In today's fast-paced business environment, it is crucial to have a clear understanding of your company's performance and customer behavior. One way to achieve this is by using the AARRR funnel, which stands for Acquisition, Activation, Retention, Revenue, and Referral. By implementing the AARRR framework, you can focus on the most critical AARRR metrics for your business and drive growth.

This article will give a detailed guide of what is AARRR funnel and the essential AARRR metrics to use in your business. Also we will show you how to quickly implement an AARRR framework for analysis using BI tool.

David McClure, founder of Practical Venture Capital, developed the AARRR framework, also known as Pirate Metrics, as a popular growth and success measurement model for Start-ups and SaaS companies. This framework segments the customer life cycle into five phases, each with corresponding metrics, to help you identify areas of strength and potential improvements in your conversion funnel, from initial customer interaction to the point where they become a paying customer. By using AARRR, you can gain valuable insights into the performance of each stage of the customer journey.

The AARRR model outlines the five key stages of the customer life cycle as follows:

• Acquisition: Understanding the sources from which customers are obtained.
• Activation: Strategies to encourage new customers to engage and become active users.
• Retention: Tactics to retain existing customers and encourage repeat usage.
• Referral: Methods to encourage customers to refer others to the product or service.
• Revenue: Techniques to convert prospective customers into paying customers and maximize revenue.

The initial encounter of a user with a company's online presence is referred to as acquisition. During this stage, users arrive from various channels such as social media, email, and SEO.

Measuring acquisition involves using metrics such as click-through rate, traffic per channel, visitor-to-lead conversion rate, and customer acquisition cost.

David McClure advises ignoring users who click-through and quickly leave the website, as they are likely accidental visitors. He suggests analyzing bounce-controlled click-through rates by channel to evaluate the quality and volume of users brought in by each channel. Are users staying on the website? Which channels are most active?

By answering these questions, businesses can allocate resources to the most effective acquisition channels.

In the acquisition phase, there are several metrics that you should track to evaluate the effectiveness of your marketing efforts, including：

• Customer acquisition cost per channel,
• Conversion rate
• Traffic is driven to the website per channel
• Click-through rate, cost per click
• Dwell time on the website
• Bounce rates
• Quality of leads

These metrics can provide valuable insights into how well your acquisition strategies are working and help you optimize your approach to attract new customers.

To achieve activation, it's not enough for a user to simply download an app or sign up for a service. They must become an active user by engaging with the product on an ongoing basis. While driving traffic is important, it's also crucial to conduct A/B tests and landing page tests to optimize conversion rates.

Tracking user behavior is key in determining what drives user commitment to a product. By analyzing user actions, such as spending more time on specific pages, you can identify areas that need improvement and optimize landing pages accordingly.

In the activation phase, users need to experience an "AHA" moment – the moment when they realize the value of the product and become motivated to continue using it. To facilitate this, it's recommended to create a quick onboarding process that allows users to quickly access the key features of the product and understand its value.

Some AARRR metrics to assess during the activation phase include the time it takes for users to see the value, the ratio of visitors to registrations, conversion rates, the number of customers utilizing important product features, the number of customers who experience the "AHA" moment, the rate of users dropping off, and the amount of time spent by users and pages viewed.

Retention is a continuous process, unlike the previous stages which are more specific moments. It involves transitioning a new user into a regular, long-term user who returns to the product or service.

Measuring retention involves examining metrics like the frequency of user visits to a website, whether a user is renewing a subscription, or how often a user is engaging with email content. To improve retention rates, McClure suggests sending regular, automated emails to new users during the first month of their site visit. These emails provide useful retention metrics, such as email open rates and click-through rates over time.

Useful methodologies in the retention phase include:

• Kano Model: This framework helps identify the most important features to customers and which ones to prioritize next.
• Journey Maps: This method helps identify the complete customer journey in your service.

Key metrics to measure in the retention phase include:

• Retention rate vs. churn rate
• Email open rate
• Email click-through rate
• Customer churn rate
• Time to recover customer acquisition cost (CAC)
• Average customer retention length (time customers remain active)
• Net Promoter Score (NPS)
• Infrequent logins

Encouraging word of mouth is a powerful way to boost brand awareness and growth. Consumers often rely on recommendations from people they know and trust, as well as online reviews. When customers are satisfied with a product, businesses can capitalize on this by incentivizing them to refer others. Referrals are an effective and cost-free means of customer acquisition, so companies should make it easy for users to refer to their products.

