“If you can’t measure it, you can’t improve it”: Peter Drucker

All too often, in an effort to be ‘data driven’, teams fall into one or more of the following traps:

  • Tracking the wrong things, or not tracking things in enough detail, leading to data that is not providing actionable insight
  • Tracking everything possible, without a plan on how to leverage it, knowing that it might be useful at some point. This can lead to code bloat, over-use of network connection in the app and potentially wasted dev cycles implementing or updating redundant tracking
  • Failing to effectively structure and process the data that is collected, leading to a mounting heap of unused or surplus analytics data that fails to add value
  • Analysis paralysis: spending tons of time slicing and dicing data, but failing to act on it (often exacerbated by unstructured or surplus data)
  • Failing to keep analytics up to date with new product updates, leading to data which does not reflect product usage accurately

Source: Minimum Viable Analytics – The Mobile Growth Stack

In the introduction to this series I made the point that Product Market Fit isn’t the only thing that matters. It is actually only one of four fits needed to grow a product to $100M+ in a venture-backed time frame.

While Product Market Fit isn’t the only thing that matters, it is important, so it makes sense that there are no shortage of blog posts explaining Product Market Fit, and how to get it.

Instead of echoing the many great Product Market Fit explainer posts out there, I’m going to focus on the 5 elements of Product Market Fit that I believe are most misunderstood and overlooked:

  • The wrong way to search for Product Market Fit.
  • Why we should be thinking about it as Market Product Fit.
  • How we defined our market and product hypotheses for early versions of HubSpot Sales.
  • What the search for market product fit looks like in reality, not just in theory.
  • Qualitative, Quantitative, and Intuitive signals of market product fit.

Source: The Road to a $100M Company Doesn’t Start with Product — Brian Balfour’s Coelevate

Etsy provides a fascinating look at a company who found traction among a very passionate and idealistic group of people, rode that wave to massive growth and an IPO and now must find growth through decisions often at odds with the beliefs of its earliest members. In this growth study, we look at how they did it in the early days, the decisions and dynamics of their business that allowed them to scale, and the company’s efforts to keep finding the new growth lever. The challenge for Etsy, now a public company with a $2 billion valuation, is to find the growth that public markets demand while doing its best to hold on to the users who made them successful in the first place. Author’s Note: A special thanks to Shana Carp for her oral history of the company’s early traction in the feminist craft community. Her feedback and insights were invaluable in creating this growth study.

Read Full Article >>

Not so long ago, websites were purely used to deliver product and services information to target customers…

But they have morphed into a point-of-sales platform where businesses leverage their website for sales. Selling products or services on websites has become de rigueur and most businesses have jumped on board this money spinner of an idea, and why not? With consumers doing most of their shopping online, it makes perfect business sense to ensure your products and/or services are sold on your website.

Since businesses began seeing their website as a sales tool, another realization dawned upon them – their website is also a great marketing machine. Building a website is not only about ensuring your brand reaches a wider audience, but also about making sure that this audience is convinced about the merits of the products or services of your brand and persuaded to buy them.

Yes, your website can be turned into a lean mean marketing machine. The kind that drives not just online sales but offline sales as well!

Read Full Article >>

Chatbots can be very useful even though they sometimes fail and make us laugh or generate frustration. I’m pretty sure you’ve had both good and awkward experiences with chatbots as well. Some people say that chatbots aren’t fully capable of replacing human beings in a normal conversation yet.

Even if it’s true that conversational bots still need to be able to fully encrypt the different facets of our language to become fully efficient and pass a superficial Turing test, they’re nonetheless pretty useful when it comes to customer support.

There are plenty of different applications for bots. For example, they’re quite practical in messenger apps and can be deployed to take an order or quickly respond to a customer in a Facebook chat or to resolve an issue via live chat on a website without human intervention.

Read Full Article >>

It’s been 7 years since the term ‘Growth Hacking’ was coined and until today, there is a lot of confusion about what it actually means. One of the problems I see is the enormous amount of misleading information you find when researching the concept online.

Growth Hacking Meme

Content like the below for example has nothing to do with Growth Hacking. However, since it’s a trending term and perfectly suited for click bait headlines, marketers have been using it aggressively to drive traffic to their articles.

Growth Hacking Tips


There are probably hundreds of definitions available online. My 3 favourite ones are:

Growth Hacking Definition by Josh Elman
Josh Elman, Partner at Greylock
Growth Hacking Definition by Morgan Brown
Growth Hacking Definition by Sean Ellis


  • Growth Hacking is not about finding ‘Silver Bullets’ or ‘Growth Hacks’. It describes a rapid experimentation process with the goal to optimise the flow between the stages of your lean marketing funnel.
  • Growth Hacking is not just about user acquisition. It is about understanding that your product itself is one of your most important growth drivers.
  • Growth teams need to be cross-functional and bring together marketing, engineering and product.

