Disruption Starts With Data
Companies that have fewer bottlenecks in their data collection and analysis processes are more likely to build a loyal audience.
Two Core Traits of Exceptional Customer Experience Teams
Empathy and humility are key attributes to having successful leadership and teams in an organization.
Acquiring Quantitative and Qualitative Journey Analytics
Both quantitative (metrics that identify broad trends) and qualitative (motivation-based) data are crucial to understanding the customer journey and improving it.
What Humanizing Data Looks Like
Metrics, analytics, and KPIs are only valuable when they are applied back to the customer experience in practical, noticeable ways.
It’s quite possible that 200 years from now, people will look back and say it was right around the year 2000 when the world as we know it irreversibly changed. Not because of social media, autonomous vehicles, or the proliferation of mobile devices. As compelling as those innovations are, it’s the explosion of data that’s truly changing the world as we know it. We’re right in the middle of one of the most drastic transitions that mankind has encountered—and I don’t say that lightly.
The tech revolution goes far beyond improving the way people communicate around the world. Technology has created a substantial data pipeline that is used on a daily basis whether we realize it or not. As individuals and organizations alike, we consume and generate data every day using the devices and platforms that are indelibly ingrained in our lives. This access to and absorption of Big Data through modern technology simultaneously reflects, shapes, and records human behavior.
Of course, conversations about Big Data and its implications have been heard in offices and boardrooms for years now. And while many businesses are trying to drink from their own fire hose of figures and data sets looking for insights that can be used in their end-to-end journey analytics, things don’t seem to be working out for most of them. According to an executive survey on Big Data from NewVantage Partners, a strong majority (85%) of leaders say that their organizations are trying to be data-driven. But of those, only 37% say they’ve been successful.
As evidenced by the graphic below, there’s still a surprising disconnect between what brands think of consumer behavior and what consumers themselves think. I call this the StoryVesting disconnect—that place where brands quickly find themselves in no man’s land when it comes to brand loyalty.
So what’s missing in this equation? Why don’t brands know exactly what’s going on with their customers? Is the idea of using massive data sets to build and retain an audience through comprehensive journey analytics just a pipe dream? Is it just a B2B buzzword that companies invoke to try and sell products or services?
Whether you believe in the hype or not, Big Data is here to stay—and the possibilities it brings with it are endless. I don’t hesitate to say that the future belongs to organizations that are able to use the right data at the right time, in the right place.
We see glimpses of savvy Big Data usage today in ways that we take for granted. News aggregators serve us the latest articles on the topics we are most interested in. We have voice assistants that can tell us where to find the closest Chinese buffet. Your favorite new music may be suggested by a machine learning algorithm. As a whole, we’re becoming increasingly expert in connecting disparate points of information to learn more about everything we know and understand. This data-fueled connection isn’t just specific to any particular industry—it’s bound to human behavior.
Big Data is real and the possibilities it brings are exciting, but what’s really going to make a mark in history is how machine learning will connect every possible data set and influence the world we live in.
Regardless of the industry, application, tactics and strategies being used with data, the most important thing a business can do when dealing with data is to remember that people are not numbers—they’re people. They’re complex, intelligent individuals driven by emotion. And the companies that are able to tap into those emotions and act on valuable insights based on the data available are the future disruptors.
Disruption Starts With Humanized Data
Companies that make data-driven decisions are above average in their performance and growth, enjoying between 5-6% more output and productivity. Not surprising, right?
But yesterday’s new ideas are today’s standard practices, so companies have been steadily catching on and dedicating resources to harness data and make more informed decisions. What sets excellent companies apart from the average ones is the ability to analyze and execute on Big Data through a tightly-woven, customer-centric culture. The companies that do this are, and will continue to be, the disruptive and transformative leaders of tomorrow.
But for every company that effectively leverages data, there are many that miss the mark or don’t try at all. They become so grossly overwhelmed with how much information is available that they are paralyzed by it. And when businesses don’t act on the data they have, they also don’t take a proactive role to try to go get more to add to their mix. This analysis paralysis often happens when data are presented in a way that looks something like this.
No matter how intelligent you are or how well-versed you are in data science, trying to absorb too many facts and figures all at once will cause your eyes to glaze over and ultimately end in a failure to glean any true insights. The inundation of dashboards, spreadsheets, and data are the primary reason more than half (56%) of decision makers admit to feeling overwhelmed by the amount of data in their marketing technologies, according to a survey of marketing executives by Conductor.
And it isn’t limited to marketing—in a breakdown of Hadoop’s role in managing massive data sets, Forrester estimates that “between 60% and 73% of all data within an enterprise goes unused for analytics.” So instead of digging in and pulling insights from these large-scale observations, companies dig in their heels by ignoring the data or just sticking with what they know. But that leaves the door wide open for insight-driven disruptors to jump in and deliver superior results.
Big Data is the great equalizer of our time, and the odds are stacked against massive, lumbering corporations that are bogged down by bureaucracy and inefficient data processes.
More often than not, we see startups in the disrupter role. Thanks to data, small companies are able to take advantage of consumer trends and grow revenue faster than ever before. They move quickly and don’t get bogged down by processes. Their success and survival depend on understanding what the market demands and meeting those expectations, all through data and journey analytics. All of this analysis leads to the ultimate differentiator—a better customer experience (CX).
