There was a great blog post on the Mode Blog, “Facebook’s Aha Moment is Simpler Than You Think,” which provided a straightforward strategy to creating Aha moments. Aha moments when a player or user understands the value of your product are widely considered the key to growth.
The key point of the post was that you create the aha moment through simple math and strong messaging; it is not a complex task that requires advanced analytics. Take Facebook’s self-defined aha moment, acquiring seven friends in ten days. In practice, this is not a binary. Some people may fall in love with Facebook after getting three friends in a week; others may need to get twenty friends in thirty days. This fact does not take away from the aha moment, it is actually the point of the aha moment. The blog post states, “Facebook’s decision to define their ‘aha moment’ in such simple terms suggests they weren’t trying to optimize it to be precise as possible. Other “aha moments”—30 follows, 1 file upload, 2,000 messages—follow the same pattern: they emphasize simplicity over science…. Because “aha moments” aren’t about precision, but about defining a core principle and a quotable rally cry for the entire company. “
Defining the aha moment
To create a useful aha moment, you need to tie it to a metric that defines customer value. Keep in mind that it is not one “moment,” Facebook’s aha moment is over ten days and requires seven different actions (friending seven people).
The most useful metrics for quantifying your aha moment are based on retention. Customers who find value come back. If you identify which actions separate retained customers from lost ones, you will know what drives customer value and then have your “aha moment.” Continue reading “Creating that Aha moment”
A recent post on TechCrunch about TaskRabbit’s roll-out of a new market structure, largely seen as a failed roll-out, offers many lessons for all types of companies. TaskRabbit rolled out a very different version of its market place last July and faced what many called a “revolt” and “rabbit revolution.” Outside of the business reasons for the change and whether it was a net positive for the company (still debatable), there are many lessons from the experience for any company.
Do not surprise your customers
TaskRabbit’s change to a new platform caught many of its customers by surprise, leading to immediate protests. TaskRabbit had tested its new platform in the United Kingdom (where it previously did not have a presence) and saw substantial improvement in its metrics. Based on these results, it decided to replace its platform in the U.S. with the new model. As TechCruch wrote, “as soon as the launch actually went live, the protests and confusion started to pour in.” The company underestimated just how strong the bidding and auction model was ingrained in its brand identity here in the U.S., and how that resonated emotionally with users. Continue reading “How to avoid a product change or new feature debacle”
I recently had a conversation with a gaming industry CEO whom I deeply respect that reinforced a MIT Sloan Management Review article, “Embrace Your Ignorance” by Michael Schrage, about how the savviest leaders promote and embrace ignorance. The thesis for both Schrage and the CEO was that you cannot accurately predict what your customers will want, like or need. Thus, you need to embrace this ignorance and run experiments to get the data.
Moneyball and The Innovator’s Dilemma
I have seen many companies where the leadership “felt” they understood the customer and would develop new products for these customers. It leads to project green light meetings very similar to the draft room in Moneyball, where people argue based on their experience which initiatives have the most potential. It is also one of the biggest contributors to the huge number of failed projects, particularly in the gaming space where we typically see more than 8 out of 10 new games fail.
This issue is actually often a bigger problem with executives who have had past successes. Even if they knew their existing or past customers very well, they do not necessarily know what a broader or new market wants. Even their existing data can skew innovation effort, which is the core point of the Innovator’s Dilemma: Companies that have been leap-frogged often create innovations for existing markets rather than new markets.
You already are ignorant—accept it
In Schrage’s article, he discusses how Microsoft’s Ronny Kohavi (a pioneer in online experimentation) challenges tech-savvy audiences when he speaks. Kohavi shows screenshots of actual A/B tests that Microsoft has run for website design. He then asks his audience to predict the outcome of the tests. Although the audience is sophisticated, they almost always fragment with different opinions. Kohavi then advises, “stop debating…it’s easier to get data.” Continue reading “Ignorance is a competitive advantage”
Since Thomas Davenport wrote Competing on Analytics in 2007, the use of analytics has evolved from a niche contributor to the central role of successful companies decision making, product development, marketing and other core functions. A great white paper published by Tableau highlights what it considers the top 10 trends for business intelligence. Of these ten trends, there are five that I agree will impact significantly companies this year.
Analytics emerge across the organization
Analytics will no longer be a domain dominated by analysts and data scientists; instead everyone in the organization will be using analytics daily for their decision making. Easier-to-use technologies that provide browser-based or mobile analytics let people answer ad-hoc business questions. Companies that recognize this as a strategic advantage will begin to support managers and front-line personnel with data, tools and training to help them do their jobs more effectively.
