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Understanding Cohort Analysis

Cohort analysis is conceptually pretty simple yet it’s one of the most important and powerful analysis approach a startup can adopt. I had in my earlier post discussed the importance of Lean Methodology for startups to minimize wastage of resources and getting to product/ market fit first before scaling up. Cohorts play a crucial role in helping us understand user behavior on each iteration or improvement to the product. There are plenty of other business questions that can be understood better using Cohort Analysis. To give you some examples:

1) How are the optimizations made to the product in a defined period affecting conversions?
2) Which traffic source is generating maximum conversions?
3) Which source tends to bring in users with maximum engagement on the platform?
4) Are customers acquired via email marketing more likely to repeat purchase or are they more likely to upgrade, compared to those acquired e.g. via AdWords marketing?

And more. Products such as Mixpanel and Kissmetrics enable us to easily create and analyze cohorts. Cohorts have never been a core part of Google Analytics, however there are certain hacks you can do to make it work. Even then there are restrictions to creating different types pf cohorts using GA, for eg: a cohort based on the date of purchase of any product on the website. With the latest update GA does allow one to segment users based on the date of their first visit.

What is a Cohort?

A cohort is simply a group of people who share something in common and is time bound, ie, they had something in common when the grouping was first made. A Cohort is very similar to a segment and often there is a lot of confusion on the difference. To understand better, you can consider a segment as “Employees working in the Marketing Department” while a cohort would be more like “Employees who joined in November 2013”.

Cohort Analysis
Cohort Analysis is very popular in medicine where it is used to study the long term effects of drugs and vaccines:

A cohort is a group of people who share a common characteristic or experience within a defined period (e.g., are born, are exposed to a drug or a vaccine, etc.). Thus a group of people who were born on a day or in a particular period, say 1948, form a birth cohort. The comparison group may be the general population from which the cohort is drawn, or it may be another cohort of persons thought to have had little or no exposure to the substance under investigation, but otherwise similar. Alternatively, subgroups within the cohort may be compared with each other.
Source: Wikipedia

We can apply the same concepts for an online portal/ startup to understand better the different type of users and their behavior on the platform. How we define the cohorts to compare and what we compare about their behavior will depend on the business question we are seeking an answer for. In the case of a Lean Startup, the basic premise is that the product is constantly iterated to find the product/market fit and then iterated on to optimize conversions and scale. This is one of the prime applications of a cohort analysis. We can use Cohort Analysis to compare the users acquired during each iteration and compare their behavior on the platform in terms of retention, engagement, conversions etc. Joshua Porter’s excellent blog post on twitter’s use of Cohort Analysis to track engagement with product improvements is a great example of this.

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If you look at the fig, it has rows for cohorts ( User acquired during each month is grouped as a separate cohort) and the columns give the engagement or retention figures for the cohort over a 12-Month period. As you can see this is the only manner in which one could clearly understand if the iterations and product improvements which twitter was rolling out on a regular basis was continually improving the engagement on the platform. Under a normal graph where in the cohorts are not present, many a times this picture won’t get reflected as the engagement from the early set of users will mask the engagement metrics of a particular group, be it in a negative or a positive manner.

The above example from twitter represents just one application of Cohort analysis. There are various business questions as discussed earlier that can be answered using cohorts. Let’s first understand the various ways to define cohorts:

1. Cohorts defined by when the user first Visits:
Many a times a user does not sign up or engage the first time they visit a platform. Grouping users based on their first visit will help one to understand the number of touches required before they sign up or engage on the platform and on what product iterations does one increase the conversion or the engagement metric based on the date of first visit. The earlier case study of Twitter is a good example of using cohorts to understand user engagement for a product.

2. Cohorts defined by when the user Converts:
By Converts, I mean any type of conversion or micro-conversion on the platform. It could be signing up, registering, making a first purchase, subscribing to the list etc.

3. Cohorts define by what channel the user was acquired on:
It’s really important to understand the best channels of user acquisition and the behavior of the users acquired through each channel so that one can focus more on the channels that yield best results. Cohorts based on the Channel of acquisition helps in this.

4. Cohorts based on User behavior:
Users can also be grouped based on the behavior they exhibit on the platform. For eg: In case of Zoomdeck, there are users who are frequent visitors and infrequent visitors. Users can be grouped in to various cohorts based on their re-visit rate and engagement on the platform. This is important as it helps us better understand them by having a look at other metrics exhibited by them. For an e-commerce companies one would need to strategize differently for frequent buyers vs infrequent buyers and this can be done better through cohorts.

5. Cohorts based on Customer Lifecycle:
For a platform having a number of stages it’s important to track various metrics like retention, Customer Lifetime Value, Engagement etc. It could be a simple game having various levels and classifying users based on the levels they are in and understanding the various metrics exhibited by these cohorts would help one take better decision to incentivize the users and make them shift levels.

6. Cohorts based on User Characteristic:
There might be cases where one would also want to create cohorts based on certain user characteristics like Men Vs Women, The Country of Origin, Age Group etc to create targeted campaigns or provide customized incentives to improve the engagement, retention or revenue metrics exhibited by them.

We have covered in general the various cohorts that can be created, although I do agree there might be a few specific ones related to the niche you are operating in. Creating cohorts form just one part of the puzzle, the most important part is to use various metrics to understand the behavior exhibited by these cohorts which enables you to take business decisions. There are various metrics one would need to track depending on the niche, type of product and the product lifecycle stage the Product is in.

Metrics most often tracked between cohorts are:

1. Measures of User Engagement:
During the early stage of a product before validation, User Engagement (including activation) and Retention becomes two of the most important metric. Cohorts based on date of first visit/ conversion, enables us to understand how product iteration is improving user engagement or if any changes made to the product has negatively affected engagement. The earlier example of Twitter was about tracking engagement on the platform. Depending on the product you can define what user action is termed as engagement or activation on your platform.

