3.4 Measuring Non-Digital Marketing Channel Metrics

Some people seemingly default to easy-to-measure marketing channels such as pay-per-click advertisements rather than choosing other channels such as print and television advertisements, whose effectiveness can be more difficult to track. However, you should not let convenience drive you to waste money on less effective channels when, with a little effort, you can also measure, test, and refine more impactful forms of marketing. It’s worth spending some time to discuss a few tactics that will allow you to measure non-digital marketing campaigns.

The key thing to remember when testing different channels is to keep your marketing message the same, so the only thing that you’re varying is that channel itself. Otherwise, you won’t know if the difference in outcomes is due to one channel being more effective than the other, the message resonating more with the audience, or a combination of the two.

Ways to Measure the Effectiveness of Non-Digital Marketing Channels

3.4.1 Simple Survey

There are many opportunities to survey customers, including immediately when they visit your website, open your app, contact your sales team, or complete a purchase. The timing of the survey greatly depends on what you intend to measure. For example, one great way to ascertain what marketing channel most effectively drove new customers is simply to ask them in a survey at the time of purchase.

As mentioned in the first chapter, marketing not only determines the number of prospective customers a product attracts, but also the likelihood of purchase (acquisition) and retention. For example, we could launch an online marketing campaign that falsely exaggerates the capabilities of a product. This campaign could drive lots of prospective customers to our website. Depending on the effectiveness of the website, we will then convert a fraction of these people. Unfortunately, due to the nature of misinformation that brought these customers in, we immediately see large drop-off rates. This example demonstrates three critical stages: initial interest, purchase conversion, and post-purchase retention. We could implement surveys at all three stages to measure how well the marketing campaign drives prospective customers to your product or website, how effective it is at targeting the right customers and conveying accurate information leading to conversion as well as retention.

If the key question that you are trying to answer relates specifically to the effectiveness of driving potential customers to your product then you will want to survey them at the very beginning of the sales cycle when they come to your website, when they contact your sales team, and so on. On the other hand, it is sometimes much more interesting to consider both how well different marketing methods convert interest to actual usage or sales. In this case, you need to survey people after they have used your product, registered for your service, or completed a sale. Finally, it is also useful to have a sense of how well different campaigns attract the right customers and retaining them. In that case, you might want to survey customers some time after they have bought or started using your product. It’s important to acknowledge that customers may not recall the marketing campaign that drove them to try the product, and the data that you get from surveying individuals a long time after they have been exposed to the campaign is likely to be less precise.

Imagine that you are interested in learning how many people that bought your product were driven by your radio advertisement versus an online advertisement. Let’s assume that you are spending $10,000 on online advertising and an equal amount on the radio campaign. After surveying customers post-purchase you find that roughly twice as many people bought your product after hearing the radio advertisement versus those that saw the online advertisement. You compare the two channels based on your survey data and estimate the cost per acquisition as in the table below.

Example Comparison of Radio vs Online Advertising

It seems that the more traditional medium, radio, is a much more effective channel for attracting and converting customers, but how confident should you be in this analysis. You were very careful to keep the messaging the same for both channels, so that should not have affected the difference. But you are concerned that maybe there is a sampling bias where those customers that bought your product after hearing a radio advertisement are more likely to take the survey. This is a legitimate worry, and might have been hugely important if the numbers were closer. However, it is not necessary to be perfectly scientific about this kind of analysis when running growth experiments. It is certainly better to do a more thorough statistical analysis and take greater pains to design a more accurate survey, but such a level of rigor might not be worthwhile in many cases.

3.4.2 Special Landing Pages, Emails, or Phone Numbers

Another very clever way to measure the effectiveness of marketing channels is to tie them to unique website pages, telephone numbers, or even email addresses. For example, you might run a radio advertisement for your product, Sergio’s Widget, and direct folks to navigate to “www.sergioswidget.com/radio.” You might also run a television advertisement which directs people to “www.sergioswidget.com/tv.” The great thing is that you can be quite certain that folks that navigated to the landing page associated with www.sergioswidet.com/radio heard your radio advertisement and not your television advertisement.

