Chapter 3: Tracking the Right Growth Metrics

3.0 Good Growth Metrics

Without good data, you won’t be able to reliably analyze the results of your growth experiments, and you’ll be back to a guessing game. Assuming that your data collection methods are sound, you still have to ensure that you are capturing the right metrics — ones that can help you understand whether an experiment has met its objective as well as your success criteria. In this chapter, we will review the characteristics of good growth metrics, and some standard metrics that can get your team started.

There are many qualities that make metrics “good,” but key characteristics to look for are the following:

  • Captures your objective
  • Easy to measure
  • Accurate
  • Actionable
  • Predictive
  • Based on your growth model

Captures Your Objective

Some might claim that it’s intuitive which metrics are appropriate for certain experiments, but this is not true. Experiments are meant to meet certain objectives and satisfy success criteria that you set. For example, there is often a tension in trying to optimize customer acquisition. Should you focus on quantity or quality? You can cast a bigger net to get more customers, but those customer probably won’t be as engaged as customers that you could get through more targeted acquisition efforts.

If your objective is just to grow the raw number of customers, you might track metrics such as the rate of new registrations or the conversion rate, which captures the proportion of people that finish the registration process to the number of people that start it. However, if your objective is to build a customer base of very engaged customers, you might prefer to measure things such as your daily active users, which captures how many of your users spend time with your product each day. Another metric that might be appropriate for capturing your objective to build an engaged customer base might be the average time that a customer is using your product in a given week. As you can see, the metrics that are appropriate for each objective are as disparate as the objectives themselves, so it’s important to take the objective into account when picking a metric to capture.

Easy to Measure

With the digital age and the proliferation of tools, many metrics have become easy to capture. However, some metrics remain difficult to capture. Those data points tend to relate to the non-digital realm such as measuring the acquisition rate of customers that saw a billboard advertisement or the retention rate of people that saw a television advertisement. Often times, it’s possible to measure the effects of a given experiment using a number of different metrics, and it’s worthwhile to focus on those metrics that are easier to measure to ensure that you do not waste effort by running experiments without good data outcomes. However, it’s important to understand that there is a danger of defaulting to those metrics that are easiest to garner and not those that provide the best basis for analysis. You will need to determine the right balance for your goals. Sometimes it is preferable to opt for difficult to measure data if it will give you a clearer picture of whether or not an experiment successfully met your objective.

Accurate

Good metrics should also be strongly tied to outcomes rather than loosely represent them. For example, let’s say that you wish to impact how many purchases customers make. You could choose to measure either the customer’s intent-to-purchase or actual purchases. It is well known that intent to purchase is not always an accurate indicator of actual purchases. Therefore, you would likely be better off measuring the actual volume of purchases rather than a virtual metric such as intent.

Actionable

All metrics should be actionable in theory, but some are definitely easier to act upon than others. How actionable metrics are greatly depends on how specific they are. Let’s say that your product is an online, subscription-based task management tool. If I told you that the average customer satisfaction score for your product is 6.9 out of 10, would you know immediately what to do next? What if I told you that 4 out of five 5 customers that register for your product start but never finish creating their first task entry? Would you have a better idea of where to focus and what to try next? I hope that the second metric would arm you with much more actionable data. It takes a lot of time and resources to conceive and implement an experiment, so it’s well worth it to give some thought to how actionable the data that you intend to measure will be.

Predictive

It is always a good idea to track data that gives you insight into your business and are outside the context of a particular experiment. These metrics are different in nature than those that you should be tracking to identify if a particular growth experiment has met your objective or success criteria. Instead, the data should help to shape your growth strategy by helping you anticipate problems before they become hugely disruptive to your business. A classic example of this is the distinction between the rate of customer service emails and customer churn rate, which indicates how quickly existing customers are abandoning your product.

Let’s go back to the example of an online task management software. Imagine that your team changes a big piece of your product, such as, how tasks are created by users. Should you be tracking the customer churn rate or the rate at which your customers are contacting customer support. The former is usually calculated after the fact, whereas you can see a spike in customer support inquiries immediately. Therefore, the rate at which your customers are contacting customer support will let you react much more quickly than the churn rate, so you should be keeping your eyes on customer support emails or calls. As you think about what metrics you should track to give you general insight into the health of your business try to think about what data can tip you off about problems earliest.

Based on Your Growth Model

Although metrics such as daily active users or conversion rates are fine, nothing beats creating a data capture strategy based on your own growth model. Doing so ensures that you have precisely those metrics that are tied to variables that most directly affect the growth rate of your business. Your revenue and customer growth models should tell you precisely what metrics your team should be tracking.

Marketplace Revenue Growth Model

Let’s consider an online marketplace with the above model as an example. At first glance, you can see that you should be capturing the following metrics:

  • The number of sellers
  • The average number of posts per seller per time period
  • The number of views of items for sale
  • The number of buyers
  • The number of times an average buyer visits the marketplace in a given time period
  • The ratio of the number of times that buyers visit an item page to the number of completed sales
  • The average fee that your company charges per transaction
  • The average sale price of items in the marketplace

Not only did the revenue model help us identify novel metrics that are perfectly suited for an online marketplace, such as the number of views of items for sale, but it also helped us make standard metrics, such as conversion rate, specific and contextual to the business.

As mentioned above, making changes to marketing, product implementation, and operations is resource intensive, so it’s well worth spending some time to identify metrics that will help your team clearly determine whether your growth experiments meet their objective and satisfy success criteria to ensure that you are not wasting time and resources. Of course, it would be ideal if all of your metrics satisfied all of the criteria above, but the reality is that many will not. In that case, it’s best to focus on ensuring that the metrics that your team chooses to capture align with your model and are accurate. Beyond your tailored metrics, there are others that have become standard practice to track. We will explore these in greater details in the following sections.

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018. Be sure to check back on tomorrow to learn about common acquisition metrics. New sections of Growthzilla are published every weekday.

 

2.5 Iterating on Previous Growth Experiments

Iterative Experimentation is Key to Growth

This post is a part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

A key part of the growth science methodology is iteration. It’s not enough to form one hypothesis, test it, and implement a change. If you stop short, you’ll be missing out on substantial optimizations that can add up to incredible gains to customer or revenue growth. The key is to keep experimenting in areas where previous changes have led to strategically significant gains.

You can either iterate on past experiments and try small variations or try the same kinds of experiments in other contexts. For example, let’s say that you try an experiment to get more people to click on the registration button on your website. In trying to increase the conversion rate on new customer registration on your web app, you don’t want to stop with just changing the color of the “Register” button to bright green. You also should double down on this line of thinking and test changing the button color to orange and red. Not only that, you might try very related experiments such as testing the label text. Perhaps you can try “Sign up” and “Get Started” instead of “Register.” And these iterations are only for one button. Not only that we haven’t even started talking about marketing and operations.

A great example of iterative experimentation leading to markedly higher increases in growth comes from a leading language learning platform, Duolingo. Gina Gotthilf, VP of Growth, recounted the story of how her team made huge strides in Duolingo’s customer engagement through iterative experimentation with their registration process.

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2.4 Running Successful Growth Experiments

Sample Growth Objectives and Experiments

This post is a part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

By utilizing experimentation to help us decide if changes to product, marketing, and operations are effective, we avoid having to rely solely on our intuition. Without experimentation, we would implement changes and hope that we’re right slightly more than half of the time. Rather than engineering growth, we would be relying on an art form, which would be dominated by a select few that had outstanding intuition such as Steve Jobs (or those who claim to have this level of intuition). Growth engineering would be inaccessible to the majority of us.

