeles, Chicago and Ohio, the Teamsters union and United Auto Workers union retirees, the newspaper said.

Lyft did not immediately respond to a Reuters request for comment.

“With a dual-class structure, Lyft is basically shielding itself and company insiders against shareholders who deserve a voice. Outsized control among an unaccountable few is an unnecessary risk — and Lyft should go back to the drawing board,” New York City Comptroller Scott Stringer said, according to the Financial Times. Stringer oversees the city’s pension funds.

Reporting by Akshay Balan in Bengaluru; Editing by Bill Berkrot

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Experimentation in product development: How you can maximize the customer experience

Experimentation in product development: How you can maximize the customer experience

Are you a product builder or a maximizer?

Hila Qu, Vice President of Growth at Acorns, has a theory.

She says there are two kinds of product managers: the builders and the maximizers.

Ideally, on your product team, you have both: the builders who conceptualize a solution in product development, from the ideation stage all the way until the customer roll-out.

WiderFunnel Product Teams as Builders
Product Builders are essential to organizations because they conceive the product that provides value to the customer in the first place.

They’re very important because, without them, there’s nothing; they are building the value from zero to one,” Hila explains.

And then, there are those that can maximize the customer’s value through experimentation in product development.

The bulk of the product value is already created, but how can you maximize that value? The maximizers help customers understand the product better when a new product or feature is launched because we often assume people will know how to use it.

Hila Qu

VP of Growth at Acorns

WiderFunnel Product Teams Maximing the Value of Customer Experience
Product Maximizers build upon the value of the product through experimentation.

It’s an interesting distinction—between developing and experimentation.

Product development is all about making your product solve your customer’s real-world problem, and the builders aim to make the process frictionless so they will create a habit out of returning.

But product development teams might not know if they were successful. Or how they could improve the experience for the users.

Experimentation enables the maximizers to improve upon the value that is already developed, to make the customer experience more delightful.

Actually, your product team can leverage experimentation where ever they are in the product development cycle: They can test an idea before launch; Or they can improve your feature after it’s rolled out to your customers.

Experimentation is iterative; your product team learns and develops as they gain insights about your business and your customers.

If you are exploring the possibilities of product experimentation at your organization; if you are a marketing leader looking to evangelize experimentation throughout the business—this post is for you.

In this post, we’re tackling the essentials of experimentation in product development. Essentials like:

  • The breadth and depth of experimentation in product development so you can start dreaming up the possibilities.
  • An understanding of the stakes when experimenting within the product development cycle, especially when those hard-earned customers are interacting with your brand.
  • And why experimentation in product development is a leap toward a customer-centric organization, a strategy for growth.

But we also wanted to show you examples. So, we talked to product managers at Optimizely, Acorns, RVShare and Stitch Fix about the opportunities for product experimentation in reaching business goals.

And they are clearly seeing results.

You might be thinking: what is product experimentation?

Client-side experimentation is based on the premise that what-you-see-is-what-you-get. It’s about visual changes to the content hierarchy of the website.

Innovative marketers have been doing this form of optimization for years, and they’ve seen the success. So what’s next?

Product experimentation is often top of mind when it comes to expanding an optimization program internally. That’s because there are more possibilities.

The product life cycle is usually more elongated than the marketing lifecycle,” explains Giannis Psaroudakis, Director, Product at Optimizely.

In the marketing lifecycle, you have a campaign and you have a very specific goal, which is sometimes short-lived – you want to increase your leads, for instance.

If you are optimizing your website to get a customer to convert, only to have a product that does not live up to the customer’s expectations—those hard-to-win customers are going to look elsewhere.

But it can be tricky.

Product managers don’t always get it right the first time, and that’s why it’s important to have a feedback loop with your customers.

It’s important to maximize the value of your product. Understanding how your customers are finding value and optimizing their journey so that they can find that value faster.

“Even well-researched products can suffer due to the gap between what customers think they want and what their behaviors reveal they actually want.

By testing a new feature or even a variation of that feature, your product team can see if it is improving the customer experience, or actually making it worse.

The experimentation mindset aligned with the premise of a minimum viable product, seeing how your customers react through a gradual or even incremental roll-out. It allows you to test new ideas before a full launch.

Don’t frame it as a product launch. Just frame it as an experiment.

Dan Siroker

Optimizely Co-Founder and Executive Chairman

If you crave that buzz of a big launch, experimentation after-the-fact enables you to get the most out of those ideas—and your organization’s investment.

By the time something this big has been built, the launch is very, very unlikely to be permanently rolled back no matter what the metrics say.

Rather, the randomized experiment, in this case, is for visibility, and to provide information that might help with making future decisions.

Announcing a feature or product through an official launch can inspire adoption—particularly if you have a large customer base to which you can communicate.

But once your product is rolled out, you can start to understand how your customers are interacting with the features at every touchpoint through experimentation.

Product experimentation should make the job of the end user easier or more helpful. If that’s not the goal in your company than you might be running it the wrong way.