Large organizations, on the other hand, are weighed down by the worry of risk and an inability to adapt—something I discussed in my post on the S curve of business. This worry stems from multiple factors, as explained in McKinsey’s article about getting big impact from Big Data:
- Frontline managers lack understanding and confidence in the analytics and hesitate to employ it
- Existing organizational processes are unable to accommodate advancements in analytics and automation
- Decision-making protocols require multiple levels of approval
Slow and steady wins the race, but companies that drag their feet when it comes to using and acting on data aren’t the tortoise in the story, but the sleeping hare.
In order to stay relevant, they should be slowly, methodically, and intelligently implementing processes and hiring team members that foster a data-aware strategy. Far too often, organizations sit on the data they have and become mired in analysis paralysis, either oblivious to the changes going on right in front of their noses or systemically incapable of making substantial changes to turn things around. These factors are less common in startups, leaving them in a prime position to create something revolutionary that outperforms the established competition.
The Supporting Role of Data
Growing companies have an uphill battle in front of them when it comes to using data to fuel insight-driven decisions. And it’s not just about scaling production or adding new features to existing products—it’s about maintaining a human connection with customers.
Startups are in the trenches, often talking directly with end users. They hit the pavement and ask questions face-to-face. They hear the consumer’s voice and they use it to iterate and innovate. Meanwhile, large enterprises build and iterate on what they have already, often distancing management and leadership from the concerns and feedback of the customer.
Now, talking to people face-to-face isn’t a complex approach. You don’t need any fancy software or tools to have a conversation with someone. But it also provides minimal data. You can’t gain deep enough insights to justify making massive shifts and taking big risks after talking to a few dozen or even a hundred people—you just can’t. Instead, you need to balance the information from those direct conversations with your Big Data to discover and validate new strategies.
At RocketSource, we believe there’s a healthy middle ground and that’s the path we’re forging. Unlike many other companies, we’d never say that we’re driven purely by data. Instead, we’re driven by insights across each unique customer journey. We combine data analytics with a comprehensive understanding of a customer’s motivation and experience to model and implement growth in leading companies.
Ultimately, the end goal is not more data, but a superior customer experience.
Sometimes, Big Data gets a bad rap because it’s seen as unreliable—and I happen to agree, at least to some extent. I’ve seen it happen time and again, where the interpretation of data takes priority over the Voice of the Customer, leading to wrong conclusions and disastrous consequences. The role of data is to analyze and accentuate the human experience with a brand. It should be used to glean insights and give confidence in your direction, not completely replace the creative process or what you know about your customers.
If your data tells you that a silver bar is gold, something is wrong in your approach.
I used this conceptual graph in the header image to show what collecting data should really lead to. The size of the database (or data lake, data warehouse, etc.) doesn’t matter nearly as much as how effectively it’s used. Everything has to be aggregated, sorted, analyzed, and modeled so that it leads to one of the 3 I’s discussed in my post on the S curve of business—invention, improvement, or innovation. There will always be outliers (shown as gray shapes) that won’t correlate as closely to the customer experience, but you’ll also find new and exciting ways to impact the brand experience and move the needle on truly important outcomes—trust, engagement, transparency, and more.
This is why applying the human experience back into data is so crucial—raw numbers alone give you an idea of what is happening, but not why it’s happening, much less how you can improve the experience. If you just look at the numbers as data points, you won’t be able to turn those observations into actionable insights.
The power of building your brand’s unique customer journey analytics is found in following the scientific process, where data can be used to challenge assumptions and biases—not the other way around.
When data are customer-centric and democratized across an organization, teams are able to move much faster on experience and transformation initiatives. Processes no longer bog you down. Instead, you’re free from silo-ridden slow downs and able to harness the talents of visionary influencers, forward-thinking boards of directors, and leading C-suite executives, contributing to initiatives that effectively leverage data to stay relevant to consumers.
Agile Operations in a Lean Organization
Influencers in an organization know how big Big Data is perceived. There’s a ton of information and insight available, but in order to pull out the stuff that will enable you to gain traction in a digital transformation or customer experience initiative, you need to get really good at understanding the meaning behind the numbers.
Let me pause for a second and acknowledge one thing—operationalizing Big Data and human-based initiatives is hard work. There’s a lot of risk. You have to go through extensive quality control, consider large costs, and more. Innovating for a large organization requires a more in-depth process. It’s hard to be as agile as startups but with a little tweaking of your thought process, you can operate a little more nimbly than in the past.
Take this widely-shared infographic from SalesForce and Pardot as an example. It attempts to describe what today’s modern marketer must be: part artist and part scientist. The artistry is in the messaging and the science is in—you guessed it—the data.
I used to think this way, that modern marketers had to be part data scientists and part artist. I figured they should be able to understand, mine, and model data to do their jobs well, but I don’t anymore. Although it sounds appealing on the surface, I’ve experienced many failures when I try to turn creative marketers into true data scientists that use Python or R. That just doesn’t work.