2. Retention:
Just like engagement is important as a metric, any successful product should have good retention figures as well. I had covered the importance of retention and how it affects virality, cost of user acquisition and customer lifetime value in my earlier posts on Virality. Cohorts help us understand retention better by enabling us to accurately define what features and user flows are improving the retention numbers. Funnel tools don’t help us track retention which needs to record user activity over longer periods.

3. Customer Lifetime Value:
Customer Lifetime Value is probably the most difficult metric to track. One of the questions we might want to understand could be the channels of user acquisition that result in giving us the max. value for CLV, the particular activity that drives a user to upgrade plans, split-test different pricing plans to understand the optimum one, features or user flow changes that results in better CLV. All of these can only be understood better using a cohort group as it allows us to track a cohort over a period of time to better understand their behavior on the platform.

4. Measuring long life-cycle events:
A product undergoes many iterations and feature roll-out. It’s impossible to measure long lifecycle events using just funnels. A prime example could be measuring revenues or retention which is typically a long term thing.

Now depending on the niche and the stage of growth your startup is in, you would have to choose the various metric that you need to track and also for the various cohorts we had earlier described. At the end of the day for any product, things finally boil down to user growth, engagement, retention and revenue. Analytics enable us to improve on each of those metric and cohort analysis is a technique that gives us great insights in measuring metric that are typically long cycle.

Cohort Analysis Presentation (Example)

I love this presentation of Cohort analysis (quoted from this Blog post) :

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What you can see immediately is that the area on the right (Period 5) stacks up the current status with users from Period 1 to Period 4. The really interesting piece of the puzzle comes into play when you are considering what exactly your users represent: active, subscribers, etc. So here is what we can infer from the chart:

  • The height of the chart at Period 5 (at 280) is the number of users currently using (or paying for) our system/app.
  • The individual stacks have a drop-off. As we can see, the drop-off is high in the beginning and then starts to level out but does not go down to zero. Since this is homogeneous across all periods, we can infer that there is something we are doing right: user behavior becomes predictable.
  • For each period 1 to 4, new users were signing up and the number of users from Period 1 makes up 17.8% (50 out of 280) of the users in Period 5.
  • The fall off of users from one Period to the next is higher in subsequent Periods, leveling out at about 25%  of the original sign-ups after 3 periods.

References:

Using Data & Analytic Tools to Better Understand Your Users – Measuring the Right Metrics

Startups be it a product or a services based one, is in an extremely competitive landscape vying for every impression it can get among the millions of potential customers available online. Getting your startup visible or discoverable is one thing, getting them to convert on your website and retain them is an even tougher task with the plethora of services and products that the consumer is forced upon. This is why it becomes so very important for startups to understand each and every activity of the user right from the first time a potential customer/ user discovers their service or product on the web to the point they convert and start coming back to their website.

There are plenty of data that’s available to a startup these days and a vast variety of analytic tools to analyze them as well. A few years back, one would have managed analytics and data tracking using just a Visitor analytics tool like Google Analytics, but that is no more the case now. With growing competition, you have far less room to fail. Based on your website and your requirements you can choose from the various Analytic Tools that’s available to you. More often than not, you would need to have a combination of these tools below to better understand user behavior. The below chart gives you the various classes of Analytic tools and their strength in measuring various parameters:

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Source: www.moz.com

It is crucial for a marketer to appreciate the insights data can provide on user behavior and take necessary actions to correct and optimize wherever required. It is also crucial for a marketer to measure the right data and understand it’s essence for better improvement of the customer lifecycle on their website.

In my previous post, we had discussed the importance of measuring the right macro metrics. For understanding and validating Product/ Market fit, one needs to measure Activation and Retention. However to completely understand the lifecycle of the Customer one needs to also measure the other three elements: Acquisition, Revenue and Referral.

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Funnels are a great way to understand user behavior on your website. They are visual, simple and map well to most of the events related to measuring the macro metrics. But Funnels alone have their limitations as well. Imagine if you wanted to measure the impact of repeated product iterations you have pushed out to during a period on the revenue. It becomes extremely difficult to track the same using only funnel, one because the impact on revenue is a long term thing and also because you would need to segment users who signed up during the period when each iteration was rolled out to effectively understand the impact on revenue for the set of users who started off with a particular variation of the product. This is where cohorts play an important part. Think, I would cover cohorts in the next post and explain in detail the methodology to track metrics like retention, revenue, impact of feature iterations on both and more. In this post, we will focus on using Google Analytics in tracking the channels resulting in any of your user interacting with your brand, converting on your product/ service and also on coming back to your product/ service. The Digital Marketing Funnel as represented in the figure earlier can be broken down in to 3 components:

  • TOF – Top of Funnel
  • MOF – Middle of Funnel
  • BOF – Bottom of Funnel

Top of Funnel:

Top of the funnel represents the first interaction a user has with your brand/ product. There are plenty of channels on which the interaction would happen and one would need to optimize for each of the channels the interaction happens on. The best solution is to always focus on at max two of the channels where the interactions seem to be most effective. With the new Universal Google Analytics Tool, you can get the channel details at Acquisition » All Traffic.

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The above table gives you a good understanding of all the various channels that drive traffic on to your platform. You can export the data to an excel sheet and then use a pivot table to understand what medium acts as the best option to drive first time traffic so that you can focus and optimize for that channel/ medium.

You can drill down further to understand the best referral sources through Acquisition » All Referrals

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Determining which sites have referred the best traffic to your website is important as it enables you to focus on those channels. You can focus on important parameters like Bounce Rate and Time Spent on site to understand the engagement of the users coming from various channels. Not only that, you can also identify websites that are similar to the ones driving traffic on to your website by doing a search on Google [ Use the search query related:”site name”]or on Similar Web to try and leverage on to the similar audience on those sites to generate traffic. For eg: If weheartit.com is a major referrer to your site, then doing a search for related websites on google gives you these results:

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The above search result gives you a healthy number of similar sites with similar target audience who would be interested in your site. Refining and cross-posting your contents across these websites can also help you in getting additional traffic. You can even automate a few of these by using a service like IFTTT where you create recipes for simultaneously posting on a number of these platforms.