Experiments make growth accessible to nearly everyone because they follow systematic ways of testing hypotheses to reach specific outcomes and are not unlike experiments in the physical and social sciences. Admittedly, growth experiments are usually not as rigorous as in academia, but the fundamentals are still the same. Anyone that learns the basic experimentation methodology can lead successful growth development at their company. Of course, you will likely be more effective with greater experience and practice, but it’s important to learn strong fundamentals from the beginning.

In this section, we will review the key components of successful experimentation. The first step is understanding what are you trying to achieve with your experiment. Are you trying to improve how long users spend on your site or how quickly the can get their tasks done? Then we will consider how to measure whether or not the changes that you implement have successfully accomplished that objective, or not. Then we will discuss ways to create a sounds hypothesis about ways to reach your objective. Finally, we’ll review the nuts and bolts steps in running good experiments as well as gathering and analyzing results.

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2.3 Strategizing and Prioritizing Experiments

Growth Strategy Cycle

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

When you model your business, you will likely find that there are many ways that you can potentially improve growth. On one hand, this is great news because you have many opportunities to grow your business. On the other hand, this is very challenging because you have two factors working against you: limited resources and a finite market size. Moreover, every change that you try will not work. This is why following a more scientific approach that includes forming hypotheses and measuring results is fundamental to growth science. Each experiment requires capital and human resources investment, and creating a well thought-out strategy and continually prioritizing experiments will be pivotal to your growth development efforts.

 

2.3.1 Brainstorm Growth Optimization Opportunities

As you begin to engineer your company’s growth, you will iteratively brainstorm new optimizations to try, create a strategy to guide your efforts, and constantly prioritize your growth experiments based on that strategy. Luckily, you have already created a framework that will help you to brainstorm and evaluate experiments. The customer journey map and growth model that you created will help guide your brainstorming. The journey map will highlight key actions in the customer interactions with your product and company, and can be juxtaposed with your growth model to understand how these actions affect overall customer and revenue growth.

Growth Strategy Cycle

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2.2 The Customer Lifecycle

Events Customer Lifecycle

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

Let’s review the customer lifecycle before we dive too deeply into strategy since it will serve as the basis for a lot of the concepts in this book. A typical customer lifecycle consists of five parts: awareness, acquisition, engagement, activation, and retention. The journey through the conversion funnel is not always linear and could jump stages. For example, an individual might use a product (acquisition), get tired of the product and contemplate abandoning it (retention), and fall back in love with the product (engagement). Notwithstanding what journey an individual might take, we need to have a game plan for each stage.

 

Customer Lifecycle - Awareness, Acquisition, Engagement, Activation, Retention

2.2.1 Customer Awareness

It all starts with awareness. One study performed by McKinsey & Company indicated that it can take five to eight exposures to an advertisement for it to make people truly aware of a product. People can learn about your product in many different ways such as through traditional marketing, through friends and acquaintances, and through organic news stories. What might be less obvious to the reader is that the product itself can help spread awareness. For example, a social sharing feature can help current customers recommend your product to your friends. Your operations can affect awareness too. For example, your customers can learn about new products that your company is offering when they call customer support.

 

2.2.2 Customer Acquisition

After a person learns about your product, they might decide that they want to try or buy it. This begins the acquisition period. It might seem that acquisition should not be a phase but rather a short point in time, but that is rarely how people become customers. For mobile apps, a new user might have to sign up. That can be a long and complex process involving filling out a registration form, validating their email, and completing their profile. Potential customers can leave the acquisition cycle in any of those steps, and it is not until they get through the whole process before they become active customers.

For other products such as enterprise software, the acquisition process can be extremely drawn out and can involve a sales team that pitches the product, a technical implementation team that helps to answer technical questions and sets up the product, a training team, and so on. The acquisition phase starts with the intent to try or buy a product and ends when the customer actually buys the product or uses the product for the first time.

 

2.2.3 Customer Engagement

Just because a person has used or bought your product does not mean that they are a valuable customer. Even popular apps like Twitter have scores of users that post a few things and then never use it again. Those are not engaged users, and having unengaged users is bad for business. Usually customers fail to become engaged because they either can’t overcome the learning curve or the product fails to meet their expectations. Luckily, it’s possible to systematically and predictably fix both.

Some products have much more compressed engagement cycles. For example, if you are in the market for a luxury yacht, you probably won’t be making a bunch of repeat purchases. However, that does not mean that all yacht owners are equally engaged. Some owners love their luxury yachts, and it shows as they merrily sail around the Mediterranean and extol their yacht’s virtues to their friends. Those are the kinds of customers that you want!

 

2.2.4 Customer Activation

Activation is a big jump in the customer’s level of engagement. The anecdote that I like to tell is my relationship with Amazon. I became an Amazon customer in 2001, when I was a college student. I realized that buying textbooks on Amazon could save me a ton of money, so every term I bought a bunch of books on Amazon. I was a pretty engaged customer. I liked that I could save money, which was tight during my college years, and I was generally happy with the service.

Fast forward a few more years, and Amazon’s offerings broadened. At that point, I was a young professional, and I would occasionally buy a DVD or a book. Once again, I was a solid repeat customer, but I would only buy things that were difficult to track down in a physical store and for which I could wait a week to be delivered. I still preferred to run down to my local BestBuy to buy electronics, DVDs and videogames since I could get it right away.

Fast forward again to 2009 when I went back to graduate school, and Amazon gave me a free Amazon Prime membership since I was a student. That year I bought my textbooks on Amazon, but my purchase behavior started to change radically. Suddenly, I was buying everything on Amazon: DVDs, video games, electronics, clothes, household items, you name it. On reflection, my Amazon Prime subscription annihilated a huge deterrent: having to wait a week for my orders. I didn’t realize at the time what a big roadblock the shipping time was, but now that I had free two-day shipping the floodgates were open. In fact, there was a time when my sons were still babies, when I’d get Amazon shipments multiple times a week filled with diapers, baby clothes, household items. That is what I call Activation!

Activation is a phase in a customer’s lifetime when they become super engaged. Many customers never get to that stage, but that is where the biggest growth can happen.

 

2.2.5 Customer Retention

Unfortunately, all good things must come to an end. Even your most activated customers may get frustrated with your product or company and will choose to leave at some point. More likely, those customers that never got very engaged will abandon your product. The good news is that abandonment does not usually happen instantaneously. There are often signs that a customer is unhappy, and you will likely have ample opportunities to make your customers happy. Even better, you can take steps during customer acquisition and engagement that will prevent your customers from becoming unhappy with your product once they convert to active customers.

I was recently trying a new online project management app. The app and the company behind it did many things right to make sure that I would not get frustrated or disappointed as I became an active user. First, when I signed up for the app, they sent me a personalized email from an individual on their team thanking me for registering and letting me know that I can contact her at any point if I have any questions or issues. Second, when I logged onto the app for the first time, they had a great video tutorial giving me an overview of the features and best practices. On top of that, they had little contextual tutorial messages as I explored the app for the first time.

I set up a simple project to test out the software and invited one of my teammates. It was a pretty solid project management app, but there were a few little things that irked me, and I stopped using it with time. The company saw that I hadn’t used it in a while and they sent me a couple emails (automated, I’m sure, but signed by my customer service representative) asking me if there is anything that they can do to help me with the app. I finally relented and replied to the email and set up a call with my dedicated customer service representative that addressed each of my complaints in turn. She couldn’t solve all of them, but she did help me find other ways to get things done. The company not only did its best to avoid abandonment, but they also successfully retained me when I was not totally happy. I’m still a loyal customer.