So while I appreciate the point this graphic is making, marketing today covers much more ground than just art and science. Successful marketers and executives today should ultimately be able to get to the why, which should always be the goal. That means you can’t have one person modeling and mining data. In order to really be able to humanize data, you have to be sure that the person who is creating the visualization is someone who really understands from an empathetic standpoint how the data works into the visuals. The data scientist has to be really skilled at telling the story of the data to the marketer who is the visual creative and designer. This requires that they each be able to put themselves in the customer’s shoes. That requires both parties to think and operate like scientists, psychologists and anthropologists.
Think Like a Scientist
Think back to when you used to do science fair projects as a child, when you were asked to form a hypothesis, conduct experiments, analyze the results, then report your findings. This elementary-level process is surprisingly relevant in today’s corporate world. For example, marketers regularly go through these motions when testing things like sales copy and design for a new initiative. Growth teams think like scientists when hypothesizing about the impact store layouts have on revenues for certain departments.
Scientific fact-finding is the most basic approach to using Big Data to make big business decisions. You make a guess, you implement a change as an experiment, and then you analyze the results. Although simplistic on the surface, it’s a great way to let the market show you first-hand what works and what doesn’t.
This approach is the most popular when it comes to data-driven decision making but it actually tends to be a little superficial. It doesn’t show you why something worked. It also doesn’t show you what consumers want from you.
Think Like a Psychologist
A scientific approach uses data to create new strategies and context to drive innovation, but psychology digs deeper. Anyone who leads a CX team must have a deep understanding of and appreciation for human psychology.
Psychological principles are applied to everything we do in growth. They drive our hypotheses about human behavior and enable us to predict future actions or events. But when an enterprise pours revenues into personalizing experiences based solely on predictions (assumptions), they usually end up wasting a lot of time and money. The idea that you can find a quick fix to a problem by identifying trends from a handful of numbers is a myth.
We have a world of data at our fingertips. We can see heat maps and monitor behavior on our website, such as clicks and page views. We can even drill down to the type of device people are using to look us up. But in spite of having all of this data, we still don’t know with certainty why someone pulls out their credit card and buys. That’s where psychology comes into play.
For example, a marketer who understands psychology is much better equipped to get into the head of the buyer and understand the factors that influence human behavior. The better we can deeply understand the psychological motivation behind a purchase, the better we can understand and improve customer experience.
Think Like an Anthropologist
“To err is human,” as Alexander Pope famously said. There’s a lot of truth to that famous quip, but here’s one more insight to add. To say one thing and do another is also human behavior—common human behavior, in fact.
When it comes to collecting data that will highlight new opportunities, you have to think like an anthropologist. This approach requires you to collect evidence from the field, such as concrete examples of consumer behavior, or audio from customer service phone calls. As you gather this evidence, you’ll uncover something far deeper than what surface level statistics can show—context.
Without context, metrics are just numbers.
Understanding how your customers use your products and engage with your services is powerful. It exposes areas where you can integrate with external products and services. It sheds light on which features your customers actually use instead of which ones you assume they’re using. To find this, you need to think like an anthropologist and do whatever you can to observe your customer in their natural habitat and augment their experience.
When you adapt these three thought processes into your organization—thinking like a scientist, psychologist, and anthropologist—you can make bigger strides. Your employees get deeper insights about both the market and your customers. These deep insights translate into being able to take a more agile approach to decision making. Because you know what the market demands and have data to validate your assumptions, you’re able to react faster than ever before.
Two Core Traits of Exceptional Customer Experience Teams
As we’ve discussed, as popular as Big Data is these days, it often fails to acknowledge one thing—the people at the heart of the facts, figures, and statistics. Hypothesizing like a scientist, looking at context like an anthropologist, and predicting like a psychologist are good stepping stones to getting into the mindset of the customer, but to truly be a leader in customer experience, brands have to be built to trigger an emotional response. And to do that, teams and leaders must possess two core traits: empathy and humility.
Note that there’s a huge difference between empathy and sympathy. Sympathy may acknowledge the problems a customer is having before, during, and after the purchase, but in a trivial, non-committal way. Sympathetic responses happen when you have blinders on and don’t look peripherally to see what is really happening in your buyer’s world. And yet, sympathetic responses are most common among organizations.
The companies that excel at customer experience don’t simply acknowledge what a person is feeling at each stage of the buyer’s journey—they put themselves (often literally) into their customers’ shoes, craft experiences that help make customers feel amazing about the time spent with the business, and treat silo-busting feedback loops as a core tenant of their culture. It’s not just lip-service for Wall Street, it’s something so much deeper within their internal value system.
As McorpCX points out in their article, every industry from healthcare to technology to retail needs to adopt an empathetic mindset when finding new ways to improve the consumer’s journey with a business. This empathetic approach towards understanding the customer experience plays a role across all businesses.
This is an empathy map we’ve been working on. Most empathy maps focus entirely on understanding the customer, but anyone who has led an organization knows just how important it is to understand what your employees are going through as well. It’s much more difficult to build a great product and an experience to match when your employees are not happy.
But empathy alone isn’t enough. Humility is equally critical.
When I think of a humble leader, I think of Richard Branson, the founder of Virgin. He’s well-known for getting in the weeds of the organization. He gets face-to-face with his customers and isn’t afraid to get his hands dirty in the operations. He humbles himself by interacting directly with his employees and customers, and he’s built an impressive business empire with this attitude.
Humble leadership is key because it enables you to be a better listener and act on feedback. A person who is humble learns to test what they are hearing and balance it with their gut feeling. True humility removes ego from the equation and helps you question your own assumptions as well as those of others.
