Article

17min read

Everything You Need to Know About Conversion Funnels

Any business selling products or services online has a conversion funnel — but not everyone realizes it. If you’re unsure what a conversion is or how you can refine yours to sell more online, you’re in the right place. In this post, we’re going to take you through everything you need to know about conversion funnels. We’ll start with the basics — what conversion funnels are and the three key stages — before moving on to some of the most effective strategies to improve your funnels to increase sales. Let’s get stuck in!

In this article, we’ll cover:

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What is a conversion funnel?

A conversion funnel is a process that takes potential customers on a journey towards buying your products or services. They’re the cornerstone of all e-commerce business models, guiding potential customers from the moment they first become aware of your brand to the moment they make a purchase and beyond.

Conversion funnel schema
Conversion funnel schema (Source)

If you’re new to conversion funnels, think about the shape of a funnel — it’s wider at the top and narrower at the bottom. This represents the flow of people through your marketing strategy. Not everyone who becomes aware of your business will go on to become a paying customer. It’s like brewing coffee using a drip filter — a large volume of coffee grounds go into the top of the brewing equipment and then the funnel filters the high-quality stuff out of the bottom into your mug. A sales funnel works in the same way. The goal is to get as many relevant leads into the top of the funnel as possible, filtering out unsuitable prospects to leave your ideal customers ready to buy from you.

When you optimize your conversion funnel, you maximize the impact of your online marketing strategy and boost sales. This isn’t a once-and-done exercise, but something you need to continually refine throughout your business life. Do you want to know how to do it?

Coffee serveware, funnel
Coffee serveware, funnel (Source)

What’s the difference between a conversion funnel and a sales funnel?

The terms conversion funnel and sales funnel are often used interchangeably, but are they the same thing? The answer to this question is no, although they are closely related. A sales funnel typically starts when a potential customer enters the sales pipeline. This can happen online (in an e-commerce environment) as well as offline. However, a prospect typically doesn’t enter your sales funnel until they’re already familiar with your brand and your products or services.

It can take a while to get to this point in the online world, particularly if you’re targeting people who have never heard of your brand before. It takes time to build a connection and trust with your audience.

This is where a conversion funnel comes in. Here, the focus isn’t just on making a sale. It’s about making a connection with your audience, generating leads, and then taking those leads on a journey with your company. Potential customers might come into your funnel cold, without much awareness of who you are or what you do. Over time, your funnel will warm them up, build trust in your offer, and get them ready to buy. It encapsulates the whole process — from the first contact through to purchasing.

The three conversion funnel stages

There are many different conversion funnel models out there. All of them broadly suggest the same thing: breaking the process down into several conversion funnel stages the leads must travel through before making a purchase. Although a customer may enter or exit the funnel at any stage, your personalized model sets out how you intend customers to connect with your business.

The exact model will look different for every organization, but here are the three stages we suggest you follow.

Stage 1: Building awareness at the top of the funnel

The top of the funnel is all about making people aware of your brand and capturing leads. This stage is arguably the most crucial. If you don’t get people into your funnel, how are you going to sell to them? This critical step is often referred to as the awareness stage, and the exact strategy you use to do this will depend on your ideal customer. Who are they? Where do they hang out? What are their fundamental problems and challenges? Why would they be interested in what you have to offer them? The answers to these questions can provide useful directions during the awareness stage. Remember: this isn’t about you; it’s about the customer. Here are a few things that should be happening at the top of the funnel.

Content marketing

To grab attention online, you’re going to need content. This content can take many forms, so it’s essential to think about the types of content your audience is most likely to consume. For example, TikTok videos will likely appeal to 18 to 24-year-olds, but they might not be the best option if you’re targeting an older demographic.

You should consider both onsite and offsite content when outlining your content marketing strategy. An effective conversion funnel needs both. Offsite content helps capture attention and attract people to your website. In contrast, onsite content engages your audience and encourages them to take the next step, such as signing up for your mailing list.

Marketing campaigns

Alongside your content marketing strategy, you should also consider the marketing campaigns you will be running to get people to engage with this content. How will you get your content seen? How will you capture users’ attention? Are you only operating online, or will you use offline marketing to generate leads?

Often, e-commerce businesses are quick to dismiss offline marketing campaigns as irrelevant. However, highly targeted offline campaigns can be extremely useful. The online marketplace is crowded! If you can think of innovative ways to reach your audience offline and direct them to your online content, it could turn out to be a cost-effective way to generate leads for your conversion funnel.

