]<\/strong><\/p>\nBy 2011, the company had 20 full-time photographers on staff.<\/p>\n
![\"airbnb](https:\/\/www.kaushik.net\/avinash\/wp-content\/uploads\/2013\/04\/airbnb_nights_booked-1.png\")
<\/p>\n
The graph is impressive, right? There were many other things going right with Airbnb’s business and business model. But the lean process was a key contributor to improving the bookings rate. Clearly, the experiment was a success. They celebrated a bit, then went on to fix the next biggest problem in the business.<\/p>\n
Case Study 2: Circle of Friends<\/font><\/strong><\/p>\nCircle of Friends was a social community built atop Facebook that launched in 2007. It essentially allowed you to create a group of friends who could interact and share content, much as people do today with Google+, before such features were part of Facebook.<\/p>\n
As a social network based on user-generated content, Circle of Friends only grew when its users were engaged. The company\u2019s creators wanted users not only to create groups, but also to send messages within those groups, invite others, and interact with Facebook elements such as news feeds.<\/p>\n
Step 1: Figure out what metric to improve<\/font><\/p>\nThe founders had several measures of \u201cengagement\u201d, from whether people attached a picture to a post, to whether they clicked on Facebook notifications, to the length of posts they wrote. Any of these actions \u2014 and several others \u2014 constituted \u201cengagement.\u201d All of this rolled up into a simple KPI: number of engaged users.<\/p>\n
While Circle of Friends\u2019 launch was hugely successful in terms of raw attention \u2014 they had 10 million users! \u2014 engagement<\/em> simply wasn\u2019t happening. None of the metrics they used to measure the health of their communities was where they wanted it to be.<\/p>\nThe rather nebulous \u201clevel of engagement\u201d was a compound metric, which can be dangerous to rely on. But one of the clear metrics they tracked was \u201cnumber of circles with activity\u201d, meaning that there had been some form of interaction within a group in the past few days.<\/p>\n
\n- One Metric That Matters: User engagement<\/li>\n
- KPI: Number of active users; number of circles with activity<\/li>\n
- Target: (not publicly known)<\/li>\n
- Current level: Less than 20% of circles had any activity after creation<\/li>\n<\/ul>\n
The challenge, here, was how to kickstart engagement. That\u2019s not an easy thing to do. Fortunately, Circle of Friends had plenty of user data to mine.<\/p>\n
Step 2: Form a hypothesis<\/font><\/p>\nCircle of Friends had huge volumes of information on its users and how they used the product. They looked at two groups of users: those that were engaged, and those that weren\u2019t. Then they looked at what those users had in common. In other words, they defined what engagement meant (i.e. \u201csomeone who has returned in the last week\u201d) and segmented users into two groups. Then they looked at other things those people had in common.<\/p>\n
What they found changed their business. It turned out that the engaged users were much more likely to be mothers. Looking at moms who use the application:<\/p>\n
\n- Messages to one another were, on average, 50% longer<\/li>\n
- 115% more likely to attach a picture to a post they wrote<\/li>\n
- 110% more likely to engage in a threaded (i.e. deep) conversation<\/li>\n
- Those who were friends of the circle owner were 50% more likely to engage<\/li>\n
- 75% more likely to click on Facebook notifications<\/li>\n
- 180% more likely to click on Facebook news feed items<\/li>\n<\/ul>\n
Before we look at what they decided to do, stop for a minute and realize that simply having data isn\u2019t that useful. Circle of Friends had all of this information. But asking the right questions of your data is a superpower. In this case, they asked, \u201cwhat do engaged users have in common?\u201d and it absolutely changed the destiny of their company.<\/p>\n
From their data, they formed a hypothesis: If we focus only on moms we\u2019ll have the engagement we need<\/em>.<\/p>\nStep 3: Create the experiment<\/font><\/p>\nTheir who, what, and why were as follows:<\/p>\n
Who is the campaign for?<\/em> Moms.<\/p>\nWhat do you want them to do?<\/em> Engage with other users, invite new users, and create content.<\/p>\nWhy do they do it?<\/em> Because they find interaction with others rewarding and compelling.<\/p>\n<\/div>\nIn the end, the founders made a huge bet \u2014 the kind of bet that only startups with nothing to lose can make. They decided to completely rebrand the company as Circle of Moms, and focus everything on attracting and engaging mothers.<\/p>\n
