Category: Social Games Marketing

Thoughts primarily related to marketing a social game

Adjacent User Theory Shows How to Supercharge Your Game’s Growth

We are all constantly looking at ways to grow our game or app like Facebook or Twitter or Fortnite, so where better to start looking than Instagram. An article by Bengaly Kaba, The Adjacent User Theory, shows the methodology he used at Instagram to reignite its mega-growth. Kaba joined Instragram in 2016 and was instrumental in helping it grow from about 400 million users to over 1 billion.

Kaba credits his success to Adjacent User Theory, where potential users (adjacent users) are aware of the product, maybe tried it, but are not engaged customers. The app’s positioning or too many barriers in the early user experience often drive the lack of traction with these customers. In Kaba’s situation, the 400 million active users of Instagram represented instances where they found product-market fit but also missed over 600 million potential customers who either did not understand Instagram or how it would fit into their lives. According to Kaba, “our insight was that it is critical for growth teams to be continually defining who the adjacent user is, to understand why they are struggling, to build empathy for the adjacent user, and ultimately to solve their problems.”

Solving for the adjacent user

If you have a successful game or product, you almost certainly have a good understanding of your customers (or else you would not be successful). Essentially by definition, you do not have the same understanding of adjacent or future users, or else they would already be customers. Also, your future audience evolves over time, so what adjacent users are not getting now from your game might be different from what keeps your product appealing outside its core in six or twelve months.

To solve for the adjacent users, either your product team or a satellite of your product team needs to focus on these potential customers. Kaba writes, “[w]ithout a team dedicated to understanding, advocating, and building for your next set of users, you end up never expanding your audience. This stalls growth, and the product never reaches the level you aspire it to…. You can think about your product as a series of circles. Each of these circles is defined by the primary user states that someone could be in. For example Power, Core, Casual, Signed Up, Visitor. Each one of these circles have users that are “in orbit” around it. These users have an equal or greater chance they drift off into space rather than crossing the threshold to the next state. There is something preventing them from getting over the hump and transitioning into the next state. These are your adjacent users and the goal is to identify who they are and understand their reasons struggling to adopt. As you solve for them, you push the edge of the circle out to capture more of that audience and grow.”

Slack represents a good example of the power of adjacent users, according to Kaba. Slack has five user states — not signed up, signed up, casual, core free and monetized — and will lose potential customers at the transition between each of these five states. By understanding why people drop off between states before becoming monetized, Slack can make product changes that moves these adjacent users from non-customers to engaged customers.

Why companies are not focusing on adjacent users

While it seems obvious that converting adjacent users is critical for growth, many companies miss this opportunity. Kaba identifies three reasons for this problem

  1. Focusing on power users. It is natural to focus your development efforts on your VIPs, especially in gaming where a very small percent of your players drive most of the revenue. Thus, product development is geared to giving these players more of what they want, rather than bringing more customers into the tent.
  2. Personas. Many companies build personas (a fictional character created to represent a player type that might use a game) to determine who they are solving for. The mistake with this approach is that personas are usually created based on existing players, do not change as customers evolve, not based on relative usage/contribution and are too broad to be actionable.
  3. The home run swing. Product teams often look for the bold beat feature that will have a dramatic impact rather than a series of changes. Kaba writes, “they get bogged down by trying to establish product-market fit for a new set of users and never fulfill the potential of their current product-market fit.”

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How to solve for adjacent users

There are several ways to mitigate the above issues and appeal to adjacent users. To overcome the bias of VIPs, product teams need to cross a “cognitive threshold” and understand the experience of non-VIP customers as well as non-customers. This includes your team putting aside their personal biases if they are using the product, they are not building it for themselves but building for adjacent users.

