Mastering Sales Funnel Analysis: Tips for Optimizing Conversions

The sales funnel organizes the prospect's (or customer's) journey into stages, so that marketing and sales know which message and which next step to offer at each moment. When you measure customer prospecting and conversions by stage, it's easier to prioritize channels, reduce waste and speed up decision-making based on data.

In this post, you'll learn more about what a sales funnel is and how to analyze data from your organization and previous strategies to be able to use this method, which is so efficient and necessary for the development of an institution.

Sales funnel and data analysis to attract students and clients

The sales funnel organizes the potential student or client's journey into stages, from first contact to closing. This helps marketing and sales know which message to use and which next step to offer at each moment. When you measure prospecting and conversions by stage, it's easier to prioritize channels, reduce waste and make decisions based on data. In practice, the funnel usually has a top, middle and bottom, with different goals at each stage, and should be adjusted with continuous analysis of metrics, indicators and sources.

• Identify which stage each lead is at and define the appropriate approach.

• Standardize events and conversions to reduce noise and compare channels.

• Choose metrics, indicators and sources that are consistent with your objectives.

• Use the data to decide where to optimize attraction, qualification and conversion.

• Monitor periodically and record changes to validate improvements.

What you'll see in today's content

• What the sales funnel is and why it follows the customer from first contact to closing.

• How the funnel is divided into top, middle and bottom, with different goals and approaches at each stage.

• Why analyzing data is essential for designing an efficient funnel and better understanding your audience.

• How to define analysis objectives and filter out data that is really useful for attracting customers.

• Which metrics to prioritize in the funnel, including standardizing conversions as key events in Google Analytics 4.

• How to plan the analysis, define those responsible, the timetable and consider budget and tools such as CRM.

• How to create hypotheses, questions and choose data sources, including standardizing steps and paying attention to the GDPR.

• Which indicators to use, how to apply the data to decision-making, use support tools and maintain continuous monitoring.

Let's read on!

What is a sales funnel?

The sales funnel is a process in which a business accompanies a customer from the moment they first contact you until the moment they buy your product or service, considerably helping you to attract customers online.

Its main objective is to provide greater knowledge of the consumer's buying journey, identifying triggers and opportunities to convert users into potential buyers more efficiently and cheaply.

How is the sales funnel divided?

The sales funnel is usually divided into three stages: top, middle and bottom. At each stage, the audience is at a different level of awareness and trust, so the goal also changes: attract, engage/qualify and convert. Here's how to identify the profile and the best approach at each stage.

• Top of the funnel: at the top of the funnel are those users who are having their first contact with the institution, often by reading a blog post or posting something on social networks.
• Middle of the funnel: when they advance to the middle of the funnel, these users already have a certain level of engagement with your brand and, above all, show the potential to become future customers.
• Bottom of the funnel: here, the focus is on turning these potential customers into actual consumers. They already know and trust your brand, so this is the ideal time to make a more direct sales approach.

How do you analyze data for use in the sales funnel?

Just as important as understanding what the sales funnel is and its importance is knowing how to design it efficiently, and this is only possible if you have an efficient data analysis base that helps you understand your audience better.

So here are some tips on how to do this analysis correctly and how to implement it in your sales funnel, with a view to using it to attract more and more qualified leads and customers.

1. Define the objectives of the analysis

The first step is to define an objective for this analysis, which in our case is precisely to use the sales funnel. This is necessary because it is possible to collect a wealth of information about the customer, the market and the company itself.

Having access to information may seem beneficial at first, but all this data will actually be useful for attracting customers, so it is necessary to filter out those that really make sense for our objective.

Which metrics to focus on for the sales funnel?

Well, the truth is that there are several metrics that can be analyzed to structure an efficient sales funnel .

If you use analytics on your website, it's worth standardizing conversions as key events in Google Analytics 4, marking as "key event" only those actions that represent real progress in the funnel (e.g. form submission, WhatsApp click, appointment booking, registration). This reduces noise, improves comparisons by channel and makes the report more actionable.

• Value and volume of opportunities (at each stage of the funnel);
• Time the user spends at each stage;
• Source and quantity of leads;
• Conversion rate;
• Among others.

