The Mobile Spoon

  Know that feeling when you're trying to create a narrative and support it with data, but data doesn't play along?  We’ve all been there, tweaking our graphs a little bit - stretching here, bending there… massaging the data to support our story and emphasize the trend... without lying of course. Manipulative graphs are 100% accurate but misleading. They show true data but go through some “tweaking” to better support the story being told.  In this post, I’ll teach you the dark art of manipulating your graphs without losing too much credibility.  And if you’re on the receiving end, this post will help you  spot dishonest graphs  immediately, so nobody could fool you with those cheap tricks.  Spreadsheets and reports lovers - this post is for you! Data trimming  They say trimming split ends can make your hair look healthier. It's the same with graphs.  For example, if your sales are slowing down, you can drop the most recent 1-2 months. If so...

If you're digging into your mobile app installation numbers, you probably ran across 2 terms that may sound similar:  conversion rate and install rate .  There are 2 types of flows leading app store visitors to install your app:  Visitors land straight on your app's Product Page (that's where all the details and images are), after clicking a referral link, or an ad. They explore the app details and some of them install it.  Visitors run across your app in one of the store lists (in search results, or while browsing categories) and decides to install it without even getting to the product page. This scenario is currently only available in Apple's App Store.  Conversion rates (CVR) CVR Definition: the percentage of users who download your app from within the product page. That's scenario 1. According to AppTweak , the average conversion rate in the US App Store is  31% , ranging from 80% in some app categories (i.e weather, and trivia apps) all the way do...

Let’s talk about user activation .  How do you define your activated users? When do they become “activated”? Is it when they sign-up? Is it when they perform an action a certain number of times? Or maybe it's when they’re making their first payment  or switching from a trial  to a paid subscription?   With some products, the activation moment is trivial and easy to track (i.e. when a user pays for a yearly subscription). In others, it might require a deeper understanding of the users' behavior and the stage in which they actually  convert.  For some products, there's a clear distinction between “ soft activated ” to “ hard activated ” users.  Example Here’s an example based on my own personal experience with Bird .  I gave it a try 2 years ago when I needed a quick way to get somewhere; I was in the middle of the street, installed the app, entered my credit card details, and started riding. It was a smooth activation: it took me 2 minutes t...

What's retention rate and what are the best ways to measure it?  What’s a negative churn, and what can you learn from it about the performance of your cohorts and the sustainability of your business? ?How does it all relate to unit economics? Answers below:  We all know the importance of retention for the long-term success of our products. Retention is the key to creating a sustainable business. It shows the long-term engagement of your most loyal users - and that’s a strong sign of a product/market fit. It influences how much revenue will each cohort produce over time and the lifetime value of each user. Higher retention = more recurring paying users. Retention is the key for creating a sustainable business.  And while tweaking your onboarding process and funnels may drive immediate improvements in conversion rates (and produce instant gratification) - retention is a long term process, it often requires some heavy lifting, deeper analysis, but...

So your product starts to show traction and you want to collect some data and start making  data-driven decisions . It’s time to think about segmentation. You know the drill: Different types of users => different usage => different needs => different priorities. Different types of users => different spend  => different priorities. Different types of users => different pain points => different messaging. The list goes on… You need a systematic, scalable way to divide your users into smaller segments based on the characteristics they share. Here’s how I think you should do it: 1. Collect as much data as possible At the early stages of a product - scale, performance, and data efficiency shouldn’t be a concern. The volumes are still relatively low, so you can store as much data as possible, even if you don’t need it right now. Don’t worry, you’ll have plenty of time to optimize when you’re successful and rich… 2. Make sure it’s all...

Trust plays a critical role when purchasing products online , but trusting the brand is not enough when booking a vacation or even ordering a massage at home, as the supplier in these cases is not the brand itself. 93% of consumers use online reviews to support their purchasing decisions ; they seek for reassurance, a social proof  that the product or service they plan to order is indeed the quality they expect it to be. Rating and review systems are expected to be accurate because they are based on high volumes, however, as we learned over the years of operating our marketplace for beauty and lifestyle services - this is not always the case. The problem When we started Missbeez , we knew we were dealing with a sensitive business. Haircuts, makeups, massages, even nail treatments - those are all personal treatments , and therefore our customers were concerned about who’s coming over, their work quality and their experience. The more expensive or personal a prod...

4 years ago we founded Missbeez : a mobile marketplace for lifestyle and beauty services on-demand.  For me, it was a significant change from leading a large B2B product to co-founding a small B2C startup.  From the very beginning, it was clear that data will play a significant role in our decision-making process. We moved fast, made a lot of experimental changes, and didn't have those large customer representatives to talk to when making our decisions. I had to change my habits and replace humans with numbers . We've embedded Mixpanel, Google Analytics, AppsFlyer, Facebook SDKs, Crashlytics, and a bunch of other tools, we created our own dashboard as well as a unique and addictive mobile dashboard, and deployed a set of real-time logs. It was fun! Over the first 2 years of our startup, we've learned the hard way that being a data-driven company is harder than it seems. I would like to share with you some of the lessons learned while working with d...