This method has some advantages over surveys. First, you do not have to interrupt customers while they are in the process of evaluating or buying your product. Instead, you send them down discrete paths from the very beginning. Second, people might misreport in a survey what piece of marketing led them to discover your product, whereas providing them with a unique link or phone number for each marketing channel forces them to remember more accurately. Third, and most importantly, providing them with a special destination opens up many opportunities to track them throughout the entire acquisition process. In other words, you can track how many people that started at www.sergioswidget.com/radio went on to successfully complete a purchase. This is hugely valuable information.

Tracking marketing channels with unique web addresses and phone numbers also has some disadvantages of which you should be aware. Just as was true with surveys, this measurement technique will give you relative measures of performance. In other words, you might learn which marketing channel is likely to perform best. It’s also very likely that users will navigate to www.sergioswidget.com even though the radio advertisement mentioned www.sergioswidget.com/radio. This might not be a big issue and will not severely skew your relative measures of channel performance if we can assume that this problem occurs uniformly across the various channels. However, if one destination is much easier to remember than the other, you’ll likely have an inaccurate outcome. For example, if the telephone number that you provide in the radio advertisement is 1-800-888-888 while the television advertisement mentions 1-800-289-0973, it will seem that the radio channel is a lot more effective, relatively speaking, than it really is because the phone number is a lot easier to remember. What this all means is that you need to carefully select the unique identifiers that you use so that they don’t end up skewing your results.

3.4.3 Promotional Codes

Another great method for keeping track of acquisition across various marketing channels is providing promotional codes that are specific to each channel. This method is very similar to providing distinct web addresses, phone numbers, or emails, but it is different in the sense that people do not have to go to unique destinations. This can be a big advantage for prospective customers since they don’t have to remember a special web address or phone number. The main disadvantage is that the promotional code must be tied to some kind of discount or reward, otherwise people do not have any incentive to enter it, and this can be costly.

 

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 next Thursday to learn about evaluation methods that you can use for growth. New sections of Growthzilla are published every week.

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.

2.1.2 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.

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.

1.2 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.

What Is Growthzilla About?

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Do you want to grow your business? Who doesn’t? We wrote Growthzilla to not only get you started but to give you a comprehensive guide that you can use to make your business thrive in an increasingly competitive landscape.

Growing a product or business is not a “hack,” which is a term that implies a trick or crude way of doing things. Instead, creating growth has become a science over the past decade, enabled by meticulous methodologies as well as technology that allows leaders to test tactics with near-scientific rigor. Companies in various industries ranging from tech to financial services are starting to employ those methodologies to out-maneuver their competitors, while the laggards are falling to the wayside. In the coming decades companies will have two options: either jump on the bandwagon and engineer growth or endure a steady decline as growth science becomes a serious competitive advantage.

Not only have companies such as Google and Amazon been systematically applying growth science to further solidify their market leadership, the scope of what they are doing has dramatically increased. Growth science now includes using marketing, product implementation, and operations to drive growth along the entire customer lifecycle from acquisition to engagement and retention. The modern field of growth science can be characterized by:

  • An iterative approach to testing and refining tactics,
  • Leveraging marketing, product implementation, and operations to drive growth, and
  • Optimizing growth over the entire customer lifecycle from acquisition to engagement and retention.

We felt that we had to write Growthzilla to give both new innovators a chance to compete with sophisticated incumbents such as Google and Amazon as well as to give existing companies a chance to adapt how they do business in this quickly changing landscape. Our hope is that the book offers a great overview of the methodology, the scope, and practical tactics that anyone in a leadership position can immediately apply to grow the product and business.

Moreover, we thought that in creating Growthzilla we should put our money where our mouth is and actually apply the lessons we present to actually making the book. Practice what you preach! That is why we are making the draft of Growthzilla available online on this site to learn from your comments and questions and create a better book for all readers.

We encourage you to read each section as we publish it and tell us what you agree and disagree with as well as what’s confusing. And if you like what you read, help us deepen our impact by telling your friends and colleagues!

We hope you find Growthzilla informative, practical, and truly transformational for you and your business! Most of all, thank you for your interest and support.

Warmly,

Sergio and Kimmy