If you want to learn more about the term and concept of Growth Hacking, check out this article by Josh Elman >>


At my company Growth Hacking Asia, our goal is to equip as many startups in South East Asia as possible with the mindset, strategies and tools necessary to develop predictable, repeatable and scalable growth processes for their ventures.

So far, we have trained and worked with 4,000+ early stage entrepreneurs across 7 South East Asian countries and observed 2 common problems:

  • Insufficient tracking setup
  • Lack of experimentation


  • How to set up goals and funnels in Google Analytics
  • How to track external links with UTM tags
  • How to set OKRs
  • How to run growth experiments


So where to start? Easy, with data! Without data, you are unable to make decisions so it is absolutely crucial to get your tracking setup right before even thinking about growth.

Growth Hacking Quote


  • Goals and Conversion Funnels
  • User Acquisition Channel Performance


It’s crucial for you to understand where along the conversion path your users drop off. If you haven’t set up your conversion funnels yet, it’s time to do it right now.

I’ll focus on Google Analytics here since it’s the tool most early stage startups use.


  • Go to your Google Analytics Admin section
  • Click on “Goals”
  • Click “+ New Goal”

Goal tracking setup

Goal Types:

  • Destination
  • Duration
  • Pages/Screen Per Session
  • Event

a. URL Destination Goals

Goal tracking setup

URL destination goals keep track of specific URLs. Each time someone loads that specific page, they trigger the goal. This Goal Type is ideal for thank you pages, confirmation pages, etc.

For example, each time a customer completes a purchase and lands on the ‘thank you’page, it counts a s goal completion or 1 successful conversion.

URL Destination Goals Details

Goal URL: This is the URL that will trigger a goal. Don’t enter in the full URL, use only what comes after the domain part. So if the full URL is http://growthhackingasia.com/contact, you only need to enter /contact.

Match Types:

Equals to:

  • An Equals to match is an exact match on every character in your URL from beginning to end. Use this when your URLs for your site are easy to read and do not vary.

Begins with:

  • This matches identical characters starting from the beginning of the string up to and including the last character in the string you specify. Use this option when your page URLs are generally unvarying but when they include additional parameters at the end that you want to exclude.

Regular Expression Match:


Setting up your conversion funnel is a must if you want users to follow a certain conversion flow and learn at what stage along the path you’re currently losing them.

You’re limited to 10 steps in your funnel. So if you need more, split it between two different goals.

Example of an ecommerce conversion funnel setup:
Example of an ecommerce conversion funnel setup

b. Visit Duration Goal

URL destination goals keep track of specific URLs. Each time someone loads that specific page, they trigger the goal. This Goal Type is ideal for thank you pages, confirmation pages, etc.

For example, each time a customer completes a purchase and lands on the ‘thank you’page, it counts a s goal completion or 1 successful conversion.

Visit Duration Goal Setup

Duration: In the below case, Google Analytics will count each website visitor that spends more than 1.5 minutes on the site as a goal completion.

Visit Duration Goal Setup 2

c. Pages/Screens Per Session Goal

Pages/screens per session goals track the number of pages each user visits before they leave. In order to trigger this goal, visitors must view more than the specified number of pages in a single session.

Screens Per Session Goal Setup

Pages/Screens Per Session: In the below case, Google Analytics will count each website visitor that browses through 3+ pages during their session.

Screens Per Session

d. Event Goals

Event goals are less straight forward to set up but can provide you with highly valuable insights. Any element that your visitors interact with can be tracked as an event, for example:

  • External links
  • Downloads
  • Time spent watching videos
  • Social media buttons
  • Widget usage

Event Goals Setup

Event Category: This is the name assigned to the group of similar events you want to track. For example, YouTube Video, PDF, etc.

Event Action: This is the name assigned to the type of event you want to track for a particular page element. For example, click, download, play.

Event Label: This is the name assigned to the page element, whose users’ interaction you want to track. Your event label can be a title of a video, title tag of a web page, name of the downloadable file, etc.

Event Value: This is the numerical value assigned to the event you want to track. For example, it can be the length of the video played, download time,etc.

In the below example, we’re tracking how often our Growth Hacking 101 guide has been downloaded.