I’m a big believer in humility because we all have blind spots—areas that someone else has more knowledge of, experience in, or just a better feel for. Humility keeps the door open to persuasion on both sides, while headstrong leaders have a tendency to ignore or misuse the evidence in front of them to charge forward on a hunch.
But the real power of humility is how it fosters empathy. Leaders who seek for honest feedback and truly listen to what their employees and customers are saying elicit loyalty. They look for and act on the data that lead to smart decision making. The end result? You mitigate risk while improving the customer experience you deliver.
Powerful stuff, right? But it’s not easy to do. There’s a lot that goes into creating a customer experience team that can merge gut reactions and hard data.
Acquiring Quantitative and Qualitative Journey Analytics
Big Data is just that—big. But having lots of facts and figures doesn’t mean you have the right type of facts and figures to get you what you need. There are plenty of quantitative data available today, but qualitative data require more effort. Quantitative data, such as clicks on your website or social media shares, comprise the data sets that help you identify broad trends. Meanwhile, qualitative data dig into human motivations on a smaller scale. Both have their pros and cons.
Now, the graphic above is somewhat simplified. The truth is there’s a lot of overlap between them. Qualitative insights can be found in quantitative data points, and qualitative observations can be quantified. Even better, we love the challenge of building quantitative models around qualitative data. For example, measuring behavioral data via web heat maps or video recordings isn’t for the faint of heart, but—as my daughter would say—”it’s soooooo worth it” to glean those insights otherwise pushed off as unactionable.
But as a general rule, qualitative is rich, personal, and gives detailed insights into those emotional triggers that drive purchase behavior. You dig into their motivations for interacting with your brand. Gathering it tends to be expensive and difficult to interpret, especially in bulk. It’s hard to make comparisons between answers and pull out those deep insights you’re looking for.
Quantitative data are less costly and easier to collect and validate. They are also more useful for statistical analysis. However, they are limited and lacking the type of depth you need to understand the psychological triggers behind the numbers. You need more data to see any statistically relevant insights and that obviously begs the questions: Which one should you focus on?
The answer is both. And anyone in and around CX needs to be able to get both qualitative and quantitative data because they are equally necessary for digging deep into customer journey analytics and understanding how each department in a company affects the customer experience.
Journey Analytics in Action: How Apple Nails It
Let’s break for a second on measurement and dive into a real-life experience with a brand we all know (and I love). I’ll never forget a consulting trip I took to the Apple headquarters back in 1999. I was there to meet with some of the top executives around a large initiative. The trip was fascinating and I was thrilled to be in their environment, but it wasn’t until I was talking with two Senior VPs that I realized why I felt such a spark in their building. It wasn’t the design. It was the culture.
During one of my conversations, I learned about the difference between Apple and everyone else. A Senior VP leaned toward me and said, “We don’t sell products—we sell experiences.”
That quote has stuck with me over the years. They’ve built their product line based on something far deeper than features and benefits. They’ve created a brand based on perfecting what the buyer sees, feels, hears, smells, and touches. That, my dear reader, is empathy at its finest.
The Apple experience is felt from start to finish. From the moment someone starts using an Apple product, walks into their retail store, unpacks a new product, or reaches out to one of their “Apple Geniuses” for help, experience is rooted in all of it—and people love it. That’s why their most loyal customers wait overnight in the bitter cold just for the chance to buy the latest product. It’s how they’ve developed loyal users who won’t even consider trying a Samsung or Google device. People love it to the point where experiences that are mundane with most brands come to life with the Apple brand. Just take a look at this video of the unboxing of the newest iPhone X, which has over 1.5 million views.
An unboxing of a phone shouldn’t be so exciting, but because it’s a flagship Apple product, it is. The boxes that Apple products come in are part of the product themselves. Unlike most products, people actually keep the packaging—not because they are planning on returning it to the store, but because the materials and design are top-notch just like the hardware they contain.
It’s not just about their products and packaging either. Their support team is well trained in the experience too. In the Apple Genius Training Manual, the team members are taught to use phrases that convey empathy to the customer. They’re advised on how to watch body language on the sales floor so they know how to react. The goal here is to create a complete experience for the customer that not only includes selling them a product, but showing genuine concern when their expectations aren’t met.
You’ll notice that every response shown in the above image starts with validating the customer’s feelings. This focus on empathy isn’t an afterthought—it’s baked into the section heading and every response. This strategy wasn’t adopted into one of the world’s largest companies just because it sounded like a good idea—it is a calculated response based on loads of data. This is just one example of how Apple fine-tunes the customer experience with their brand, right down to the smallest detail.
Now, my guess is you’d like to be more like Apple, right? You’d like to create a culture of experience but you also want to do it in a way that enables you to create exceptional products and continue innovating. But innovation is risky, and to mitigate that, you’ll need data to drive your decision making.
But these insights don’t come from simple metrics. How do you balance broad figures and specific anecdotes to pave a clear path towards growth?
Gleaning Insights From Quantifiable Data
With quantitative data, you can’t always take the data at face value. If the context and purpose around the data are off, it could lead to missed opportunities as well as skewed insights, and an incomplete overall picture. For this reason, it’s critical that there is a lot of care and comprehensive understanding of how data are gathered, as well as how it’s mined and modeled.
