Slide #4
HEART stands for:
H – Happiness: How happy is the user with the product and users perceive the product as easy to use and are likely to recommend.
It is a quantitative metric and can be measured via NPS or simple surveys.
E – Engagement: How engaged are the users? How frequently do they use your product.
You rely on data analytics suit like google analytics to discover this. For eg. visits/session, search/session and so on A – Adoption: The percent of users who adopt your product after signing up (user onboarding) and then use the product or a feature (feature adoption)
You measure this also via analytics.
R – Retention: The percent of users who came back after the first session to use the product.
You measure this also via analytics
T – Task success How successful was the user in doing the task he used the product for. How long did he take and what was the error rate.
You measure this via user surveys.
Slide #5
In the example shared in the last discussion, I am sure a part of you was screaming for a structure that can be followed. HEART is a framework shared by Google Ventures to measure the quality of UX, which is a fancy way of saying customer satisfaction with the product. GSM (Goals Signals Metrics) is also a framework shared by Google Ventures to convert goals into appropriate metrics. HEART combined with GSM is a powerful framework to quickly brainstorm and come up with the metrics most relevant for your product or feature at hand.
Slide #8
AARRR is also known as pirate metrics and was proposed by Dave Mcclure of 500 startups. Pirate metrics and Heart framework, essentially have the same message. Lets understand the AARRR framework and then look at the two. Refer: https://blog.autopilothq.com/pirate-metrics/
Slide #10
Generally speaking, AARRR framework offers you 5 means of measuring the customer lifecycle. Here is a simple example. A website is getting 1000 visitors per month (Acquisition), and its Activation (conversion) is 70%, so that they are getting near 700 users/month. Only 20% of 700 users will return after first visit (Retention). From 20% (it is 140 users), only 10% are paying (Revenue). Only 14 users are paying each month. Near 10% of all visitors will tell their friends about that website (Referral). It means: This Site has a good Activation (conversion) rate of 70%, which means they are exploring a real problem and have a working solution.
Also they have a convincing landing page. In order to increase signups they search ways to improve Acquisition (new visitors) by adding more acquisition channels or try to attract more users working on SEO. Their Retention is only 20%, so they develop some new features like email newsletters, white papers, gamification to return users. Then they understand that users are not coming back because the service doesn’t solve users’ problem or customers don’t know how to use it. Site owners make easy-to-understand guides to help users. The process of converting users into paying customers (Revenue). In order to increase it the company adapts its’ pricing policy, improves the client-communication process, and takes other steps to convince users to buy, thus becoming customers. Finally they check why their service is not being recommended (Referral) to increase the number of referrals. The goal is to make customers like the product enough to recommend it to others (by sharing, commenting, verbal recommendations etc.)
Slide #11
In Lean Startup, Eric Ries talks about three engines that drive the growth of a startup. Each of these has associated key performance indicators (KPIs). Sticky Engine The sticky engine focuses on getting users to return, and to keep using your product. It’s akin to Dave McClure’s retention phase. If your users aren’t sticky, churn will be high, and you won’t have engagement. Engagement is one of the best predictors of success: Facebook’s early user counts weren’t huge, but the company could get nearly all students in a university to use the product, and to keep coming back, within a few months of launch. Facebook’s stickiness was off the charts.
The fundamental KPI for stickiness is customer retention. Churn rates and usage frequency are other important metrics to track. Long-term stickiness often comes from the value users create for themselves as they use the service. It’s hard for people to leave Gmail or Evernote, because, well, that’s where they store all their stuff. Similarly, if a player deletes his account from a massively multiplayer online game (MMO), he loses all his status and in-game items, which he’s worked hard to earn. Stickiness isn’t only about retention, it’s also about frequency, which is why you also need to track metrics like time since last visit. If you have methods of driving return visits such as email notifications or updates, then email open rates and click-through rates matter, too.
Virality Engine: Virality is all about getting the word out. Virality is attractive because it compounds—if every user adds another 1.5 users, your user base will grow infinitely until you’ve saturated all users.* The key metric for this engine is the viral coefficient—the number of new users that each user brings on. Because this is compounding (the users they bring, in turn, bring their own users), the metric measures how many users are brought in with each viral cycle. Growth comes from a viral coefficient of greater than one, but you also have to factor in churn and loss. The bigger the coefficient, the faster you grow. Measuring viral coefficient isn’t enough. You also need to measure the actions that make up the cycle. For example, when you join most social networks, you’re asked to connect to your email account to find contacts, then you’re given the option to invite them. They receive emails, which they might act upon.
Those distinct stages all contribute to virality, so measuring actions is how you tweak the viral engine—by changing the message, simplifying the signup process, and so on. There are other factors at play with virality as well, including the speed with which a user invites another (known as the viral cycle time) and the type of virality. We’ll dive into these later in the tutorial. Paid Engine The third engine of growth is payment. It’s usually premature to turn this engine on before you know that your product is sticky and viral. Meteor Entertainment’s Hawken is a multiplayer game that’s free to play, but it makes money from in-game upgrades. Meteor is focusing on usage within a beta group first (stickiness), then working on virality (inviting your friends to play), and finally payment (players buying upgrades to become competitive or enhance the in-game experience). Getting paid is, in some ways, the ultimate metric for identifying a sustainable business model. If you make more money from customers than it costs you to acquire them—and you do so consistently—you’re sustainable. You don’t need money from external investors, and you’re growing shareholder equity every day. But getting paid, on its own, isn’t an engine of growth. It’s just a way to put money in the bank.
Revenue helps growth only when you funnel some of the money generated from revenue back into acquisition. Then you have a machine that you can tune to grow the business over time. The two knobs on this machine are customer lifetime value (CLV) and customer acquisition cost (CAC). Making more money from customers than you spend acquiring them is good, but the equation for success isn’t that simple. You still need to worry about cash flow and growth rate, which are driven by how long it takes a customer to pay off. One way to measure this is time to customer breakeven—that is, how much time it will take to recoup the acquisition cost of a customer.
Slide #12
Unlike a traditional business plan, you should use and update the Lean Canvas continuously. It’s a “living, breathing” plan, not a hypothetical tome of nonsense that you throw out the minute you start actually working on your startup. Once you’ve filled out the Lean Canvas (or most of it), you start running experiments to validate or invalidate what you’ve hypothesized. In its simplest form, think of each box as a “pass/fail”: if your experiments fail, you don’t go to the next box; rather, you keep experimenting until you hit a wall completely or get to the next step. The only exception is the “Key metrics” box, which is meant to keep a record of the most important metrics you’re tracking. You don’t run experiments on this box, but it’s important to fill it out anyway because it’s definitely open to debate and discussion.
Slide #14
Additional reading: https://drive.google.com/open?id=1t3b4pa_JAOkGr3wxluZu0oTNlMQuTBTu https://www.kaushik.net/avinash/lean-analytics-cycle-metrics-hypothesis-experiment-act/ https://www.slideshare.net/GeeksOnaPlane/startup-metrics-for-pirates-startonomics-beijing-june-2009-1566974 https://www.slideshare.net/gueste94e4c/dropbox-startup-lessons-learned-3836587 https://www.slideshare.net/startuplessonslearned/eric-ries-the-lean-startup-google-tech-talk