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.

1.4 What Kinds of Growth Can You Engineer?

You can engineer growth for digital and physical products and even services.

The most obvious application of growth science is in software, which includes popular categories for innovation such as mobile apps and cloud-based software. Growth science is particularly enabled in this space by excellent tools for conducting quantitative experimentation on the design of these interactive products. Product owners can test variations of design (e.g. which button color gets more clicks) as well as functionality (e.g. does adding a particular function increase key engagement metrics such as total time spent using the app).

Similarly to product development, a lot of marketing for physical products and services is done online where a great ecosystem of experimentation tools also make it possible to optimize marketing channels and messages. For example, it is now very easy to test variations on messaging in email marketing or to compare the effectiveness of channels such as pay-per-click advertising to email marketing.

Beyond tools, there is rich community of growth hackers, marketers, and product designers that share ideas and best practices. In our opinion, all these factors have led to the advancement of a holistic approach to growth in the software space, which other industries can certainly emulate.

The growth science methodology can just as effectively be applied to physical products and services. As mentioned above, the big advantage with interactive products is that it’s easier to make changes to the product on the fly, and while this might not be as feasible with physical products, we can certainly experiment with marketing, customer support, sales, and other aspects of operations.

Even though it’s more challenging, we absolutely conduct experiments on physical products as well as services. With physical products, you don’t necessarily have to test variations of the actual product to see which version is most likely to resonate with customers. For example, let’s say that you are going to be selling sunglasses and you are not sure with which styles and colors you should launch. You could simply create variations product pages for various combinations of styles and color in your online store and measure which ones register the most number of clicks on the “Buy” button. Voila, you just tested product design of a physical product!

We could also try variations to how a service is delivered to test what works best. For example, let’s say that you are an executive at Goliath Bank. What if I told you that you could grow your business by optimizing how your employees deliver banking services to customers at bank branches? Let’s say that my hypothesis is that customers would be happier if they performed more straight-forward tasks such as depositing checks on Goliath Bank’s mobile app rather than going through the trouble to travel to a branch and standing in line. We could test various ways to encourage late adopters to try making check deposits via the online app. For example, we could try showing tutorial videos on monitors at some bank branches while patrons wait in line. By randomly assigning which bank branches show the video tutorial, we could see if it gets customers to make more check deposits via the mobile app. Along with usage, we could measure customer satisfaction and really see if these changes are making customers happier with your service.

While this book focuses on businesses that build software products, the growth science methodology can be applied to practically any other kind of product or service offering.
With physical products and services the main challenge is finding ways in which one can try variations of product or service implementations.

Be sure to check back next Monday to learn what we’ll be covering in the remaining chapters of the book. New sections of Growthzilla are published every weekday.

1.3 Growth Science Pillar 3: Optimization Across the Entire Customer Lifecycle

Optimize across the entire customer lifecycle to reach the greatest growth.

The biggest mistake that business leaders make in trying to drive growth is focusing on just customer acquisition through marketing. This is a problem because their customer retention and engagement are usually suboptimal, which means that the resources that they leverage to increase acquisition are squandered when those hard-won customers abandon the product right away. This is called the “leaky bucket problem,” which perfectly captures the futility of this approach. For this reason, the third building block will be learning how we can drive growth by optimizing each stage of the customer lifecycle– the third pillar of growth science.

Exclusively focusing on optimizing acquisition through marketing is unfortunate because it has led businesses to miss out on the full potential of growth optimization. The main thing to understand is that the effects of growth optimization are multiplicative and improvements compound. This means that you can often accomplish greater total growth by making many little improvements rather than just one big one. In particular, it’s better to optimize the entire customer lifecycle including acquisition, engagement, and retention. Let’s explore how this works.

Let’s imagine that you are a very good marketer and are able to improve your customer acquisition by thirty percent by experimenting with various marketing messages and different channels such as online advertising and inbound marketing. That’s great progress, but you can do better.

Diagram showing limited effects of optimizing just acquisition and retention.

You realize that many of your hard-won customers are abandoning your product right away, so you talk with the customer support folks and realize that many new customers have trouble with the initial setup. You work to improve the customer support for new customers and you are able to increase how many customers your business retains, which translates to another thirty percent growth. Since those two optimizations are compounding, you will increase your overall customer growth by a total of eighty-two percent. You don’t have to stop there. You can try optimizing customer engagement with better product design, retention with more targeted marketing, and so on.

If you look at the diagram below, you can think of the full universe of possibilities as having nine dimensions:

  • Improving customer acquisition through better:
    • marketing,
    • product design and implementation such as sharing features, and
    • operations such as sales support.
  • Improving customer engagement through:
    • marketing such as more accurate targeting,
    • product design and implementation, and
    • operations such as training.
  • Improving customer retention through better:
    • marketing such as promotions,
    • product design and implementation such as fewer usability problems, and
    • operations such as pro-active customer support.