Remember, it’s always a good practice to tag the various URLs you use to drive traffic from various campaigns on referring sites. You can use the standard URL builder which google provides to generate tags.

By generating campaign URLs, you can identify the source of referrals to your website, whether visitors found the link from within a newsletter, social media post or other marketing campaigns. By naming the three main campaign tagging elements:  source, medium and campaign, Google Analytics will display information about where the referral originated. Simply complete the tool’s three-step form.

Here are just a few examples of valuable KPI data points you might consider tracking as part of acquisition:

  • Organic Search (SEO)
  • Paid Search Marketing (SEM)
  • Social Campaigns
  • Banner Campaigns
  • Links from External Sites
  • Links from Online Videos
  • Email Recipients
  • RSS Subscribers

Another important parameter which you would want to track is the landing page and how you can optimize them for better conversions. Google analytics helps you identify the most important landing pages on your site and the user flow thereafter. This would give you a better understanding on which pages are performing badly and helps you understand what you can do to further improve user interaction on those pages. [Behavior » Site Content » Landing Pages or Content Drill Down ]

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On Improving weak landing pages:

  • Optimize the content to make it relevant if it’s outdated.
  • If it’s your main landing page, change the message or positioning if required. Use the heatmap tool to better understand the user interaction on the pages and optimize your page accordingly.
  • Make the content more comprehensive so that more people will find it interesting and informative.
  • Build more relevant internal links to the weaker pages to give them more link juice.
  • You can prompt the user to sign-up for email newsletters or at least try and convert them on any of your micro-conversions before the user leaves.

Middle of Funnel:

Middle of Funnel in the Digital Marketing Funnel is the point where in the user is moving from an initial product or brand interaction to a first sale/ to any major interaction on the platform. You might not be able to get a user to convert during this stage but it’s crucially important for companies to target micro-conversions during this stage.

It’s important to track the sources or channels through which the users come back to your site during this stage and it’s also important to measure the paths taken by the users in completing the micro-conversions or goals set on your page. For understanding user paths, GA has an option called Visitor Flow under Audience that visually represents the user path on the website and the drop-offs at each stage. The Visit Flow Report is a nice and a better representation of the traditional click path report. One can view the visitors moving between nodes. One also has the option to view particular segments of users based on region, campaign, traffic source, country etc and their flow/ browsing pattern on the website.

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You can also create your own funnel for any of the goals you have set using GA to better understand where the users are dropping off. For setting up goals or micro-conversions in your site, you would need to clearly define the business objectives for creating goals (micro-conversions). Few examples of good engagement goals to track:

  • Account signup
  • Email signup
  • RSS subscription
  • Watching video
  • Content interactions (e.g. photo zoom, faceted search attributes, etc.)
  • Product Purchase

The goals would vary based on the type of website you are measuring for. To set up these goals, you can login in to the admin panel of your Google Analytics dashboard and then click on the Goal tab.

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You have different goal types to chose from: Destination, Duration, Pages/ Screens per visit or Event. In case of an E-commerece website for eg, if the marketer needs to track how many users complete the check-out process, then he/ she would have to chose the type of the goal as “Destination” in the first step. In the second step he/ she would have to define the destination page which would complete the goal (Conversions).

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For creating the funnel, you would need to specify each step (page) the user traverses before completing the final goal. The funnel visually represents each stage in the micro-conversion process also specifying the drop-offs at each stage. You can create, based on your requirements, multiple mini-conversions and funnels to better understand user flow during this middle stage of user lifecycle.

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[Fig: A funnel representation of a goal set to White paper Downloads from the start page clearly indicating the conversions and drop-offs at each stage.]

In the middle of the funnel (MOF) for the Digital Marketing Funnel, it’s also important to analyze the most effective and popular channels that bring the user back. For this, GA provides Multi-Channel attribution tools under the “Conversions” section. There are various attribution models one could use. For a full guide refer this. The Linear Attribution Mode, which gives equal weightage to any channel in the funnel irrespective of where it appears,  gives us great insight in to which channel accounts for the most revenue overall. You can use the Model Comparison Tool in GA to find this out:

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For figuring out the most popular channels in the MOF, we would have to do some manipulation using excel to weed out the first and the last interaction channels.

Bottom of the Funnel:

The bottom of the funnel is the last touch before someone buys. These channels are very important as it let’s you identify which channels to focus on to complete conversions. You can find this data in Conversion > Attribution > Model Comparison Tool and select your model as the Last interaction.

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You can use these data on the best channels for driving traffic on to your website to further improve and optimize.

Segmenting:

In addition to standard segments that are available in GA to chose from ( You would have noticed this when we discussed the User Flow path), there are also a wide variety of custom user segmenting options that lets you better understand each set of users. You can create your own segments from the dashboard by clicking on the drop-down next to the All Visits tab that’s present as default. GA with the latest update now has the ability to segment visitors and not just visits, which is something GA lacked compared to tools like Kissmetrics and Mixpanel.

Now click on the Create Segments Icon to define your segments. There are a wide variety of parameters you can use to create segments or else you can use any of your own created events as well to define a segment.

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Refer this post for a great list of custom advanced segments which you can use.

Using segments, you can slice and dice your audience in ways never imagined before. You can create segments based on first purchase value, browser being used, platform being used, device on which the visitor opened the site, purchase value during a period etc. I can very well use this data to do a cohort analysis which is very important at an early stage especially if you are on a lean methodology and constantly iterating, measuring the behavior of the set of users who come in during each of these iterations. Even otherwise, there is tremendous amount of insights analyzing segments will give you. I will cover Cohort analysis in detail in the next post.