There has been a huge amount of innovation across the data space, resulting in mixed environments for everything from data storage to analytics to business applications. Although there will not be one system or application for all of your needs, the different analytic systems will be more integrated and easier to use, making them more accessible across your company. You will laugh at the multiple logins and clunky processes you had to use during analytics 1.0. Continue reading “Analytics in 2015”
While almost everyone now accepts the value of analytics and metrics-driven decision making, one area where it is often neglected is in implementing innovation. Even data driven companies are hampered in implementing innovation because their data is backward looking. In the absence of sufficient data to inform decisions about proposed innovations, managers often rely on their experience, intuition and conventional wisdom, and none of these is necessarily relevant. Although many of my readers are from the mobile game space, where much is tested, even in the game space pure innovation often is not. An article in the Harvard Business Review, “Increase your chances of success with innovation test-drives” by Stefan Thomke and Jim Manzi, does a great job of showing how to test these hypotheses.
Most companies do not conduct rigorous tests of their risky overhauls because they are reluctant to fund proper business experiments and have considerable difficulty executing them. Although the concept of experimentation is straightforward, there are many organizational, cultural and technical challenges to implementing experiments. While running an A/B test on a website is simple, many business need to deal with complex distribution systems, sales territories, bank branches, etc. Business experimentation in such environments suffers from many analytic complexities, most importantly that sample sizes are often too small to be significant (e.g., only a few stores). Continue reading “Testing innovation opportunities”
As more companies use virtual events as part of their growth and engagement mix, it is increasingly important to be able to evaluate the effectiveness of the event. I have used virtual events multiple times and it is an element of our growth mix.
Simply having the event is not a success; you need to measure and evaluate it. Has the event been worth the time and resources devoted to it and should you replicate the event? How can you optimize future events so they have a bigger impact on your business? I read a recent article on MarketingProfs that offers some great advice on what you should track on your next virtual event.
Unlike physical events, with virtual events you can measure much of the attendees’ behavior. You can see which content they interact with and how they engage with other attendees and speakers.
By tracking attendee engagement, you see what worked and what did not, as well as seeing which potential customers you are most likely to convert. In a physical event, you assume the people playing on their phones are the least engaged. In a virtual event, you can see if they are progressing their slides with the presentation, asking questions, whether they clicked on links you provided etc. Continue reading “Evaluating your virtual event”
There are many applications for machine learning, but one of the most exciting is using it to create hypothesis to test. An article in the Economist, “Computer says ‘try this’,” discusses many of the ways computers are now creating hypothesis to move medicine, farming and even cooking forward.
There are already many projects using machine learning to generate valuable and novel hypotheses. One project, BrainSCANr, suggests research topics for neuro-scientists by looking at millions of peer-reviewed papers. Research published by Baylor College of Medicine researchers used machine-learning software to review over 150,000 papers on a technique to curb the growth of cancer (proteins called kinases). The algorithm led to seven new kinases that researchers had missed. The technique is even being used to analyze search terms in Bing and Internet Explorer to determine potentially dangerous pairings of medicine.
What it means for you
While machine learning is already benefitting many tech and game companies, using it to help develop hypotheses for your business is invaluable. If you are a mobile game company, think of the value of having a machine-learning system suggest that rather than focusing on your premium exchange rate, you should test changing the frequency of your free-coin bonus. Continue reading “Using machine learning to develop your hypothesis”
A recent article in the Harvard Business Review, “Making Charity Pay” by Michael Norton and Jill Avery, shows how business can use philanthropy to improve profitability. They point out that charity can enhance customer loyalty, brand awareness and sales. To impact metrics positively, however, you need to implement the cause-based initiatives appropriately.
Norton and Avery analyzed both successful and unsuccessful charitable initiatives and determined that success is driven by companies aligning causes that resonate with customers in a way that drives sales.
The first part of the equation is aligning the cause with the customer. With most successful initiatives, it means looking beyond causes that are important to you, the leadership team or maybe even your local community. You need to talk to your customers and understand what causes are important to them. One simple technique I have found particularly successful is surveying your users and asking them to rank the causes you are considering for the initiative. Continue reading “Using charity to improve revenue”
I am a huge proponent of using analytics and other metrics to drive business decisions, but I repeatedly see people making a huge and avoidable mistake. Instead of using the data to determine the best strategy, they use data to justify their intuition. A good analyst can use data to draw virtually any conclusion and if the analyst is pushed in a certain direction by the business leader, all the data does is provide people with cover for the decision rather than leading you in the optimal direction.
The same situation applies to financial analysis. I have seen people frequently manipulate numbers, often with the approval or even encouragement of the target audience, to tell the story people want to hear. I have seen this manipulation in sales, in corp dev and in internal forecasting. In all situations, it is actually just a rationale to make a decision the person already wants to make.
The first part of the problem is manipulating the data. I am not talking Enron here, but more subtly and maybe not even intentionally. People will often select the data that supports their position while discounting the other information. If you want to greenlight a certain feature, you may look at the impact on retention while neglecting the impact on monetization and rationalize it by saying it is a retention feature. Regardless of whether it is a retention or monetization, your goal is to optimize lifetime value (LTV) so you need to look at the data holistically. Continue reading “Changing the numbers does not change the reality”
For those of you who foolishly decided to take a vacation this summer rather than stay at home and read my blog, I wanted to summarize what I feel were my top ten posts this summer (and below will also summarize the rests of my posts since I am sure you will want to catch up on all of them). Continue reading “My top ten summer posts”