In the previous part, I have talked about the risks of data-driven culture, we have discussed the biggest challenges such as the “Dirty” data, Vanity metrics, and why all of those challenges could lead to the end of your company. But, we are in the age of big data, the market value of Big Data Analytics is around 215.7 Billion dollars and it is expected to get to 274.3 billion dollars in the year 2022. Isn’t it good to prove that using data in our workflows could be a game-changer? Well, my point is not to stop using data, but the thing that separates great companies from the rest, however, lies in how much of the thinking they do themselves, and how much of it is left to the data. Having high data dependency could lead to disasters and big challenges as mentioned in the previous part.
Companies with huge data dependency usually struggle with having a bigger scope to the problem which ends in them being stuck over-optimizing small details (ex: Adding Web- chat), this happens because data shows you the symptoms of the problem, not the root cause of it. So, how could we use data to help us achieve our goals and avoid all the challenges related to data-driven culture? Introducing the Experience Driven Culture.
With the rapid increase of technology adaptation, consumers have dramatically moved towards digital channels. Interactions are now more digitals than ever according to McKinsey and Company “To say that at least 80 percent of their customer interactions are digital in nature”. With this gigantic shift in customer behavior, customer experience is becoming a key source of competitive advantage, as companies look to transform how they do business.
Research shows that 89% of consumers have switched to doing business with a competitor following a poor customer experience. It is not enough nowadays to just sell a product or service, companies must truly engage with their customers. In today’s Consumer Savvy world, having a good Customer Experience (CX) is a must. The problem with data-driven decisions is, it lacks empathy for the customer. Which results in superficial Products or Services, you have to understand the “why” behind the needs of Customers. An article published by Jeneanne Rae on HBR shows how companies who have been investing in understanding their Customers have outperformed the S&P Index by 288% over 10 years.
To be more Experience-Driven means you are not solely making decisions based on data, but you are trying to really understand the needs of the customer by asking more questions and looking behind the obvious answers.
Creating an Experience-Driven Culture
To create an Experience-Driven Culture you need to have the right infrastructure and decision-making framework which is going to be critical to building more compelling products and services that resonate with your customers. In this article I am going to discuss the three most important components that you need:
1. Data Governance.
We are living in the Era of Big Data, Companies nowadays are acquiring Data from multiple sources, and Data comes in different shapes and sizes. Much of your data will arrive in unstructured forms, or incompatible formats. What is more important is the ways you gather and treat your data. The biggest misconception I keep noticing is, that companies think that Data Governance is assigning a product manager or a data scientist then all their problems will be solved, this misconception indicates the vague understanding of “Dirty Data”, and neglecting the human factor within the problem itself. (You can check the Second part to understand more about Dirty Data.)
With the growing iterative and fast pace startup culture, lots of companies are using Macro – Services to build their products, of course, it is faster and cheaper but, the data that you gather from those different API’s, if it was neglected it could become a nightmare for the company accumulating storage cost and insane maintenance bills and this is one of many examples.
So how can we do Data Governance the right way, an article published by David Opolon on the World Economic Forum talks about the “Big Data Trap”, where David explains his approach to avoid the big data trap called “thinking in new boxes”. The idea behind his concept is to question everyday assumptions by examining data sources, revisiting business KPIs, and “pain points,” which can be rich sources of inspiration.
After you have identified a list of potential data uses, it is time to start prioritizing the uses by weighing the benefits against the feasibility. This will help you to start finding opportunities that could enhance your products, services, or even create new ones. But, you need to determine the necessary types of data. Different opportunities require different types of data and formats. Therefore, David advises to create and follow a range of dimensions to analyze your data including the following:
- Validity, the degree to which the data conforms to logical criteria
- Completeness, the degree to which the data required to make decisions, calculations, or inferences is available.
- Consistency, the degree to which data is the same in its definition, business rules, format, and value at any point in time.
- Accuracy, the degree to which data reflects reality.
- Timeliness, the degree to which data reflects the latest available information.