 

2.2.6 Engagement Versus Retention

Not everyone will agree with defining retention as simply a measure of keeping customers. To some retention means the act of keeping customers coming back, while engagement is a measure of how intensely they use your product at any one time. For example, they would consider Google Search to be a product with low engagement and high retention given that users use Google Search briefly, but then keep coming back.

Consider a student that is writing a term paper, who uses Google to help them find resources and information, and then never uses Google again. Because they were doing a ton of queries while writing the term paper, we would say that their engagement was high but their retention was low because they never used Google again. I think the majority of people would not consider this person to be highly engaged even if they did submit dozens of searches in a day. Wikipedia users are similar. They use the online encyclopedia intensively but infrequently.

I find it more useful to think of engagement as a measure of how much a customer is using a product, which is a function of intensity and frequency, whereas retention indicates if I am still an active customer. For example, which user is most engaged, one that uses your product once every day or another that uses your product ten times a day every ten days? What about a customer that uses your product twenty times over the span of two days but never touches it again? I would say that the user that used it twenty times and never again is not engaged because he is not an active customer while the other two are roughly equally engaged.

It’s often very ambiguous whether to consider some customers as being active or having abandoned your product, and every company has it’s own cutoff point depending on the nature of your product. For example, many years ago, I signed up for Flickr to share my photos with friends and family but stopped using it after a while and have not logged on for over four years. I still have an account, so am I still an active Flickr customer? It depends on Flickr’s definition of what they consider to be an “active customer.”

 

2.2.7 How Acquisition, Engagement, Retention Work Together

In 2007, a recent Yale graduate, Justin Kan, came up with an idea to stream every minute of his life over the internet. With the help of Emmett Shear, Michael Seibel and Kyle Vogt, he created Justin.tv, which would become the world’s first prominent livecasting service. The team grew Justin.tv to be a fairly solid business employing about twenty-five people, but at some point the founders realized that their idea had hit a ceiling. There were only so many things that it made sense to livecast, and they had tapped out all of them.

Justin.tv rose to about 30 million unique visitors per month, but the majority of their users weren’t super engaged, except for one tiny segment. Emmett Shear made the realization that video gamers, which made up just 3% of their user base, were fanatical users. They would sometimes spend hours streaming themselves playing video games and others would watch them playing for long stretches of time. The four founders decided to focus on addressing just the video game market and created a new version of Justin.tv called Twitch, which became one of the hottest startups in Silicon Valley and sold to Amazon for $970 million.

Justin.tv had the perfect product all along, but they were distributing it to the wrong customer base. Not too many people were super engaged by watching Justin sleep or others sit around in front of the computer, but the video gamers were completely hooked. Once they focused on acquiring the video gamers their product growth exploded. This is one of the best examples of how acquisition, engagement, and retention are related.

Many people forget that the concept of product-market fit is composed of two parts and focus, instead, on just the product. However, it’s not just about building the perfect product. Finding the right customer whose needs your product meets most adeptly is just as important.

If you target the wrong customer, it does not matter how good your product is, you will always have problems engaging those customers because they won’t really have the problem that your product is trying to solve. It’s like selling goose down parkas in Miami Beach. You might have the most stylish, warmest goose down parka in the world, but if you sell it to folks in a place that is constantly hot, your customers won’t love your product and will eventually shove it in their closet or give it away to Goodwill. On the other hand, if you sold that same amazing down parka in St. Paul, Minnesota, your customers would wear it all winter long and would love it for years to come. Their engagement and retention would be off the charts.

To put it simply, if you are effectively targeting and acquiring the right customers for your product, they will appreciate it and will be highly engaged. Customers that are highly engaged, are very unlikely to abandon your product. How much do you like Google? I bet you’re a highly engaged Google user. Are you very likely to stop using Google. Probably not unless another even more amazing search engine comes along.

 

2.2.8 Key Points in the Customer Lifecycle

At the beginning and end of each phase of the conversion funnel are key events that demarcate the end of one and the beginning of another. These points are hugely important because you can influence these events to supercharge your growth.

 

Events Customer Lifecycle

The First Time Someone Hears About or Sees Your Product

A good first impression is trickier than you might think, and it will set the stage for the rest of your relationship with you customer. First, you want to make sure that you’re reaching the right people — those that have the problem that your product is solving. You also want to make sure to communicate how your product is better at meeting their needs or solving their problems. Finally, you should tell them how to get your product or what to do next. Without meeting these three fundamental awareness goals, acquisition becomes an uphill battle. Even engagement, activation and retention can be adversely affected by a poor first impression.

The First Time Someone Uses or Buys Your Product

Usability and customer onboarding make the biggest difference when it comes to creating a great first experience. There is not much that you can do if your product doesn’t have the right features to meet the needs of a customer. Certainly, you can add important missing features with time, but by that time many of the dissatisfied customers will be long gone. Not only that, as soon as you add a new feature, customers will want others, and you will be constantly playing catchup. However, I have found that for a significant set of the customers the right features do exist, but they are hard to find or understand how to use. Moreover, bad usability often forces customers to abandon products long before they learn their full utility. Both of these problems are preventable, and addressing them will go an extremely long way to improving your engagement and retention.

The Customer Uses Your Product More or Buys More

A user will very rarely discover how great your product is in their first use. It often takes many interactions to find out if and how well it will work for the customer. Using or buying a new product is an awful lot like moving to a new city. It takes a while to learn where the good restaurants, parks, and hangout spots are.

Often times we are pleasantly surprised about what the city has to offer. Other times we are disappointed to find that there is not much more to the new place than initially meets the eye. It’s not that the place has changed in the time since you moved; you just have learned it more deeply. Often people discover new things with the help of their friends or colleagues. With your product, you want to be that friend that guides their discovery. Your marketing, product, and operations should be aimed at helping the user discover and learn your product as quickly as possible, so they appreciate it for what it really is.

Customer Becomes a Habitual User or Buyer

The holy grail of growth is the point at which an individual becomes a habitual user or buyer. This is when they enter the activated phase of their customer lifetime. We all are very familiar with this concept. Earlier, I told my story about how Amazon was able to activate me, though free two-day shipping, to become a weekly shopper. If you are on Facebook, you are very likely a habitual user, checking your account at least a few times per week.

On the other hand, there are also many products that I use sporadically. For example, there is a personal finance software that I have been using for the past five years, but I only log onto it when my spending really gets out of hand. According to many reports, there are a great number of Twitter users that are like that. Folks sign up, write a few tweets, follow a few users and then hardly do anything else. On the other hand, there are many Twitter users that post seemingly every minute. The latter are the kinds of customers you want to cultivate with great product experiences, effective marketing, and world-class operations.

Customer Becomes Unhappy with Your Product

Even the best products in the world have unhappy customers. For example, the average customer satisfaction score in the United States is 87%.  That means that about 13% of customers are unhappy at any one time. If your product is a social media app, your customer satisfaction score is likely around 78%.

Disappointment and frustration can be factor of both product experience as well as marketing. You can have the best financial management tool in the world for middle income customers, but if you have high net worth or very poor customers, they will likely become unhappy with your product. On the other hand, you could be reaching the perfect audience, but perhaps your product is difficult to use. This will also not work in your favor.