 

Diagram showing the effects of optimizing across acquisition, engagement, and retention.

If you had made even modest gains along all nine dimensions, those gains would accrue to tremendous total growth. For example, even if you improved growth by ten percent in each of the nine areas, that would result in 136% total growth, which is a lot higher than improving just acquisition by thirty percent.

Growth science is iterative, data-driven experimentation to optimize the entire customer lifecycle through marketing, product design, and operations.

Fortunately, some companies are starting to realize that growth optimization spans far beyond just marketing and product design as well as beyond just acquisition or retention. Those companies are systematically applying iterative experimentation to marketing, product development, and operations to improve customer acquisition, engagement and retention. All three of these pillars together is what makes up modern growth science.

Be sure to check back tomorrow for the next section: 1.4 What Kinds of Growth Can You Engineer? New sections of Growthzilla are published every weekday.

1.2 Growth Science Pillar 2: Optimization Across Disciplines

Growth is multidisciplinary

While corporate structure continues to silo departments, growth has quickly become an interdisciplinary practice requiring functions such as marketing, product development, customer support, sales, and even logistics. The third pillar of growth science is employing all of these different functions to spur customer and revenue growth. It is important to recognize that growth isn’t just driven by marketing but rather by multiple functions that are interrelated and must work together to have a positive impact on the business.

Sean Ellis, who coined the term “growth hacking,” was hired at Dropbox right as the company had decided to open the product to the general public. The biggest driver of growth at Dropbox at that time was when the company created a referral program giving existing customers additional storage space if they got their friends to try the product. While the strategy and vision for this kind of initiative might be driven by marketing, implementation often relies on the product, such as a website or app, that makes it possible to send and track invites and to reward users. That means that this tactic relies on both marketing and product development functions, and the two teams must work closely together to successfully implement it.

Uber’s customer support is another example of how different functions within a company work together to affect growth. One of the first times that I used Uber, I requested a ride, and the driver never showed up to pick me up. What made things worse was that I cancelled my ride after waiting for a long time and was charged a cancellation fee. Needless to say, I was furious contemplated deleting the app. Uber could have easily lost me as a customer then, or at least I might have been more likely to use a competitor such as Lyft. However, Uber made it extremely easy to request that the cancellation charge be reversed. It took only a few taps of my finger, and the refund request was submitted. Not only that, I was granted the refund that same day. Uber was able to retain me as a customer through great product design and lightning fast customer service.

In many regards, job boards hint at the interdisciplinary nature of growth science, and you can often see postings for “growth hacker,” “growth manager,” “growth engineer,” and “head of growth.” These titles did not exist a decade ago, but now the demand for these kinds of roles is ballooning. Business leaders created roles such as  “growth manager” and “head of growth” because those responsible for these efforts needed to oversee across departments and traditional roles that focused on a narrow domain didn’t fit. What is critical to understand is businesses are better able to identify new strategies when they understand how all functions impact growth.

Be sure to check back tomorrow for the next section: Growth Science Pillar 3: Optimization Across the Entire Customer Lifecycle! New sections of Growthzilla are published every weekday.

1.1 Growth Science Pillar 1: Iterative Experimentation

Meme: Never stop testing, and your growth will never stop improving.

The origins of growth science could be traced back to David Ogilvy, one of the most well known ad men of the twentieth century, who proclaimed back in 1963, “Never stop testing, and your advertising will never stop improving.” Ogilvy’s statement embodies two important concepts–continually test and improve–that have paved the way to modern growth science.

The idea that advertising is not a static creation is critically important because challenges one to continuously improve tactics. Although this philosophy was popularized in advertising it is just as relevant in product development, operations, and even sales. Since the 1960’s some marketers have embraced this ethos by iteratively trying variations of tactics, channels and messaging. Moreover, the practice of iteratively implementing improvements has become more commonplace in other business functions such as product development, customer support, and sales. However, It’s not enough to keep trying different tactics and implementations.

The second key concept that Ogilvy proposed is actually testing new variations and improvements by carefully measuring associated outcomes. Once again, this practice is not just limited to marketing activities, and one can test variations of product implementation, operations, and sales. It’s not surprising that many business leaders have adopted both iterative experimentation and rigorous testing across business functions, and it’s now just as common to test variations of product design (particularly in the digital realm) as it is to test different advertising messages.

This paradigm shift has been supported by an ever burgeoning ecosystem of data collection and analysis tools that have allowed companies to track key performance indicators for new customer acquisition, such as click-through rates on online and email marketing, as well as for engagement and retention of existing customers. Now, one can easily test variations to marketing emails or to webpage designs to see which ones lead to the greatest gains in key metrics such as the total number of new sign ups or daily active users.

