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Last year, I published a series of posts on the importance of knowing your users’ or players’ lifetime value, the key components and how to impact them and techniques to increase the accuracy of your customer lifetime value (LTV) predictions. I intentionally did not publish a formula for calculating LTV—while it is always a factor of retention, monetization and virality—as it is different by product and there are many alternative ways to get to an accurate customer lifetime value. Prompted by an infographic that I came across (see below) I did want to go into some details of the mechanics of calculating LTV.

The first step is to obtain your key variable metrics as averages across all users. The ones I prefer are ARPDAU (average revenue per daily active user), day 1 retention (how many people who use or install your website, app or game come back the next day), day 30 retention and k-score (how many free/organic users does a user bring in).

The second step is to estimate your “constants,” as Kissmetrics refers to them in the infographic. The key constants you need to understand and estimate are average customer lifespan (and days played in that lifetime) and customer retention rate. Lifespan is how long the customer will remain a customer, which is derived and estimated from your retention data. For a game, you may want to start by assuming six months and then adjust that number as you acquire better data on your users. The second metric, customer retention rate (also often referred to as churn), is the percentage of customers that, given a certain period of time, will return when compared to an equal period of time. As the infographic shows, there are other constants you may want to incorporate into your model (and I have discussed some of these in previous posts) but the core of an accurate LTV prediction for a tech company is understanding your user’s lifespan and churn rate.

Once you have your data, both the variable and constant metrics, it is time to estimate your LTV. As I have written before, always keep in mind that your calculation will be a prediction and actually represents a range of expected values of a new user. Also, to generate actionable data, it is more important to understand LTV of users from different ad sources and cohorts. That said, once you have the data either aggregated or by source/cohort, you then build a function to calculate your customers’ lifetime value. The infographic shows several formulas based on the metrics their example (Starbucks) considers useful. If you have focused more on the metrics that I mention above, an oversimplification would be ARPDAU times number of session in the lifetime of a player (which will incorporate churn rate) times their k-score.

Key takeaways

  1. Step one: obtain metrics for your variable data (average of players), such as ARPDAU (average revenue per daily unique user), day 1 retention, day 30 retention, etc.
  2. Step two: determine the constant metrics, most important being customer lifetime and churn rate.
  3. At its simplest, your customer lifetime value will be ARPDAU times number of session in the lifetime of a player times their k-score.

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How To Calculate Customer Lifetime Value
Source: How To Calculate Lifetime Value

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While growth hackers are continuously looking for the sexy, new trendy way of obtaining or reactivating users, they often neglect one of the most effective methods: e-mail. A recent article published by McKinsey & Co., “Why Marketers Should Keep Sending You E-mails,” makes a strong case for e-mail marketing. In fact, the article shows e-mail is 40X more effective to acquire users than Facebook and Twitter combines (though maybe 1/40th as cool). The argument is very consistent with what I wrote about Bayes’ Theorem: The underlying baseline data is very powerful in driving results. In this case, the “40X more effective results” assertion states that about 91 percent of US consumers use e-mail daily, and that e-mail prompts purchases at three times that of social media with an average order value that is 17 percent higher than from other sources. Given e-mail’s power to improve your user-acquisition efforts, there are three keys to making it a successful channel.

Focus on the customer journey

Understand the recipient’s journey from the time they receive your e-mail to the final desired action. This action is not opening the e-mail or clicking on a link but it is potentially installing an app, making a purchase, etc. While it is good to optimize every part of the e-mail, from the subject line to the images to the copy, you should focus on optimizing the entire customer journey. Once they click on a link in the e-mail, do not stop optimizing. Rather than taking them to a generic landing page, keep their experience consistent with what persuaded the user to click on the e-mail in the first place. And ensure the experience is just as good on a mobile device, given that 45 percent of all marketing e-mails are opened on a mobile device. According to Google, 61 percent of users are unlikely to return to a mobile site they had trouble accessing and 40 percent visit a competitor’s site instead.

Learn, learn, learn

You should use every e-mail as an opportunity to understand better your customer. Define clear learning objectives for every campaign, capture data and share it across your company (with your product team, design team, VIP group, etc). By sharing hits and misses from your e-mail marketing campaigns, you uncover what does not work and improve messaging in all channels. It also helps ensure future e-mails are more successful.

Get really personal

Adding a customer’s first name does not make an e-mail personal, but really customizing it for the user has a huge impact on effectiveness. The article discusses how Gilt Groupe sends more than 3,000 variations of its daily e-mail, each tailored based on user click-throughs, browsing history and purchase history. Keep in mind that building this level of customization and targeting abilities is not easy; it requires specific capabilities and supporting infrastructure. A targeting engine must be built to guide the right message to the right person. Your e-mail team also has to be able to create and send 3,000 different e-mails daily, which is significantly more difficult than one mass e-mail blast.

Key takeaways

  1. E-mail marketing is a grossly underused marketing channel, 40 times more effective than social media.
  2. When building your e-mail campaign, focus on the customer’s journey. It is not only about getting your customer to open or click, but ensure that once they arrive in your game or on your site they have a great experience.
  3. Learn from every e-mail and constantly improve, not just your e-mail campaigns but use it to optimize all of your growth efforts.

