Growth Strategy

Growth and acquisition may be synonymously used but to encapsulate the entirety of ‘growth’ we need to break it down as:

Acquisition: How do you get people in the door?

Paid Acquisition has real efficiencies and enables the business to meet target under time pressure and assists in accelerating growth. To keep bringing on new customers — the business needs to keep on writing checks. It all depends on which stage of growth the business is at.

At early stages of growth investing in organic methods is recommended as primary driver for acquisition. Organic acquisition goes hand in hand with a solid retention strategy (doesn’t make sense to start one without the other).

Before we delve into acquisition — let’s classify types of prospects:

The first two buckets of prospects is what we should be focusing on.

Users who have never heard of you

Let’s say you launched a new product and by regular means of generating awareness (e.g. PR, content marketing via blogging and social media, SEO etc) your ecommerce site gets users.

The key to acquisition is to understand how people discovered and shared your product — and then mapping your product tactics to match the specific behavior you want to be driving.

Product discovery can be classified into the following:

For the outbound marketing — this will be covered later under paid acquisition (discovery in this case relies heavily on how strong your multi-channel attribution is).

For the former — a personal example best illustrates this: Hailing from Pakistan where tailored men’s clothing is the norm for a regular guy — I have always been interested in finding an easy fit for budget service and so I searched ‘made to fit men’s clothing’ on google — and I get the following organic results:

I click Trumaker — and get hooked on to the simple, easy and highly customized site that is spot on in addressing my problem:

Sources of Traffic

Understanding such product discoveries involves observing sources of traffic via your analytics tool (for google analytics this will be acquisition > all traffic > source/medium > changing secondary dimension to user type we can isolate new users only):

New users’ best identifies prospects who have never heard of your product before. The above shows outcomes per traffic and we observe that google/organic has the highest volume of new visitors. By going through the channel view you can also get a breakdown of keywords new visitors are searching for.

With this information — you can infer (to an extent) why users are coming to your page (intent). If the keywords searched are matching with the purpose of your ecom site — you’re doing a great job with SEO and if that’s not the case — this is an opportunity for adjusting SEO, so that users can search you appropriately.

This also paves way to explore your Head-Tail graph (every ECOM business will have this) which enables you to create a flexible framework for search keyword portfolio.

Users who know about you but aren’t using you

For these prospects it’s key to understand the audience more deeply including how they view and experience product end-to-end. ‘Winning these users who have not converted yet is dependent on your ability to paint a picture around who they are, what they have to go through before going to your site (and during)’ [1]

In understanding users deeply — we first need to examine our understanding of conversion, need to understand the inherent friction in the user’s experience and level of engagement demonstrated by the user.

Defining Conversion

If users know about you — in a measurable sense that would mean they have visited your site — and them not using you can be defined by examining your definition of conversion.

Conversion rate (unique purchasers/# of unique visitors) is a great metric but it’s limited in conveying actionable insight and minority (<50%) of visitors come to the site to buy. So the question is: “If you solve for conversion rate are you solving for all your traffic and are you improving the website experience for all your customers?” [2]

In Avinash’s (author of two best selling web analytics books) words “My answer to that last question is a big whopping thumper of a No. We are guilty of causing sub-optimal customer focus by using conversion rate and web analytics, and I recommend that we stop that.”[3]

Avinash further recommends replacing conversion rate with ‘Task Completion Rate by Primary Purpose’. This requires figuring out why people come to your website and then figuring out if they were able to complete task for their associated primary purpose.

Primary purposes can vary from researching products/services, purchasing, looking for company information, looking for support etc. These segments can be created by looking at the sources of traffic, type of pages viewed and time spend on these pages (among other metrics) and the clusters these visitors form by doing an unsupervised cluster analysis (in statistical term cluster analysis is the practice of gathering up a bunch of objects and separating them into groups of similar objects. By exploring these different groups — determining how they’re similar and how they’re different — you can learn a lot about your visitors from a previously amorphous pile of data you had)

By doing this — you’ll improve the overall website experience by focusing on task completion of each different segment.