There are also times when customers simply haven’t been able to figure out how to use your product to truly meet their needs. Luckily, that’s something that you can fix by systematically listening to your customers, experimenting with better ways to help them get the best of your product, measuring the impact that those changes have, and keeping only those that move the needle in the right direction.

Individual Stops Using the Product

Social scientists believe that the last impression is more important than the first. That is why you should make sure to end on a good note as much as you can. If you can do a good job saying goodbye to a customer, you might still get them back in the future. Perhaps they stopped using your product because it was missing a key feature, and when you finally build that feature you want them to try it again! Just as importantly, ending on a good note might mean that they will refer your product to their friends and colleagues. The might say, “It didn’t have this one thing that I really wanted, but it’s still a great product and a caring company!”

 

2.2.9 Marketing, Product, Operations through the Customer Lifecycle

Marketing, product design, and operations can each affect all stages of the customer lifecycle. It’s obvious that marketing can drive awareness, but we also saw with the above examples that product features and even customer support can drive awareness as well as acquisition. Conversely, how good a business is at turning interested folks into customers depends greatly on their ability to market to and draw the right customer. It is important to remember that marketing, product implementation, and operations could potentially affect all parts of the customer lifecycle as we consider how to engineer growth.

 

Marketing, Product, Operations over Customer Lifecycle

Be sure to check back tomorrow to learn about strategizing and prioritizing experiments. New sections of Growthzilla are published every weekday.

2.1.4 Customer Growth Models

Sample Full Marketplace Growth Model - Growthzilla Book

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

In the previous section we covered common revenue growth models to understand how companies make money on different types of products. In those models, revenue was driven by essentially the same three factors:

  • the number of customers making purchases,
  • the number of purchases that those customers make, and
  • the size of those purchases.

The number of customers that a company has depends on how well it acquires new customers and retains existing ones. On the other hand, how often those customers buy your products and how much they spend per transaction is based on how well the product and the entire customer experience is able to engage them. The models in this section focus specifically on increasing the customer base and how much customers buy.

Lifecycle in Revenue Growth Model

 

Even though revenue depends both on how many customers a business has and how much they purchase, many business leaders focus on growing only the number of customers. Moreover, they tend to focus on growing the customer base by acquiring new customers without giving much thought to retaining existing ones. This trend is unfortunate because increasing how often customers buy and the size of those purchases can also be an extremely effective way to grow revenue.

 

Basic Customer Growth Model

The concept that your customer base will grow bigger if you improve how well you acquire new customers is a simple, universal belief. Perhaps this is why many businesses start with trying to improve acquisition in order to grow the customer base and revenue. However, the rate at which the customer base grows is not just a function of acquisition but also depends on how well the business is able to retain existing customers. Neglecting existing customers can really drag down your growth because you are essentially fighting an uphill battle. Every time you get more customers, you lose some number of them, and the resources that went into acquiring them are squandered. Consider the major cellular providers such as AT&T and Verizon. They know that retention is critical to the profitability of their business, so they bake it into their business model with long-term contracts. Moreover, they give great deals to existing customers when their contracts run up to make sure that existing customers stay loyal to them. Of course, no business can retain all of their customers, but the ones that do a better job hanging onto theirs have a leg up on the competition.

Basic Customer Growth Model

Let’s take a simple example to see how both acquisition and retention factor into the growth of your customer base. Imagine that you are able to acquire a hundred customer per day. At the same time, you lose one out of four of those customers the very same day. Perhaps they sign up for your product, but then realize that it does not really suit their needs and cancel their account right away. Therefore, the effective customer growth rate is seventy-five customers per day. In reality, the dynamic between acquisition and retention is a little more complicated.

First, the acquisition rate is rarely linear over time, so the business might be acquiring a hundred customers per day one month and three hundred customers per day the following month. Often times, seasonality plays a factor in acquisition rate. However, these nuances only matter when you are trying to develop more rigorous models for predicting growth. Just understanding the concept that acquisition and retention play a part in the net growth rate is enough for a high-level model of your business that will feed into your growth strategy.

Second, the retention rate is also not typically a simple function like described above. Instead, retention is almost always a function of time where the more time passes, the more customers abandon the business. For example, let’s say that your business acquired a hundred new customers today. It might be that ninety-eight of them will still be active customers tomorrow, but only half will be your customers in a year from now. Therefore, it is important to compare acquisition and retention rates over the same time periods to get an accurate sense of overall growth rate.

Third, the retention rate is often calculated in terms of the overall customer base rather than by cohort. This is a critically important distinction because how you calculate it will often give you vastly different results. Let’s consider the cohort example first. Imagine that you acquire 1,000 customers in a month, and the cohort retention rate is 75% for the second month. That means that of the thousand new customers that you acquired in the first month, you will have 750 in the second month.

Cohort vs Absolute Retention

Now let’s consider an absolute retention rate where your business loses 25% for your entire customer base every month, so you retain 75%. After a few years in business, you have 10,000 existing customers and your acquisition rate is a thousand new customers per month. That means that you business will be adding 1,000 new customers but losing 2,500 existing customers resulting in a net loss of 1,500. As you can see, the result is very different in this case than in the cohort analysis, and this is important to keep in mind when more rigorous calculations are needed.

As you can see, we are continually zooming in. We started with the big picture and revenue equations. Then we focused on how acquisition and retention work together to drive customer growth. In some cases, however, it’s important to focus solely on the acquisition part of the customer growth equation. The next three models provide insight into factors that drive acquisition rate.

 

User Generated Content Growth Model

Many websites and apps rely on user generated content to provide value to their customers. Products such as Twitter, Wikipedia, and YouTube all rely on some of their users creating new content for others. The most successful of these are able to build thriving businesses by creating a sustainable ecosystem of content creation and consumption.

The way user generated sites work is quite simple. The majority of users will just consume the content that is being created. However, a small portion of the overall user base will create content. As that new content is created, it will draw in even more new users, and some of those new users will also create new content repeating the cycle, which is modeled by the equation below.

 

User Generated Content UGC Growth Model

For example, let’s say that we’re starting out with 1,000 users in the first period. Most of those users will just view content, but on average, one post will be created for every ten users. Those posts might get indexed by Google, and each of the new posts will then attract fifteen new users when they search for related topics. That means that in the second period, we should have 1,500 new users or a total of 2,500 users, and the cycle repeats agains. The important things to note from the above model is that the rate at which new users are acquired is driven by the rate at which new content is created as well as the rate at which that new content attracts new users.

 

Viral Growth Model

Some products such as social networks rely heavily on existing customers inviting others to grow their customer base. This acquisition model is often called the “viral model” although to be truly viral, each user has to successfully get more than one new user to sign up, on average. All the same, viral growth can be modelled similarly to user generated growth, where the number of new users is a product of the existing users, the average number of invitations per user, and how many of the invitations are accepted.

Viral Growth Model

 

For example, if we have a thousand users in the first period, and each of those users invite an average of two people in that period, they will send out 2,000 invitations. Let’s now suppose that only one in four of their invitations get accepted by their acquaintances. That results in 500 new users in period 2. Therefore, you will have a total of 1,500 users in the second period, and the cycle repeats.

One thing to note is that the invitation and acceptance rates need to be over the appropriate periods of time. For example, we could figure out the average number of invitations that existing customers send over a day, week, month or a year. We would need the same period length for comparing the acceptance rate. In other words, if we are calculating the invitation rate over a week, we need to use the acceptance rate over a week as well.