Making your products Viral : Understanding Virality

One of the major challenges for a marketer or an entrepreneur is to get users and grow for an eternity. Paul Graham would tell you that you ain’t doing it right if you are not growing by a minimum of 5-7% Week-on-Week. And there are plenty of channels one could use to grow, be it the Press, Text Ads or Visual Ads, Partnerships. All of these techniques require money to be spent proportionally to the amount of visits/ click throughs or conversions you are going to get. Wouldn’t it be so much better if we could get hundreds of users for an eternity for virtually no marketing spend. This is where the inherent Virality of products help.

 What is Viral growth? Viral growth is nothing but an existing user bringing you new users either through a generic invite sent on any of the platforms the potential user is on or by directly using the product ( sharing a file link on dropbox) or by any means possible. Google with gmail was phenomenally successful in creating a viral growth. Google initially started with a base of 1000 people who were given a limited number of invitations to share with friends/ family. Gmail finally went public in the year 2007 but by April, 2006 Gmail had through viral referrals grown phenomenally to a base of 7.1 million users. Quite incredible. Products like Instagram, Dropbox, Youtube etc grew rapidly to a million users through virality.

As with any product the key to being successful in growing virally is to have a world-class product, a product people would love to use and would love to share with their friends. Word of Mouth is a great, free channel for products to grow. But that’s not the only way to build virality in to your products. Look at products that grew phenomenally and you would understand that they built in and utilized at least one or two incredibly viral features in their products. Let’s examine the various viral features a product could have:

1) Inherent Virality : It’s incredibly difficult to achieve this type of virality in all products. There are certain products and niches where the products are inherently viral like gmail or Whatsapp or facebook. These products thrive on users inviting others users because the user gets no value out of them without his families or friends or someone else. But do understand that the easier you make it for a user to invite his friends or family, the more invitations they send out whereby increasing your virality.

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2) Signature Virality : Remember the messages “sent from my Blackberry” or “Sent from my ipad”? This type of virality encourages people to include the messages as signature because they think it makes them cool. Again, you would need a world class product that people would aspire to use to truly achieve this. Could you imagine someone using the signature “sent from my Nokia?” Kidding. But yeah, the point is to spread the message like Hotmail did with a simple “ Get your free email at Hotmail” signature and grew rapidly from a nominal base to 1 million in 6 months and in the next 5 weeks to 2 million. Remember this was a time when there were only 70 million Internet users and in 18 months they had about 12 million users.

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Paypal with their autolinks on ebay is another great example. It automatically inserted Paypal logo to the bottom of each of the listings of the sellers who used Paypal. This was incredibly successful in making Paypal grow virally.

3) Incentivized Virality: Companies like Fab.com or Dropbox are great examples of this. They incentivized their users to send invitations to their network for either monetary benefits or extra storage space in the case of Dropbox. It worked and people brought in an incredible number of referral traffic. Think of Affiliates as well. They thrive on this. The company grows and sells products by incentivizing the affiliate marketer to sell more or bring him more buyers. Amazon has achieved an incredible amount of success through their affiliate networks.

My facebook feed is filled with shares from this new to be launched service :Trevolta

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Of course one is going to share this with their friends, there is no better thing in this world than travelling around the world on someone else’s money! 🙂

4) Embeddable Virality: The biggest example of this is Youtube. Youtube was not the only video sharing website available during its initial stages but what made Youtube a leader was when they made the videos embeddable. People started embedding Youtube videos on their website and with it Youtube amassed massive views and made itself visible to an incredible number of people. This shifted the balance in youtube’s favor and there was no looking back.

5) Social Virality: In this case, Products depend on Social Network like facebook, twitter, pinterest etc to rapidly spread their base. There is a psychology behind Social virality. The key here is always to give people a set of tools to create something awesome which they would want to flaunt with their social graph. Instagram exploded because they could make photos beautiful and people loved flaunting their good looking self to the world. Services like twitter or Scoop.it grew virally because they allowed people to project a certain persona. Even the content shares that are done on any of these networks is in effect a way for a user to project a certain type of persona. If one could get this aspect right, then the product is a sure shot bet to grow virally. What I like about Twitter or Tumblr is the re-tweet or re-blog option which enables a user to create content effortlessly while actually he or she is curating content. It increases engagement on the platform and also gives a sense of satisfaction to the user that he or she is actually creating content.

I guess it’s easy to understand virality but its difficult building virality in to a product and even more difficult trying to measure it accurately.

For measuring Virality, one needs to understand two components:

  • Viral Coefficient
  • Viral Loop time

Let’s assume the scenario where:

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This implies that each user brings you an additional user within a time frame of 10 days ( the Viral Loop time), which is absolutely incredible if you are able to achieve it! J As we had discussed earlier there are different types of virality and in this case we are assuming a simple scenario where each user is sending out invitations to get their friends in (it could be incentivized or simply because your user loves your product)

Now if we were to look at the growth the product would have by the 20th day:

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Understanding Viral Loop time is important because Virality is inversely related to it. The shorter the Viral loop time, the better virality one would be able to achieve. Imagine if the Viral loop time in the earlier case was 1 day, ie, each user invites a set of users and the new user signs up all in a day’s time. That would make the user acquisition 5 times faster than the earlier scenario and your table would look like this:

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Let us plot a graph to understand our growth curve in the first scenario:

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Assuming a product has a viral coefficient that is equal to or greater than 1, it results in a steep upward growth curve. In reality a product having 1 or a number greater than 1 as its viral coefficient throughout its lifetime is impossible although there might be intervals during which the product shows such a viral coefficient. In reality a viral coefficient of 0.4-0.6 for a product is extremely good. Now let us consider such a scenario where the Viral coefficient is 0.5 assuming the rest of the numbers remain the same from our earlier example.