Understanding Inherent Friction in User experience

Let’s take my experience from Trumaker — I explored the home page and visited their ‘Journal’ (tumblr blog). After a few days I returned to their home page again and saw their product video which beautifully explains how their service works:

Then I click on their most prominent call-to-action ‘Get Started’:

Which takes me to the following options:

While I do care about looking good — I just don’t have the time to shop — which takes me to my fit problems and I fill in the radio buttons:

It then takes to me ‘What stuff interests you most?’ — during this part of the process, my interest is slightly waning out — but I still proceed to see where it takes me:

I like it all — which takes to this step:

This is where I dropped off — not because of a lack of interest but because I am busy and I made a mental note that I’ll come back to the site to do this some other time.

In my particular case — this calls for your retention initiatives which we’ll address later. For now — how best do we identify drop-offs in a customer’s journey? How should we understand why people are dropping off?

Path Analysis (see image above) is way of identifying drop-offs but it’s important to understand that visitors don’t follow the path we want them to follow. For instance — in my case, I spent a longer time on the ‘Journal’ before I engaged with the commercial aspect of the site. The reason for doing so was that it gave me a sense of the brand identity and aesthetic/artistic sensibilities of the brand. Enforcing an idea of an optimal path is not the way you would want to view your visitors as different users will follow different paths — your job should be to facilitate them in achieving their purpose.

Also, the combination of pages and the number of possible paths that can be taken by a visitor (click forward, back to home, click forward, go back to three pages ago, hit buy) is insanely huge. Current tools aggregate traffic into one bucket — when in reality each segment of traffic behaves differently. [4] (* if you’re storing browse data — segmenting traffic to specific visitors and mining the path flow is possible but is a lofty undertaking. Storing historical data is not always optimal as your visitors, computations, systems, website and people (may) change too much.)

There is one exception to this rule: For structured experiences with next-next-next-submit approach — path analysis can identify where the ‘drop-off’ happens and which page is most influential in moving people to the next page. It still doesn’t explain ‘why’ the drop-off happens for which we need qualitative analysis that provides window into the minds of a customer (this can be done by usability testing, experimentation/testing and surveying etc). [5]

In Trumaker’s case — once I click ‘get started’, the top navigation disappears — which allows for the path analysis to be effective. It would be super awesome to be able segment the path analysis according to user engagement.

User Engagement

Theo Papadakis (Marketing Executive at cScape in London) makes an insightful point that engagement can be segmented as:

Kind: Customers can be positively or negatively engaged with a company/product.

Degree: The degree of positive or negative engagement lies on a continuum that ranges from low involvement to high.

From web analytics we capture degree of engagement by looking at unique visits, frequency of visits, recency of visit, depth of visit, time spent on site, subscribing for email, registering, giving feedback, rating/bookmarking content or downloading content etc. The degree of engagement is in fact a synthetic metric composed of several basic metrics that is ranked in comparison to the site average.

A quick basic way to segment user engagement is:

High engagement: Visit site longer than average + views more than average

Moderate engagement: View few pages but stay on site for long time

Sporadic engagement: Hit lot of pages in short-time (seems to be looking for something and not finding it)

Low engagement: Visit fewer pages than average + spend less time than average

Understanding how your audience experiences the product end-to-end per each different segment enables actionable insight for you to drive them to achieve the task their trying to complete.

Tactics to convert these users

Partner with experts on ground: Hire local generalists with strong product intuition and understanding of user taste, preferences and needs. For instance, for Trumaker does a great job in recruiting outfitters in different states. An outfitter in LA will understand the needs much better for that local geography than an outfitter in New York.

Test your product, constantly: This is the rule even in times of significant growth. While everything may seem fine when you’re looking at top-level dashboards — it requires effective data mining to deep-dive into data and identify areas of testing opportunities across cohorts.