Each dimension should be weighed according to the business benefits it delivers, David also recommends using a multitier standard for quality. Then you can start performing a gap analysis every now and then, which will help you to reveal the differences between the baseline, and your business goals for each data source.
I would highly recommend you to read “The Big Data Trap” article By David Opolon, he does a superior job explaining Data governance.
On the other hand, Data Owners, PM, or whatever the job title is going to be; have to make sure data stays transparent all the time. But, no matter what concepts, strategies, and methods you are planning to follow, it will not work out perfectly except, if there is support from the management. Data quality has an expensive price, thus, you will find lots of hurdles coming your way from the management. You need to make your case.
2. Have a Goal, Stop Looking into Data Aimlessly.
The decision-making process you build for your team is going to be critical. Decisions should be more grounded in reality. Data shows you what is going on with the product in real-time. It makes more sense to be data-driven. But, there are several blind spots for the data-driven culture that could have a long-term downstream impact on the quality of the product you build.
Being too reliant on data to make your own decisions will cause you to have a tunnel view product vision, where you are stuck playing the feature game thinking this is the only way to grow your product. Sooner or later you will hit the “Local Maximum” and start over- optimizing stuff that doesn’t matter (ex: Adding Web Chat ), without understanding the behavior of the user behind that data, you are just making decisions based on assumptions. Humans in nature are not good statisticians, and we are full of biases, any decisions based on gut feelings or instincts can, and will go wildly wrong.
Being Experience-Driven means that you let data check on your intuitions to make sure you are on the right path. Instead of just relying on data, which, most of the time is not capturing the behavior of the user accurately. You are required to have multiple points of view which will help you to identify, understand and, tackle the right problems.
Building great products is inherently subjective, it requires the organization to have a great understanding of the users. While having multiple mental models. The goal should be to engage users to drive business and digital product goals and strategies.
But, how do you scale subjectivity in an organization?
Understanding the root cause of the problem would require you to abandon your current assumptions, and look at the problem from a different perspective. Introducing The First Principles Thinking is an article published by Mayo Oshin, where he has demonstrated the 3 steps to how to solve more complex problems.
“Usually, when we’re faced with complex problems, we default to thinking like everybody else. First-principles thinking is a powerful way to help you break out of this herd mentality, think outside the box, and innovate completely brand-new solutions to familiar problems.
By identifying your current assumptions, breaking these down into their basic truths, and creating solutions from scratch, you can uncover these ingenious solutions to complex problems, and make unique contributions in any field.” I would strongly recommend you to read his article.
However, to start developing this type of mental model within your organization, you need to start embracing a research culture within the company, while being supported by the management. User empathy, design, and product sense along with data should always be your guide in solving users’ problems. Of course, it can be tough to figure out a problem if it is real or not with insufficient data, people could point out that the conclusions can’t be statistically proven or it is not an accurate representation of the user’s experience. But, there is a technique used in social-science research called Triangulation. Which is the practice of using multiple sources of data or multiple approaches to analyzing data, to enhance the credibility of a research study. An article written by the NN group explains how to use the technique and when should you do it also, I strongly recommend you to read it.
But, what if I don’t know what is the problem? This is where the discovery phase comes in handy. Discovery is a preliminary phase in the UX- design process that involves researching the problem space, framing the problem(s) to be solved, and gathering enough evidence and initial direction on what to do next. Discoveries do not involve testing hypotheses or solutions, your goal here is to generate as many insights as possible while being solution agnostic. A discovery should start with broad and vague objectives, which will allow the research team to question every assumption known to the organization.
3. Hypothesis Design
In 1957, Leon Festinger came up with ‘The Theory of Cognitive Dissonance,’ which exposed our psychological tendencies to reframe conflicting evidence in support of our deeply held beliefs, instead of changing our beliefs. Rather than owning up to our mistakes and learning from them, we tend to invent new explanations as to why the mistake occurred, or ignore the conflicting evidence altogether.