Lloyd Melnick:

Great post on how to improve your chances of raising funds from institutional investors.

Originally posted on VentureBeat:

In my last post, I shared eight tips for creating the perfect pitch deck. After such an overwhelming response (tens of thousands of readers, 45 questions via Twitter/email, and almost 5,000 shares via social media) I decided to follow up with a second piece on a topic that’s discussed even less than creating a pitch deck: actually standing in front of potential investors and pitching for capital.

As part of my five-year journey building Bigcommerce, I’ve raised three rounds of venture capital financing: a $15 million series A in 2011 from General Catalyst, a $20 million series B in 2012, also from General Catalyst, and a $40 million series C last year from Steve Case’s Revolution Growth for a total of $75 million.

For each round of financing, we created a pitch deck and went on a road show. For all three rounds, we received term sheets very early in the process and cut our pitching short, allowing…

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With the ever increasing costs of acquiring users (either to a game, an app or a retail establishment), the ability to acquire users by referrals becomes increasingly important engine for growth. Jason Bosinoff, an engineer at Airbnb, one of the fastest growing tech companies around, recently posted about how it built its successful referral program.

Airbnb referral screen

Not only does a referral program help you get users, it generates users who usually have a very high lifetime value. This happens because word of mouth is a very directed user acquisition channel, people will refer a product or game only to friends they believe are likely to value the product.

Airbnb’s referral program is pretty straightforward, with both the sender and recipient getting $25 travel credit when the invited user completes their first trip (or $75 if the recipient hosts). One of Airbnb’s keys to success is that users can send and accept referrals on all platforms (web, iOS and Android), a lesson many game companies would be smart to replicate.

According to Bosinoff’s post, there were five steps to creating the successful referral program:

  1. Know what success looks like. The first step is to define success. What metrics are you trying to impact and what would represent a positive result. Create three cases: good, better and best.
  2. Measure. Integrate robust analytics into your program so you can constantly be tracking how you are performing versus the success metrics that you have set. You want to be tracking everything on the customer journey from when they first see the prompt to create a referral to when the accepter rates their experience after using your product. Airbnb built a rich logging taxonomy of over twenty user events that happen during the referral invitation and sign up journey. With this tracking in place they could follow an invitation from invite page impressions to referred users’ making bookings or becoming hosts. They could then easily review an metric or view it graphically.
  3. Test and improve. You then want to test the product on a subset of your user base. This testing both allows you to improve the stability of the referral program and think of additional functionality that could improve metrics. Some functionality that Airbnb added during its testing process included personalized referral codes and landing pages as well as customizing the experience based on what the user clicked to enter the experience.
  4. Go live. Launch your referral program and compare with the results you targeted in step 1. As your analytics are implemented across the product, you should easily see how you are doing compared with plan.
  5. Iterate, iterate, iterate. Compare the analytics you are getting with the plan you established in the first step. Where you are trailing your projections, optimize the referral program to overcome the weakness. For example, if you are seeing fewer invitations per user, increase the incentive for using the program. If you are seeing too few senders, increase surfacing of the program. If you are seeing a weak acceptance rate, change the copy of the referral or increase the benefit to the end user.

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I recently came across a fantastic presentation on startup metrics by Andreas Klinger. It is embedded below but given its length I wanted to highlight the key takeaways:

  • The biggest risk for a startup is not failing to create a good product with a market; it is having a competitor come up with something a little better. Great example is Lyft, which I am sure is a little envious of Uber.
  • There are four stages for a startup to succeed. The first is discovery, generating the product idea. The second is validation, making sure the market wants the product. The third is efficiency, being able to supply the product cost effectively in quantity. Then there is scale, delivering the product to millions.
  • To look at it from the user perspective, there are two key elements: finding the product the market needs and then optimizing (the former encompassing discovery and validation, the latter representing efficiency and scale). To find a product the user needs, you need to understand these needs and create something that will be sticky (i.e., that they will return to) and viral (they will talk about). To optimize, you then need to build out the right revenue model and level, and then scale.
  • According to Klinger, 83 percent of startups are in the discovery phase (empathy, stickiness and virality) while most analytics are around revenue and scale.

    Andreas Klinger

  • A/B tests, funnels, referral optimization, etc., are about optimization, not innovation and cannot replace creating a great product that people want.
  • There is a way to get product insights from data to create that innovative product and you can do it with a much smaller number of users. They key is looking at whether people stay on your site or in your app, in other words, whether they are hooked.
  • Focusing on improving metrics creates a false positive, you can always improve ad conversions or funnels but what looks good for investors does not necessarily improve the product. You may be converting or funneling the wrong users.

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Although founding a company is always a challenge, there has never been a better time than now for starting a business. The acquisition announced last month of WhatsApp by Facebook for $19 billion illustrates this opportunity. It is not the size of the deal; there have been huge deals that have made founders incredibly wealthy for decades. What is exciting is how WhatsApp achieved this huge exit.

What is amazing now is that you can build a $19 billion business quickly without a huge investment because of cloud computing. When you look at Microsoft and Google (and even Facebook), it took them thousands of engineers to build their businesses. WhatsApp has just 32 software engineers, which means that each one supported about 14 million users.

WhatsApp logo
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