A common problem I see is dirty quantitative data—data sets that were collected, aggregated or mined poorly. For example, if you’re gathering data for a retail store through face-to-face surveys, you have to be methodical about where you stand, how you ask the question, the verbiage you use, and even the body language you use. Changing up any element could cause the same person to give you a different answer. If you ask a shopper at the beginning of her experience in the store about how likely should be to recommend the store to a friend, chances are she’d say very likely. But, if she has a bad experience while shopping, her answer could easily change at the end of her visit. In order for quantifiable data to be clean, they have to be collected correctly.
But proper data gathering strategies are just the beginning. Eliminating variables in the collection process is only one task in the greater world of proper data humanization.
Another problem I see consistently is gathering data that doesn’t take context into consideration and just scratches the surface on what’s driving human behavior. Many collection methods fail to consider human motivation and psychological triggers—triggers I often refer to as cognitive biases. One of the most common examples of this is the Net Promoter Score (NPS), which typically looks something like this one-question survey from Buffer.
I’m not trying to call out Buffer here, and I’m not saying NPS scores are useless. In the end, it comes down to how this data is being treated. Businesses using NPS have to recognize that a single question is not enough to get deep into the mindset of the consumer. Not even close.
The Limitations of NPS Scores
A few months ago, I took my wife out to dinner for our anniversary. Something you should know about me and my wife is, we are foodies. I knew I wanted to wine and dine her, so I found a restaurant that featured Michelin or Michelin-esque chefs. Each a la carte plate was about $25 to $30, which was reasonable for the quality of food and atmosphere of the restaurant, so my decision to pay a visit there was easy.
When we got there, we were promptly seated and looked at the menu. I love trying gourmet dishes, which means that when I go to a restaurant like this, I love ordering samples of…well, everything. Before dessert was brought out, the chef came out to check in with us. He asked us how we liked the meal and indulged us in talking about how he prepared our dishes. We never felt rushed, like we might if we had we gone to a restaurant like Texas Roadhouse. The entire team, from the greeter at the front to the chef in the back, always made us feel welcomed with open arms. The meal set me back a few hundred dollars but it was worth it for that entire soup-to-nuts (pun intended) experience.
The next day I opened my email and saw a message from the restaurant. In it, they asked one question: On a scale from one to ten, how likely would I be to recommend them to my friends and family? This was a typical NPS survey, which is used in every industry these days. I quickly and enthusiastically responded with a 10, remembering how nice the evening was, and went about my day. But the response I gave was not the absolute truth. It’s not that I lied, but the truth is much more complicated than that 1-10 scale.
The fact of the matter is, as you start to drill down, you can see the inherent weakness with this particular data collection methodology, as well as the data analysis model.
Inaccurate Predictions of Future Behavior
My answer didn’t accurately represent the reality of what my future behavior (or that of my family and friends) will look like. Yes, my experience had been fantastic and I plan to take my wife back there again sometime soon, but that wasn’t the question. The question was, would I recommend the restaurant to my family and friends? In reality, I would but only to a few and only if they asked.
Let me explain.
There are a slew of situations in which I would not run to a friend and enthusiastically talk about my experience at this restaurant. First, it’s not a good recommendation for some people in my network. Some of my friends are fast food fans. They love sinking their teeth into a juicy burger in the comfort of their car and don’t share the same affinity for carefully prepared, uncommon gourmet foods as I do. I also wouldn’t recommend this particular restaurant to anyone who’s recently asked to borrow money, or someone who owed me money, since I wouldn’t want either group to necessarily know how much money I’d spent or think I didn’t need them to pay me back. On top of that, I’m not the ideal demographic for recommendation mining. Like many in my generation, I tend to shy away from flaunting how much money I’ve spent on a meal. I also typically spend more on meals than other diners, since I like to explore a variety of dishes making me an atypical diner.
What all these minor details build to is that there are certain circumstances surrounding the act of a recommendation, and it goes beyond “Would you recommend this?” The answers collected by the restaurant’s NPS survey were susceptible to inaccuracy, and it didn’t tell the marketing team what I was really thinking, and it certainly didn’t indicate future behavior. Assume 1,000 of those NPS surveys came back. That’s a statistically significant number and yet I would bet my partner’s Porsche that the restaurant doesn’t have any great actionable data to grow their ideal demographic. This lack of accurate feedback is a severe weakness with the NPS score.
The Context Behind the Data Collection
There’s another inherent weakness when it comes to collecting NPS data. The methodology of collecting data isn’t clean because of the devices consumers use to deliver their responses to the NPS survey. As consumers, we’re almost always in a hurry. When I got this survey, I was checking my email on a mobile device. I wasn’t inclined to sit and think realistically about whether or not I’d recommend the restaurant, and to whom—I just wanted to answer and go about my day. I answered this NPS survey half-heartedly and with a knee-jerk reaction because of the context in which I received it—on my mobile device.
I’m an advocate for ultra-clean data collection, aggregation and mining processes. As you recall from earlier, I mentioned the importance of collecting data in the same place at the same time, and at the same point in the buyer’s journey. The NPS score doesn’t do that. The data can quickly get dirty because every person answering the question is doing so at a different place and time, and on a different device. Timing and placement is everything, which is why leading intelligent organizations hire us to take the time to build the proper foundation for customer journey analytics. For us, it’s a simple equation: The increased time invested in building actionable journey analytics via data looping is directly proportional to Brand Euphoria within StoryVesting.