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And if we were to plot this on a graph, the growth curve would look something like this:

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The growth curve flattens out after a particular interval. It’s important for growth hackers and marketers to understand that in reality for most of the viral product this is how the graph would look like if they only depend on user acquisition through virality. So it’s important to plan out the metrics in such a manner that you constantly boost up user acquisition from other channels as well to have a steep growth curve which a product requires to be successful.  Remember Paul Graham and his number for the ideal growth rate for a startup? Utilize not just the virality of the product but also other channels like Press, Market Places , Creation of Viral Content, Paid Advertising or anything that boosts traffic and discoverability of your product/ service which drives conversions in order to maintain an upward trending growth curve.

Now if I were to simply consider the scenario earlier described with users sending ‘n’ invites and x% converts from them giving us a Viral Coefficient of K=n*x%, then the User Base at any particular point of time would be (considering only viral growth):

User Base (t) = User Base(0) * (K ^ (t/vlt +1) – 1)  /  (K-1)

(where vlt is the Viral loop time)

[Reference: David Skok’s article]

The above is not a comprehensive model as there are various things we have left out which includes:

  • The sending invitations process is always staggered. We have just assumed it to happen in one go. If I were to give an example – Imagine dropbox, you will always end up inviting people in a staggered way as you interact with them and share docs with them. It does not happen in one go. And If I were a user of dropbox and If I were to stop using it all together one fine day, then dropbox loses out on any referral signups from me.
  • The churn your product will have as it affects the above mentioned parameter.
  • We have not considered virality across the many channels and the different forms of virality.
  • We have also not included the saturation of a particular channel. If I were using a platform which has a total base of 10Million as the target base for sending out invitations, once I cover the entire user base I can’t rely on the formula.

The Viral Loop

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The viral loop highlighted in the diagram is what can be called as the single viral loop. It’s important where it’s possible to have a double viral loop to fasten your user acquisition. This is possible especially in case of Social networks. Like we discussed earlier, retention is a key component that defines the Viral Coefficient. An increased retention will result in increased Viral Coefficient and hence a faster user growth. There are simple techniques one could do to improve retention and engagement on the platform. This forms part of the double viral loop. Re-connection always increases engagement and retention and hence it’s important to re-connect people by prompting them as well as by making it easy for them.

For ex on LinkedIn, after we sign up, it prompts us to export contacts from our address books and re-connects us. This removes the friction normally people will have in searching for people and then connecting with them. Also, it helps in retaining dormant users. This is a technique employed by many of the Social networks to bring back dormant users on to the platform. Notifications on follow improves your chances of brining back dormant users.

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This simple step resulted in an increase of 16% in the number of invitations sent. Check the stats below:

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Source: http://www.slideshare.net/joshelman/josh-elman-threegrowthhacksgrowconf81413

Or in case of twitter they take you step by step through the various things one can do on twitter and by it helps you in getting content on your feed and making you follow a few popular people on login itself. It alleviates any friction the user will have initially to engage on the platform and also interacting with the popular users sets the context for them to get active. In doing so Twitter achieves more invitations and requests sent to users and prospective users and also re-connections and engagement between existing users. That’s a double viral loop.

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Similarly, it’s important for any marketer to understand the viral loop of their product, one would have to iterate and measure to understand in detail the parameters and the best possible viral loops.

At Zoomdeck, we are creating a platform for making photos interactive. A User can spot anything interesting inside photos to ask a question or add notes or add spots to highlight an interesting story or experience about an element inside photos, then make it much more engaging by linking the spots to audio, video, products, people, places or any link relevant. Users would be able to discover and share the stories and elements in photos using interactive spots and have contextual conversations around each spot. We have a web version, an iOS app and an embedding option which along with viral content shared across various Social Media sites would be a key driver of traffic and user acquisition for us. When I look at the various channels of virality for Zoomdeck, I have:

  1. Embedding: Bloggers and Publishers embedding Interactive Photos on their website. Similar to how Youtube utilized embedding as an important element of their Viral growth.
  2. User joins Zoomdeck, takes photos and makes them interactive by adding spots. Shares it with their friends and family (Invite). ( This will have a longer Viral Loop time) Similar to how Instagram or Pinterest built their viral loop.
  3. Directly recommends the product to their contacts through the invite option in the app or in person.
  4. Sharing of Interactive Photos they find interesting on Zoomdeck( Content) on Social platforms ( Facebook, twitter or Pinterest). Their network discovers, finds it interesting and shares with their friends. ( This will have a much shorter Viral Loop time) The advantage of having content that is viral in nature is the Viral Loop time significantly reduces as you are providing ready made things for people to share and not asking them to create which is always time consuming and requires an effort and hence would always have friction. A Youtube or Twitter is a great example of this.

The four basic viral loops in the case of Zoomdeck as mentioned above would each have different conversion ratios. While the first option and the fourth option would enable Zoomdeck to reach a much larger base of audience and that too multiple number of times, the conversion percentage is going to be a lot lesser than the second and third option where in our chances of conversions are much higher. Similarly, the Viral loop time for the first and fourth option would be much lesser than the VLT number for the other two. So measure the various parameters continuously and optimize for the ones that give best results.

Importance of Seeding :

Imagine for calculation purpose the current user base of a product as 5000 and consider only the 2nd and the 3rd channels of virality listed above as the growth channels for easiness in quantifying. (Assume a Viral Coefficient of 0.6 and a Vlt of 10 days) We would have a table that looks like this

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And our user graph would look like this:

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Now, assume acquisition of a constant number of users from other channels, we have (all values are hypothetical):

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Seeding initially is critical as that’s what enables the viral growth to kick in. In the first example we flatten out our user base after 2 months. This is why seeding should always be an ongoing process to leverage maximum value from virality or else we should have a viral coefficient greater than 1 to have an eternal upward curve for our user graph which is very difficult to achieve throughout the lifetime of the product. There would be short bursts when the viral coefficient is greater than 1 and would result in phenomenal growth especially if you are on a larger base as well but not through the lifetime of a product.