To avoid having your product team focus on the huge wins, Kaba writes that they must “[r]emember…adjacent users are the users who are struggling to adopt your product today. Non-adjacent users could literally be everyone else in the entire world. Sure, non-adjacent users might be a larger market, but the barriers to their adoption are also dramatically higher. Companies that try to go too big too soon and often, skip the next obvious steps and fail to solve their current adoption problems.”

Identifying and defining your adjacent users

Once you understand the value of adjacent users and how to build for them, you need to find them. You need to look at cohort decay, keeping in mind the different circles or user states for your game. You need to look at these variables (ie. registering to purchasing) and identify the decline in each cohort. That declines represents the adjacent users.

You then need to build a theory on who they are and why they are struggling or not converting. You also need to realize you will not have perfect visibility into the answer. Kaba describes the process, “is to lay out multiple hypotheses of who the adjacent users are, choose which one to focus on strategically, force your team to look at the product through their lens, experiment and talk to customers to validate and learn, then update the landscape to make your next choice. I like to think about it as a snowball. You know very little at first, but as the snowball turns you collect more information about the adjacent user, which helps you collect more snow (users).”

It is also important to gain a deep understanding of your current users. If you find that your current users are predominantly, male, 35-50 years old and from the northeast in the US, you can then look at each of these attributes to find adjacent users. For example, maybe you are losing women. Maybe you are not appealing to 50+. Maybe users from the Midwest are not connecting.

Once you have hypothesis on your current users you can create multiple hypothesis of adjacent users. Kaba recommends, “starting with a bottoms-up analysis of your data. You do not need to spend weeks talking to users to get a sense for who your adjacent user is. Look at what is happening on the edges of these states in the data. The data will help you identify places in the product that people are dropping off. This is the starting point to help you develop hypotheses about why different segments of users are dropping off.”

Understand the why

Once you have identified your adjacent users, you need to understand why they are dropping off. To achieve this knowledge, your product team needs to empathize with the adjacent users. This is usually a challenge as they often think like your VIPs and current users and find it difficult to put themselves in the heads of people who do not like their game or product. There are three ways, though, to overcome this problem:

  1. Be the adjacent user. Your product team needs to be experiencing your game in the conditions and settings that the adjacent user is experiencing. Some examples are new user flows, empty states, and product states that require different levels of play.
  2. Watch the adjacent user. This is done by usability testing. It is best to do this in the adjacent users environment, as focus groups and in-office usage creates artificial conditions.
  3. Talk to the adjacent user. Once you have identified adjacent users, ask them why they are trying to use your product, what jobs they are trying to solve, and what other games they play or have tried.

Prioritizing adjacent users

Once you understand your adjacent users and have identified what they need from your game or product, you need to prioritize. You cannot solve all of their issues at once, instead you need to focus on creating the most long-term value for your company. You want to do it in the correct order or sequence so that your efforts build on each other.

First, the adjacent users you should approach are ones different on only one or two attributes. You will only have to make limited changes or additions to your product to appeal to them.

Second, they should align with your strategy. Will the changes you have to make to appeal to this segment help you achieve your long-term strategy and vision for the product. You cannot please everybody always and trying to do so could potentially take away from what is making your game successful.

Third, start with adjacent users already in your funnel, customers you are losing, rather than trying to appeal to a completely new segment.

Not a one time exercise

Once you have identified and made product improvements to appeal to your adjacent users, do not proclaim mission accomplished, as the pool of adjacent users is constantly evolving. Efforts to build for one segment will identify new segments. These efforts will also bring in new adjacent users who you can then convert. Finally, these efforts will create a new or enhanced value proposition, which means that both current and adjacent users will have a new equation in deciding whether to engage. You will then want to adjust based on how they react.

Key takeaways

  • Adjacent users represent a great opportunity for growth. These are potential players (adjacent users) are aware of the product, maybe tried it, but are not engaged customers
  • You solve for these players by looking at different states of your product (i.e. registration, play, purchase) and seeing who drops off at each of these states, then understand why these potential customers are dropping off.
  • You can make your product attractive by putting yourself in the place of the adjacent user, watching them use your game or product and talking to them.

