This is a blog series that I originally wrote as a book. I’m sharing the full text online for free. Each blog post is a chapter. Please send your feedback: growthmarketingmap@gmail.com. If you’d like, get on the email list.

All chapters:

  1. Intro
  2. Marketing Strategy and Management
  3. Business Growth (we’re here)
  4. Research and Analytics
  5. Campaigns and Tactics

In this chapter:

  1. Marketing Strategy That Hits Business Goals
  2. So What Is a Business Goal?
  3. Accounting 101
  4. Basic Metrics: Acquisition, Retention, and Virality
  5. Growth Model
  6. Setting The Right Goals
  7. Customer Lifetime Value
  8. Alternative Metrics
    1. Marketing Funnel
    2. Image Indicators
    3. Market Shares
  9. A Quick Recap
  10. More on Growth

Managing a business is a little bit like flying an airplane. Keep your eyes on the speed, altitude, drag, and fuel and you’ll get to your destination fast. Try flying blind and you’ll crash and burn.

Marketing Strategy That Hits Business Goals

In the previous chapter, we established that we should always start with customer and business goals when crafting a successful marketing strategy.

In the next chapter’s section on marketing research, we will outline the main methods of learning about customer needs. We will also discuss marketing analytics – getting and analyzing data that will be needed to build growth models like the one described in this chapter.

In this chapter we will revisit main business metrics, build a growth model, and calculate customer lifetime value. We will also consider alternative marketing metrics, such as funnel metrics, market share, and image indicators. Let’s start with business goals.

So What Is a Business Goal?

If you are an entrepreneur, you are probably in a position to define the main business goal yourself. If you are a marketing manager, you can get business goals from top-management. Either way, it’s important to be clear on what we are trying to accomplish in order to develop a marketing strategy.

Microeconomics teaches us that all firms have one main goal: profit maximization.

The real world, however, is a tiny bit more complex than that, and management often cares about other things. For example, managers might choose to sacrifice some profits for sustainability or to support corporate responsibility. Or they might sacrifice the short-term profits to acquire more users, then leverage network effects and generate more profits in the long-term. Or the opposite might happen, and a publicly traded company might prioritize short-term profits to look good by the end of a quarter.

Most often what we have to work with is a variation of a “growth goal.” For instance, we might want to grow revenue, profits or users.

It is important to understand our business goal in detail to develop a marketing strategy that will hit it. For example, if we are talking about user growth, what types of users do we want to acquire? Are we interested in any particular demographics? Do those users have to be tech-savvy to appreciate our product? Maybe we only want to acquire users who are most likely to be retained. Or users that are most monetizable? Or perhaps users who have the most viral potential, meaning that they are the most likely to attract other users?

Let’s pick one business goal as an example. Let’s assume we start with the most generic and high-level business goal possible: make more money. Or, if you prefer financial terms: maximize net income.

Accounting 101

Once we are absolutely 100% crystal clear on what the business goal is, we can dive into the data to figure out what marketing goals we should set to be in a position to achieve it.

First, some second-grade math. As you already know, profit = revenue – cost.

To be a little bit more specific:

Profit = (revenue per unit – cost per unit) * (number of units) – (fixed cost).

Another way to look at this, especially if your company, like many other tech companies, receives most of its revenue on a recurring basis, is the following:

Annual profit = (annual recurring revenue) – (annual costs).

To be more specific:

Annual profit = [number of active customers] * [ (annual subscription price) – (annual cost per active customer)] – annual fixed cost

No matter how we look at this, we need to grow the number of customers with high paying potential without the costs growing out of hand. So, the key metrics that are relevant in our case are:

  1. The number of active paying customers (increase!)
  2. Price paid by a customer (increase!)
  3. Marketing costs (decrease by increasing spent effectiveness!)

Notice how these are already more actionable. If you’ve spent some time working in marketing, ideas on how to improve these metrics might already be emerging in the back of your mind. You might already be thinking about buying some Google ads to get more customers or offering some additional services to existing customers to increase prices. But let’s not get ahead of ourselves.

