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.

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.

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

What Is Web Analytics? Here is All You Need to Know!

Web analytics is the analysis of how users interact with and behave on a website. Web analytics solutions track a variety of aspects of user activity and behavior on a website, including the number of visitors, the length of their visits, the number of pages they see, and other key performance indicators relevant to a website’s goals.

An organization’s key performance indicators (KPIs), such as the purchase conversion rate, click-through rateand bounce rateare measured and benchmarked using web analytics tools.

What Is The Significance Of Web Analytics?

In order to provide an optimized user experience, data must be collected on how those users interact with a website. That data can then be used for things like design iteration, Conversion Rate Optimization, and Marketing Campaign Optimization.

By using the insights provided by web analytics, organizations can properly promote the right products to the right people. The data-backed decision-making provided by web analytics takes the guesswork out of website optimization and allows for companies to maximize ROI.

How Are Web Analytics Used?

There are a variety of web analytics tools that allow for simple integration and provide meaningful insights on User Behavior. The complexity of these tools can vary greatly depending on the depth of insights desired on a particular website.

Many web analytics tools use click-counting tags to get a high-level overview of which pages users visit on a website. The tag may also collect other information, such as the kind of device, browser, and location of the user (via IP address).

Cookies may also be used by web analytics services to keep track of individual sessions and assess whether a particular browser is returning to a website repeatedly.

Although cookies have been a popular method of tracking web analytics, many users are now blocking cookies from their browsers through things like ad blockers and VPNs. This makes cookies an increasingly unreliable source of data.

Best Practices For Web Analytics To Improve Your Website

Finding meaningful insights through web analytics and reporting those findings to others in your organization can be a tricky process. Here are a few things to remember while working in this area:

1. You Should Not Simply Provide Traffic Updates

Reporting on traffic, page views, top sources, or top pages merely scratches the surface. If there is more traffic or time spent on the site, it doesn’t always imply that the site is successful. When it comes to measuring the performance of your program, having 7 million visitors is a non-essential metric.

2. Insights Should Always Be Provided Alongside The Data

It’s a waste of time and effort to provide analytics to your stakeholders without providing any context to your company or user objectives. Show how your site’s data points to areas of success and development by highlighting the data’s relevance to your site.

3. Avoid Focusing On A Single Point In Time While Reporting

There are more complicated and deeper web experiences occurring today than can be captured by looking solely at visits or a given time period. It is possible to assess how well your website is performing as it develops and interacts with visitors, particularly recurring ones, by looking at metrics such as visits, user-lifetime value, and other values that give a more long-term knowledge of people and their use

4. Be Unambiguous In Your Communication With Key Stakeholders

Make sure your stakeholders are aware of the system’s flaws and that the information you offer is accurate and up-to-date, and that you are aware of your audience.

To learn more about how Kubit can help with your analytics, click here!

What Is Retention Rate? – Here’s What You Need To Know

Retention rate, also known as customer retention rate, is an important metric that measures how well a company keeps its existing customers. But what is it and how is it measured?

What Is Retention Rate? 

Retention rate is a metric that measures the percentage of customers who return to use a product after they have already used that product. An example of this could include how many past customers return to make another purchase.

It’s calculated by dividing the number of customers who make a second purchase by the number of past customers. So if 50 out of 100 former customers return to buy from you again, your retention rate would be 50%.

Your retention rate can tell you whether or not customers are happy with your service or product, whether they feel satisfied with the value they receive, and if they have any gripes or issues that need to be addressed. You can use this data to make changes in your business practices.

Why Should You Care About Your Retention Metrics?

Active customers are more likely to purchase more products and refer friends to your company. They’re also more likely to stick around longer than people who aren’t engaged with your brand.

This form of user behavior data can help you understand why customers might stop using your product by looking at their behavior patterns after they leave.

For example, suppose 80% of users exit the app within 30 seconds.

  • They give your insight into your customers’ behaviors.
  • They help you understand what they like about your product or service.
  • They reveal which features are most important to them.
  • They can help you predict churn rates and plan for it before it happens.

Customer Retention Strategies

Customer retention strategies are the key to a successful business.

Don’t let your customers slip away! Here are five ways you can keep them coming back for more:

  • Be Responsive 

If a customer has a problem or question about one of your products or services, don’t make them wait on hold for hours or go through multiple phone calls before answering their question.

  • Offer Great Customer Service 

A good reputation is everything in business. If your customers have a negative experience with your business, they will tell their friends and family, who will say to their friends and family, etc. If they have a positive experience, they will tell that to others.

  • Communicate Regularly 

Customers like knowing what’s going on with their order status and when it will arrive at their doorstep (or doorstep). It also reduces confusion if there is an issue with their order or delivery schedule (which happens more often than not). You can communicate with customers via email newsletters or social media platforms like Facebook.

  • Understand your Customer’s Behavior

Getting to know how your customer interacts with your product is a key aspect to keeping them engaged. Using Product Analytics tools like Kubit can provide your team with valuable insights that will help refine your product experience and provide an optimized experience for your customers.

Bottom Line

To summarize, the definition of retention rate is the sustained or repeated use of a product. To reach an effective and long-lasting business, you need to retain your users perfectly.

Retention can be utilized to improve user loyalty to your product. In addition, it can be considered a tool for driving sales and revenue in your company.

Click here to learn more about Kubit and how it can help optimize your retention rate.

What is MAU and Why is it Important? 

What is MAU?

Monthly Active Users (MAU) is an engagement metric referring to the number of unique users that have interacted with a product or service within a given month. As a key performance indicator (KPI), MAU is an important aspect of measuring the health of many businesses.

The definition of terms like “users” and “active” might vary from business to business, since there is no industry standard. But, below are some general guidelines of how they are determined

Users: when a business is determining MAU, a user is an individual who has performed some sort of in-app or on-site action during a 30-day period. This action could include logging in, completing a specific conversion, or executing a certain number of interactions. This individual will only be counted once regardless of how many interactions they have.

Action: Any business can determine how they want to define an action. But, in many cases, an action included in MAU can be opening an app, logging in, or performing a set number of interactions with a product or service.

Why is MAU important?

For most businesses, it is important to know how users interact with their product or service. A high MAU can generally indicate a good rate of product engagement and positive retention over a certain period of time.

By measuring MAU, a business can also determine the effectiveness of its marketing strategies, product iterations, and customer experience. MAU can also be used to determine other engagement-related KIPs including:

  • Retention
  • Churn
  • Growth rate
  • Conversion rate
  • Revenue per active use

On top of the many internal uses for MAU, it can also be used by outside sources to determine the overall health of a company. Public organizations like Facebook and Twitter regularly publish their MAU numbers in order for investors to view the performance of their user base.

Why Kubit Uses MAU

Kubit uses MAU as the key metric to determine pricing for our customers. Using MAU as a basis for pricing gives Kubit’s customers cost predictability and freedom to use all of the collected data transparently. Other vendors in the Product Analytics space would typically charge per data volume which leads to unpredictable costs and sometimes would force users to start sampling their data. Kubit looks to avoid these issues by providing an easy, scalable, MAU-based solution to pricing.

To learn more about Kubit’s services and how we calculate our pricing, click here.