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The Business of Social Games and Casino

How to succeed in the mobile game space by Lloyd Melnick

Tag: survey

Customer analytics tips for gaming companies

Customer analytics tips for gaming companies

While social and mobile gaming companies are generally at the cutting edge of applying analytics, I recently took an online course from Wharton on Coursera that provided some additional insights in how to best use analytics in online gaming. These takeaways range from ways to improve your UI to how to calculate LTV more accurately.

Make your players wander

One of the most interesting takeaways from the course is that efficiency is not always the desired player behavior in the online world. In traditional retail, retailers found they enjoy much higher revenue when customers wander around the store rather than quickly find what they have come for. In the studies cited, about 75 percent of movement inside a store is not required. Sixty percent of purchases are items people had no intention of buying when they went into the store. Instead retailers optimize for “jiggliness,” as people with the most jiggliness buy the most.

jiggliness

There are uses of this concept for online gaming and iGaming companies. Rather than optimize your lobby and UI (user interface) to ensure your players find what they are looking for, take them on a journey around your game. If it is a social casino, rather than finding the slot they know and love, expose them to some other content, they may find something they prefer.

Higher customer satisfaction may not improve profitability

While customer satisfaction is positively correlated with profitability, the relationship is not linear. Companies with a low level of customer satisfaction, referred to as the Zone of Pain, experience a strong impact on revenue when making improvements. That is, the firms with awful customer service see big benefits just moving out of the Zone of Pain.

On the high end, companies that provide great customer service and differentiate themselves with it experience positive ROI by making the experience even better. These companies are what is referred to as in the Zone of Delight. Retailers like Nordstroms, which enjoy high margins due to their customer service, see a huge impact when they find even better ways to provide a WOW experience.

nordstrom

When customer satisfaction is only a small part of a company’s value proposition, improvements do not necessarily have a positive return. There is a large flat region where increasing satisfaction does not increase profitability. The key takeaway is that the relationship between customer satisfaction and profitability is not linear, but starts with a Zone of Pain, then hits a sizeable flat region, and then moves to a Zone of Delight.

Correlation does not equal causation

We should all know by now that just because two variables have related movement, you cannot assume one is causing the other. I see this mistake made frequently, including by BI experts. Correlation only shows a relationship between two variables. Causation, more critically, shows that one variable produces an effect on the other variable. It is crucial to remember there are three requirements for causality:

  1. Correlation
  2. Temporal Antecedence. X must happen before Y.
  3. No third factor is driving both. Need to control for other possible factors.

Use analytics for pricing

I am surprised at how often pricing strategy in mobile games (the cost of in-app purchases) or in iGaming (RTP and bet levels) is driven by competitive analysis and intuition rather than analytics. Regression, however, can be used to set optimal pricing (including for virtual goods) at the level that boosts profits. Regression can predict demand at prices that have not been tried, thus you can determine profitability for different options. As predictions can be completed for different future prices, you can then determine optimal price. Effectively, you answer the question what you can charge to make the maximum profit (and with virtually zero marginal cost for online products, can be simplified to maximum revenue).

Preparing better surveys

While market research is a less than reliable way to understand customer intent, it still provides valuable insights into your players. Surveys are a good way to learn about potential customers and are relatively low cost. Some best practices include:

    • To improve reliability of surveys, test and then retest. If the results are consistent, it shows you are getting reliable results (people still may not know what they want though).
    • There are multiple ways to ask questions in a survey (comparative, rank, paired comparison, Likert, continuous, etc.) and you should understand your end goal when deciding which format to use. Advantages of open-ended questions allow for a general reaction that can help interpret closed end questions and may suggest follow up questions. Closed end requires a lot of pre-testing but is easier to administer.
    • Focus on drafting high quality questions. Use simple, conventional language and avoid ambiguity. Do not ask any questions more than 20 words. Most importantly, avoid leading and loaded questions (i.e. How bad a job is Lloyd doing?).
    • Pay attention to sequence and layout. Start with an easy and non-threatening question. Have a smooth and logical flow. Have the questions go from general to specific. Keep the sensitive or difficult question at the end.
    • The key to using surveys effectively is validity, how well it predicts variables you are interested in. If you find surveys effectively predict certain behavior, then they are an appropriate tool for predicting that variable.
    • Make sure your results are generalizable to an appropriate population. You need to define clearly the population, choose a representative sample, select respondents will to be interviewed and motivate them to provide information.
    • Pre-test your survey. Ensure respondents understand each question and the questions make sense.
    • Collect data on non-respondents as they may be systemically different. Try to convert them to responding.

