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Traditional marketing conversion funnels explain the stages every consumer goes through when they engage with new products and brands, from the first contact with a product, to the initial purchase.

This simple chart is a great way to break down the whole purchase process, so we, as marketers, can take action to influence each of the stages, where we feel we will make the biggest impact.

marketing-funnel

Generating an impact to influence the widest part of the funnel is accomplished with traditional advertising (TV Ads, Press Ads, Out of Home, and so forth). The issue with this activity is it is very hard to measure.

“Half the money I spend on advertising is wasted; the trouble is I don’t know which half” – John Wanamaker

Web Conversion Funnel

In the digital era, conversion funnels are a great tool to understand how our visitors and potential customers engage with our brand touch points. Looking at the conversion funnel and its different stages allow us to answer questions like:

  • Do our customers get stuck at some point of the checkout process?
  • Do our customers try our product but don’t buy it?
  • Do our customers don’t engage with our product and end up registering?

web-marketing-funnel

Conversion Funnel – Defining Stages

Now, let’s take our conversion funnel and apply it to all the visitors to our hypothetical site.

To understand this approach let’s think of an e-learning provider like Lynda or Filtered as an example. The stages that a customer goes through from being a visitor to an active user are:

Now, each of the stages above will contain a certain number of leads. Our objective as a company is to push everyone down the funnel to become, “active user,” who are the most valuable for us.

Growing the Business – quantity Vs quality

When it comes to drive growth through your website, there are 2 ways that this can be accomplished. We can drive more visitors into our funnel (quantity) or we can try to push the ones that have already engaged with us down the funnel so they jump to the next stage where they are more valuable to us.

How about growing without investing in extra traffic? This is how our conversion funnel would looks like in our example:

web-marketing-funnel-not-optimised

  • Visits: 1,500
  • Check Out: (1,500 x 10%) = 150
  • Free Trial:  (150 x 30%) = 45
  • Pay User: (45 x 25%) = 11.25
  • Active User: (11.25 x 50%) = 5.6

We are going to analyse each of the 4 stages and improve the conversion to the next stage.

1. Visitors to check out (10% to 12%)

We measure here how many of our visitors are interested in what we have to offer so they actually go to the process of signing up. There are many factors playing a part here, however most of them can be tested. Your intuition should help you decide what to test. Create a hypothesis and test it!

E.g. I think users would sign up if our courses were shorter. Can we create a shorter version of our most popular course and test performance?

Take into account that in this step there are unlimited factors that can play a part and many of them can be under the product testing field rather than pure conversion rate optimisation.

2. Check Out to Free Trial (30% to 35%)

This is the classic example of CRO (Conversion Rate Optimisation). The user landed on our site, he liked what we have to offer and went to the check out. However not everyone ended up signing up for a Free Trial. What went wrong?

First of all we need to understand our visitors’ behaviour using tools like Session Cam which allow us to record how they engage with our website. This way, we will see whether they get stuck at some of the checkout process, they are put off by long forms, etc.

Once we understand what is preventing our visitor from going ahead and finish signing up with us, we can create a new variation to the page which can be tested with a specific percentage of our visitors. One great way to do this is using Optimizely, as it allows us to move and change elements of our page to carry out tests based on our hypothesis.

E.g. I think people don’t like having to give their phone number in order to start the free trial. Let’s remove it and test if this outperforms the original variation.

So our prospect has landed to our site, went to the check out and signed up to the free trial. Let’s suppose that the free trial has limited access to courses. Only 25% of those that signed up for free trial end up paying for using our platform. There is something we have from these prospects, their email address. What about doing some email marketing to increase this rate?

3. Free Trial to Pay User (25% to 35%)

The key to do this effectively is segmentation. Do we have any information about their interests?

A few boxes asking for their interests in the checkout process will help drastically to be able to know why they signed up in the first place. Are they interested in business courses, design courses, education courses?

With this information we can send teaser emails with extended limited access to some of the courses so they engage with us and end up signing up.

E.g. I am really into photography and that’s why I signed up for the Free Trial but I haven’t use the site enough to see the benefit of paying for it. A limited access to a full course for a week would urge me to take advantage of it and find the time to engage with the site. Was it worth it? I’ll probably want more.

4. Pay User to Active User (50% to 65%)

We would be wrong if we thought that a user paying on a monthly basis is going to stay with us if he is not using the service. Think of a gym, many people sign up but they don’t really go so you don’t need to be a rocket scientist to guess that they will cancel their subscription soon. We need active users.

There are different things we can try to get more users to actively engage with our courses. Email campaigns, social media campaigns, competitions. The sense of urgency is something that works really well. Coursera is a free e-learning platform that only allows you to take a course when these are released, and then they get closed down so you need to take it during a specific time frame or you will miss it.

By doing this we assume an improvement from 50% to 65% from paying users to active users.

Optimised funnel

This is our new funnel after optimising the conversion rates for each of its stages.

web-marketing-funnel-optimised

If we apply this conversion rates to our website given that we drive 1,500 visitors a month from our PPC campaign we would get:

visit-to-users-data

Cost and Income

Let’s assume that our traffic comes from a PPC campaign. We drive visitors at £1.50/visit with a monthly budget of £2,250 which means 1,500 visitors/month.

  • http://cilaiscialis20mgcoupons.accountant cialis coupons CPC = £1,50
  • Budget = £2,250
  • Visits/month = £1,500
  • Monthly Fee/user = £20
  • Av. length paying user = 6 months
  • Av. length active user = 9 months
  • Value (Paying User) = £120
  • Value (Active User) = £180

We are going to assume a cost for using our platform of £20/month. The average time that a user stay with us is 6 months for the passive users (those who don’t login every week), and 9 months for the active users (login at least once a month).

Cohort Analysis

cohort-analysis

Note how each of the optimisations done in each stage have an impact in all other stages improving the conversion rate.

We see how the first optimisation “Check Out Page” has only impact in month 4, but the next stage gets benefits as the previous stage is feeding it with more leads.

If we look at the conversion rate from visitor to active users we get an improvement with the optimisation from 0.4% to 1%.

Profit Graph

Looking at the profitability numbers below, we see how with the conversion rates we started the campaign with, we would not drive any profit as the cost would be higher than the income.

However, as the optimisation phases kick in, in month 6 we manage to break even for the total investment vs total income.

In month 9, thanks to the impact of the email marketing campaign improving the conversion rate from Free Trial to Pay User, the profit figure increases dramatically.

profitability-graph

Special thanks to Eric Ries for his brilliant book “The lean Startup” whose Cohort Analysis was the inspiration for this article.

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