You could also consider how you might automate some of your marketing campaigns. Creating evergreen campaigns that can run in the background while you and your employees focus on other tasks is useful to maximize profits. In essence, it means you can be generating leads for your business while you sleep.

Lead capture

Lead capture is the final step of the awareness stage. It’s where you move your prospects from the top of your conversion funnel to the middle. Once you’ve directed a potential customer to your website and encouraged them to engage with your content, what’s next? Each piece of content your audience engages with on your website should have a call to action — something that tells them what action to complete next.

To achieve this, you might want to consider a lead magnet. This can be something as simple as a discount code. But, for maximum results, you could develop something that helps solve a problem directly related to the product or service you’re offering.

Not only does this ensure you’re capturing highly qualified leads, but it also means people are likely to sign up even when they’re not ready to make a purchase. Given the point of a conversion funnel is to get them ready to buy from you, this is a vital point to consider when outlining your content marketing strategy.

Once you have that email address, it’s time to move on to the second stage of the conversion funnel: nurturing your audience to build desire for your products or services.

Business dashboard
Business dashboard (Source)

The best ways to build awareness

To maximize the number of leads you’re capturing, you should focus your stage one activities across a range of digital marketing channels. Here are some of the most popular options:

Social media

Given there are almost 4 billion social media users worldwide (over half the world’s population), it’s no surprise social media marketing is one of the most popular ways to generate leads. That said, it’s important to note it isn’t an easy option! Many business owners expect social media to be a fast and cheap way to grow an audience. Still, it takes time and persistent effort to get results — just like any other marketing strategy.

Work with a professional to develop a social media marketing plan that helps you stand out from the crowd. Many businesses use social media to attract people into their conversion funnel, but few do it well.

Paid search

What’s the first place you turn to when you need information? It’s estimated there are around 2 trillion Google searches every year — so advertising your content on Google could potentially be very lucrative! Unlike social media marketing, people using search engines are actively looking for the information you’re providing. To get the best click-through rate, make sure the phrases you’re targeting are directly relevant to the content. And test campaigns with a small budget before increasing your spending.

Organic search

It’s also a good idea to optimize your content for organic search. While this isn’t a short-term strategy, Search Engine Optimization (SEO) can deliver large volumes of traffic to your website over time. Focus on creating evergreen content — content that doesn’t become irrelevant or outdated and can appear in organic searches for many years to come. When you gain website visitors organically from search engines, you improve your ability to build a list of qualified leads, improving the quality of people entering your conversion funnel.

Stage 2: Nurturing your audience

Many online businesses make the fundamental mistake of pushing for a sale too soon. While you can (and should) always have an option for potential customers to buy from you on their terms, you should design your conversion funnel to nurture your leads, building trust with your brand before moving them into the sales pipeline.

Staying in contact

Once a potential customer has told you they want to hear more from you, it’s essential to stay in touch with them. If you can, you should aim to use multiple channels to do this. Encourage them to follow you on social media, re-target them with relevant online content, and send them regular emails. Research consistently shows the more opportunities a potential customer has to engage with your brand online, the more likely they will buy from you.

In short, it’s not enough to let people know you exist. If you want to sell to them, you need to put in the work to keep them engaged!

Positioning your products and services

As you stay in touch and nurture your audience, you should also ensure each lead is familiar with your products and services. This step isn’t about pushing for the sale — we’ll come back to this in the next stage — but you should be introducing your offering interestingly and engagingly. Essentially, we need your leads to be ready to make a purchase when you deliver your sales pitch. To get to this stage, they need to know what you’re selling.

Building a desire to buy

And finally, throughout the nurturing stage, you should be gearing up your audience to perform the desired action. In most cases, this is completing a purchase. How do you do this? Use emotion.

Humans are emotional beings. Remember earlier when we discussed the problems and challenges your product or service can solve for your customers? What are the emotions behind that problem? Aim to appeal to these emotions when engaging with your audience, and make it clear that you’re here to help them overcome these feelings to foster more positive and desirable emotions. How will your product or service make them feel? Can you impart some of these feelings with your content?

As well as feeling emotion, people have an inbuilt desire to be understood. The more you can show them you understand them, the more they will connect with your brand, and the more desire they will have to do business with you.

Throughout this step, you should be keeping your competitors in mind, especially if you’re operating in a competitive niche. Why should your audience choose you above your competition?