Recency is incredibly important

When looking at the future value of a customer, the three keys are how recently they made a purchase (recency), how many purchases they have made (frequency) and monetization (size of the purchase) recency is by far the best predictor of future value. Frequency is then significantly more indicative than monetization. Thus, focusing on increasing the size of a purchase (up-selling) is the least valuable strategy you can pursue to increase your customer’s lifetime value.

Include clumpiness in your LTV analysis

I wrote several weeks ago about the important of clumpiness in determining a customer’s future value so will not go into too much detail again. 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, as I discussed above. This analysis is based on customers making purchases in a regular pattern, i.e. coffee, diapers or milk. 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 modeling.

Key takeaways

  • People who wander around a retail location spend more than ones who immediately find what they are looking for and retailers optimize to create this jiggliness. Online casinos and games can also build in jiggliness so players find new games and offerings rather than simply quickly go to the one they are looking to play.
  • While satisfaction with customer service positively impacts profitability, the relationship is not linear. Improvements have a strong impact when players are highly dissatisfied (and that is corrected) or when customers with great service make further improvements, companies in the middle often do not see a positive ROI on CS improvements.
  • A relationship between two variables does not show one is causing the other, to have causation there must be a relationship plus temporal antecedence plus the absence of a third variable driving both factors.

Slide1

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Author Lloyd MelnickPosted on July 15, 2020June 27, 2020Categories Analytics, General Social Games Business, General Tech BusinessTags analytics, Clumpiness, customer service, Jiggliness, pricing, recency, survey3 Comments on Customer analytics tips for gaming companies

When to use qualitative research

When to use qualitative research

In the mobile gaming space, being data driven is largely synonymous with using quantitative data, with qualitative data (such as user research) relegated to third class status. Most game companies believe their decisions should be based on user activity in their product. They often act and allocate resources only to analyze their existing data, with design thinking and user research considered old fashioned and not as accurate. These companies, however, are missing an opportunity.

As I have said before, all data should be used to make decisions and optimize your product. Gameplay data is extremely valuable but so are qualitative data sources. Young children learn quite early that when you add a positive number to a number, the new number is larger. Thus, there is no way adding qualitative data to your quantitative data can make your analysis weaker. As long as the qualitative research does not detract from the analysis, it makes you stronger.

There is a great article, Qual vs Quant: when to listen and when to measure by Laura Klein, that explains when qualitative research is most useful. The article explains that you should constantly use both qualitative and quantitative data, but different situations require different focus.

Slide1

When to focus on qual research

Qualitative research is important to understand why you are experiencing a problem or situation. Quantitative research tells you that you have a problem and what the problem is (i.e. D1 retention is down 20%).

What method in which situation

Klein highlights three different high level scenarios and then discusses whether qualitative or quantitative research would help most. In the first scenario, you are only considering changing one variable. You may be deliberating whether to change the maximum bet on slots in your casino. In this situation, quantitative analysis is optimal. With a change this small, users in a testing session or discussion will not give you any actionable information. Qualitative feedback will not provide failure to compensate for the time and money it takes to set up interviews, talk to users, and analyze the data.

The second scenario is when you are planning a multi-variable or flow change. You may be planning to implement a new feature, like a progression system, that would among other things require you adjust the user interface and journey. While you could launch it and measure performance, you would never know why it failed or succeeded. Qualitative research allows you to learn before launching what elements of the feature are compelling, what is confusing and how you can optimize the feature before you launch the feature.

The third scenario is prioritizing your roadmap. In this situation, a combination of qualitative and quantitative research is optimal. As Klein writes

The key here is that you want to look at what your users are currently doing with your product and what they aren’t doing with it, and you should do that with both qualitative and quantitative data.