Here’s another challenge with the NPS model. It doesn’t take into account the human drivers behind the demographic they’re targeting. They failed to ascertain the aspects I truly enjoyed about the experience. The Michelin quality of the dishes at reasonable prices, the attentiveness of the staff, and the ability to sit for long hours without being rushed out the door are all elements that build the strong experience this restaurant has to offer. Although I’m the right demographic for that kind of experience, not everyone is. Some people don’t want to be bothered during their meal and appreciate prompt service that will allow them to get out the door faster.
There’s also a generational gap to consider. Middle-aged people usually don’t want to go out and express to the world how much money they spent. They don’t crave that attention. I’m not saying that the opposite is bad, but it’s all about understanding the customer’s motivation. If the motivation is to look cool, and you have a customer base of youngsters who love to put their experiences on Snapchat and Instagram, then that’s the marketing approach you should be going after. It’s all about understanding the human behavior you’re trying to measure.
These types of errors in methodologies like the NPS score are probably why data aren’t as clean or reliable as many data scientists think they are. Data scientists were taught a different way of using data. Those teachings and industry standard methods for collecting data aren’t wrong, but at RocketSource, we do things differently. We dig deeper. We take the methodologies that are taught in business school and build on them to use data to uncover emotional-to-logical triggers that drive real buyer behavior.
We recognize that the NPS score isn’t everything. This is just one example. There are plenty of other metrics that could be used instead, but the inherent problem remains: The data can’t tell you what you need them to if they are not handled or interpreted properly. A tool is only as good as the person using it and giving a powerful but dangerous table saw to someone who’s never used it before is asking for problems. Data will rip through business productivity if they are misused.
The key here is to really understand how the demographics and cohorts truly fit into a business’s messaging, which in turn enables businesses to innovate and sell more products and services that customers really want via an experience that is far superior to that of the nearest competitor. Many times, the best returning growth models are built around a well-planned and executed data looping process.
What Surveys Should Look Like
In it’s most simplistic form, data looping starts with a great survey. It’s possible to get some good information from a single-question survey, but it has to be very dialed-in to a specific need or question. Lengthier surveys are almost certain to get fewer responses than their NPS-style counterparts, but you’ll be much happier with the accuracy and depth of the responses you get. Here’s just one section of a sample survey from Qualtrics that really dives in.
This range of questions allows an organization to get much more insight into what the respondent is thinking. We’re digging deeper to understand how happy they are with the training the CSRs receive, the supervision they have, the professional standards of conduct, and so on. Then, based on those answers, you can create visuals that explore the areas where your CSRs are excelling and where they need some improvement.
But this sort of survey doesn’t have to be limited to customer sentiment. You humanize your customers by treating their data points as a way to iterate and improve the customer experience. This same approach can be done for your employees by actively seeking feedback to uncover important pain points and improvements. Think back to the empathy map I shared earlier as an example. You spend plenty of time and money on your employees as well, and they have their own unique experience with the brand that can be tapped into for insights that contribute to a better workplace and product.
Here are some sample metrics and corresponding questions that you can dig into with the help of feedback from your own team:
- Net Promoter Score (NPS). On a scale from 1 to 10, how likely are your employees to refer a friend to work here?
- Satisfaction Scores (ESAT). How happy are your employees at work?
- Customer Effort Score (CES). Do your employees have enough flexibility and authority to resolve problems quickly? Or are their hands tied by scripts they’re forced to follow, or red tape they have to cut through in order to get something fixed?
This is the first step in humanizing data—collecting and analyzing data for the right reasons. If your primary goal is to make more profit by earning new customers, the data and findings will skew towards the initial sales and conversion process. Meanwhile, if your focus is to build a better product and an enjoyable workplace, you’ll seek out the honest feedback of customers and employees alike and sincerely make an effort to make changes for the better.
Once you have a data set to work with, it’s time to turn it into something that’s easy on the eyes.
Visualizing Quantitative Data
I want to talk about how you take the data gathered and visualize it—but not in a comprehensive, torturous breakdown of each possible chart you could use. I want to focus on the best methods to quickly put data points into a usable format.
For displaying quantitative data from a range of metrics, I love the style and subtle complexity of radar charts, which are most often used for measuring the performance, digital maturity or preparedness of an organization.
It’s pretty easy to absorb what’s going on here. You have several different points plotted that reflect an assessment of different parts of a company. At a glance, you can tell where the weak and strong points are. But it doesn’t tell us much on its own beyond that, and the results may not come as much of a surprise to anyone familiar with the company.
Here we have something a little more detailed. This example overlaps multiple data sets and uses colors to indicate different areas of the company. Now we can contrast and compare more information at a glance. It’s better, but we can go much deeper.
This is how we use the radar chart. We build visuals from quantifiable metrics on the employee and customer side that contribute to a holistic approach to CX. We’re mashing results from surveys, heat maps, and touchpoint data that are shown as points in each section, shaded in to show the range, and averaged to give an indication of performance. It’s much easier to visually pick out deficiencies here, and observations from one or both charts can lead to important conversations about how to improve the entire experience—from back office (for the employee) to front office (for the customer).