The risks of market research

I have written multiple times recently about the perils of market research and a recent article by Kristen Berman and Dr. June John, [Don’t] Listen to Your Customer, builds on these perils. Berman and John write, “a sole reliance on customer input and feedback is built on an antiquated model of human decision making that assumes humans are rational,” and time again we see that people’s decision are often not driven by optimizing utility. Thus, talking to people and understanding their desires and aspirations to design products fails because most people do not know their desires and aspirations.

People do not answer accurately

The core problem with market research is you do not get true answers. In research done by Berman and John, they found that a small minority answered a question correctly about their own intent and importance. They asked people why they were or were not saving for retirement. 86 percent of people responded that saving for retirement was important. That response conflicted with the reality that in a typical company (without automatic opt-in), less than 50 percent of people would sign up for the retirement program. The authors write, “[t]he real reason people save for retirement or (don’t save) is because they’ve been defaulted into a savings plan. Companies who automatically enroll employees in a retirement savings plan increase savings rates by 50 percentage points. It has nothing to do with a preference for the future or not having enough money.”

Data is both inaccurate but also does not reflect motivators

Not only do people provide a response inconsistent with their actions, they often do not understand underlying causes of their behavior. In the retirement plan example, Berman and John followed up by asking why they had not enrolled in their company plan. Although data showed the key driver was whether it was or was not an opt-in plan, only 6.2 percent of respondents cited anything related to form design or default enrollment.

Basing decisions on these responses could have ruinous results. As the authors point out, “if we relied solely on data from these traditional qualitative and quantitative research methods, we would likely spend a lot of time and money building solutions that would miss the mark, and for sure be less effective than just designing a default enrollment.”

People are not lying

It is not that people are trying to mislead you when they respond inaccurately, the fundamental issue is that we are not conscious about the mental processes leading to decisions. We are also not cognizant that we do not know our own decision making processes.

Another study cited in the article showed over 70 percent of people did not know the underlying reason for a decision they made. Berman and John asked people if a default setting for organ donation and retirement savings would impact their choice. Despite data that shows such defaults nearly doubles the chance of someone opting in, 71 percent of people who said the default would not impact their retirement or organ-donation decision were “very” or “completely” confident in their answer.

The authors cite another study that reaffirms how confident people are in their incorrect responses. “They showed participants two female faces and asked which face was more attractive. After the participant pointed to a face, the researcher secretly swapped photos to the one they didn’t choose (think David Cooperfield). Then, the participant was asked to explain why they chose the face (which they didn’t actually pick). A majority of participants didn’t even notice the face swap. And furthermore, they went on to explain in great detail why they preferred that face. Most participants made up the reasons—good reasons—for liking a face that they didn’t actually choose. Only a third of people realized they had been tricked.”

Why it happens, ask Kayleigh McEnany

Given that people are not intentionally trying to be dishonest, you can ask why they are so inaccurate predicting and understanding their own behavior. Researchers have given this occurrence the name the Internal Press Secretary. Berman and John explain, “[t]he brain’s Press Secretary must, like a president or prime ministers’ press secretary, explain our actions to other people. “I don’t know” is simply not an acceptable answer. Many times the brain’s Press Secretary is correct, but other times, it’s just guessing, piecing together clues in order to give a plausible answer. The tricky thing is, we don’t know when our Press Secretary is using facts and when it’s just guessing.”

Kayleigh

Don’t ask, test

Given the challenges people face understand and explaining the actions they will take, product managers and designers need to find alternatives to traditional market research. One of the most powerful is to test what people will react to different choices. This testing can take the form of an ABn or multi-armed bandit test, where you present the options to a test and control group. It can be a preference test where you show customer or potential customers different concepts and see which one they are most likely to purchase or would spend the most for.