Stage 3: Convert potential customers into paying customers

Stage three is what it’s all about — securing the sale. Without this stage, your business is nothing — without paying customers, you have no profits. But we hope you now appreciate why it’s important to take your audience on a journey through the preceding stages before you attempt to convert them. Once you’ve optimized your funnel, your leads will now be ready to buy from you.

Continue to nurture leads

It’s crucial to be aware of this: you don’t stop nurturing your prospects once you get them to the end of your funnel. This stage should continue as long as your leads — and eventual customers — are in contact with your business.

Work at your potential customer’s pace

It’s also important to remember your potential customers will all travel at their own pace. Some will be ready to make a purchase sooner than others. For this reason, you should think of your conversion funnel as a process. It isn’t about throwing leads in at one end and spitting them out at the other side but about fostering connections that will help your organization thrive over time.

If you attempt to trigger a sale, but your customers aren’t ready, you should continue to engage and nurture them — and try again further down the line. Similarly, if none of your prospects are buying from you at this stage in your conversion funnel, it’s a sign something needs tweaking — we’ll get back to this in a little while.

Trigger a Sale

Now it’s time to encourage your leads to become paying customers, but how should you do it? As always, there are many options here. Finding the right approach will likely involve some trial-and-error. It’s a good idea to test out a few sales tactics and see what works. For some, a simple email or retargeting campaign on social media might do the trick. But for other businesses, you might need to come up with something more personal or creative.

What makes a good call-to-action?

Calls-to-action are the lifeblood of any effective conversion funnel. But how can you make sure yours are effective? Here are some tips to get you started.

Be clear and concise

Your call-to-action shouldn’t be too wordy. It would be best if you were direct. Use short sentences and tell your audience exactly what you want them to do. Use verbs like “buy,” “shop,” or “download.” Telling someone to “shop the new collection” is likely to result in more sales than something like “our new collection is now live on our website.”

Ask yourself why

As you develop your call-to-action, put yourself in your potential customer’s shoes. Why should they do what you’re asking them to? This is where the copy in the rest of your sales pitch comes in. The call-to-action is the final piece of the puzzle. By the time your lead gets to this part of your content, they should already be ready to hit that button. Make it a no-brainer for them.

The role of the shopping cart

The shopping cart on your website can be one of your biggest assets for driving sales. Did you know you can follow up on abandoned carts with your email subscribers? If not, you’re missing out on one of the most effective conversion tools available to e-commerce businesses. Research suggests around 70% of all shopping carts are abandoned online. Think about it: these are leads that have been through the conversion funnel and are almost ready to make a purchase. What is it that stopped them? It might have been something as simple as an interruption. Get back in touch and ask them if they’re ready to complete their purchase. The results may surprise you.

Evaluating your funnel with conversion funnel metrics

As we mentioned at the start of this post, a conversion funnel isn’t something you can create and then forget about. It’s an ongoing, interactive process that you must refine over time. The digital marketing world is dynamic and ever-changing — and your conversion funnel will need to evolve alongside industry trends and technological advances. Evaluating your funnel is an essential part of this, enabling you to improve each stage of the process to generate more qualified leads and convert more of them into paying customers.

Your first step should be to set up Google Analytics to track your conversion funnel. When you do this, you can track a lead from the moment they join your funnel until they make a purchase. This gives you an overview of how well your funnel is performing, as well as helping you access some of the key conversion funnel metrics that help you decide what to focus on next, such as:

Cost per acquisition (CPA)

Marketing costs money and the expenses associated with your conversion funnel can quickly mount up. It’s vital to understand the benefit these investments bring. What is the return on investment (ROI) associated with your conversion funnel? To understand this, you need to calculate your cost per acquisition. To calculate this, divide the costs associated with your conversion funnel by the number of paying customers the funnel generated in the same time period. For example, if you invested $500 and generated 10 paying customers, your CPA would be $50.

You can then compare this with the average spend to figure out whether your conversion is profitable or not. Using the example above, if the average customer spends $200, your funnel is profitable. On the other hand, if the average lifetime spend is $20, the funnel is operating at a loss.

Conversion rate

Google Analytics calculates your funnel’s conversion rate by working out how many of the visitors went to the goal page (e.g., “thank you for your purchase”) as well as one of the pages associated with the earlier stages of your conversion funnel. This provides you with useful insight into how well your funnel is working over time, which can help you evaluate any changes that you make to optimize the funnel.

Are you ready to optimize your funnel?

In summary, conversion funnels are an essential asset to all e-commerce businesses. If you want to improve sales, optimizing your funnel is often the best place to start. What steps will you take after reading this post?