We first need to understand what specific marketing goals we should set. In reality, you might focus on all three above. But for the purposes of this book and to save you some time, let’s focus on one that we expect to be the most impactful.

Let’s take “number of paying customers” as the number one metric we are trying to improve. Where do we go from here?

We can break this metric down even further and build a simple growth model. The number of paying customers is really a function of three things: acquisition, retention, and virality.

 

Basic Metrics: Acquisition, Retention, and Virality

  • Acquisition = the number of users you acquire during a certain time period
  • Retention rate = the percentage of users that you retain during a certain time period
  • Churn rate = the percentage of users that churn during a certain time period = 100% minus retention rate
  • Virality coefficient = how many additional users you acquire for free via word of mouth per one user acquired

You may have an intuitive understanding that you want to acquire as many users as you can, retain as many users as you can, and have the highest possible virality coefficient. What you might not be so sure about is the relative importance of these three factors. So to address the former issue, let’s model several scenarios and visualize the results.

Growth Model

Here is the spreadsheet I built to illustrate the growth dynamics. Feel free to download it or save a copy to the Google Drive and play with it. Of course, the reality is much more complex than this oversimplified model but it will suffice for our purposes of illustrating key patterns.

Let’s run a few scenarios to get an intuitive grasp of the interplay of these metrics.

Example #1: Acquire 100 users each week and retain all of them

Let’s start with the simplest scenario and assume that we can only acquire 100 users per week. This limitation might be due to our budget constraints or the number of friends we have.

Let’s also assume that our product is not viral at all, i.e., virality coefficient = 0. In other words, nobody recommends our product or nobody sufficiently trusts these recommendations to sign-up.

Now, let’s analyze churn. The first scenario is ideal: we retain 100% of users we acquire. This is how our ½-year growth chart looks:

image00

We end up with 2,600 users by week 26. Not quite a hockey stick, but a solid linear growth rate.

Example #2: Acquire 100 users per week and retain 80%

Now, let’s suppose that our weekly churn rate is 20%. In other words, we lose 20% of our users each week and retain the remaining 80%. With this type of churn, we will only have 399 users by the end of week 26 instead of 2,600, even though we acquire them at the same rate:

Example #3: Acquire 100 users per week and retain 90%

What if we could improve our churn rate? Let’s say we can get it down to 10%. In other words, we will still lose 10% of users each week but will retain 90%. In this case, we will end up with 842 users by the end of week 26. It’s much better than 100 but nowhere close to 2,600:

Example #4: Acquire 100 users per week and retain 80% with a virality coef. of 0.4

How about viral growth? You might have read stories about Facebook, Twitter, and Hotmail growing this way. So let’s see how this variable can impact our growth prospects.

Let’s still assume that we lose 10% of our users. But let’s also assume a virality coefficient of 0.4. This means that when we acquire 100 users via our regular customer acquisition efforts, we get additional 40 more through the viral loop.

Under this scenario, we will have 1,395 users by the end of week 26:

Example #5: Acquire 100 users per week and retain 80% with a virality coef. of 1.0

Now, what if we could create a truly remarkable product experience leading to a virality coefficient of 1.0? In other words, for each acquired user, we would get another one “for free”.

Whoa, we’ll have 15,823 users by the end of week 26 with the same acquisition rate of 100 users per week and with the same churn rate of 10%!

Example #6: Acquire 100 users per week and retain 80% with a virality coef. of 1.2

What if could get our virality coefficient above 1? Let’s say we could achieve 1.2. Here is how it might work: each user sends an email to five friends and, on average, 1.2 out of five sign up.

Still assuming the same churn rate of 10%, we will end up with 201,730 users by the end of week 26.

Now, this is much more exciting, isn’t it?