Key-points:

  • Virality is something that should be inherent in the product. It’s important to design and incorporate virality during product conceptualization itself.
  • Always measure and track various metrics to understand what works best and dig deep into those channels.
  • Iterate as fast as possible to understand the best viral channels. The longer the iteration cycle, the longer it will take for you to spot your best viral loop.
  • Reduce the number of steps required to do any action that results in virality. Make it as easy as possible for the users to send invitations. Understand that the easier you make, the better your metrics would look.
  • Two factors that influence Virality are: Viral Coefficient (K) and Viral Loop time (Vlt). Increase ‘K’ and decrease ‘Vlt’ for rapid growth.
  • Retention and re-connection are important factors that help in Viral growth.
  • Important to seed users initially.
  • Exponential growth from Virality kicks in after a threshold limit. Make use of various channels for seeding the initial audience.
  • It’s very difficult to achieve sustaining growth through virality where you require a viral coefficient greater than 1. Hence, compensate for this and balance it out by seeding users through other channels as well – if required paid channels also to maintain momentum.

References:

http://www.forentrepreneurs.com/lessons-learnt-viral-marketing/

http://www.linkedin.com/today/post/article/20130402154324-18876785-how-to-model-viral-growth-retention-virality-curves

http://andrewchen.co/2007/07/11/whats-your-viral-loop-understanding-the-engine-of-adoption/

The SaaS Customer Acquisition Conundrum

Building a successful SaaS startup and putting it on a growth trajectory involves a great amount of effort and a thorough understanding of the market dynamics. To begin with one would need to build a product and achieve product market fit. Once you have a product market fit, you know there is a target segment that you can sell into. The question then becomes – How do I reach out to my target audience and start converting them as paying users?

An easy answer some would say with the amount of options a SaaS company has to interact with its customers in today’s digital world. But is it so? To understand SaaS customer acquisition and business viability one would first need to understand a few basic metrics that become extremely crucial to understand the health of a SaaS business:

  • LTV – Lifetime Value of a Customer
  • CAC – Cost to Acquire a Customer
  • Sales Velocity – Number of days required to acquire a customer from the first point of interaction
  • Payback – Time taken to recover the CAC
  • Churn(monthly) – % of customers you lose in a month

A well balanced business model will always have a LTV value greater than the CAC. If it’s not so for your SaaS business, then it might be a good option to reconsider the business model you are building.

One in a million SaaS startup would be able to depend upon the inherent virality of the product to scale up. But even in case of these startups there is an initial threshold value for the customer count that they need to hit for the virality to kick in. Rest of them, well they have to use of mix of SEO, SEM, Social Media, Direct Sales, Channel Sales, PR etc to hit their audience. Let me tell you, the CAC for majority of the SaaS companies have shot up so much that it becomes really difficult during the initial days of its incubation to rely on the Paid channels to acquire customers.

So, when does Paid Channels Work for a SaaS Startup?

Paid Acqusition works only when LTV > CAC

To elaborate, let’s assume a startup which relies on Google Adwords for eg to generate leads and works with the following numbers:

Scenario-1Scenario – 2
Total Traffic80008000
Cost Per Click$1$4
Conversion to Trials10%10%
Conversion to Paid Customer5%5%
Number of Trials800800
Number of Paid Customers4040
Cost of Converted Trial$10$40
CAC$200$800
No. of Sales & Marketing Resource710
Cost per Employee per Month1000010000
Total Cost Incurred Per Month( Without salary)800032000
Total Cost Incurred Per Month( With salary)78000132000

 

As you can see the CAC is a function of CPC, Conversion % at each stage, Sales & Marketing Overheads, Churn and the Sales velocity. You would want to have a high velocity and a churn percentage close to zero to reduce the CAC. Similarly, with more human touchpoints, your CAC value goes higher. Touchpoints could be any of email followups, sales calls or demos the inside sales team does. The CAC value goes even higher with direct Sales team involved, however in such cases your Average Contract value will also be much higher. In short it’s important for any company to keep their CAC < LTV to sustain.

While you do your math and try and figure out the CAC number during the early stages of your business, another important metric to track is the payback period. Let’s assume in the above example your avg. revenue per customer is $400/ year.

Scenario 1: Payback Period = 200/400 = 0.5 years

Scenario 2: Payback Period = 800/400 = 2 Years

 

Implies, in Scenario 1, you would take 6 months to recover the amount of money you had invested in acquiring the customer, while in Scenario 2, you will take almost 2 years to recover the money you spend on getting your customers. For a Startup at an early stage having limited access to capital the second Scenario is a sureshot bankruptcy contender, so while you analyse your business keep in mind the payback period and the amount of capital you would need to sustain your business. A simple workaround in Scenario 2 would be to collect the payment upfront – For more, refer Pricing Strategy makes or breaks a SaaS startup.

Always be asking yourself the following questions before you decide to spend on acquiring customers through Paid Channels:

  • Has my product already achieved Product/ market fit?
  • What’s my CAC for a paid channel?
  • How much Capital do I have?
  • What’s my runway with the Capital I have?
  • What Pricing Strategy can enable me to sustain the business if I am relying upon my cash reserve to acquire customer.
  • Do I plan to raise more money or am I planning to run the business through the revenue generated? If you are planning to raise funds, burning money to show momentum might be a good idea. You may want to read this article The Incremental Customer

Find answers to these questions to have a clear understanding on why and when to use paid channels for customer acquisition. Remember you will have a sustainable business only when:

  • CAC < LTV (general rule of thumb is that LTV should be 3 times your CAC)
  • Payback period < 1 year

Building a Product from an Idea : The Lean Startup Way

We all have ideas. We all have felt the need of having something more to an existing solution or an alternate way of doing something which we often do. Startups and products are born out of this. The good part or maybe the sad part is that there are thousands of such ideas and products that spring up every day and it becomes increasingly difficult for these products to succeed in the market.