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Article

13min read

Better Understand (And Optimize) Your Average Basket Size

When it comes to using A/B testing to improve the user experience, the end goal is about increasing revenue. However, we more often hear about improving conversion rates (in other words, changing a visitor into a buyer). 

If you increase the number of conversions, you’ll automatically increase revenue and increase your number of transactions. But this is just one method among many…another tactic is based on increasing the ‘average basket size’. This approach is, however, much less often used. Why? Because it’s rather difficult to measure the associated change.

A Measurement and Statistical Issue

When we talk about statistical tests associated with average basket size, what do we mean? Usually, we’re referring to the Mann-Whitney-U test (also called the Wilcoxon), used in certain A/B testing software, including AB Tasty.  A ‘must have’ for anyone who wants to improve their conversion rates. This test shows the probability that variation B will bring in more gain than the original. However, it’s impossible to tell the magnitude of that gain – and keep in mind that the strategies used to increase the average basket size most likely have associated costs.  It’s therefore crucial to be sure that the gains outweigh the costs. 

For example, if you’re using a product recommendation tool to try and increase your average basket size, it’s imperative to ensure that the associated revenue lift is higher than the cost of the tool used….

Unfortunately, you’ve probably already realized that this issue is tricky and counterintuitive…

Let’s look at a concrete example: the beginner’s approach is to calculate the average basket size directly. It’s just the sum of all the basket values divided by the number of baskets. And this isn’t wrong, since the math makes sense. However, it’s not very precise! The real mistake is comparing apples and oranges, and thinking that this comparison is valid. Let’s do it the right way, using accurate average basket data, and simulate the average basket gain. 

Here’s the process:

  • Take P, a list of basket values (this is real data collected on an e-commerce site, not during a test). 
  • We mix up this data, and split them into two groups, A and B.
  • We leave group A as is: it’s our reference group, that we’ll call the ‘original’.
  • Let’s add 3 euros to all the values in group B, the group we’ll call the ‘variation’, and which we’ve run an optimization campaign on (for example, using a system of product recommendations to website visitors). 
  • Now, we can run a Mann-Whitney test to be sure that the added gain is significant enough. 

With this, we’re going to calculate the average values of lists A and B, and work out the difference. We might naively hope to get a value near 3 euros (equal to the gain we ‘injected’ into the variation). But the result doesn’t fit. We’ll see why below. 

How to Calculate Average Basket Size

The graph below shows the values we talked about: 10,000 average basket size values. The X (horizontal) axis represents basket size, and the Y (vertical) axis, the number of times this value was observed in the data.

It seems that the most frequent value is around 50 euros, and that there’s another spike at around 100 euros, though we don’t see many values over 600 euros. 

After mixing the list of amounts, we split it into two different groups (5,000 values for group A, and 5,000 for group B).

Then, we add 3 euros to each value in group B, and we redo the graph for the two groups, A (in blue) and B (in orange): 

We already notice from looking at the chart that we don’t see the effect of having added the 3 euros to group B: the orange and blue lines look very similar. Even when we zoom in, the difference is barely noticeable: 

However, the Mann-Whitney-U test ‘sees’ this gain:

More precisely, we can calculate pValue = 0.01, which translates into a confidence interval of 99%, which means we’re very confident there’s a gain from group B in relation to group A. We can now say that this gain is ‘statistically visible.’ 

We now just need to estimate the size of this gain (which we know has a value of 3 euros).

Unfortunately, the calculation doesn’t reveal the hoped for result! The average of group A is 130 euros and 12 cents, and for version B, it’s 129 euros and 26 cents. Yes, you read that correctly: calculating the average means that average value of B is smaller than the value of A, which is the opposite of what we created in the protocol and what the statistical test indicates. This means that, instead of gaining 3 euros, we lose 0.86 cents!  

So where’s the problem? And what’s real? A > B or B > A?

The Notion of Extreme Values

The fact is, B > A! How is this possible? It would appear that the distribution of average basket values is subject to ‘extreme values’. We do notice on the graph that the majority of the values is < 500 euros.

But if we zoom in, we can see a sort of ‘long tail’ that shows that sometimes, just sometimes, there are values much higher than 500 euros. Now, calculating averages is very sensitive to these extreme values. A few very large basket size values can have a notable impact on the calculation of the average. 

What’s happening then? When we split up the data into groups A and B, these ‘extreme’ values weren’t evenly distributed in the two groups (neither in terms of the number of them, nor their value). This is even more likely since they’re infrequent, and they have high values (with a strong variance). 