Marketing professionals have been talking about “word-of-mouth marketing” for decades to refer to marketing that your customers do for you. In a way, the “viral loop” is simply re-branded “word-of-mouth marketing” with more emphasis on deliberately designed product features that reinforce virality.

Of course, achieving this type of virality would require an outstanding product and remarkable marketing. In fact, very few companies were able to achieve this kind of growth.

The practical takeaway is that product experience and marketing and highly interrelated. When users love the product, marketing campaigns become much more effective as acquired users stick around and bring even more users in.

 

Setting The Right Goals

How do we decide on which metrics to focus on? One way to do it is to benchmark our metrics against market-averages.

Here is just one example. Let’s say we find ourselves in this situation:

Our company Estimated market averages
Acquisition 3,500 per week 2,200 per week
Retention rate 70% (churn = 30%) 90% (churn = 10%)
Virality coef. 0.2 0.2

As you can see our retention is lagging behind the competition. Let’s estimate how it impacts our business by plugging these numbers into the spreadsheet.

An average competitor can expect 23,105 users by week 26.

We can expect only 10,207 even though we acquire users at a higher rate.

What if we could improve our retention rate from 70% to the industry standard of 90% while keeping all other metrics at the same levels?

In this case, we could expect 36,758 users – more than 3x the original number.

So would we be able to confidently set the goal of improving retention rate in this case?

This, of course, depends on the context. Here are some questions you might ask:

  • What’s the root cause of the low retention rate? E.g. the product itself, the quality of onboarding materials and customer support, the pricing strategy or marketing effectiveness.
  • What exactly are customers unhappy about?
  • How much investment is required to improve retention?
  • How is the market evolving – will this still be important in the future?

Practical Takeaways

Improving retention and virality coefficients can have a substantial effect on growth, independent of customer acquisition. But it doesn’t mean that acquisition numbers are irrelevant. On the contrary, the rate at which you acquire users will often be the number one factor determining future success.

How so? Here are some possibilities:

  • Certain companies might be unable to improve the churn rate in the short-term because of certain fundamental issues with the product that would take months to address.
  • Certain teams can substantially improve the acquisition rate but be limited in how much they can affect churn rates and virality.

In other words, the answer is “it depends”. So, the most important takeaway is that modeling your specific situation will allow you to make much better decisions.

Customer Lifetime Value

Understanding how changes in acquisition, retention, and virality impact the number of active users is a great start. Now, let’s look at one of the ways we can assess the impact of user growth on profits. One of the best ways to assess this impact is to estimate customer lifetime value (usually abbreviated as CLV or LTV).

CLV is simply dollar value a company can earn from serving one customer.

When we know this number, we can decide exactly how much we can spend on the acquisition of an average user and still break even.

It’s important to understand how CLV of a business compares to customer acquisition cost, abbreviated as CAC.

  • If CAC is higher than CLV, the amount of money we need to spend to acquire one user than the amount of money we can earn from serving this user. This is a bad business to be in unless we can either increase CLV or decrease CAC in the future.
  • If, on the other hand, if CLV>CAC, our marketing investment is justified: we can invest in acquiring new users because the return on investment is positive.

CLV Formula

So how is CLV derived?

In some industries, calculating this number is super easy and straightforward, but sometimes it is moderately, or even extremely, complicated. The most generic formula looks like this:

screenshot.png

Where:

  • “GC” = yearly gross contribution per customer = Sales minus Cost of Goods Sold
    For example, the annual subscription to a service minus the cost of providing this marginal subscription (which is often close to zero for some tech companies).
  • “M” = retention costs per customer per year, if relevant. Often, M=0.
  • “n” is the horizon (in years)
    It depends on how comfortable we are as a company extrapolating far into the future, based on the historical data that we have. So it’s a very arbitrary number. Using “infinity” makes mathematical sense, but we can also start with something more conservative, for example, three or five years.
  • “r” is the yearly retention rate
    This is a critical component. The longer customers stick with us, the higher their CLV because we can expect to receive payments for a longer time. This is why it is so important to build a product that customers want and like to use.
  • “d” is the yearly discount rate
    The discount rate is a financial term that might deserve a separate chapter. But for our purposes, we can assume that d = the interest rate we need to pay to raise capital. In other words, if we finance our business by getting a bank loan at 4%, our d = 4%.
  • “i” means a certain year, i=1 means year number one and i=n means year number n