Of course having a great founding team with the right mix of technical, marketing and design skill set would go a long way in helping the product to wade through the clutter and be noticed but it still doesn’t guarantee the success of the product. It’s often easy for a founding team to lose direction early on in terms of what’s the right product that people are willing to use, or better, willing to pay for. There is an even worse scenario which I have often seen among founders when they try and convince themselves that the features and the products that they are building is the right solution based on intuition and practically zero metrics to back their claim. That’s suicidal.

It’s imperative for a startup to follow the Lean methodology’s Build-Measure-Learn loop. But before you enter the build phase, your first step should always be to do Research and understand the market you are going to target.

Research

First things first. One wouldn’t want to waste a significant amount of resource on an idea which has relatively zero market potential. So always begin by understanding the true market potential of your idea. You can start by asking yourself a set of questions initially:

  • Will my idea address a genuine pain point, if yes, what is it?
  • Who will be my potential customers and where can I find them?
  • Who are my competitions?
  • How different is my idea from what my competitions have?
  • Will I pay for a product like this? Would anyone pay for the product I intend to develop?
  • Are there are regulatory constraints?
  • What would my rough budget be and what would be the resources required for a basic product?

I’m sure you won’t get comprehensive answers to a lot of these questions but then the point of asking yourself all these questions initially is it helps you understand the market and the opportunity you are going after and sets the context right. Googling will give you sufficient inputs which will enable you to take a call on if it’s worth pursuing further. If you want to understand things a little deeper, do a survey or shortlist a set of people who will in the future be interested in the product and try and get their opinion on if they would actually pay for such a product (To be honest, at this stage it’s difficult to really understand if the users will pay for it at this stage, but do get opinion from people nevertheless.)

In already established markets there would be a fair number of research reports which you can leverage to understand in detail the market you are going after. An easier way would be to use Google Keyword Tool or Market Samurai to understand the demand for your idea. It’s always easier for a startup to build something in a space where there is an existing demand and is not fully saturated than to carve out an entirely new market. I am not saying that’s not possible but with limited resource at your disposal in your early days, trying to create a new market might not be the best option.

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Just did a search on Google KWT for the term Video Games and see the results. It has a fairly good number of searches worldwide. The KWT also gives you a set of related keywords which might help you even segment the entire market.

It’s important at this stage to try and segment the market you are going after. Having a generic solution won’t help at an early stage. Segmenting the market you are going after gives you a much better chance of validating your idea. The Idea can be expanded on to other segments as and when you grow and become mature. Also, make a shortlist of your competition and their offerings. This would give you a fair bit of understanding on the current market demand for various features and would allow you to understand how your product is different from your competition.

All of this helps you in getting a Problem/ Solution fit. It’s good to get a feel of the market even before you start prototyping and building a product. Like Eric Ries mentions in his Lean Startup methodology, it might be a good idea to just create a landing page and put up a “Register to get an Invite Option” and check how many click through to register and actually register. This is a trend followed by a lot of online startups and especially Apps. One of the most important tactics for an app’s pre-launch marketing strategy is to build up a landing page with an option for the users to subscribe to be notified when the app goes live. This would also enable you to get a feel of the solution you are suggesting for a problem. Again, the problem here is that often people without proper segmentation and without trying to get their target users to come on to the page would conclude that the idea has no demand in the market. This is why it’s important to segment your market and know your core group of audience. Hunt for them on forums, groups or anywhere they are available if you want to make people discover your webpage for free or else use Google Ad words or any of the Ad solutions to target your core group of audience. Understand if there is a demand for your solution.

The next stage in the product lifecycle is to develop an MVP (Minimum Viable Product) that would actually enable you to reach out to customers, engage with them and understand better the demand for the product.

Minimum Viable Product

The concept of a Minimum Viable Product was introduced by Eric Ries, the man behind the Lean Startup movement. In his own words :

The idea of minimum viable product is useful because you can basically say: our vision is to build a product that solves this core problem for customers and we think that for the people who are early adopters for this kind of solution, they will be the most forgiving. And they will fill in their minds the features that aren’t quite there if we give them the core, tent-pole features that point the direction of where we’re trying to go.

So, the minimum viable product is that product which has just those features (and no more) that allows you to ship a product that resonates with early adopters; some of whom will pay you money or give you feedback.”

According to me, it’s always a difficult task clearly understanding what exactly is “minimum viable” as far as your product/ idea is concerned. It would be different for each idea and category. Understand that if the product is as is any other competitor and there is no differentiation then the product you are shipping is in no way a “minimum viable” product. Focus on your core value proposition and how your product is different from the rest. If your differentiation is purely the experience that you give your users then ensure that when you ship out your MVP, you enable your customers to have that experience. Minimum Viable Product does not mean that you roll out a crappy product. In fact that would be suicidal as with Social Media these days it does not take a lot of time to completely kill your product or brand with a negative word of mouth. Of course the MVP can have bugs and there would be hundreds of features that could be added later. The early adopters that you manage to get are always going to give you a leeway and that’s because they genuinely need and value the core experience or the core feature your product provides. So ensure that the core proposition is in its entirety is reflected in the MVP.

Steve Blank in his book outlines the four stages to the Customer Development process with the following success end goals:

  1. Customer Discovery – Achieve Problem/Solution Fit
  2. Customer Validation – Achieve Product/Market Fit
  3. Customer Creation – Drive Demand
  4. Company Building – Scale the Company

This is a great framework for someone operating with the Lean Startup methodology. The initial research phase and the development of the MVP falls under the first bucket where in one achieves the Problem/ Solution fit. This does involve effort however you do significantly cut down on the unnecessary resource you would have spent otherwise on trying to create something which has no demand in the market only to realize that after you have pumped in all of your money and effort.

The Second phase of Customer Validation is where one achieves Product Market fit. This is the stage where in you actually try and sell your MVP and or make your customers to use it to tweak and bridge the gap between the Product and the Market.