You can also look at responses in similar circumstances. How did a similar product or service perform? Did competitors launch comparable features? I often find that looking at adjacent or even completely different industries helps understand people’s true preferences. The key is using one or multiple tools that show actual decision making rather than relying on what customers believe is their preference.

Key takeaways

  1. A sole reliance on customer input and feedback, traditional market research, is built on a model of human decision making that assumes humans are rational, while in practice we are not.
  2. Not only do people provide a response inconsistent with their actions, they often do not understand the underlying causes of their behavior.
  3. Use one or multiple tools that show actual decision making, such as ABn testing or looking at reactions to similar initiatives in adjacent industries, rather than relying on what customers believe is their preference.

Lifetime Value Part 30: Why clumpiness should be one of the KPIs you focus on

When calculating customer lifetime value (LTV),there is one KPI that is often neglected, clumpiness. Clumpiness is a term coined by Eric Bradlow, a marketing professor at Wharton. Think of clumpiness as binge-watching Ozark (or, if you must, Tiger King). Rather than an extended, constant period of consumption, a short, intense buying burst. By understanding and tracking this behavior, you have a significant weapon for improving LTV.

Defining clumpiness

Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable. When calculating customer value and segmentation, we focus on analysing recency, frequency and monetization of the customer (what Bradlow refers to as RFM). This analysis is based on customers making purchases in a regular pattern, i.e. coffee, diapers or milk. Bradlow’s analysis, however, shows that for certain products (and I would classify social and casino games here), customers actually monetize in bursts. Thus, you need to add C for clumpiness to your RFM modeling.

Why clumpiness is important

Bradlow researched how to predict the future value of customers (predicted LTV). Bradlow explains, “[l]et’s imagine you want … to predict who are going to be the valuable customers in the future. And you have four things you can use to predict it. As I mentioned: recency, frequency, monetary value and let’s say the marketing spend towards the customer. Those are the classic ways in which companies build what are called scoring models…. [T]he findings of my research suggest that higher clumpy customers are worth more out of sample, meaning in their future value, even after controlling for RFM and marketing expenditure — which means we have found another variable that firms should track [concerning] our customers and use it to predict their worth in the future.”

This finding is particularly interesting as most companies, especially in the game space, currently base their LTV projections on the RFM model. While this calculation may have worked in the traditional retail economy, consumption has evolved, especially for digital goods. Binge consumption is a fact of life in the entertainment space, and gaming sits squarely at the center of the modern entertainment environment. This analysis is consistent with Bradlow’s findings, where he says, “[i]f you look at historically purchased goods, clumpiness really isn’t there. But if you look in the new wave, the new economy, clumpiness is pervasive in every data set I’ve analyzed.”

Using clumpiness insights

Calculating clumpiness should be easy and not require tracking any new events. It is the same data you are using to calculate R, F, M and LTV. There are then several applications for this insight:

  • Incorporate it into your segmentation to get a better understanding of who your VIPs and high value players are, then focus your premium treatment (and benefits) on these players.
  • Use clumpiness to predict better what players are likely to become VIPs. This will help you concentrate your early retention efforts and reinvestment.
  • Focus reactivation on your clumpy players. Bradlow explains, “[i]f you reactivate them, they’ll come back and be clumpy again, and do a lot of stuff in the future. “
  • Do not simply rely on the KPIs you have always been trusting. Do not rely on simple theories of how your players behave, also look at things about arrival time or when people play. People who come in bursts, then go away and then come back in bursts and then go away are fundamentally different and have a different LTV.

By understanding clumpiness, you have a more accurate predictor of LTV. Once you know how to predict your LTV, you can then impact it by making changes that drive these variables.

Key takeaways

  1. We normally focus on analyzing recency, frequency and monetization of the customer but by adding a new KPI, clumpiness, we get a much better understanding of their expected value.
  2. Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable.
  3. Clumpiness can help you better segment players, predict VIPs and target your reactivation efforts and spend.