Some might think that this is unnecessarily complicated. If you do, here is a simpler, more intuitive way to think about customer lifetime value:

  1. Subtract the cost associated with serving one marginal customer for a year from the price that a customer pays for a subscription per year.
  2. Multiply this number by the number of years you expect an average customer to keep using the service.

For example:

  • Annual subscription = $69.99
  • Annual cost to serve one more customer = $5 (for example, incremental hosting costs)
  • An average customer uses our service for two years

CLV = ($69.99 – $5) * 2 = $129.98

Of course, we can also use days or months instead of years.

I’ve included built a CLV calculator on the second worksheet in the same Google Spreadsheet.

Using CLV for Business Decisions

Now we can estimate the dollar impact of improvements in acquisition, retention or virality metrics by multiplying the number of users by CLV of each individual user.

Remember how we planned to improve retention from 40% to 70% because 70% was the industry average? We expected this improvement to increase anticipated user count by week 26 from 333 to 1,166. In other words, we expected 833 incremental users.

If each user is worth $129.98, our expected incremental profit is $129.98 * 833 = $108,273. Another way to interpret this number is that we can spend up to $108,273 to improve retention and still break even as long we get to at least 70%.

Advanced Uses of CLV

CLV is even more powerful when calculated for distinct customer segments. For example, a SaaS business might find out that CLV of its enterprise clients is higher than CLV of SMB clients. At the same CAC can also be higher for enterprise clients.

CLV and CAC metrics also vary by marketing channel. In other words, it might be more expensive to acquire users through paid search advertising but these users might also have higher CLV that would justify the cost.

Having visibility into these granular metrics can help companies make strategic decisions, such as deciding what segments to target or what marketing channels to double down on.

What We Learned So Far

  • There are multiple variables that will define business growth dynamics
  • Main ones are acquisition rate, retention rate, virality coefficient, customer lifetime value, and customer acquisition cost
  • Customer lifetime value is primarily determined by how much you charge and how long you retain users
  • At any given time you might choose to prioritize some of these metrics over others
  • This decision will depend on the business strategy, available resources, customer feedback, stage of the product lifecycle, and hundreds of other factors
  • Models like the one shown here can assist you in making better decisions by letting you see where your business will be at in the future under different scenarios

Alternative Metrics

There are no universal models or metrics, though. So if for some reason the ones described above cannot be used in your industry or company, you might consider some of the other popular frameworks described below or employ all of them.

Marketing Funnel

One of these alternatives is marketing funnel, used widely in all types of industries and companies but in different versions. Some companies even create their own customized versions to better describe their business.

The overall idea is simple, though: users move through different stages, such as: unaware -> aware -> consider buying the product -> trial -> purchase -> regular purchase -> loyalty -> advocacy.

Each next stage is included in the previous one. For example, everyone who is considering buying a product is, by definition, aware of it. In turn, everyone who has tried a product had considered it, and so on. This is a seemingly simple framework, but a very powerful one.

You might wonder where we can get the data. We will talk more about marketing research and about marketing analytics in the next chapter. But here are some options. A consumer goods company might conduct a wide offline survey of their targeted audience. A tech company might utilize data generated by app usage and complement it with an online, in-app survey.

What matters is that at the end, we get something like this:

screenshot.png

Use the spreadsheet I created to play with the numbers.

What does it all mean? As we discussed above, the marketing funnel simply illustrates how your target audience falls into different buckets relative to the competition.