Another point to note is that the acceptances have to happen after the invitations, so in order to be truly accurate we have to remember to take into account a time period lag. For example, if current users send the invitations in period 1, the acceptances will happen in period 2. The above model captures this in a simple way, but you might have to be more explicit in your equation to be more rigorous.

 

Paid Acquisition Growth Model

There are a number of products that cannot greatly rely on growing their customer base through viral features or user generated content spurring search engine traffic. These products are often in the software as a service (SaaS) space or other commercial product space. In these cases, those companies have to rely on paid acquisition such as paying a partner company money for getting their customers to try your product. This strategy works great when existing customers pay for the acquisition of new customers through the revenue they generate for the company, which the company reinvests in paid acquisitions.

The cycle transpires as the following. First, existing customers generate revenue for the company, which translates to profit after we subtract the expenses of serving them. The company then decides how much of that profit to reinvest into paid acquisition — the investment rate. Together, this determines the total amount that the company is going to spend on acquiring new customers in a given time period. However, that’s not the end of the story since a dollar does not necessarily buy you one customer. Each new customer will cost you a certain amount of paid acquisition spend, which we call the acquisition rate per spend. Now, putting all these parts together will give you how many new customers you can get in the second period if you start with a given number of customers in the first period.

 

Paid Acquisition Growth Model

Let’s imagine that we have 1,000 customers in period 1, and each one generates an average of $10 of profit for the company for a total profit of $10,000. The company decides to reinvest one out of every five dollars for paid acquisitions, costing $10 for every new customer. In period 2, the company can expect 200 new customers or a total of 1,200 customers.

The great thing is that the above model can apply to any paid marketing activity that is subsidised by profit earned from existing customers. Simply generalize paid acquisition to paid marketing, and the model still holds true. The acquisition rate will take into account the probability of acquiring a new customer if the conversion rate is not one hundred percent.

 

Lifetime Value (LTV) Model

The last model that we’ll cover in this section is not really a customer growth model nor is it a model for the total revenue. Instead, the customer lifetime value model shows us how the average revenue that a company earns from an individual throughout the time that he is an active customer is the product of the average revenue that a customer brings in a given time period and the average lifetime of the customer. The customer lifetime value model helps us see that we can grow revenue by either increasing how much customers spend in a given period or how long they stay active customers. For example, imagine that the average amount that your customers spend per week is $15, and they tend to remain customers for ten weeks. (In other words, your company loses one customer every ten weeks — the churn rate.) That means that the average lifetime value (LTV) is $150 per customer.

 

Lifetime Value LTV Model - Growthzilla Book

It’s worth noting that the above model is a simplified version of a more rigorous model that takes into account interest rate for a discount rate and a gross margin. Nonetheless, this simplified model provides great insight into how retention rate affects revenue.

 

Combining Customer Growth and Revenue Models

As we saw with the revenue models in the previous section, the number of customers that you have greatly determines how much money your business can make. While the revenue models provide a broader picture of the different ways to influence revenue growth, a key driver is often simply how many customers your business has. The customer growth models in this section can be plugged into the revenue models in the previous section to give you a deep understanding of factors that contribute to increasing the number of customers, which in turn influences your company’s revenue.

 

Sample Full Marketplace Growth Model - Growthzilla Book

For example, we discussed how the total revenue that the company makes from the marketplace is a product of the number of items available for sale from sellers, the probability of a sale, and the average transaction fee. If you wanted to see how a feature allowing users to invite their friends might play into that model, you can simply plug in the viral growth model into the marketplace revenue model to see how virality can affect the number of sellers, which drives the number of products for sale and ultimately revenue. While this is one combination, you can combine any of the customer growth models with the revenue models to gain deeper insights into factors that you might optimize as part of your comprehensive growth strategy.

Be sure to check back tomorrow to learn about strategizing and prioritizing experiments. New sections of Growthzilla are published every weekday.

2.1.3 Common Revenue Growth Models

SaaS Revenue Growth Model

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

When modeling a business, it’s best to begin with its core: how it generates revenue. By starting at the most fundamental level, you can progressively drill deeper into the variables that affect revenue to understand what factors are ultimately responsible for the success of your company. Furthermore, you can map key stages of the customer lifecycle to the factors that drive revenue. For example, one common realization that business leaders make when they really evaluate their revenue model is that increasing how often existing customers make purchases can be just as effective as garnering new customers. You could also just focus on modeling pure customer growth, which we will cover in the next section, but starting with how your business makes money will help you to tie everything together. In this section, we’ll start with the simplest revenue model and explore how it can be applied to different types of businesses.

Simplest Revenue Model

How much money a company makes quite simply depends on how much stuff it sells and how much revenue it makes per unit. This is the most basic revenue equation known to mankind, and even though it’s trivial, it’s the perfect jumping off point for us to delve deeper into more sophisticated models. We can start by breaking down what factors drive the number of transactions (or sales). The more active customers, the higher the number of transactions. Moreover, if each customer makes more purchases, that will also increase the total number of sales. Therefore, the number of transactions that a company gets is a product of the number of customers that it has and how many purchases each of those customers makes.

Simple revenue growth model

The amazing thing is that even by digging one level deeper, we can start to see insights! Since the number of transactions is a function of the number of customers and their average purchase volume, we can ask ourselves how can we get more customers or how can we increase how many purchases each customer makes. We can start to form hypotheses around increasing each of those factors independently. For example, we can assume that we can get more customers by increasing marketing spend or by introducing a feature where customers can invite their friends for cash credits.

The underlying factors that drive overall revenue vary with different business types, which we will explore next, but I will also note that there is no right way to break out the revenue model for any business. The examples below are simply meant to give you a starting point, and you’ll need to contextualize them to your business. Another important note is that some of the models below do not take time into account, but you can simply add a temporal component by applying the equation to any period of time be it a month, quarter, or year.

E-Commerce Revenue Model

In order to model an e-commerce business or any retail business you should explore what drives the volume of transactions as well as average revenue per transaction. There are many ways to think about this, but one common perspective is to consider the transaction or sales volume to be a function of the total number of products that are being sold in the online store as well as how likely they are to be sold. The probability with which a sale is likely to be made could also be influenced by how many products the average customer views as well as how likely they are to purchase each product. You could dive even deeper and assume that the number of product views is a function of how many visitors you get and how many products each one views, on average.

Ecommerce revenue growth model

Let’s not forget that we could also increase revenue by increasing how much an average customer spends. What drives the revenue per transaction? That question might be a little more tenuous. It could be the nature of the goods you’re selling (budget vs. luxury), how easy it is to find products, the checkout conversion rate, or the amount of cross-selling that you do on each product page. In reality, probably all of these things have an effect on the average revenue per transaction, and the best way to figure out on what to focus is to go back to your trusty customer journey map and identify how your customers perceived each of those experiences.

SaaS Revenue Model

The following model has a time component since software as a service businesses tend to worry about revenue on a monthly basis given the typical billing cycle for this class of products. At the simplest level, monthly revenue for SaaS companies is a product of the total monthly subscribers and the average monthly subscription cost. One could break down the number of monthly subscribers as being the product of the total number of customers that are paying for subscriptions and the average number of accounts or seats that each buys. Delving a little deeper into the model, one could assume that the number of customers is a function of marketing effectiveness or the size of your sales team. Furthermore, the number of subscriptions that each customer buys might be greatly affected by how easy it is to invite colleagues.