2. Make a plan

Once you have defined your objective, you need to make the data analysis planning more complete, so you need to define a timetable for carrying out this process and who will be responsible for it.

A timetable will make it easier to structure data analysis and ensure that your team (or you yourself) don't waste time on things that aren't necessary for the objective you set.

Defining who will be responsible for collecting this data will help you identify a competent person to carry out this task, who probably already has a knowledge of data analysis or even the sales funnel.

Another point that can also be seen during the planning stages is the allocation of budget for this activity, if necessary (as in the acquisition of CRM tools).

See also:

• The use of structured data in customer retention
• Learn how chatbot AI can help your educational institution
• Data Analysis: Really get to know your customer and make fewer mistakes

3. Define your hypotheses and questions

Another way to further target the data collected to optimize the sales funnel is to ask some questions and formulate hypotheses that must be answered by the information that will be worked on.

Questions such as:

• "When is the lead discovering that they should invest in my solution to solve their problem?"
• "Where are the leads coming from?"
• "How long is it taking for a user to progress through the funnel?"
• "What are the objections that leads are stating to not becoming customers?"

They help you not only understand your customer better or create a better funnel, but also identify problems and opportunities for improvement that will make this consumer journey faster and more assertive.

With these and other relevant questions in mind, it will be easier to identify what data should be collected to answer them, as well as what sources will be used to gather them.

4. Choose the sources of the analyzed data

Another issue that must be well defined for the data analysis process concerns the sources from which the information will be collected, promoting greater confidence in the final report.

When collecting browsing data and forms, also include a check for compliance with the General Data Protection Act (LGPD). On websites, the ANPD Guidance on Cookies helps to differentiate between necessary and non-necessary cookies and to define transparency and legal basis, especially when there is measurement, advertising or embedded content.

Data sources for the funnel can come from internal systems and digital channels. As well as databases, data lakes and data warehouses, consider CRM, analytics platforms (websites/landing pages), paid media, email/automation, chat/call centers and social networks. The ideal is to standardize the names of stages and events in order to compare performance between channels without "mixing" criteria.

The sources for collecting information will depend on the type and size of the institution, since a smaller institution, for example, will not be able to or need to deal with a large database.

However, all the help you can get will help you find the information you need to answer the questions listed in the previous topic, as well as getting to know your own customers better.

5. Create indicators

With all the planning done so far, it's time to list the indicators that will be used to collect the information and analyze this data correctly, with a view to using it in the sales funnel.

Again, you need to consider the purpose of the data collection and the questions and hypotheses raised.

For example, to answer the question "where do the leads that convert the most come from?", you can use the indicator "conversion percentage by channel", which will show which of all the communication channels used are giving the best results.

Below are some other indicators commonly used to analyze them and the sales funnel itself:

• ROI (return on investment): shows how much of the money invested in a particular strategy is coming back to the organization. A high value means that the method adopted has been effective.
• CAC (cost per customer): represents the amount being spent to win just one customer, which also involves costs related to marketing and sales strategies.
• NPS (Net Promoter Score): this is a great indicator of customer satisfaction, as it analyzes the likelihood of this consumer referring the company's products or services to other people.
• Conversions at each stage of the funnel: although conversions happen more frequently at the bottom of the funnel, it is also possible for this process to occur at other times, something that should be analyzed and used to the advantage of the strategy itself.

6. Use data to make decisions

Once the data has been properly collected, it's time to use this information in practice, to make decisions about the sales funnel and other marketing and sales strategies.

If you notice that the majority of your conversions into leads are coming from the blog, for example, it's worth making decisions aimed at optimizing the attraction of audiences derived from this communication channel and, at the same time, investigating why the others aren't performing so well.

7. Use support tools

Collecting, analyzing and monitoring (something we'll talk about in the next topic) this data is not such an easy task, especially if it's done completely manually. That's why it's worth looking into the possibility of investing in tools to optimize this process.

A CRM system, for example, will make it easier to monitor conversions and sales made by the institution using the sales funnel strategy. Some of them even allow you to have a profile of each lead.

Image: Tracking metrics and graphs helps you understand each stage of the sales funnel and improve conversions.

8. Keep monitoring the information

If you think that all these steps only need to be done once, you're wrong: your audience's preferences change constantly and it's important that your sales funnel, as well as the whole business, is constantly updated.