Qualitative Approaches:

  • Watch users with your product on a regular basis. See where they struggle, where they seem disappointed, or where they complain that they can’t do what they want. Those will all give you ideas for iterating on current features or adding new ones.
  • Talk to people who have stopped using your product. Find out what they thought they’d be getting when they started using it and why they stopped.
  • Watch new users with your product and ask them what they expected from the first 15 minutes using the product. If this doesn’t match what your product actually delivers, either fix the product or fix the first time user experience so that you’re fulfilling users’ expectations.

Quantitative Approaches:

  • Look at the features that are currently getting the most use by the highest value customers. Try to figure out if there’s a pattern there and then test other features that fit that pattern.
  • Try a “fake” test by adding a button or navigation element that represents the feature you’re thinking of adding, and then measure how many people actually click on it. Instead of implementing an entire system for making friends on your site, just add a button that allows people to Add a Friend, and then let them know that the feature isn’t quite ready yet while you tally up the percentage of people who are pressing the button.

What to do

There are some situations where you should rely on the quantative research, other situations where qualitative research is best, but generally a combination of the two is the optimal way to formulate your product strategy. By using both, you are collecting all available information and using this data to drive optimal decisions.

Key takeaways

  1. Qualitative research (survey, user panels, etc.) is often neglected by game companies who prefer to use quantitative data (KPIs) to drive decisions, but this approach neglects that more information is always better.
  2. In situations where you are only impacting one variable, quantitative data is the answer; if you are adjusting flow or multiple variable, lean on qualitative data; and if you are prioritizing your roadmap, use a robust combination.
  3. There are some situations where you should rely on the quants, other situations where qualitative research, but generally a combination of the two is the best way to formulate your product strategy.

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Author Lloyd MelnickPosted on October 29, 2019October 7, 2019Categories Analytics, General Social Games Business, LTVTags content roadmap, qualitative data, quantitative data, surveyLeave a comment on When to use qualitative research

Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development

Most of my posts about the reasons and methodology for creating accurate customer lifetime value (LTV) predictions have focused on the numbers and metrics, but a key element to predicting accurately LTV is observational (qualitative) data. It all comes down to more data is better, so predictions with qualitative data are going to be more accurate than those that rely solely on quantitative data. A mistake that is commonly made in the analytics world, and particularly in gaming, is to disregard anything that is not a quantitative value.

Creating the most accurate projections

Some examples of incorporating effectively qualitative data

The example that had the most impact on me is that Billy Beane and the Oakland A’s, the subject of Moneyball (and multiple blog posts by me), has one of the highest scouting budgets in baseball. Scouts provide data on variables, like mental make-up and desire to win, that are not evident in the historical metrics. So although Beane makes personnel decisions based on metrics, he has also invested large sums in getting qualitative data (scouts watch players and prospects and then report on how they perceive the player’s skills). This approach has proven successful, as Beane’s A’s again surprised people by winning their division last year. What Beane has mastered is finding a way to incorporate the scouting reports with the available quantitative metrics. Continue reading “Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development”

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Author Lloyd MelnickPosted on June 11, 2013June 18, 2013Categories Analytics, General Social Games Business, LTVTags focus testing, Hal Varian, LTV, Moneyball, Nate Silver, qualitative data, surveyLeave a comment on Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development

Get my book on LTV

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Understanding the Predictable delves into the world of Customer Lifetime Value (LTV), a metric that shows how much each customer is worth to your business. By understanding this metric, you can predict how changes to your product will impact the value of each customer. You will also learn how to apply this simple yet powerful method of predictive analytics to optimize your marketing and user acquisition.

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Lloyd Melnick

This is Lloyd Melnick’s personal blog.  All views and opinions expressed on this website are mine alone and do not represent those of people, institutions or organizations that I may or may not be associated with in professional or personal capacity.

I am a serial builder of businesses (senior leadership on three exits worth over $700 million), successful in big (Disney, Stars Group/PokerStars, Zynga) and small companies (Merscom, Spooky Cool Labs) with over 20 years experience in the gaming and casino space.  Currently, I am on the Board of Directors of Murka and GM of VGW’s Chumba Casino

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