Growth Hacking 101 Guide

For Google Analytics to be able to measure an event, you always need to tie it to a website element. You can do this by using what we call an ‘event handler’, the most common one being onClick (meaning an event occurs when a visitor clicks on an item on the webpage).

<button onClick=“ga(‘send’, ‘event’, ‘guides’, ‘Download’, ‘Growth Hacking 101’, 10;”>Download Our Growth Hacking 101 Guide</button>


Growth Hacking Event Handler Sample

There’s a lot more to event tracking but I don’t want to go into too much detail here as this is meant to be an introductory article. If you want to learn more about how to set up advanced event tracking for your site, check out this awesome guide >>

Where to find my goals and funnels once I set them up?

Google Analytic Goals and Funnels
Conversions → Goals → Overview
Google Analytic Funnel Visualisation
Conversions → Goals → Funnel Visualisation


The first part of this article focused on setting up tracking for CRO (Conversion Rate Optimisation) purposes. Goal is to track the actions visitors take from the moment they land on your site to the moment they convert. Based on the insights extracted from your conversion data, you’ll then be able to run A/B tests and increase the overall conversion rate from visitor to customer.

However, we’ll also have to make sure to measure the effectiveness of our acquisition channels and tactics to optimise our CPA. This is where UTM Tags come into the picture.


The part starting after ‘&’ is the UTM code and it tracks multiple variables, including traffic sourcemediumcampaign and content.

Please note that adding the UTM code doesn’t impact the actual page. In the above example, the website http://growthhackingasia.com would load as always but thanks to the UTM code we added, we’ll know exactly which campaign or user acquisition experiment the visitor came from.

Google Analytic UTM Parameters Cheat Sheet
Source: Funnel.io

Once you have created a UTM code, you can track it in Google Analytics by going to Acquisition -> Campaigns -> All Campaigns.

Google Analytics - UTM Tracking Guide
Acquisition → Campaigns → All Campaigns

How to create UTM codes?

There are multiple ways to create UTM codes, my preferred one is using this spreadsheet:

UTM Link Builder Spreadsheet

Rather than having everyone in your company create and document their UTM tags in separate sheets, it makes sense for just one person to ‘own’ this document and set the rules to make sure the data collected is complete and naming of the parameters is consistent.

To learn more about how and when to use UTM Parameters, check out this article >>

What’s Next?

Once you have completed your tracking setup and collected some acquisition and user behaviour data (e.g. where in the conversion funnel are users currently dropping off the most), it’s time to focus on experimentation to optimise each stage of the customer journey.

OKR Framework – Set Goals

Based on your data, you’ll be able to identify areas for improvement. Choose the most urgent one first (based on how much it can potentially improve your One Metric That Matters.

The framework we use for goal setting is the so called OKR framework. It sets ONE goal, which is then broken down into a few different measurable results, within a certain time period. Here’s the basic layout:

OKR Framework
Source: HubSpot

The best part about the OKR framework is that it keeps you focused on what you should be working on. These OKRs are designed for each division (e.g. content team, product team, etc), as well as individuals within each division.

Here’s a great example of how Swipely applied the OKR framework to their startup >>

Once you’ve created your OKRs, it’s time to get started with the fun part — experimentation 🙂

Make sure to watch this video with Brian Balfour: How to Design & Track Viral Growth Experiments. Until today, it’s one of my favourite videos on Growth Hacking!

The Experimentation Process

Once you know your OKRs, it’s time to become active and run experiments to hit your key results. Below you can see the process you need to apply.

OKR Experimentation Process

a. Brainstorm

During the first phase, you need to compile a list of all the ideas you and your team have to achieve the key results.

Brian Balfour suggests four ways to generate growth ideas:

  • Observe (How are others doing it?)
  • Question (Why? What if? How?)
  • Associate (Connect dots between unrelated things)
  • Network (Build a network of growth people)

All ideas need to be documented in a backlog. Here’s a Google Spreadsheet template for your Backlog Document (make sure to copy it before you start to edit).

b. Prioritize

Your backlog can easily have 80–100 ideas so the question is — what to start with?

Sean Ellis, first growth marketer at Dropbox and Founder of GrowthHackers.com, uses a simple way of prioritizing: The ICE-score.

  • (I)mpact — What effect will implementing this idea have on the metric you’re looking to improve?
  • (C)onfidence — How confident are you that it will work?
  • (E)ase — How easily / quickly can the idea be implemented?

Growth Hacking - The ICE-Score
Adjusted from: https://www.slideshare.net/SamMartin21/johannes-radig-implementing-a-hightempo-experiment-process-at-truly-learnings-challenges-results

Once you gave each factor a score between 1 and 10, the average of these three scores is the ICE-score. Check which idea scored the highest and get started with it.