There are various ways to implement referral strategies, such as offering cash rewards to users who refer new customers, hosting social media contests that involve tagging friends or sharing posts, and asking for reviews. The Net Promoter Score (NPS) is another useful tool for measuring customer loyalty, by asking users how likely they are to recommend the product on a scale of 1-10. Companies can also follow up with customers to ask for feedback on why they rated the product in that way.

Several AARRR metrics can be used to measure the success of referral strategies. These metrics include the percentage of customers who refer their friends, the number of referred customers, the percentage of total purchases made by referred customers, and the lifetime value of referred customers. Additionally, positive reviews, social media shares, sent invitations, successful invitations, viral coefficient, viral cycle time, and Net Promoter Score are also important metrics to track in the referral phase.

Although customer satisfaction is important, product managers must also prioritize revenue generation. If a feature or product does not contribute to the company's financial success, it could have negative consequences.

In the revenue phase, two metrics are critical. The first is customer lifetime value, which calculates the total amount of money a customer spends on the product during their lifetime.

The second important metric is customer acquisition cost, which measures the cost of acquiring a new customer.

To sustain growth, the objective is to lower customer acquisition costs while simultaneously increasing customer lifetime value. This will enable the company to generate sufficient revenue.

In the revenue phase, several AARRR metrics can be used to measure the financial success of your business, including customer lifetime value, customer acquisition cost, monthly recurring revenue, the conversion rate of free customers to paying customers, average order value, repeated purchases, revenue churn, and expansion revenue. These metrics can help you assess how much money your customers are spending, how much it costs to acquire and retain customers, and how much revenue is being generated by the business.

The above are the five development stages of the AARRR model (AARRR Funnel), a data analysis framework that marketers or product managers can use to analyze product acquisition and profitability. It creates a closed loop of a product or service from customer acquisition to revenue and is supported by clear development stages and metrics for measurement and optimization. This helps marketers or product managers continuously improve the product's revenue model and promote beneficial growth for the product.

Each product may have unique characteristics, but overall they all go through these 5 stages of development. Instead of just talking about it, let's use an example to see how to apply the AARRR model (AARRR Funnel).

In this article, we will use the business intelligence self-service analysis software FineBI to analyze the 5 development stages of the AARRR Funnel for a grocery shopping app.

FineBI is a self-service analysis platform that can quickly build various business models and is a widely used enterprise-level business analysis tool for various data analyses.

It is professional, concise, and easy to use, with a clear interface and workflow, and each module has a clear functional partition. With the self-service data set function of FineBI, ordinary business personnel can drag and drop data to filter, slice, sort, summarize, and achieve the desired data results flexibly, and select intelligent push charts and dashboards to visualize the data.

Using FineBI, it is easy to build various classic data analysis models, such as the BCG matrix analysis, RFM model, ABC Analysis, etc., to help businesses gain insights. FineBI also has various types of built-in data analysis model templates, you can easily use these templates and build your business reports dashboard within one click.

FineBI provides business theme analysis scenarios for different industries, such as manufacturing, pharmaceuticals, retail, finance, and more. By analyzing and displaying business indicator data, relevant managers can easily grasp business dynamics.

FineBI frees business personnel from the tedious tasks of data processing and visualization, allowing them to focus more on data analysis, data management, and business communication.

Today, we are going to use FineBI to create an AARRR dashboard to conduct AARRR framework analysis. The dashboard preview is in the picture below which demonstrates the AARRR satges of an APP used for buying grocery online.

Gaining customers refers to drawing in new clients, which involves raising awareness about the app and persuading users to give it a try. Typically, there are various channels available to enhance product visibility, but the challenge is to identify the optimal channel and leverage it with minimal resources to achieve the greatest traction. Conducting a channel analysis is the initial step.

The analysis of channels generally encompasses two aspects: the quantity and quality of the acquired customers. (In this article, we will employ the average time spent browsing the app as the benchmark for assessing the quality of acquired customers.)

Conclusion: From the analysis, it is evident that offline activity promotion has the highest costumer quantity and quality, and thus increasing investment in offline activities is recommended. Offline activities in supermarkets or vegetable markets are the most favorable option.