So their hypothesis was:<\/p>\n
Find out if moms<\/strong> will join, relate and engage in groups<\/strong> because of a community targeted specifically at them<\/strong> enough to improve the number of active circles and engaged users<\/strong> by X%<\/strong>.<\/p>\nThey were betting that if they stopped trying to please everyone, and instead focused specifically on mothers, they\u2019d have a higher percentage of engagement, which would in turn lead to the company\u2019s overall success.<\/p>\n
![\"circle](https:\/\/www.kaushik.net\/avinash\/wp-content\/uploads\/2013\/04\/circle_of_moms_lean_analytics_cycle_occams_razor.png\")
<\/p>\n
Step 4. Measure performance<\/font><\/p>\nIt took a few months to carry out the experiment, since it involved re-launching the entire company. The initial result was a huge drop in users, of course, since most of the communities weren’t made up of mothers.<\/p>\n
They lost millions of users.<\/p>\n
They feared this would happen, but they knew that if they didn’t get engagement where it needed to be they might as well close up shop.<\/p>\n
If they’d been focusing on the wrong KPI, such as number of subscribers, they’d have quickly concluded that the experiment was a disaster. But because they’d been deliberate about their hypothesis, they knew that the most important metric was engagement. Sure enough, the users that remained were engaged. Overall engagement and active circles climbed significantly, to the point where the business model was healthy.<\/p>\n
Of course, having achieved the engagement they sought, the next experiment was about whether they could grow a more narrowly focused community to a decent size. By late 2009, that experiment was a success, too; they’d climbed back up to 4.5 million users, with strong engagement.<\/p>\n
Case Study 3: High Score House<\/font><\/strong><\/p>\nHigh Score House is a tool that lets parents manage household chores and rewards for their kids. The founders envisioned a world where parents and children sat down each day to identify chores, keep score of what the kids have done, and drool over rewards they might claim.<\/p>\n
Step 1: Figure out what metric to improve<\/font><\/p>\nIn the early stages of launch, the most important metric was the number of families who fired up the app at least once a day. Those who did were considered “active”, while those who didn’t were assumed to have abandoned the application. Unfortunately, while High Score House was getting installations and great press reviews, users weren’t active.<\/p>\n
No active users, no business.<\/p>\n
\n- One Metric That Matters: “Active families”<\/li>\n
- KPI: Percent of families who’ve used the app in the last 24 hours (AKA “percent daily use”)<\/li>\n
- Target: Over 60%<\/li>\n
- Current level: Below 20%<\/li>\n<\/ul>\n
Step 2: Form a hypothesis<\/font><\/p>\nThe founders went around the Lean Analytics Cycle several times:<\/p>\n
\n- They tried changing the look and feel of the application.<\/li>\n
- They tried sending people messages urging them to come back to the application.<\/li>\n
- They tried adding new features.<\/li>\n<\/ul>\n
None of it worked; fewer than 20 percent of families who were part of the early release used the app each day.<\/p>\n
Each time, their hypothesis went something like, if we send a reminder email to parents each day, the percent daily use will exceed 60%.<\/em><\/p>\nStep 3: Create the experiment<\/font><\/p>\nThe High Score House team went dutifully through the motions of the Lean Analytics Cycle. For the notification hypothesis, the who, what, why looked like this:<\/p>\n
Who is the campaign for?<\/em> Current installed base of users.<\/p>\nWhat do you want them to do?<\/em> Use the app daily.<\/p>\nWhy do they do it?<\/em> An email will remind them to launch the app and sit with their kids, making it a daily activity that becomes part of their routine.<\/p>\n<\/div>\nIn other words, their experiment (for the notification model) was:<\/p>\n
Find out if parents will use the app daily because we remind them to do so enough get the percent daily use to 60%.<\/p>\n
They ran the experiment.<\/p>\n
![\"high](https:\/\/www.kaushik.net\/avinash\/wp-content\/uploads\/2013\/04\/high_score_house_lean_analytics_cycle_occams_razor.png\")
<\/p>\n
Step 4. Measure performance<\/font><\/p>\nThe experiment disproved the hypothesis. Email reminders didn\u2019t measurably improve percent daily use. Like many of the other changes the team had tried, they couldn\u2019t move the needle.<\/p>\n
