Funnel Analysis and How to Use it to Boost Conversions?

Picture this – you get 10,000 visitors to your website, and they all purchase or subscribe to your most expensive plan. Does this not sound ideal?

Unfortunately, more often than not, this doesn’t happen. There are countless reasons why your visitors drop off without reaching the end goal. Sometimes, they could be browsing without the intent to buy or might not find what they need in your content copies. According to Forrester, online retailers in the US alone lose $18 billion annually due to cart abandonment.

But as marketers, your KRA is to bring in conversions to justify the ad spends and impact overall P&L. You need to find ways to achieve higher conversions. So how do you achieve that?

The answer lies in funnel Analysis. Let us learn all about how to use funnel analysis to optimize customer journeys and increase conversions.

What is Funnel Analysis?

A funnel represents the sequential stages a user goes through to complete a journey, whether on an app, a website, via email, or in a physical store.

The study of this journey via visual representation is called Funnel Analysis.

Funnel Analysis Examples

Consider you’re an email marketer focused on driving clicks on your email CTAs. Here’s how the stages would typically unfold:

Step 1 → Email Delivered

Step 2 → Email Opened

Step 3 → Email read

Step 4 → Clicked on CTA

Now, visualize this on a funnel.

In this funnel, you can identify that the problem is the Click-through rate (CTR). Your recipients receive the email; most open and read it but do not click the call-to-action button.

That’s the primary use case of funnel analysis. There’s much more to funnels, but we’ll explore them further later in this blog.

Let’s now look at an example of a buyer’s journey with a D2C brand. Here is what it would look like:

Step 1 → Sees ad on Facebook 

Step 2 → Lands on the website  

Step 3 → Get on the product page 

Step 4 → Adds product to the cart 

Step 5 → Reaches check out page

Step 6 → Makes the payment

Visualize this in the funnel:

In this funnel, you can see that the problem occurs when the user reaches the checkout page.

The 3 Stages of a Funnel

Here are the three stages of a funnel:

ToFU (Top of the Funnel) represents users who are just starting their journey at the awareness stage.

MoFU (Middle of the Funnel) represents users who have moved further along in their journey and are in the consideration stage.

BoFU (Bottom of the Funnel) represents users who are at the decision stage and ready to purchase or complete a conversion.

Benefits of Funnel Analysis

You can iteratively test and refine these elements and see what translates to increased conversions.

Steps to Perform a Funnel Analysis

Step 1: Define Clear Objectives

Your objectives must be crystal clear before you start building your funnel. Do you want to analyze the conversion rate of a particular channel or conversion of a product category? Both journeys will have different paths and events that will be tracked. You must know what is required to get the best outcome from your funnel.

The best practice is to pen down the metrics that impact your goals. For e-commerce brands, it could be transactions; for insurance, it could be persistency ratio; and for media, it could be average watch time.

Step 2: Evaluate the Right Segments

Aligning customer segments with your funnel’s overall objectives is important when selecting customer segments. For instance, when measuring ROAS (return on ad spends), you must distinguish between users who came through performance spends and those from referrals. The journey for people acquired via Google ads will differ from that of those acquired through referrals. Mixing these segments can skew your ROI calculations.

Step 3: Identify paths/touch points that lead to the end goal (Conversions)

List all the touchpoints in the customer journey that lead to the end goal and include them in your funnel. If a conversion involves five steps, add all five to your funnel. Skipping any step may skew the outcome, leaving you with half-baked information.

Here, I’d like to introduce you to two new concepts:

InFlexible Funnels: Measures users who follow a single path to reach the end goal.

Flexible Funnels: Measures users who may take a detour but reach the end goal.

Let’s take an example.

For insurance renewals, there is a single defined path. You land on the website, enter your policy number, validate your PII, check your premium and make the payment. This is called an inflexible funnel, where users follow a single path to reach the bottom of the funnel.

An example of a flexible funnel would be a beauty and wellness brand journey. The end goal is the transaction of a product. However, there need not be a single uniform manner in which the user reaches that step.

Based on the type of your customer journey, build any of the two funnels.

Step 4: Create sub-segmented funnels for Deeper Insights

(An example of cohort analysis)

To understand your consumer behavior in depth, you should create sub-segments of the larger segments you’re building a funnel for. For example, to determine the conversion rate of users acquired through Google ads, consider breaking them down by tier 1 or tier 2 cities, gender, or even AOV by gender. This approach gives you a clearer picture of how different customer groups interact with your brand.