Martijn Scheijbeler

Vice President of Marketing at RVShare

Those learnings can be brought forth into future product evolutions—whether that’s changing copy to highlight your value proposition, or smoothing out the funnel flow to reduce friction.

And you can even experiment after your customer logs into your experience, especially when confidentiality is essential for your business.

Do what your customer wants: A painted door experiment example from Optimizely

We created a painted door experiment informing customers we intend to give you this feature, sign up to get early access. We wanted to test if its value proposition resonated with our users,” explains Giannis Psaroudakis of Optimizely.

For those that don’t know–a painted door or a fake door experiment is a way of gauging customer interest in a feature, service or product without building anything.

When the customer clicks a call-to-action button or a link, or even registers with their information, they are notified that it has yet to exist. And they might see something like:

Hey! We haven’t actually built this feature yet, but are you interested?

Of course, you have to know if your customers would be open to this type of experience. You would have to make the experience intriguing enough so that it doesn’t cause frustration.

But you get the data to see if customers find that appealing. Or at least made them curious enough to click or register. And for Optimizely, this worked:

We had several customers enthusiastically send us feedback. It was overwhelming—we received dozens and dozens of requests which is quite uncommon and we even had a customer send us a photo of a thumbs up.

WiderFunnel Experimentation in Product Development Optimizely painted door experiment example
Experimentation is a feedback loop with your customers, but in Optimizely’s painted door experiment, they were able to delight their users with the hope of a new feature.

A painted door experiment, like this, truly exemplifies the power of running a cheap form of experiment as a gauge for the next step.

It gives you the confidence that you’re moving in the right direction, without a single line of code or effort by an engineer.

But more importantly, it’s that feedback loop with your customers.

Relevant resources

The scientific method of product experimentation

Let me first clarify: you don’t test features in product experimentation, you test hypotheses.

Product experimentation takes an optimizer’s brain: It takes an analytical mind to be able to see the opportunities for product experimentation. It also needs the scientific method to turn ideas into measurable hypotheses.

Move away from the mental model of thinking of new products and new features you want to build, in the form of a list of requirements, and instead, think of them as hypotheses.

Giannis Psaroudakis

Director, Product at Optimizely

Rather than saying, “we need to build feature X that has this requirement and that requirement, because customers asked us to”, you start with presenting these same requirements in the form of if/then statements.

This is so important because it sidesteps any HiPPO (highest paid person’s opinion), and it persuades you to think about your product—how you’re building the product—in the form of experiments. In other words, hypotheses that you can validate with data.

Making data-driven decisions is important in product experimentation because the stakes are higher.

You are experimenting within your product experience on your many already acquired customers so you need to have high quality assurance and more guard rails to what makes it to the experimentation stage.

We have a more rigorous review process for product experiment hypotheses,” recalls Giannis Psaroudakis.

We have an experiment review, which is more of an advisory process that allows us to surface new product experimentation ideas and specifically review our hypotheses.

Anyone in the organization can submit their “experiment briefs”—customer challenge, hypotheses, and metrics of success—through Optimizely’s Program Management platform.

And we revise these experiment briefs in the weekly experiment reviews to make sure that whoever is planning to run a product experiment has carefully thought about the hypothesis and the metrics of success.

The scientific method ensures that you can make the data-driven decisions on how your product—and the customer experience—should evolve.

Free Worksheet

Identify opportunities for maximizing your product’s value.

When you use the scientific method in experimentation, you don’t test features; you test hypotheses. Start planning your first product experiment with our hypothesis worksheet.

Digging deep into data to find that first product experiment opportunity at Acorns

When I joined two years ago, my goal was to improve customer retention,” recalls Hila Qu. “But it is a very broad goal: What do we mean by improving customer retention?

Hila described how she talked to her co-workers—those stakeholders who have been exploring the problem. She asked them the reasons people left their app and their opinions as to what might improve customer engagement.

But she didn’t stop there.

I also worked with our data analytics team to do a quantitative analysis. Basically, I was trying to identify which customer behavior encouraged people to stay.

For example, I looked at the retention curve after a customer completed action A in the product, and compared that to the retention curve of people who didn’t complete action A in the product within their first month.

Using that methodology, I was able to compare different behaviors, because for any digital product, there are a lot of actions user can take.

I was able to narrow down to one particular user behavior where I saw that if the user completes that action, their retention is much better that if the user doesn’t. I also saw that many customers weren’t actually completing that action.

Through her analysis, Hila identified that if a new customer completed action A at the onset of their journey, it had an influence on the customer’s future behavior.

It had a much bigger impact because at that moment, we had people’s attention and they were excited. They wanted to get started with the product.

Hila then mapped out the journey, considering the many paths that a customer could take to complete action A. And there were several:

“For example, you can go to the menu, click that menu item and use that feature; Or, you can basically send them an email with that call-to-action to use that feature. I identified all of the paths so I could understand which one I could focus on first.