SaaS Revenue Growth Model

What is a bit unique in the SaaS model is that it’s often more difficult to affect the monthly subscription cost if the business is selling just one product, in which case the pricing is often set by market conditions. In other cases, when the business sells multiple products or tiered plans for access to advanced features, it is possible to affect the monthly subscription cost. Moreover, the cost of enterprise subscriptions is often tied to the size of the client company, so larger companies often pay higher subscription fees for bigger teams. For SaaS companies that have tiered pricing, as most do, it’s wise to create a model for each tier when performing more rigorous analysis or creating predictive models.

Marketplace Revenue Model

Marketplaces such as eBay or Uber bring buyers and sellers together and make money by charging a transaction fee. The total revenue that the company makes from the marketplace is a product of the number of items available for sale from sellers, the probability of a sale, and the average transaction fee. Each of these factors can be further broken down into underlying factors that drive them. For example, we can assume that the number of items for sale in the marketplace is driven by the number of sellers on the platform as well as how much each seller posts, on average.

Marketplace Revenue Growth Model

 

 

We can also think of the probability that a sale will be made as a function of how many times buyers view the item as well as the conversion rate for purchases (e.g. how many purchases are made for every thousand views of products). The number of views of product also depends on how many buyers are on the marketplace and how engaging the buying experience is. For example, if the product images are really unattractive, buyers probably won’t spend a ton of time browsing products.

Finally, the average transaction fee can also be affected by a number of factors. The two most obvious ones are the fee that the company is charging as well as the average sale price since many marketplaces charge a percentage commission. The average sale price could also be determined by the type or quality of goods that are being listed on the marketplace. It’s also worth noting that, in some cases, the marketplace charges flat fees. For example, many job sites charge a flat fee for every job posting that the employer publishes.

Community Revenue Model

There are a large number of online and mobile products that enable people to connect and share content with each other. The class of products include social networks and user-generated content sites such as Facebook, Twitter, Instagram and Quora. One thing they have in common is that they make money by displaying pay-per-click advertisements on their pages and in their apps. The revenue that these kinds of companies make is a product of the total number of clicks on ads and the average cost per click (or revenue per click). This model, with slight variation, can also apply to any business driven by cost-per-click advertising.

Community Growth Model

The easier of the two variables to optimize is usually the number of clicks on ads, which we can think of as a function of the average ads per page, the number of times that users view those pages (page views), and the rate at which those users click on the ads. Diving a level deeper, we might assume that the number of ads on the site or in the app is determined primarily by the number of advertisers and the average number of ads that those advertisers buy. The number of pages that are viewed by users is probably a function of the number of users and how engaging the content on those pages is.

The average cost per click is a little more difficult to model, but it is usually driven by two key inputs: the number of advertisers bidding on ad placements and the categories that are being advertised. For example, costs of ads for lawyers are usually a lot more than for common commodities such as grape jelly. Another thing to note is that the cost per click of ads almost certainly impacts how many of those ads or impressions advertisers buy.

Key Takeaways for the Sample Revenue Models

The main point to understand is that the above models are just simple examples, and although we hope they are very relevant for your business, you should use them as starting points to develop your own model. As you are creating a revenue model for your business, you should lean heavily on the customer journey map that you developed since it will provide you with great insights about what variables are important for growth and how they relate to each other. Finally, it’s important to remember that other growth models such as those for viral growth usually plug into the left hand side of the above models and drive the number of customers that a business has. We’ll cover customer growth models and other useful models in the next section.

Be sure to check back tomorrow to learn about different kinds of growth models that you might use. New sections of Growthzilla are published every weekday.

2.1.2 Modeling Your Business for Growth

Example of a more complex growth model

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

If there is one concept that we hope to convey, it is that fostering growth for your product should be systematic and strategic rather than guesswork. Therefore, the next topic that we’ll cover is how to model and understand the engines of growth for your product and in your company. It is tempting to rush right into experimenting with changes to your marketing or product implementation, but that often leads to less impactful initiates or wasted effort.

I learned this lesson the hard way in one of my former startups, AtmaGo, which was a social network for poor urban communities allowing members to share information, buy and sell, and to organize around local initiatives such as community neighborhood watch. We made the rookie mistake of focusing primarily on acquiring new users, which seemed like a no-brainer. We focused on testing various marketing initiatives ranging from grassroots outreach to partnerships with community organizations as well as on product improvements to make the signup process as easy as possible, but user growth only increased marginally.

Without really understanding our growth model, we reasoned that users were not able to find the content that was interesting to them, so we started experimenting with the homepage to make the search more prominent. But growth still didn’t really budge. Next we tried making the search less prominent and replaced it with a filter that would display the most popular posts. Again, this change led to little discernible progress, so we hypothesized that maybe the posts themselves were not engaging enough to get readers to sign up. We tried changing the posts by making the pictures bigger and re-arranging post titles and buttons. This went on for a while, but it was not really making a huge impact on our user growth. Acquiring new users felt like pushing a boulder up the hill, and we felt like we were spinning our wheels.

We decided to take a step back and really determine the true sources of growth for our social network. We broke down how AtmaGo worked as though we were trying to explain it to our grandparents.

AtmaGo was an information hub for urban communities where users created posts about topics in their neighborhoods such as crime reports, events, and tips. Other users read that content, voted on it, commented, and shared it. AtmaGo was essentially a marketplace where the product was content and the consumers were readers. We assumed that interesting, substantive posts are more likely to get views, votes, and comments. Furthermore, we hypothesized that post authors are more likely to post again if readers voted up or commented on the posts that they wrote previously. This was the full cycle of how AtmaGo worked as a kind of marketplace for content about one’s neighborhood.

Example of a simple growth model

In modeling our business, we realized that our growth didn’t just depend on acquiring new users. More fundamentally, AtmaGo only worked if the site had engaging posts, since that was driving the network effects: when readers engaged with the content, it encouraged authors to write more. In addition to this, after reviewing our website analytics, we found that the majority of new users were finding AtmaGo through search engines like Google, which was sending them directly to specific posts. Content was not only driving user engagement and the entire cycle but also our main marketing channel.

Example of a more complex growth model

After finally modeling our growth, it dawned on us that we were focusing on the wrong areas. It’s not that optimizing the registration process was a complete waste of time, but we should have directed most of our effort on making it easier and more rewarding for authors to post new content. We should have spent more time on improving the design to encourage readers to vote and comment on posts, which would have provided authors with greater incentives to post. We also should have focused on making the writing process easier and asked ourselves, “How else do we give affirmation to the content creators, so that they will create more posts?” On the marketing front, we should have focused our efforts on trying to reach leaders that would write engaging content. Taking some time to really think about our business and to model it, would have made our growth efforts much more efficient and effective. Creating a growth model for your business will not only help you strategize, but it will also help you uncover new areas of potential optimization that you may not have thought about previously.

Every business is slightly different, and every business leader has a different way of thinking about not only how the business functions but also about improving growth. This means that there is no “correct” model. Rather, the right model is the one that makes sense to you and your colleagues and helps you all effectively develop growth. There are, however, some best practices to help you get started.