Define a periodicity for analyzing the funnel data (weekly, fortnightly or monthly, depending on the volume of leads) and record what has changed in each round. This way, you can validate whether optimizations, such as adjustments to pages, ads and commercial approach, have really improved conversion, cycle time and lead quality.

To sum up: in order to analyze the data that will be used in the sales funnel, you need to define your objectives, define which metrics, indicators and sources will be used, as well as using this information for strategic decision-making and monitoring your results.

This way, by knowing the best tips on how to analyze data and use a sales funnel, you'll be able to better understand your audience and what adaptations you need to make to your strategies, with the aim of improving the customer experience with your brand and prospecting more and more customers!

And to help you work better with this data to achieve your company's goals, check out this other post on our blog, in which we explain how to apply SMART goals to your business.

Frequently asked questions about sales funnels and data analysis

What is a sales funnel?

The sales funnel is a process in which the business follows the customer from the first contact to closing the purchase. It helps to understand the buying journey, identify triggers and opportunities and make conversion more efficient and cheaper.

What's the point of organizing the journey into stages?

Organizing the journey into stages allows marketing and sales to know which message and which next step to offer at each moment. It also makes it easier to measure prospecting and conversions by stage, prioritize channels, reduce waste and speed up data-based decisions.

How is the sales funnel usually divided?

The sales funnel is usually divided into three stages: top, middle and bottom. At each stage the public has a different level of awareness and trust, which is why the goal changes between attracting, engaging or qualifying and converting.

What characterizes the top of the funnel?

At the top of the funnel are users in their first contacts with the institution. This often happens through reading a blog post or posting on social networks.

What changes in the middle and bottom of the funnel?

In the middle of the funnel, users already have a certain level of engagement with the brand and show the potential to become future customers. At the bottom of the funnel, the focus is on turning potential customers into consumers, with a more direct sales approach, because they already know and trust the brand.

Why is analyzing data essential to using the funnel effectively?

Analyzing data is essential because designing an efficient funnel depends on understanding your audience better and making decisions based on useful information. Without well-defined objectives, metrics and sources, data collection can generate a lot of data that doesn't help to attract leads and customers.

How do you define objectives for analyzing funnel data?

Defining objectives means choosing a clear direction for analysis, such as using the sales funnel to guide marketing and sales actions. This is important because there is a plethora of possible information, and you have to filter out what really makes sense for the objective.

What metrics can be used to structure an efficient funnel?

Metrics can include the value and volume of opportunities per stage, the user's time at each stage, the source and quantity of leads and the conversion rate. It is also possible to standardize conversions on the site as key events in Google Analytics 4, marking only actions that represent real progress in the funnel.

How do you plan data analysis and define responsibilities?

Planning the analysis involves creating a timetable and defining who will be responsible for collection and follow-up. This helps avoid wasting time on activities that don't contribute to the objective and makes it easier to choose people with knowledge of data analysis or the sales funnel.

How do hypotheses and questions help optimize the funnel?

Hypotheses and questions help direct what data to collect and what to investigate to improve the journey. Examples include understanding when the lead realizes they need to invest in the solution, where the leads come from, how long it takes to progress through the funnel and what objections prevent conversion.

What can be the sources of funnel data and what should be standardized?

Sources can come from internal systems and digital channels, such as CRM, analytics platforms, paid media, email and automation, chat and customer service and social networks, as well as databases, data lakes and data warehouses. It is also advisable to standardize the names of stages and events in order to compare performance between channels without mixing up criteria, and to check compliance with the GDPR when collecting browsing data and forms.

What indicators are cited and how do they help with decision-making?

The indicators cited include ROI, CAC, NPS and conversions at each stage of the funnel. They help to answer questions such as which channels bring in the most converting leads and to make decisions, for example by optimizing the blog if it is generating the most conversions into leads.

How do you use data in practice and keep the funnel up to date?

Using data in practice means making decisions about the funnel and marketing and sales strategies based on the results and investigating underperforming channels. To keep the funnel up to date, you need to periodically monitor the information, record what has changed in each round and validate whether optimizations have improved conversion, cycle time and lead quality.