NB: when running an A/B test, website visitors are randomly assigned into groups A and B as soon as they arrive on a site. Our situation is therefore mimicking the real-life conditions of a test. 

Can this happen often? Unfortunately, we’re going to see that yes it can. 

A/A Tests

To give a more complete answer to this question, we’d need to use a program that automates creating A/A tests, i.e. a test in which no change is made to the second group (that we usually call group B). The goal is to check the accuracy of the test procedure. Here’s the process:

  1. Mix up the initial data
  2. Split it into two even groups
  3. Calculate the average value of each group
  4. Calculate the difference of the averages

By doing this 10,000 times and by creating a graph of the differences measured, here’s what we get:

X axis: the difference measured (in euros) between the average from groups A and B. 

Y axis: the number of times this difference in size was noticed.

We see that the distribution is centered around zero, which makes sense since we didn’t insert any gain with the data from group B.  The problem here is how this curve is spread out: gaps over 3 euros are quite frequent. We could even wager a guess that it’s around 20%. What can we conclude? Based only on this difference in averages, we can observe a gain higher than 3 euros in about 20% of cases – even when groups A and B are treated the same!

Similarly, we also see that in about 20% of cases, we think we’ll note a loss of 3 euros per basket….which is also false! This is actually what happened in the previous scenario: splitting the data ‘artificially’ increased the average for group A. The gain of 3 euros to all the values in group B wasn’t enough to cancel this out. The result is that the increase of 3 euros per basked is ‘invisible’ when we calculate the average. If we look only at the simple calculation of the difference, and decide our threshold is 1 euro, we have about an 80% chance of believing in a gain or loss…that doesn’t exist!

Why Not Remove These ‘Extreme’ Values?

If these ‘extreme’ values are problematic, we might be tempted to simply delete them and solve our problem. To do this, we’d need to formally define what we call an extreme value. A classic way of doing this is to use the hypothesis that the data follow ‘Gaussian distribution’. In this scenario, we would consider ‘extreme’ any data that differ from the average by more than three times the standard deviation. With our dataset, this threshold comes out to about 600 euros, which would seem to make sense to cancel out the long tail. However, the result is disappointing. If we apply the A/A testing process to this ‘filtered’ data, we see the following result: 

The distribution of the values of the difference in averages is just as big, the curve has barely changed. 

If we were to do an A/B test now (still with an increase of 3 euros for version B), here’s what we get (see the graph below). We can see that the the difference is being shown as negative (completely the opposite of the reality), in about 17% of cases! And this is discounting the extreme values. And in about 18% of cases, we would be led to believe that the gain of group B would be > 6 euros, which is two times more than in reality!

Why Doesn’t This Work?

The reason this doesn’t work is because the data for the basket values doesn’t follow Gaussian distribution. 

Here’s a visual representation of the approximation mistake that happens:

The X (horizontal) axis shows basket values, and the Y (vertical) axis shows the number of times this value was observed in this data. 

The blue line represents the actual basket values, the orange line shows the Gaussian model. We can clearly see that the model is quite poor: the orange curve doesn’t align with the blue one. This is why simply removing the extreme values doesn’t solve the problem. 

Even if we were able to initially do some kind of transformation to make the data ‘Gaussian’, (this would mean taking the log of the basket values), to significantly increase the similarity between the model and the data, this wouldn’t entirely solve the problem. The variance of the different averages is just as great.

During an A/B test, the estimation of the size of the gain is very important if you want to make the right decision. This is especially true if the winning variation has associated costs. It remains difficult today to accurately calculate the average basket size. The choice comes down soley to your confidence index, which only indicates the existence of gain (but not its size). This is certainly not ideal practice, but in scenarios where the conversion rate and average basket are moving in the same direction, the gain (or loss) will be obvious. Where it becomes difficult or even impossible to make a relevant decision is when they aren’t moving in the same direction. 

This is why A/B testing is focused mainly on ergonomic or aesthetic tests on websites, with less of an impact on the average basket size, but more of an impact on conversions. This is why we mainly talk about ‘conversion rate optimization’ (CRO) and not ‘business optimization’. Any experiment that affects both conversion and average basket size will be very difficult to analyze. This is where it makes complete sense to involve a technical conversion optimization specialist: to help you put in place specific tracking methods aligned with your upsell tool.

To understand everything about A/B testing, check out our article: The Problem is Choice.