Achieving Product/ Market Fit:

How exactly does one determine whether you have achieved product/ market fit? Different people will give you different definitions for Product/ market fit

“Product/market fit means being in a good market with a product that can satisfy that market,” according to Marc Andreessen

Andrew Chen

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Sean Ellis has created another metric for determining Product/ Market fit. He suggests asking existing users of a product how they would feel if they could no longer use the product. According to him, achieving product/market fit requires at least 40% of users saying they would be “very disappointed” without your product.

For me the whole idea of getting a Product Market fit is nothing but getting to a point with your product when a particular segment of the market which you have identified as your initial target segment embraces your product so that you can grow your company/ product scalably. Achieving Product/ Market fit as early as possible is crucial for any product as it allows you to then focus on company growth and not on iterating and pivoting the product. Spending significant money and effort on growth and marketing at this stage before product/ market fit is not an advisable strategy.

For a consumer company like Zoomdeck, Andrew Chen’s numbers stack up well. It’s important for startups to constantly measure during this stage and understand the behavior of their users. One needs to craft and test several value propositions, user flows, conversions, user interactions to effectively achieve a product/ market fit.

The priority here is to focus on the macro metrics, the right ones. Understand that optimization of micro-metrics comes at a later stage once we achieve product/ market fit. There are various macro metrics that matter; you may refer to Dave McClure’s AARRR model.

  • Acquisition – How many people landed on your website coming from a marketing campaign or through viral channels that you are tracking and then you acquire the user.
  • Activation – The user uses your product and completes a core action on the platform.
  • Retention – What is your churn? How many of the users you have in your user base are active? How many stopped being active and why?
  • Referral -How many of the users that are using your product are willing to refer to others?
  • Revenue -How many users are willing to pay you of the ones that are using the service?

During this stage out of the 5 macro metrics Dave suggests, there are only two that needs to be tracked comprehensively. They are: Activation and Retention.  Of course Acquisition is important as well because for measuring and optimizing activation and retention there needs to be sufficient users. But then the idea here is to not spend and focus on acquisition but to focus on Activation and retention in a core segment by minimizing your acquisition cost and optimizing it. Try and figure out the best and most effective channels to let your target audience discover your product and allocate a budget accordingly. Social Media these days provide a great channel for enabling your target users to discover your product, so utilize it to the maximum effect possible.

Try and map out the important actions on the platform that corresponds to the macro metrics : Activation and Retention.

Activation:

For a product like Zoomdeck, the activation process typically starts with the user signing up, uploading a photo and then adding a spot or by adding a spot on to another photo on the platform. ‘Spots’ are annotations on images which can be used by people to highlight any interesting element or story on photos. One can even link the spots to video, audio, maps, products, wikis, social profiles and more

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One has to decide on the percentage numbers for each of these macro-metric: Personally I consider 30% active users the day after signing up as a healthy sign for a consumer internet product

Retention:

Retention is nothing but getting the users back on the site regardless of the engagement they have on the site. You can define retention as mentioned or tie it to certain key actions on the platform. In general for a consumer product which is both creation and consumption based, it might be good to just consider the activity of the user coming back to the site as retention. For eg: Facebook or twitter might consider retention as the case of users just logging back in to the site. Engagement, however, is a different concept where a platform like Facebook or twitter would want the user to perform any major/ core user action on the platform like sharing content, liking or updating status or tweeting etc.

A good retention rate would be different for different consumer products/ apps depending on the nature. It would also depend on the customer usage cycle which tends to be shorter for a social gaming app while it tends to be a little longer for a platform like Zoomdeck. So based on your product’s customer usage cycle and general trend in your niche/category decide on your target retention number/ time frame ( 1 Day, 7 days or 28 days) to achieve.

Measure and iterate on both these macro-metric to get to Product/ Market fit. Use Funnel and Cohort analyses to better understand the user flows and the churn at each stage so that you can identify and improve/ rectify the non-required or wrongly crafted features and flows. Breakdown each user flow to understand in depth any issue there is. The idea here is not optimization for efficiency but the idea here is to validate your MVP. People often relate A/B testing with changing colors of the Sign Up button, yes, that might be a good way to improve on the conversions in some cases, but getting to product Market fit is all about validating your MVP, to get people to buy into the features or the experience it provides and then make them repeatedly come back to the platform. There would various broad scenarios:

Have high arrivals but low Conversions: Tweak your messaging and positioning to check if that helps in conversion. Also, ensure that the incoming traffic is composed of people you assume to be your target audience.

Have low arrivals but high conversions: Work on the channels to bring in more targeted traffic. Groups, Forums, Meetups etc of target community would be a great start. Try and improve on the keywords you chose for your PPC campaigns.

Have high conversions but low activation: Ensure people understand the interactions on the platform. Is it too difficult to understand or complete the core action on the platform? Would an interactive guide in the beginning help the user understand user actions on the platform?

Have low conversions but high activation: Are you bringing in the right traffic on the platform? Is the messaging right on the front page? Is the signup process easy enough or have you made it too difficult? Is there a clear call to action on your landing page?

Have low activation but high retention: A good sign to have a high retention number. However lower activation would mean either people are not interested in the core activity you have considered or people are not given an easy enough option to complete the core activity on the platform.

Have high activation but low retention: Low retention could be due to lack of interest in the product and it’s core feature. A product which genuinely solves a problem for a sect of people would have high retention numbers. Products which are not a must but is a luxury like Quora would need to constantly remind people and get through the clutter to improve on their retention numbers. Work on either.

The whole cycle would look something like the figure below. Keep measuring all the important metrics, learn and iterate on important features/ flows till you get to product/ Market fit. The earlier, the better.

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Post achieving Product/ Market fit, the company can focus on user growth and leverage their marketing spend to speed up the entire process. Utilize the best and the most effective channels to scale further. In the next post, I will cover in detail utilizing Funnel and Cohort analysis to measure each of the important macro-metric described by Dave in his startup pirate metrics, two of which we comprehensively track during the process of achieving Product/ market fit.