Start with the Customer Journey Map

This chapter started with how to create a customer journey map because it is an extraordinarily useful tool for uncovering the details your entire business as well as understanding how each of the stages in the customer journey affects growth and profitability. In reviewing the customer journey map, pay special attention to the following factors:

  • At what point do your customers experience substantial value from your product or service? For some things such as news sites, the value is almost immediate when readers navigate to it and read an article. For other products such as cloud-based project management software, a user might not experience real value until far down their journey. The user might have to register, join a team, post tasks, assign tasks, and see those tasks completed before they finally appreciate how useful the software is.
  • What elements of the product or the overall experience generate value for your customers? Let’s consider Facebook as an example. I would say that the most valuable part of the experience is seeing my friends’ updates about important events in their lives such as finishing a degree, getting married, or having a baby. However, I also find other features very useful such as being able to plan events and invite my friends to them, and there are many other ways that Facebook is valuable to me. A related questions to ask is, “How does one customer’s actions improve the experience for others?”
  • What elements of the product or overall experience detract from the value you are providing? Focus on areas in the customer’s journey where they might run into roadblocks such as signup, checkout and payment, reordering, content creation, sharing, and searching. Often times, improving these areas will result in greater value for the customer and strong growth for your business.
  • How does your business make money? This might seem like a ridiculously simple question, but the trick is to delve one level deeper within the context of the customer journey map. Let’s say that you are running a user-generated content website such as Twitter and you earn revenue from pay-per-click advertising. On the surface, the answer is obvious: the more users click on the ads, the more revenue your business generates. But how do you increase the number of clicks? Perhaps you need more content, better content, or more readers.

Seeking answers to the above questions from your model will help you isolate the key engines of growth for your company.

Abstract Your Model

A helpful next step is taking parts of the customer journey map that most directly drive value and creating a simplified diagram. This will allow you to focus on the key parts of the overall customer experience as well as how they relate to each other. For example, let’s imagine that you are tasked with developing a growth model at Facebook. An abstracted diagram representing growth at Facebook might look like the following.

Example of what a growth model for Facebook might look like

It starts with one Facebook user. That user invites his friends. The group of friends post updates about their lives and share pictures, videos, and even news articles from online publications. The friends enjoy seeing updates from each other and invite more friends to their network, which means that they will be having even more interactions on the site, and the cycle starts over. Furthermore, Facebook makes money through pay-per-click advertising and the more users it has, the more content they create, the more those users visit Facebook, and the more ads they click. (By the way, there are countless other ways that you could model growth at Facebook.)

The guiding principle in creating a good growth model is to abstract enough to focus on the key drivers of growth and revenue without including too many details such that the model is difficult for others to understand. Your model should be simple enough for your CEO or board to understand, and you should always test your growth strategy with it to ensure that your tactics are driving the key variables.

It’s important to keep in mind that you can always delve deeper into what underlies the variables as you are developing your growth strategy. Also, in what format you capture the growth model for your business does not matter much. I always prefer to have both a diagram that shows how the variables are related to each other as well as a simple equation that can be used for creating more rigorous models in spreadsheets allowing your to gauge how changing the variables will affect the overall growth metrics. You should use the model that suits the context. You might use the simpler equation for a board meeting and the rigorous spreadsheet model with your team. Whatever the format, your model will help to inform and ground your growth strategy.

Be sure to check back tomorrow to learn about different kinds of growth models that you might use. New sections of Growthzilla are published every weekday.

2.1.1 Understanding Your Customers’ Journey

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

To identify the opportunities for growth along the customer lifecycle, it is first important to understand the customer’s experience engaging with the company and its product or service. A customer journey map is an illustration of exactly these experiences. The map can tell the full story covering the entire customer lifecycle from initial contact to activation, engagement, and beyond or focus on only a part of the story that lays out interactions or touchpoints critical to a subset of the customer’s experience.

There are actually several forms of journey maps, all defined by the scope of the investigation. The different types are:

  • User Experience Journey Maps: to chart the digital experience
  • Sales Journey Maps: to chart the path through the sales funnel (awareness to purchase)
  • Customer Journey Maps: to holistically examine the full experience

We will focus on the last of these as it is the most expansive, most used, and often the most efficient in identifying big impact areas, and understanding your consumer’s full experience.

Customer journey maps can be further broken down by the stage of the product or the customer perspective. They can be either:

  • Retrospective Maps: in the case of existing products with actual users. These map observed behaviors, or
  • Prospective Maps: in the case of new products where we map expected ways that the consumer will behave

For our purposes, we will be focusing on retrospective maps that are built upon more concrete data.

What makes customer journey maps unique to traditional funnels is that they focus on the customer’s points of view, questions, feelings, and motivations. These all blend together to explain and provide insights on the customer’s behavior. Customer journey mapping is a critical step in understanding your customers’ needs, desires and pain points. They allow you to stay focused on the consumer, and to identify the ways that you can better serve them.

Before we get into the specific steps to building out the customer journey, remember that a customer journey map does not have to be a work of art, but it does need to communicate the critical things that illuminate the customers’ behaviors, thoughts and frustrations, and where the opportunities lie. Although adding pictures and making these more visual are nice, it is more important to be substantive.

Assemble a Cross-Functional Team to Build the Map

The journey mapping process should involve stakeholders that represent a key cross-section of functions. If selected carefully, these team members will be able to offer unique perspectives in understanding the customer. For instance, product marketing and business representatives can bring key behavior metrics and market factors, while customer support can best illuminate customer pain points with both qualitative and quantitative evidence. Having a diverse team as part of the process throughout can speed the ability to identify both organizational gaps in knowledge to drive the research phase, and opportunities for improvement throughout the lifecycle.

Define the Goals and Scope of the Customer Journey Map

With a cross-functional team in place, the next step is to create focus and alignment within the group. As such, the mapping process must start by clearly articulating the goals and identifying the scope of what will be mapped. The scope is defined by two dimensions: who the primary target is and what key experiences need to be traced.

First, the assembled group of people in your business must agree on whose experiences are being mapped. Like a protagonist in a story, these subjects will be the ones whose perspectives, behaviors and experiences are being captured. The chosen persons should map one-to-one to a target customer segment or persona. (Personas are fictional characters who represent a specific target segment, that is, a group of customers that share key attitudes and behaviors.) Be sure that your groups are well-defined by being truly distinct and homogenous. For instance, if you are a company such as Uber offering a ride-sharing service, your personas may be a typical rider and a typical driver. These could be even further narrowed down based on usage of the application (possibly influenced by spending patterns, propensity to travel, geographic density, employment, etc.). To keep the exercise targeted, personas should likely be limited to no more than three.

Second, the cross-functional team must decide on what experiences should be included in the map. The customer journey map should include paths that may be critical to the success of the business and its growth potential. Again, if your offer a ride-sharing service, critical paths for a rider could include first ride or usage, paying for a ride, various repeat ride scenarios, rating a driver, requesting a refund, sharing a location, referring a friend, deleting the app, etc. The chosen set may be influenced by the businesses priorities, however it is important to remember that some of the internal biases are meant to be challenged in this process, so a more expansive view is always better. Undoubtedly, insights can be uncovered in unlikely places.

Create an Initial Map Based on Current Knowledge

Once the scope has been clearly defined, the process usually starts by going through the steps of mapping using existing knowledge. This allows us to understand the internal ways we think about our customers before seeking out external information.

The cross-functional team should bring together their own knowledge and data gathered to date in their area of focus. For example customer support could bring analysis reports of support calls and emails. Marketing could bring prior satisfaction surveys, brand perception, and analytics. Sales could provide data from the field from partners and direct customer contact. All of this can be supplemented with stakeholder interviews to capture as much institutional knowledge as possible. The information that we should be collecting at this point includes:

Empathy Maps:

The empathy map (see below) further elaborates on the personas by capturing the feelings gleaned from all the surveys and customer observations. It serves as a good tool for helping to put oneself into the frame of mind of the customer and understand their challenges.