The Experience Driven Culture requires you to make changes in your mindset, since the world is fundamentally complex and consistently changing. The “Art of Given Up” becomes a critical skill essential to every decision-maker. Most of us, are unwilling to question our assumptions and deeply held beliefs, Which will lead to devastating results.
Scott D. Anthony in his book The First Mile said: “The most frequent reason why innovators make wrong turns is the lure of fool’s gold white space”. The goal here is to overcome our cognitive biases, before committing an idea that might be devastating to the business. An interesting example given by Laura Klein in the LSC 2014 was Webvan, it is a company founded in the ‘90s where it delivered products to customers’ homes within a 30-minute window of their choosing. Webvan was committed to a concept that was later on proven wrong, which resulted in losing 400 million dollars. “They could have figured out earlier that people didn’t need their solution to that particular problem”. Your objective is to design a system that allows you to validate, or invalidate ideas before committing to them.
But first what is an Assumption, what types of Assumptions we might have, and how to categorize them?
An Assumption: is a thing that is accepted as true or as certain to happen, without proof. And, in business we might have multiple assumptions, going back to Laura Klein, there are 3 types of Assumptions that are important to keep in mind:
- Problem Assumption: is an assumption where there is a market that has a problem that we are trying to solve.
- Solution Assumption: is an assumption that the way you are solving the problem is the right way.
- Implementation Assumption: is an assumption which you can build and deliver value and make money out of it.
At this point, we need to start building our hypothesis statements. An article published by the NN Group explains how to write problem statements. So what is a problem statement, “A problem statement is a concise description of the problem that needs to be solved”. It helps you to keep the team focused, and it is a great tool that can help you to communicate your plans to the stakeholders and get some buy-in.
Building hypotheses and iterating on them is an indefinite process that ensures your organization’s continuous learning curve. What you need to keep in mind is that your customer behavior will be changing with time, so do your strategy and product. You need to keep asking yourself what are your objectives, what do I need to learn about my customers? And, what data do I research while following scientific methods that will help to avoid research hazards?
As I have mentioned earlier, our objective is to minimize your product waste, it takes so much of your money and time that could be invested in better solutions. Most of the time, we build features or products tending up rarely or never used by the users.
A great way to avoid this type of problem is by building an Evidence-Based, Backlog this concept was introduced by John Pagonis where he explains that assigning Risk and Value measures to each product requirement, assumption, etc. will help you to calculate your opportunity cost, and what would happen if this assumption is wrong, this will prevent you from making bad decisions based on speculative and ” in Just case” opinions.
But, Success comes to those who quickly identify, and systematically eliminate risks in the right order, using the right level of resources and the right methods. A great article published on the HBR explains the types of risks every entrepreneur has to deal with. According to the article, not all risks are created equal:
- Deal-killer risks
- Path-dependent risks.
- Risks that can be resolved without spending a lot of time and money.
As it turned out, the demand identified by market research depended on customers’ being able to access the broadcasts through low-cost radio receivers—which turned out to be impossible. The radio receiver required complex features such as multimode playback, a keypad for ordering subscription services, and—worst of all—professional installation, which made the device unaffordable in most of the developing world. Having failed to identify this fatal vulnerability, the company invested hundreds of millions of dollars to reach consumers who couldn’t pay for its service. The business limped along before ultimately going bankrupt. The company should not have left this key deal-killer assumption so utterly untested until late in the life of the venture. Quick-hit market research and rapid prototyping could have provided early warning signals.
In the end, Experiments should help redirect a venture, not confirm that your initial ideas were correct.
An Experience-Driven Company has a deep understanding of the needs of its Customers, not afraid of questioning their deeply held beliefs. While keeping a close eye on their Data. In times of uncertainty, Experience-Driven Companies flourish. John Boyd once said, “What Is the Aim or Purpose of Strategy? To improve our ability to shape and adapt to unfolding circumstances, so that we (as individuals or as groups or as a culture or as a nation-state) can survive on our terms.” At the of the day, Experience-Driven Culture is a Strategy that will help you survive on your own terms.