What was once driven entirely by intuition can now be informed by experimentation and testing. Leaders across marketing, product development, and operations are forming hypotheses and conducting experiments to see which tactics yield the most positive results. This rigorous methodology is the first pillar of growth science and underlies the entire field allowing us to call it a “science.”

What is shocking is that after more than half a century, most marketers still do not make data-driven decisions according to a survey conducted by Google in 2017.  I can empathize with those that do not put this philosophy into practice. While the field has burgeoned, it has also become a lot more difficult to navigate. There are now countless tools that allow people to experiment with marketing and product design. The scope of growth science has also greatly expanded across disciplines–the second pillar of growth science– from marketing to product implementation and operations. The application of this methodology has also expanded to include not only new customer acquisition but also to engagement and adoption. The optimization of the entire customer lifecycle is the third pillar of growth science.

We will cover the other two pillars of growth science in subsequent sections. Hopefully by explaining the three elements of growth science and providing a comprehensive guide, we can empower you to successfully employ growth science to supercharge your business.

 

Be sure to check back tomorrow for the next section: Growth Science Pillar 2: Optimization Across Disciplines! New sections of Growthzilla are published every weekday. 

Chapter 1: An Introduction to Growth Science

I remember looking at a picture with colorful tiles overlaid over a mock webpage. In the upper left there was a red-orange tile and another one right below. Across the top and down the left-hand-side of the page there were more orange and bright green tiles. As my eye gazed across to the right and down the mock page, the tiles got blue and finally purple. I was at Google in 2005 where my job was helping website owners figure out how to implement Google AdWords ads on their site. The picture that I was inspecting was a heatmap, which was derived from aggregate statistical data showing which ad placements resulted in the highest number of clicks.

I was astonished. I realized that design on the web was a big statistical game. Users had tendencies such as where they tended to look first on a screen and what colors tended to catch their eye. It was just a matter of using experimentation to figure out what those tendencies were and designing products that played to those preferences rather than worked against them.

This idea that we can test the effectiveness of product design resonated with me. Having studied physics and economics in college, I had a strong preference for proof and hard numbers over subjective intuition. My academic background led me to adopting a data and research-driven approach to designing user experiences for interactive products like websites.

I soon discovered testing tools such as eye-tracking, which allows one to track what a user is looking at on the screen, as well as A/B testing, which allows one to compare how effective two versions of a page are in relation to each other. I also read results of experiments that others were performing with headlines such as “Placing the security logo on the upper left between the search box and the navigation bar increased conversions 8.83%.” 

Later, during my tenure as director of user experience in a Silicon Valley startup in 2007, I decided to use experimentation to drive product design. The startup was a document sharing site that was one of the most visited web properties in the world. I suggested to Senior Director of Product that we should try experimenting with the registration button on the home page. He laughed and told me not to waste my time, but luckily the founders were supportive of the idea, and we did it anyways.

One of the senior engineers (a brilliant gentleman that later became a very successful entrepreneur) developed our in-house A/B testing platform. First we figured that maybe moving the registration button to a more prominent place might help our conversions–the proportion of people clicking through to the registration page. So we moved the button from all the way on the right of the menu bar to the center. Lo and behold, our conversion rate increased by more than 70%! I was stunned that simply moving the button to the left of the page by a few hundred pixels (effectively about two inches on a monitor) made such a huge difference. I think we all were.

Given our initial success, we decided to keep going to see what other optimizations we could make through testing variations. We next decided to change the design of the button itself. Originally the button was a bland shade of blue, so we decided to try making it bright green based on the hypothesis that users would more readily notice a brighter color and would, thus, be more likely to click it. Another round of A/B testing revealed that the green version gave us about another 50% increase in conversion rate. This moment in time changed my life. At that moment, I thought that soon everyone would be experimenting and testing product design because it had such unbelievable potential to help businesses grow.

Over the next decade, two important trends occurred. First, leaders figured out ways to experiment with changes to not only product implementation but also to marketing and operations such as customer service. Second, many new tools appeared in the market allowing businesses to experiment with things like marketing messaging and even customer service process. Some companies have taken full advantage of the methods and tools to dominate their competitors. Unfortunately, these exciting advances have not yet reached a mass audience, and many businesses continue to fall behind.

In speaking to friends, clients, and colleagues, we heard the same refrain: the possibilities seem so numerous and the methods so opaque that many folks simply do not know where to start. In addition, many people had heard of terms like “growth hacking,” and incorrectly believed that they should replicate hacks that other businesses have used rather than adopting a broader process that they can contextualize to their business and implement to reliably grow their customer base and revenue. Our aim in writing this book is to give everyone an overview of the three pillars that make up growth science as well as practical tactics for growing your business.

 

Check in tomorrow for the next section of Chapter 1.