Each experiment you kick off from here needs to be added to your Pipeline Document, a list of past, present and future tests, actual results and resource estimations.

You can find a template for the Pipeline Document here >>

c.  Test

Before you can dive headfirst into testing, you need to create a structured Experiment Doc (you can find the template here >>).

You need to:

  • construct the hypothesis
  • outline the experiment design
  • estimate the required resources

After you are done with your experiment you’ll need to come back to this Experiment Doc and add the results as well as the learnings you gained.

d. Implement

This should be pretty straight forward. Go ahead and run the experiment.

e. Analyze

Once you have gathered enough data (i.e. your results are statistically significant), you can move into the analysis phase and make decisions.

You can use 3 factors to evaluate your results:

  • Impact: What were the results of the experiment?
  • Accuracy: How close was your prediction to the actual outcome?
  • Insight: Why did you see the result that you did?

f. Systemize

If your experiment was successful (i.e. close enough to the expected results), you have 2 options: Productizing or Creating a Playbook.

Productizing means that the experiment will be directly embedded into the product (e.g. improvements in the onboarding flow).

If you can’t do that, build it into playbooks. A playbook is a set of instructions for repeatable growth tactics (e.g. product distribution best practices).

Example: Hello Bar

Hello Bar
(source: https://www.slideshare.net/hiten1/lean-analytics-36766210/45-Research_Tools_Taccs)


To identify which metric to improve and how to potentially improve it, the Hello Bar team collected both quantitative and qualitative data.

FOUNDATION 1: Analyse the conversion funnel for drop offs

Hello Bar Funnel Metrics

Metric to Focus on: Installation Rate

Potential OKRs (these are not included in the original case study but could look like this):

Objective: Increase the installation rate

Timeframe: 60 days

Key Results (simplified):

  • Increase conversion rate from ‘Completed Registration’ to ‘Installed’ by 40%
  • Increase conversion rate from ‘Completed Registration’ to ‘Installed’ by 60%
  • Increase conversion rate from ‘Completed Registration’ to ‘Installed’ by 80%

FOUNDATION 2: Gather Qualitative Data To Understand Why The Installation Rate Is Low

Quantitative data showed the team that there was a problem with the installation process of the tool. Keep in mind that quantitative data can only tell you that there is a problem, it can’t tell you why.

This is why the Hello Bar team started quantitative data collection to better understand WHY their visitors were dropping off.

“Why didn’t you install Hello Bar?”
Why didn’t you install Hello Bar?
“What would have made it easier to install Hello Bar?”
What would have made it easier to install Hello Bar?

The most relevant insight was that more than 40% of survey participants said they wanted to install Hello Bar through a WordPress Plugin. A second finding was that potential users seemed to look for the option to email instructions to their developer.

These and any additional insights gathered from surveys allowed the team to populate their backlog with experiment ideas.


STEP 1: Brainstorming

Backlog Document Sample
This is just an example of what their Backlog Document could have looked like.

STEP 2: Prioritization

Backlog Document Sample

Based on the ICE scores of the ideas, we see that Experiment #1 (‘Add multiple installation options’) has the highest score and is therefore the first test to run.

STEP 3: Testing & Implementing

Below is the Experiment Doc breaking down how to test whether the Control Variant (= existing installation options) or Variant B (= multiple installation options) generates a higher installation rate.

Growth Hacking Asia Experiment Document
Hello Bar Installation Screen

STEP 4: Analysis

Data showed a 40.15% increase in the installation rate.

Data analysis for the installation rate

To make sure that the results were statistically significant, they needed to run the numbers through isvalid.org. It showed 100% significance, meaning it was time to move on to the next step – Systemisation.

The results statistic

STEP 5: Systemisation

Hello Bar replaced the old installation screen with the new variation that generated a higher installation rate during the A/B Test.

Hello Bar Installation

I hope that after reading this article, you’re able to better understand the term Growth Hacking and the underlying experimentation process. In the coming weeks, I’ll share more actionable content about strategies, tactics, best practices and tools to grow your company. Stay tuned and happy growth hacking 🙂

AdWords Expanded Text Ads must be AB split tested. This guide will tell you how to AB test these new expanded ads and how this differs from your current AB testing methods. AdWords has released Expanded Text Ads to all accounts, we are now left to look at our data and determine which creative options will work best. But how?<

Read Full Article: How to AB Split Test AdWords Expanded Text Ads