There are tremendous insights you can pull from the data you see in these charts, but there’s still a nagging problem: It’s all quantitative—it fits beautifully into a chart, but it doesn’t dig into motivational behavior. You need qualitative data too in order to draw out human thoughts and interests. We consider these radar charts a starting point because they are limited to simply showing you what is happening, not why.
This is where the rubber hits the road for humanizing data. The data points in a radar chart are just that—data points. The outcome you’re looking for isn’t a fancy chart—you want actionable insights that contribute to improving your brand. The deeper you’re able to go into the data and mine, aggregate, collect, correlate, and then begin to visualize and share and communicate across an organization, the more likely that you’re going to be able to create the kind of human-centered, customer-centered experiences that the data science process is designed to promote.
Getting to the Why By Quantifying Qualitative Data
If you know me, you know I love the value of a deep conversation. I’m fascinated by the way people think, feel, and react to certain situations. So it makes sense that one of my favorite forms of data collection is through conversation. I prefer to go out in the trenches and talk to people one-on-one, either through polls or focus groups. I love getting honest responses straight from the source and an opportunity to dig deeper into the concerns and motivations of the person on the other end of the conversation.
But I’m also realistic. Talking with your customers is just not feasible in every situation. The limited data discovered via polls and focus groups has to be balanced and reconciled with the massive data sets collected through surveys. Regardless of the methods used, you want to prioritize ending up with clean, usable, and enough data.
Another way to gather this type of qualitative data is to work as a customer service representative for a few days. Customer service departments are a goldmine for customer-centric data. By sitting shoulder to shoulder with your representatives, you’ll find out which features people want the most, common friction points in their journey to buy your product, where you can optimize touch points, ideas for product innovation, and more. Talking to the customer will help uncover the why behind the data’s what.
Uncovering human behavior forces you to move beyond Big Data and have the one-on-one conversations that will expose motivational triggers in the customer experience. Then, once you have acquired this information, you can use the data to visualize the buyer’s journey. As I mentioned earlier, qualitative data identify human motivation, which is difficult to quantify. So you need to find a way to quantify it. But that’s not all—specifically, you’ll need to:
- Quantify assumptions and observations with relevant metrics
- Dig deeper by applying psychological principles
- Visualize and democratize the results
As an example, let’s say you want to analyze the performance of the key sales page on your website with the goal of increasing sales. There are many ways someone might begin this process—call an executive meeting, get some insights from leaders in various departments, or even comb through emails to sales for top-of-mind questions.
Unfortunately, none of these steps lead to any quantifiable metrics. At best, the conclusions are based on limited data or anecdotes, at worst they rely on misguided assumptions.
So here’s what you can do instead. Implement a research tool such as Hotjar to track and analyze user activity on the sales page. Once configured, it shows you the aggregate clicks, movement, and even scroll behavior of your users. But that’s just the beginning.
This gives you some numbers to work with, but don’t settle for an extremely basic metric like a conversion rate. Think about it like a psychologist would—when you visit a website, do you feel that your only options are to buy or not buy? Of course not. You can refer to different parts of the website for more information, check out reviews, research alternative products, or just leave the tab open for when you have more time. Presenting this metric as a true/false dichotomy in your analytics oversimplifies the factors at play here—the consumer’s genuine thoughts, feelings, actions, and more.
So consider the ways you can really dive into what’s going on here. Instead of tracking and acting on the conversion rate, assign a weighted score to the most prominent user actions, where clicking the “purchase” button is the highest score followed by other options such as clicking the pricing page, subscribing to the newsletter, visiting the blog, and more. When you’re ready to experiment with the page by running A/B tests, you’ll be able to see if changes to the page lead to an increase in desirable user behavior based on those weighted scores.
You can go even deeper by using platforms that offer account-based marketing, which lets you get more granular with individual prospects. But no matter what tools or methods you use, remember that the goal to sell your product can only be more effectively achieved when you objectively observe, understand, and assess real human behavior through data, and turn all of that into an improved customer experience. Doing this will yield deeper insight into how people think and feel when buying from you and help you adapt with less risk and iterate without fear.
Visualizing Data as Insightful Journey Analytics
Acquiring data is only a small part of the equation. Before you can derive any meaningful insights from your customer journey analytics, you need to present your data in a way that is visually appealing and digestible. Many businesses—both disruptors and large corporations—are drowning in numbers, stats, and figures. How can you show these metrics in a way that is descriptive enough to be useful and easy enough for anyone to understand?
Start by getting it out of spreadsheets and lengthy reports as soon as possible. Instead, use colors and charts to present complex data in a way that is much easier to digest. Imagine using something like this going into a quarterly meeting instead of sifting through pages of spreadsheets.
This is just a small insight from a much larger customer journey visual of what I call experience mapping. It is designed to mash together qualitative and quantitative data into meaningful, agile insights. Presenting the complex in a format like this makes it easier to absorb than any combination of documents, PowerPoints, spreadsheets, and so on.