Activation is not synonymous with successful registration. Activated users are those who actively engage with the core functions of the product, and not just passively registered. For instance, in short video software, a new user needs to watch videos for a certain duration, while in chat software, a new user needs to complete a conversation to be activated. In the case of grocery-buying APP, users who have made at least one purchase are considered active users.

To analyze the number of newly added users each month, we use FineBI to create the line chart to analyze the data:

Conclusion: Based on the analysis, it can be concluded that the activation rate witnessed a decline in October and further investigation is necessary to identify the underlying reasons. Additionally, efforts should be made to conduct targeted operations for new customers, such as displaying personalized product recommendations on the homepage to attract and retain users.

Retaining activated users is crucial because if they are not retained, their activation is essentially pointless.

Therefore, analyzing user retention is equally important. By referring to retention analysis, we can calculate the one-week, two-week, and 30-day retention rates of activated users.

In FineBI, data processing is also very convenient. Through functions such as filtering and merging calculation in FineBI, data can be processed quickly without writing SQL statements or other codes.

For example, let's take a look at how user retention rate is calculated in Finebi.

Click "Dashboard> New Dashboard", set the name and location, and click "OK", as shown in the figure below:

Click "+", select the "User retention analysis" dataset, and click "OK", as shown in the figure below:

Note: The sample data has already calculated the activate_login time difference. If the existing data has not been processed, you can use the new column time difference calculation.

3.2.1 Current day retention rate

Click "+", add a calculation indicator, and enter a formula: COUNTD_AGG(IF(Activate_login time difference=0, contact number, NULL))/COUNTD_AGG(contact number). The name of the input field is "Current day retention Rate", click "OK".

Formula description:

3.2.2 Retention rate in the first week

Click "+", add a calculation indicator, and enter a formula: COUNTD_AGG(IF(AND(Activate_login time difference>=1, activate_login time difference<=7), contact number, null))/COUNTD_AGG(contact number). The input field name is "Retention rate in the first week", click "OK".

The retention rates in the second week, third week, and fourth week are calculated in the same way.

Drag the "Earliest activation date" into the dimension axis, drag the retention rate indicator into the indicator axis, and set the "Earliest activation date" to be displayed as "Year Month", as shown in the following figure:

Get the monthly retention rate of activated users with the earliest activation date as the dimension.

Conclusion: The retention rate in the first week has dropped by more than 35% on average compared to the same day. It is necessary to increase user stickiness and increase product use value.

The relative decline of the retention rate in the fourth week has slowed down, indicating that a part of the conversion has been carried out, and it is necessary to carry out refined operation and management of these users to help users stabilize the conversion.

After a user is activated, it is important to focus on how to generate revenue and achieve profitability. The profitability of food shopping software is determined by various factors. One of the main ways to increase revenue is to boost users' purchasing activity.

To facilitate analysis, users can be classified into three categories: low active users, regular users, and members. These categories can be visualized using a funnel chart in FineBI, as illustrated below:

FineBI supports over 50 chart styles, basically covering all the basic chart types on the market, with excellent dynamic effects and powerful interactive experiences. When used, it can set various characteristics according to the needs, and can also perfectly adapt to display on mobile devices and LED screens.

Conclusion: there is a large number of low active users with great potential, and it is crucial to activate them and retain member users.

When a product reaches a certain number of users, it is important to consider ways to encourage spontaneous communication among users. The self-propagation data index, known as the K value, is a recommended coefficient calculated by multiplying the average number of invitations sent by each user to their friends with the conversion rate of those who received the invitation into new users.

K = (the average number of invitations sent by each user to his friends) * (the conversion rate of those who received the invitation into new users)   

The K value directly reflects the level of self-propagation results, with a value greater than 1 indicating a strong force of self-propagation. The larger the K value, the stronger the force. Conversely, if the K value is less than 1, the transmission level gradually weakens until it disappears. The K value of the APP can be calculated, as shown in the figure below.

In conclusion, the APP has a K value that exceeds 1, indicating a strong self-promotion power. Operational activities such as "invite to receive a red envelope" can be used to further increase the K value and accelerate the dissemination speed.

By understanding and implementing the AARRR funnel, and using Finebi analytics tool, you can effectively measure and optimize your business performance. You can track your customers' behavior, identify areas for improvement, and make informed decisions to drive business growth. Try Finebi today and see how it can help you achieve your business goals.