An intelligent way to benchmark ourselves vs. the competition is to look at conversion rates. Those simply illustrate how well users convert from one stage to another. Look at the “Aware -> Consider” conversion rate for our company. It’s 50% because 80% of customers are aware of the product, but only 40% consider trying it. So 40%/80% = 50%.

So what is going on here? As you may have noticed, Competitor 2 is a very widely known brand. Almost everyone knows about it. They must have spent millions of dollars building awareness. They might as well be Coca-Cola. But it seems that the message is not resonating with many customers. Relatively few people want to try it, then relatively few of those who tried, want to purchase it, and so on.

Competitor 1 is more like an iconic brand in some circles. It is not as widely known, but those who know about it and those who tried it are more likely to become regular customers.

And then, there is our company. We seem to be doing ok overall; 30% of our customer base buy our product regularly. However, we seem to be losing a lot of customers after the first purchase and the customers who purchase regularly do not seem to become loyal, i.e., they don’t abstain from buying other brands if ours is not available. In other words, we don’t retain our users very well. It might be a product problem or we might not engage our customers well enough through marketing channels.

Funnels are incredibly common and are used in all types of industries because they allow marketers to identify specific problems that can be addressed later on.

By the way, if somebody tells you that traditionally, marketers only focused on acquisition and that retention only became a focus very recently with the rise of so-called growth hacking, feel free to laugh out loud. As we have seen in the funnel above, conversion from “Purchased” to “Regularly purchase” is exactly that: retention. And it has been a priority for decades. In turn, conversions into “Loyal” and into “Advocates,” in essence, might serve as proxies of virality metric.

Image Indicators

Image indicator is yet another metric that is widely used. We can get these numbers by conducting a survey and asking customers to rank the importance of different attributes and rank main products by these attributes. For example, we might ask about the three most important factors a customer considers when choosing a product. Then, for each of these factors, we might ask to evaluate products on the market.

At the end, we should get something similar to this table:

screenshot.png

Here is how to read it:

  • “Importance” column tells us how our target audience makes purchasing decisions – what matters most to them.
  • The next three columns show how we compare against the competition.

Sometimes getting lower rankings on certain indicators can be a strategic choice. But sometimes these low rankings reveal a huge opportunity to improve in the eyes of customers.

Use the spreadsheet if you’d like to play with the numbers.

Market Share

Sometimes simply focusing on user growth is not the best idea.

When the total addressable market expands or contracts over time, you might be better off looking at market share too.

Here is an example to illustrate this idea. Imagine that we operate in an emerging market that grows 10x a year. Let’s say that it grew from 10,000 potential customers to 100,000 potential customers last year. Now imagine that we only look at the number of users and see that we have increased the number of customers from 2,000 in the beginning of the year to 6,000 at the end of the year. The natural reaction is to congratulate ourselves: we have tripled the number of customers! This is 200% growth! But our market share has also collapsed from 20% to only 6%. This is a 70% decrease (or 14 percentage points drop!). Suddenly, the conversation of how amazing we are might turn into a conversation about ways we can improve.

On the other hand, there are mature markets that are slowly declining year over year. In these markets, even brilliant marketing might lead to a declining number of users. Market share, however, might stay the same or even increase. As long as our company declines slower than the market as a whole, we are winning the competition and providing more value to customers. Even though it is not the best field to be in, it is better than the number of users might suggest if considered in isolation.

Let’s review what we have learned. We discussed multiple metrics in this chapter. We subdivided user growth into acquisition, retention and virality. We talked about marketing funnel and image indicators.

We always started with some numbers to illustrate the point. The next chapter on marketing research and analytics will show how we can obtain some of these numbers, as well as learn more about our customers.

A Quick Recap

  • Rule #1: understand “why” behind customer needs
  • Rule #2: identify company goals and capabilities
  • Rule #3: achieve product-market fit
  • Rule #4: think strategically to create a sustainable competitive advantage
  • Rule #5: foster a collaborative culture between engineering and marketing
  • Rule #6: use data to set goals and inform marketing strategy

More on Growth