But creating stunning visuals isn’t easy. It requires the right tools and the implementation of proprietary algorithms to pull out data aligned with the key performance indicators you’d like to track. These metrics must all point to your company’s North Star metric, or the core data point that determines whether you’re succeeding in your market. Again, this all requires that you’re gathering clean data that points to your buyer’s why. Why are they buying from you? What are their human motivations towards recommending you to a friend, or making a repeat purchase? With data visualization, you can highlight these insights, showcase the human motivators behind the data, while simultaneously keeping your team in the know and quickly monitoring how well you’re moving the needle toward your goals.
What Humanizing Data Looks Like
Using people-centered experiences to drive growth is not a fantasy—it’s the future. From brick-to-click and click-to-brick, the demand for a more sublime experience will rule today, tomorrow, and for years to come. Let’s look at a case study to paint a picture of a business using data to improve a brand experience.
In late 2015, a handful of cases of E. coli were traced back to chicken salad sold at Costco. 19 people were impacted by the infection according to the Center for Disease Control. That number could have been quite a bit higher had Costco not collected the data or acted on it quickly enough. As soon as they learned about the infection, Costco was able to reach out to the affected members who purchased the chicken salad during a specific time frame. Where other companies might have shifted the blame or attempted to cover up the problem, Costco reacted to the situation quickly and turned a potential PR disaster into an opportunity to show that they value their customers’ safety.
Today, the bar has been raised. Based on positive experiences with brands that use data, consumers are increasingly expecting more attention and care from the brands they use. They want companies to use their data responsibly and intelligently to improve their overall journey with a brand, not just when disaster strikes. The balance of power is actively shifting away from corporations over to consumers, who are empowered to gather information and make buying decisions regardless of price or location. It’s up to businesses to win them over and deliver on their expectations.
Above you’ll see what I call the bowtie funnel. This represents the entire journey buyers go through today with brands. Unlike a traditional sales funnel, it’s an ongoing loyalty loop. In the first half, businesses work to create positive experiences that lead to a purchase. But the journey doesn’t stop there. After the purchase, businesses still have a responsibility to continually offer the best customer experience by understanding, documenting, and improving the buyer journey to both increase new sales as well as keep existing customers.
This process doesn’t simply rely on data. It relies on humanizing it, and rolling that back into the customer journey by inventing, improving, or innovating in ways that directly impact the brand experience.
Collecting all the data in the world means nothing if you don’t execute on it.
One way we analyze and kick-start a path to execution on Big Data at RocketSource is through a UX/UI architecture and vision. We want to use data to better understand how people are engaging with an app or a website. When we start working on a project, we’ll take all of the data related to the user experience and map it out so that it looks something like this.
On the surface, these charts and arrows might look daunting, especially if you’re not familiar with this real-time user mapping approach. For us, it’s insightful. Each of these arrows are navigation mapping points. They signal the flow a person takes through the website or app. The idea here is to get a deeper understanding of how people are using an app or a website and we can do this long before a page goes live in order to collect and act on the best data possible. It’s taking data points and pulling out the human behavior behind the numbers. We’re not just looking at statistics—by observing the usage behavior of the tool and learning from the psychographic data collected, we’re uncovering valuable insights that can be applied back to the customer experience.
Contrast that with the traditional mindset behind metrics, which prioritizes getting new customers and optimizing the initial sales processes. There’s nothing inherently wrong with acquisition, but as the company grows it becomes more and more difficult to implement initiatives that focus on retention. If the structure, process, and mindset aren’t there from the very beginning, then you’ll have a lot of catching up to do.
The same lesson applies for Big Data. Whether it’s being used for acquisition or retention, instilling this mindset in an organization early on ensures that key processes—including hiring and operations—account for factors such as data democratization and literacy.
One brand that really stands out in this regard is Monday.com (formerly dapulse), a team management platform that we use in-house. They’ve developed an internal BI tool they call BigBrain, which makes every KPI, A/B test result, and account health score available to each team member. On top of that foundation, they’ve been able to build and sell the product without a sales team.
Along with other data innovators, they are finding success in ways that appear unrealistic or even impossible to other companies. And it all has to do with using the data they have in a way that informs their understanding of and improves the customer journey—humanizing data.
Our Methodology for Gathering and Using Journey Analytics
Here at RocketSource, we’re big believers in leveraging data and data science to uncover powerful, game-changing insights. That’s why we’re actively developing tools that visually map out the kind of data you need to make smarter decisions for your customers. This process of taking data and using it to truly understand the customer journey for your business is just a part of my own growth framework I call StoryVesting.
StoryVesting is a methodology that I have developed and refined over the last 15 years. It’s a system I’ve learned from the best, gleaning bits and pieces from some of the most successful leaders of all time. Using this proprietary approach, I’ve created a way to incorporate data without relying on it for every decision. It’s a way for organizations to not get bogged down by data-driven decision making, yet simultaneously mitigate risk. It’s a way for large organizations to be more customer-centric and agile. It’s a way for corporations to become disruptive again.
But StoryVesting—or any other sustainable customer-centric growth framework—relies on having empathetic and humble team members, processes to gather, visualize, and analyze data, and make constant improvements to the customer experience. Without those elements, you won’t be able to achieve the kind of growth your company is truly capable of.
Interested in having a deeper conversation about your organizational goals? We’d love to offer you a complimentary assessment to help get things moving in the right direction. Fill out the form, share some information with me and my team, and we’ll be sure to set you on the right path forward.