When you slice and dice a funnel based on deeper sub-segments, you must understand averages and weighted averages.

Why? Let’s first understand what they are.

Averages

Averages represent the mean values calculated at various stages of the funnel. These averages are crucial for understanding typical performance metrics and spotting trends. By examining these figures, you can gain insights into how users are interacting with each stage, identify patterns, and pinpoint areas for improvement.

Example:

To find how much time your users take to move down the funnel, you need to measure the average time per user. To calculate this, you sum the time taken to transition between each step for all users and then divide by the total number of users. This calculation provides insights into user behavior and helps identify potential delays or bottlenecks in the funnel process.

Weighted Averages

Weighted averages consider the importance or frequency of different values in a data set. This is useful in funnel analysis because not all stages or actions are equally significant.

Example:

Consider you’re analyzing customer purchases. Some customers buy more frequently than others. You can give frequent buyers more importance (or weight) to understand the typical purchase amount better.

By leveraging weighted averages, you understand which users in your segments are bringing the growth and which are pulling it down.

Mastering Funnel Analysis to Boost Conversions

Till now, we have learned what funnels are, how to build them and analyze them. We’ll now add more depth to funnel analysis. Here are a few hacks:

1. Measure Conversion Velocity

Now, we’ll add the time dimension to the funnels. We’ve established that it’s necessary to add all steps to conversion to your funnel, but you would also need to understand how long your users take to reach the end of the funnel.

Why is it important?

If users take too long to reach the end of the funnel, it directly impacts ROAS and cashflows. In that case, you need to intervene when they start lagging. If a user adds a product to a cart and then takes days to purchase it, you should add them to your Email or WhatsApp list or remarketing campaigns and try to fast-track the conversion.

2. Identify Mini-Trends

Funnels are powerful when you go deep and unlock mini-trends. Let’s understand this with an example: Consider building a funnel that shows you a conversion rate of 10% on your website. By all standards, that is an excellent number. But as a marketer, how do you act on this? You need to know where these customers came from, which campaign caught their eye, which landing page brought maximum conversions, etc.

It’s best to combine funnel analysis with path and cohort analysis so you understand the journey they took to make the purchase and how long it took to convert a customer.

All these insights will help you double down on the areas that bring you growth.

3. Analyze, Optimize, Repeat

Scientists and marketers have much in common – they’re both driven by experiments. They’re both on the hunt for breakthroughs that lead to unprecedented growth. But to reach there, you must experiment, experiment, and if you get tired, take a break and experiment again.

No marketing cheat sheet can ensure success. Instead, developing a hypothesis, conducting experiments, evaluating the outcomes, and collecting data is essential. Using a customer engagement platform that offers real-time analytics of your campaigns can be highly valuable. These insights can then help you build your next experiment backed by data. By continuously refining this approach, you’ll better understand what truly drives results.

Common Mistakes and How to Dodge Them

1. Trusting Hypothesis over Insights

When you see a sharp drop in users in your funnel, it’s easy to think it might be clunky content or a UI glitch. While that can be the case, it’s just a guess without data to back it up. A good approach is to use exit intent pop-ups at high drop-off points. This way, you can ask your customers directly what’s causing them to leave. These insights will give you a clearer picture of what’s tipping them off.

2. Half baked optimization measures

Many marketers excel at extracting insights from funnel analysis but struggle when it comes to translating those insights into actionable measures. For instance, if you identify a significant drop-off in your funnel due to a technical glitch, you might fix it and improve conversions, but what about the users who dropped off in the process? If there are multiple issues on your website, leading to the loss of, say, 100 users per issue (a total of 500 users a day), the potential cost of lost opportunities and impact on Return on Ad Spend (ROAS) can be substantial.

The key is to retarget these users and bring them back to your site while your brand is still fresh in their minds. This strategy is crucial for optimizing Customer Acquisition Cost (CAC) and maximizing the return on your initial investments.

Conclusion

The concept of funnels has existed for a very long time, but its significance has only increased. In the digital world, funnel analysis is the basis of building and dismantling martech strategies.

When choosing tools for funnel analysis, look for ones that are easy to use and help you turn your insights into actions. The Netcore Customer Engagement Platform (CEP) is an excellent choice for these needs. It’s among the few tools that are exceptionally intuitive and allows you to export users as segments, which helps you act on your insights swiftly.

If you’re looking to maximize the value of funnel analytics, Netcore CEP is definitely worth considering.