At this point, Kyle, the founder of the company, decided it was time to ditch the data and go qualitative. He picked up the phone and started talking to users. And this simple, messy, unquantified, anecdotal process yielded an amazing insight:<\/p>\n
Many families would set aside a specific day of the week to plan their chores and count their rewards.<\/p>\n
Kyle was floored. Families were getting value from the product. They were just using it in a different way from what the designers intended. Kyle now had new information which he could use. But it wasn\u2019t for another experiment. This time, Kyle was going to move the goalposts\/target.<\/p>\n
Changing your target should never happen lightly. It\u2019s only OK to change the goal when the customers have given you new, validated learning from which to make the change. In this case, since your goal is tied back to your business model (remember the $5 glass of lemonade?) changing the goal means changing your business model<\/p>\n
In this case, HSH was going to have to change the way their application was designed, making it more suitable for week-by-week views and a different usage pattern. But by moving the goalposts, the team had a new KPI: Percent of families who’ve used the app in the last 7 days (AKA “percent weekly use”). And with this new definition, over 80% of the early adopters were using the application regularly.<\/p>\n
We\u2019ve seen three case studies. The first one, about Airbnb, shows a straightforward example of a hypothesis and an experiment without data. The second, about Circle of Friends, shows an example of an experiment based on data. And the third one, about High Score House, shows how to adjust the goalposts when they aren\u2019t set properly in the first place. All three are examples of the Lean Analytics Cycle at work.<\/p>\n
______________________________________________________<\/strong><\/font><\/p>\n\nAmazing, right? Life’s pretty cool when you have a process!<\/p>\n
For me the lean analytics cycle is, at it’s very core, about driving change quickly, regardless of how much you know about your users or how much data you have. It discourages waiting for perfect data or perfect understanding of every variable or perfect understanding of what the business is trying to solve for (because in all three cases you will never get perfection!).<\/p>\n
While it was developed with startups in mind, you can see the attraction of speed, learning and constant improvements for businesses of any size.<\/p>\n
It is important to note that the cycle, and the Lean model as a whole, isn\u2019t advocating chaos. They might deal with uncertainty, but they’re not random.<\/p>\n
There is great deliberation in step one to identify the KPI that will be a guiding light for us (embrace “One Metric That Matters”). There is a lot of deliberation in step two on ensuring that we have an optimal hypothesis to work from. Then figuring out how we are going to experiment with deep clarity from defining the who<\/em>, what<\/em>, why<\/em>. Finally, understanding whether we actually succeeded by measuring performance. Then, then … you learn. You internalize. You win.<\/p>\nAnd how can you be immensely incredible at this? Go get Alistair and Ben’s book Lean Analytics<\/a> .<\/p>\nAs always, it is your turn now.<\/p>\n
Do you follow a structured process inside your company to ensure data-driven decisions are being made constantly? If you were to apply the Lean Analytics Cycle, what challenges might cause problems inside your company? As you look at the cycle, is there anything that might be missing, or tweaks you would make to improve it?<\/p>\n
Please share your thoughts, critique, ideas, and musings via comments.<\/p>\n
Thank you.<\/p>\n","protected":false},"excerpt":{"rendered":"
Lean Analytics a new book by my friend Benjamin Yoskovitz. The book introduces a wonderful process called the Lean Analytics […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[11,383,384,9,12,10],"tags":[299,430,106,171],"class_list":["post-5726","post","type-post","status-publish","format-standard","hentry","category-advanced-analytics","category-big-data","category-digital-analytics-2","category-web-analytics","category-web-insights","category-web-metrics","tag-actionable-analytics","tag-business-optimization","tag-experimentation-and-testing","tag-key-performance-indicators"],"acf":[],"yoast_head":"\n
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This should not be news to you. To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. Online, offline or nonline.<\/p>\n![\"the](https:\/\/www.kaushik.net\/avinash\/wp-content\/uploads\/2013\/04\/the_lean_analytics_cycle_occams_razor.png\")
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