 

The elements included in the empathy map are, what the personas are:

  • Thinking and Feeling: How do they feel about the experience? What anxieties, joys, and hopes do they have? For example, for shopping online: “I hope I don’t have to wait to get this order” or “I’m confused and can’t find what I’m looking for.”
  • Seeing: What do they look at while using the product, in what order and why? For example, for online banking, they may look at their account summary first to see their balances before doing any task.
  • Hearing: What do they hear from friends, family, and the masses that influence their thoughts
  • Saying and Doing: How do they behave? What are the words that they use to describe the experience?
  • Pains: What obstacles and frustrations stand in the way of accomplishing their goals?
  • Gains: What are they hoping to accomplish?

Touchpoints and Channels:

What touchpoints does the user have with the company? Direct sales, email, support, in-person events, social media etc.? The team can start considering additional touchpoints that could be used to enhance the experience.

With this internal knowledge captured, the team can now lay out an initial draft of the map. This first pass will expose gaps in the knowledge of the journey, for example, what drives repeat behavior or how the customer overcomes problems with using the product. The team can now articulate theories regarding how to improve the experience or turn pains into opportunities. For example, at the end they may determine that there are not enough touchpoints or that the onboarding is failing to drive users to use the product (i.e. to activation). The goal of the next steps will be to validate, adjust and fill gaps in the journey. Additionally, we will test these theories based on research.

Gather and Analyze Research

Now that we’ve collected some internal insights, we need to conduct research to fill gaps and to test any of the theories from the internal mapping exercise. This should be done with a combination of qualitative and quantitative measures: talking to and surveying customers, prospects, former customers etc. Key tools at this point are research methods that allow us to see how the user behaves in their natural environment and while using the product in the real world. These include, contextual inquiry and ethnographic research.

Both contextual inquiry and ethnographic research rely on observation and building and in-depth the story directly from the customer. For the latter, the person conducting the research will observe users in their day-to-day or arrange for participants to create detailed diaries over a set amount of time. Often these two approaches are used together.

For contextual inquiry, the researcher observes participants individually, while using the product or service in their regular context, i.e. in their home, office or other environment. There is an additional component of unstructured interviews that can happen throughout the observation period to aid in understanding what the user is thinking and why they perform certain actions.

At the end of the research step, we should be able to articulate the key elements of the journey map: who, what, where, how, and why?

Who

Who is the customer is for whom you are mapping the experience? There may be a couple that may warrant more than one map, but personas should not be ambiguous or a large number.

What

What are your personas goals are and what they are doing throughout their engagement with your company?

Where

Where are they in the journey and where all the touchpoints in each of these steps?

How

How they achieve (or don’t achieve) their goals, and how important each of the steps may be along the way? Their feelings should also be drawn out here

Why

Why they behave they do and use your product/service? This is the crux of what motivates them and the things they do!

Illustrate the Customer’s Journey

Based on all the data you have gathered, you should now be able to map out the stages by which you can define the customer experience and the facets of what they entail for the customer. The key touch-points within each of these stages should be catalogued although they may not all be detailed in the final distilled map. As with the empathy map, for each step we want to immerse ourselves in the persona’s experience. Therefore, for each step you will catalogue:

  • Stage: What stage of the journey does this represent?
  • Key Activities: What is the persona doing? What is the online and offline behavior?
  • Touchpoints: What touchpoints are employed to try to accomplish the steps?
  • Thinking: What questions do they have? What opinions?
  • Feeling: Are they happy? Sad? Frustrated? Confused?
  • Opportunities: What areas for improvement have we identified?

A simple example map is shown below:

Distill Areas of Opportunity

Once the journey is mapped out, and areas of opportunities identified, it’s time to act. The ideal way to focus on the most critical areas is to prioritize within the context of the business model. This will be the focus of the next section.

Chapter 2: The Iterative Experimentation Process

Understand the customer journey and your business model to efficiently drive growth.

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.

In this chapter, we will go over the fundamental toolkit that will make your growth development a science rather than an art. We will cover the iterative, data-driven experimentation approach as well as qualitative and quantitative evaluation methods that will help you to understand what is working and why. Finally, we will finish with some tools that are available in the marketplace today that you can start using immediately.

2.1 Understanding Your Business

A mistake I’ve seen business leaders make in developing growth is jumping right into experimentation without understanding the details of their business or having a comprehensive strategy. I’ve made this mistake a number of times myself before I learned that it leads to waste and inefficiency due to focusing on tactics with low potential or implementing changes that work against each other.

To understand the potential hazards of charging ahead without sufficient insights or strategy, let’s imagine that you are the head of marketing at a business that sells cloud-based project management software that allows your users to assign and track tasks with their co-workers. The board of directors is unhappy with the company’s customer acquisition rate and have mandated you to drive more growth. You waste no time and propose three high-priority initiatives:

  • Increase the overall marketing spend
  • Experiment with marketing messages and channels
  • Work with engineering to improve the signup form

At the surface, the above tactics sound very reasonable. However, after a few quarters have passed and you’ve implemented your strategy, you find that the growth rate ticked up just a little bit.

Rather than pouring more resources into the above initiatives, you decide to retrench. Why were the gains so marginal? After speaking to the customer support staff at your company as well as to a number of current and past customers, you discover a few clues. The marketing optimization coupled with a higher spend did result in many new leads, and the improved signup form helped with converting those leads to new customers. However, you also learn that the most avid users of your product usually come from their teammates inviting them to projects. At the same time, you learn that creating and sharing new projects is riddled with bugs and usability errors forcing new customers to abandon your product right away.

It hits you that rather than focusing on new signups, you should have been working with the engineering team to make the creation and sharing of new projects flawless since that’s your main engine for growth. Now it’s time for a difficult conversation with the board to explain that you should have focused your efforts elsewhere, and that your team will need another few quarters to show the kind of growth that they are expecting.

By first understanding your customers and business before creating solutions, you will start to see patterns about where customers hit roadblocks and what can be done to get rid of them. You will also start to identify what elements of the product and overall experience delight your customers and drive growth. Perhaps it’s how well your customer support staff helps new customers to get onboarded with the tool that is the strongest engine for growth, and you need to double-down on making it even better. Those are exactly the kinds of insights that you need to make a roadmap, so you don’t waste resources on things that won’t greatly impact your business’ growth trajectory.

There are two activities that seem to be particularly helpful in establishing a baseline understanding of how one’s business works. The first is customer journey mapping, which aims to detail every step in the customer experience with your product and business. It starts with the customer’s first exposure to your product and continues through their entire lifecycle as an active user and customer. It’s critical to map out the complete journey from prospective customer to customer and beyond because each step is a candidate for growth optimization. Customer journey mapping essentially provides you with a laundry list of things to try to improve!

The second key component to understanding your business is understanding your business model, so you can tie each step in the customer journey to key variables in how you make money. For example, it’s obvious that new purchases will drive revenue, but it’s also possible to affect revenue by increasing how often existing customers make purchases. It’s important to understand what actions the customer and your company take that add to both revenue and costs. For example, perhaps your customer support is not only helping to retain customers but is also helping to drive new customer acquisition because your current customers can’t stop raving about your outstanding support. Making this association will help you build intuition about what improvements are likely to have the biggest effect on your profitability.

Be sure to check back tomorrow to learn about customer journey mapping. New sections of Growthzilla are published every weekday.