For example, let’s take Trumakers product page for a particular shirt:

The product page is killing it — I am in absolute love with ‘Namesake’ (as it adds personality to the product and I am a big fan of American Jazz) + ‘Works with’ (because this gives me quick tips and helps me visualize what I’ll be wearing this shirt with).

If I hover the mouse over the product — it zooms the product AND by clicking the arrows it shows me details and different views of the product:

Moreover, the site allows me to build the shirt from scratch:

I can decide custom collar, cuff, pocket styles (or no pocket at all), placket or no placket, pleats etc and I get the option of adding my own three letter monogram on the inside of collar (luckily my name is three letter as well).

After all this awesome experience — I realized, perhaps I need to change the color. I don’t have that option from the current set-up. I would have to go back, select the shirt of desired color and re-do the entire process of building from scratch. As a busy New Yorker — I might leave and think of doing it again some other time.

A quick A/B test of adding a color feature can quickly enable us to see if adding this product feature is going to improve the user experience. Or if color addition is not backed by your inventory — having the feature to store ‘from scratch’ settings created by users will enable them to not re-do the entire process if they desire to repeat it.

Similarly, expanding on the ‘works with’ section and adding a list of products at the bottom of the product to complete the final look is likely to improve the user experience — testing this is the way to go as comparison against control allows us to see the incremental intended impact of the product modifications and new features.

Retention: How do you keep users keep using the product and make them willing to come back ?

User acquisition is pointless without solid retention. “If you push people into product experience that they’re going to churn out of — you’re going to lose more thank you think.” [6]

Retention is all about showing users the right things at the right time. For example — a user new to instagram may not be connected with other users right away. If you’re connected your account to to another service (e.g. facebook) — you’ll get a notification someone you know just posted a photo — you’ll probably check it out. Relevancy and timing in reaching out to users is key for retention.

For apps, retention will be focused on push notifications while for ecom sites — email will be a primary source of retention. It’s key to create an email eco-system around various product experiences to boost retention. For instance — let’s take Trumaker as an example:

Primary means of email collection from the site are:

  1. Footer of the site

2. Get started section of the page

From the two email collection sources the following flow of emails is observed:

No email was received from signing up from the footer — which is an area of opportunity to boost account creations. By consolidating visitor cookie with email address (this requires an integration process between your email and web analytics vendors) — each user that dropped off in the ‘get started’ sequential section of the page can be sent ‘trigger’ emails (provided these users signed up via the footer) reminding them of their fit problems and how they’re one step away from solving them (by creating Trumaker account).

Trigger emails are efficient, automated and scalable means of getting users to come back to your site. For instance — if a product is out of inventory, trigger waitlist emails is the norm in the industry. Similarly, if a user abandons cart — trigger emails containing product added to cart is pretty standard as well.

Purchase related emails (order, shipping & feedback) have the real-estate opportunity to have the user come back and re-engage with your site by including strip of related products and tips/advice related to the product. For instance — this post ‘How to Care for White Shirts’ is going to be extremely useful to someone who bought a white shirt and it’ll build a relationship with the customer beyond pimping products to them.

For regular standard cadence emails — it’s best to consolidate browse and transactional data per each subscriber to create personalized email experience to improve on customer lifecycle (read more on customer segmentation here). Understanding the optimal timing of sending out emails would require investigating your data and setting A/B tests to reach data-driven conclusion.

This is it for Part 1. More to come on Paid Acquisition and other parts of growth strategy. Part 2 will also focus on the mechanics and organizational culture involved to enable growth.

[1] Here’s What a Real Growth Strategy Looks Like — Road Tested by Facebook and Remind

[2] [3]Stop Obsessing About Conversion Rate — Avinash Kaushik

[4][5] Path Analysis: A Good Use of Time? — Avinash Kaushik