Funnel Analysis

What is funnel analysis?

Funnel analysis is an essential way to observe and describe a customer journey as a process with different stages that users go through. It usually involves several steps, from entering an app or web page to performing a particular action. It is called a funnel because of its shape that becomes narrower and narrower. By observing your funnel, and analyzing and adjusting its parameters, you will be able to improve conversions and customer satisfaction.

The funnel is an excellent tool for marketers, product managers, sales, and data scientists to understand user behavior better. Whether you wish to convert visitors into customers or you want your customers to buy more of your products, or even make them stay more in your app, funnel analysis is essential. Having a good understanding of your funnel is like using a GPS to guide you to a place you want to visit. It will show you the speed, the direction, and whether you’re on time or going to be late.

Conversion rates can help you understand the number of visitors who came to your website and bought a product or performed an action, such as watching a movie, downloading a document or submitting a lead form

How does funnel analysis work?

There are steps you would expect your visitor to take, from entering your website or mobile app to taking an action, such as making a purchase. With a simple funnel analysis, you can visualize your visitors’ steps to convert. Creating a funnel allows you to observe where exactly visitors or users are dropping.

First, you collect data through user tracking, SEO, email campaigns and other methods. Note that you need to have your data available and ready for funnel analysis. Then you define the steps that will be evaluated.

A simple funnel tracks how users convert from entering a landing page to checking out, or watching a movie, or another goal conversion. The funnel itself is usually presented as a bar graph. You will know where to look next when you see a decline or drop in your funnel. Usually, this is the time when an imaginary light bulb shines above your head. Understanding each step of the funnel and making necessary adjustments to see what works and what doesn’t will eventually lead to more conversions.

Funnel Analysis Stages

When creating a Funnel there are a few things you need to consider.

1. What is the purpose of funnel analysis? Our goal may be to generate more leads, to achieve more transactions, to make people more engaged with your product, or something else? If you provide a streaming platform maybe daily active users (DAU) are your focus.
2. What is the conversion rate you expect? This is your benchmark to work from. Let’s say you’ve finished an outstanding marketing campaign. You forecast a significant increase in conversions. What will your funnel look like? You can compare older funnels, Q1 to Q2 sales. If, for instance, you’re starting a business in a new country, you can correlate conversion rates.
3. What could you improve to raise the conversion rate? I.e. How can you optimize your conversion rate? Maybe an ad campaign, discount offers, introduction of a new product or new service, additional benefits…. Should they do the work? A/B tests your theories with real data examples. And finally, use those improved results to achieve your goal. Segmenting users and comparing funnels will give you a deeper insight into what the next step should be.

Please refer to Kubit’s Documentation to learn more about how to create a funnel

Types of Funnels

Sometimes your customers don’t follow the path you have created for them. Users can enter your funnel in a variety of different ways. That’s why it is important to define your funnel. There are a few types of funnels:

• Open funnel – where users could enter any step and still be counted in the analyses
• Closed funnel – to be counted, a user should go from step 1 to step 2 to step 3, and so on. But there might be other steps, for example, between seeing a product and buying that product, a visitor might read a blog, compare products etc.
• Strict funnel – as the name says it means there are no other steps between the steps you define and if the visitor is not following that route, he won’t be counted in the analyses.

Do’s and Don’ts of Funnel Analysis

Funnel analysis shows you whether people are dropping off, but it doesn’t tell you why they do it. Brainstorm the potential issues. Is the registration process too long and hard to fill, do you have clear messages and good product descriptions, are there too many steps and blanks to fill in before ordering a product, etc.

Quality over quantity – it is vital to not just attract visitors but also to make them stay. If you want to increase the quantity, then observe from where your audience is coming – social media, Google ads, Internet search, etc. Increasing the number of visitors doesn’t guarantee higher conversion. But, proper maintenance of the funnel will help get the most out of your new users.

Fine-tune your filtering! Some customers might be looking for a particular product while others are just browsing. Filter out those who are not your target.

Conversion windows. If you are selling shoes, from first opening a product page to placing an order, it might take a couple of minutes, but if you are comparing streaming services, it will take a little longer to complete the task. Keep that in mind.

Funnel Analysis Benefits

• Allows you to determine key events on customers’ journey
• Improve customer experience and satisfaction
• Track any changes in your visitor habits
• Helps you decide where you can increase budget and where you can scale back
• Compare conversions between different dimensions like countries, genders, age buckets, app versions, and many other segments.

Example

A visitor is coming to your website, and he’s seeking products, then if the products are interesting enough, he will add them to the basket and then end up buying them. Let’s explore three stages of a user journey:

1. Awareness stage – a user is on your website or an app, and they have a problem. You get the attention of your future customer or user.
2. Review – the visitor is on the product/service page and scrolling up and down. He gets familiar with your product or service. Maybe he is comparing prices, using provided filters, and estimating how much he needs the product. Anyway, you’ll never know what’s in his head. As a result, he sees the solution for his problem in your website or app.
3. Finally, he makes a decision. The visitor wants your product or service and makes a purchase. Congratulations, your visitor is now a customer.

That’s the ideal route, but sometimes visitors can go back and forth on the steps. Let’s dig a little deeper. Here’s where the true funnel analysis happens: If visitors are dropping off between 1st and 2nd steps, you should check whether there is enough information about the product. Are you providing helpful information like a help menu or a chat box, or anything visitors can use as guidance? If a visitor is dropping between the 2nd and 3rd steps, you should focus on prices, check competitors, and make sure you have a unique product or service. By observing where your visitors are dropping off, you can define your weak points and discover places where improvements should be made.

There is one final step – coming back or re-engaging. After seeing a customer making a purchase, you will want to invite them on another journey of being your repeat client. You can offer a discount on their next purchase, and sign for your newsletter, and like your social media page to support your cause.‍

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Pro Tip‍

High drop-off usually means UI problems. But before scratching everything and returning to the drawing board, examine the audience who are dropping. Are they teenagers or elderly? Are they your targeted audience? If not, it’s a better idea to remove them from your funnel analysis.

Conclusion

Funnel analysis is a useful process that will support you on your way to building an exceptional product. However, it’s not the final phase. If you want to know the middle steps that your user or a visitor takes, consider doing a Path analysis and examining where your users are getting confused. Path analysis is an integral part of conversion rate optimization. We’ll cover this in a future post.

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Want to see how Kubit can help you understand your user’s behavior? Get in touch with an expert to learn more.