So, my hypothesis was based on the data; it seems like if we can get more people to complete action A, the retention will be better. I narrowed down to new customers and if I could get more of them to complete action A, it would be most impactful and easier to influence people’s behavior.

Her first experiment was simple. She didn’t have the development or engineering resources since she was only initiating a product experimentation program.

She hadn’t yet proven experimentation’s value internally, so she needed to identify an opportunity where she could show business impact from a small investment of resources.

We already had a modal in our registration flow that asked people to take action A, but the modal was at the very end of the flow. And you only see the modal if you completed another action. So, I narrowed down to this particular area.

I wanted to test copy. That experiment had the potential for a very high ROI. If it worked, it could have a big impact, but also the effort required was relatively small. It was really just changing the copy and testing different copy against each other.

And it worked.

We launched it and saw over 60% improvement in conversion rate.

Simple or complex—product experimentation can have a big impact.

Further resources

Experimenting broadly and deeply in product development

When it comes to experimentation in product development, you have more opportunity to optimize your experience. That’s because you can experiment broadly and deeply into the product development cycle.

WiderFunnel Experimentation in Product Development Cycle

There are plenty of opportunities to experiment in the product development cycle.

Share the insight:

Consider the stages of product development:

First, you have the Discovery stage where you ideate new features or products and this is particularly relevant when you ideate in the form of hypotheses.

According to Meg Watson, Product Manager at Stitch Fix, her team also conducts an analysis to determine if they should proceed into product development.

Next, you move into the Design stage, according to Giannis Psaroudakis, where you can evaluate your hypotheses through early user studies or rapid prototyping. This prototyping is where experimentation begins in terms of usability.

Then, you go on to the Development stage where you dive into coding or testing your actual code.

And afterward, you have the Roll-out stage, when you make sure your hypotheses still stand after launch and there’s nothing negatively affecting the quality of the final product or the customer experience.

In traditional product development, this is where you might stop. Your ideas might win, but they also might fail. And there’s no defined evaluation for where you can improve.

That’s why in product experimentation, you also have the Maximizing stage, that Hila Qu described: What can be improved? What can we evolve? What can make the customer experience frictionless, more delightful?

When I’ve looked at some of the things we’ve started with and usability testing, and then compare that to the product that ships, X months later, a lot happens in between those stages. We start with sending a prototype through usability and then we have all the different optimization tests that run after something goes live.” confirms Meg Watson.

Throughout the product development cycle, with the experimentation mindset, you are constantly exploring and validating the possibilities for new features or products by generating that feedback loop with your customers.

But perhaps more importantly, experimentation in product development means you can test deep in your stack.

Unlike client-side experimentation, with its what-you-see-is-what-you-get approach, you can test what you can’t see. Product experimentation incorporates server-side testing:

You can test changes on your server side and improve the performance of your back-end systems,” Giannis Psaroudakis affirms.

If you’re running any machine learning models that give recommendations to customers, you can experiment with those, for instance.

In client-side experimentation, a variation is run through the web browser or mobile app using a visual editor and a single JavaScript line. Once the variation is proven, you can build it out by implementing a code pack.

But in server-side experimentation, the variation is coded on servers. So, it can be more resource intensive because you build up-front.

WiderFunnel Experimentation in product development client-side server-side
You can see that server-side experimentation requires that you build upfront. (Source: “Why Experiment Server-Side?” by Optimizely)

That way, you can experiment with how a product functions, like the machine learning algorithms that enables you to deliver a personalized experience.

And that’s why product experimentation has so much potential.

Learn more from these innovative organizations

When it comes to experimentation, it’s all about the customer experience.

Ultimately the goal of all this, the reason we’re doing this, is to give the customer a better experience with Stitch Fix and to make sure that it’s effortless. It’s fun. it’s delightful. And that they truly have a good experience with all of our products.

Experimentation in product development allows you to get deeper into the stack, deeper into your customer’s experience so that you are delivering the best possible solution and a delightful one at that.

For many organizations, it’s an untapped source of growth.

The gap between product and marketing teams is becoming smaller and smaller, and from a customer experience perspective, it’s blended.

The customer experience has to be consistent and compelling, and work seamlessly between the pieces that are controlled by the two teams, in order to have the customer finish that journey from a random visit or from your ads, to the point where they use the product for the first time and they know the value, they see the benefit.

WiderFunnel Product and Marketing Customer Experience
If the customer doesn’t view marketing and product as separate parts of their journey, organizations shouldn’t either.

But we need to stop siloing product and marketing.

With an experimentation mindset, organizations need to unite internally to spread those insights throughout every customer touchpoint.

That means experimenting constantly—both client-side and server-side—and sharing the insights across both marketing and product development teams.

Because your customers don’t see your landing page or your product funnel as distinct experiences. Instead, you need to focus on the entire customer journey.

What are your burning product experimentation questions? We’d love to hear them below.

Author

Lindsay Kwan

Marketing Communications Specialist

Benchmark your experimentation maturity with our new 7-minute maturity assessment and get proven strategies to develop an insight-driving growth machine.

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