Setting up a sales funnel and example use case

In this article, we’ll take a close look at why the sales funnel is so important to a business and how to optimize your marketing plan with its help while solving the problem of poor lead conversion.

A sales funnel is a consumer-oriented marketing model that illustrates a buyer’s theoretical path to purchasing a product or service. By understanding all the steps leading to a purchase, you can control the behavior of customers, unobtrusively stimulating their interest and encouraging them to buy. The term sales funnel was proposed by Elias Saint-Elmo Lewis as early as 1898.

The purpose of ​​defining a sales funnel is to determine the main stages in the decision to purchase and then build communication with the customer, taking into account what decisions they have made at each stage.

The standard structure of a sales funnel:

In reality, buyers may follow a non-linear path – they may return to previous stages, lose interest, or no longer be able to pay. However, the sales funnel is an indispensable element of any marketing analysis because it allows you to find problem sales stages and eliminate them.

To build a funnel, open the Administrator section in Google Analytics and select the Goals option.

On the page for creating a new goal, set the name of the target and set the type as Destination.

In the Goal Details section, specify the last step: a visit to the page whose URL contains the order_id element. The goal will be considered achieved when an order is successfully placed. Each order is assigned a unique identifier. In addition, most sites have a thank you page after the purchase, the URL of which can be used as the last step.

Next, click on the Sequence switch and set the remaining steps in the funnel. Each step corresponds to specific URL that the user visits on the conversion path.

The first step can be made mandatory. In this case, the goal will be fulfilled only if the user has viewed a page of any product category (for example, the “Diaries” category).

With the current settings, the goal will be considered achieved for all successful orders – that is, for any visit to a URL containing an order_id – regardless of whether other steps were performed.

Each step in our funnel has its own URLs. But what if there are steps for which specific URLs cannot be set? For example, clicking on the Buy button (step 3) and checking out (step 5)?

In such cases, a special Google tool called virtual pages is used. To implement it, a code is added to the Buy button or some other element. When clicked, the button or element sends Google Analytics a view for a non-existent page with the URL mysite.com/buy: onclick = “ga ('send', 'pageview',‘ / buy ’);”

The same applies to the Checkout” button:
onclick = “ga ('send', 'pageview',‘ / order_start ’);”

If we set up the target correctly, Google Analytics will start collecting statistics and display them in several reports. The most useful for us will be the Visualization report. It shows how often users interrupt the buying process at different stages of the funnel.

In addition, this report shows that not all visitors follow the sequence of actions we’ve defined. The left column shows the number of users who first came to the funnel at this stage, as well as the login page. The right column shows the number of users who left the funnel at a given time.

We already wrote about this in our article on how to marry up your crm with google analytics, so in this article we’ll look at the general points.

First, a CRM and ERP contain detailed information about your customers: gender, age, hobbies, children, cars, pets, etc. Some may argue that Google Analytics also shows the gender, age, and interests of the audience. True. But this information is not tied to specific users and their Client ID or User ID. In addition, thanks to information from your own internal system, you can conduct RFM analysis and merge customers into segments depending on the duration of the last purchase, frequency of purchases, and the amount spent.

You can send user data and RFM analysis results to Google Analytics in order to build new user reports and segments as well as to create audiences for remarketing. For example, you can offer a loyalty program and prepare special offers to your best customers who buy often and spend a lot. For those who haven’t bought anything from you for a long time, you can remind them of your company with some interesting email with a call to action. For those who often make inexpensive purchases, you can offer related products. Speaking of segments, read the story of how online car parts store Boodmo optimized advertising costs and increased LTV using cohort analysis.

Secondly, a CRM and ERP contain detailed information about your products: internal classifications (which often differ from what’s presented on the site), suppliers, and detailed features. By adding this data to Google Analytics, you’ll be able to track, for example, sales of products of specific suppliers through traffic channels. Obviously, you don’t specify profit margins on your website. But with this information, you can build reports in Google Analytics and see which traffic sources generate more profit, not revenue.

Also, don’t forget that sales information in Google Analytics may not coincide with the sales in your ERP, since Google Analytics does not have data on cancelled orders, returns, and offline purchases, including in-store and via a call center. In addition, some orders may not get into Google Analytics because they did not execute JavaScript code on the site. If you send this information from your CRM directly to Google Analytics, it may be distorted, since Google Analytics doesn’t support data reprocessing.

That is, you can’t change the amount or add a transaction for a past period.

Step 1. Upload data from your internal system to Google BigQuery.
To transfer data from your CRM to Google BigQuery, you can use ready-made libraries and applications (see BigQuery help for details). In this case, you can upload and update data automatically (i.e. you’ll always have current data in Google BigQuery). Recall that OWOX BI has a Salesforce → BigQuery stream.

Step 2. Make the necessary settings in Google Analytics.
Create user-level user parameters in Google Analytics (Resource –> User definitions –> User parameters –> + Special parameter). Then create a new data set to import data from Google BigQuery (Resource –> Import data –> Create). You can read more about Google Analytics settings in this article and how to set up importing RFM analysis results in our help section.

Step 3. Prepare a SQL query.
This query will select the data you need in a key-value format. For example, say user 2346 is a robot. Save the query in your OWOX BI project so you can simply specify it when setting up the stream.

Step 4. Create a stream from Google BigQuery in Google Analytics.
This stream will automatically upload the data selected by the query to Google Analytics. Set up a stream once and all subsequent downloads will occur without your active participation (for more details, see the help section). Information about the status of downloads can be viewed in the OWOX BI interface on the stream page.

As a result, the movement of data will look like this:

In essence, this means reducing the number of steps needed to make a purchase. The more steps a user needs to take, the more likely they are to leave and seek solace from your competitors. Therefore, make the entire purchasing process as simple as possible.

Remember: Don’t overdo the simplification and remove really important information from the site. For example, don’t remove product specifications or a description of the return process. Otherwise, visitors will decide that your product isn’t good enough and will choose your competitor.

This analysis is necessary to maximize the usability of the site for visitors.

The reason for a low conversion rate may lie in poor equipment or poor functionality of the online store:

• No shopping cart on the site
• A lot of hype
• Long registration form required in order to access the catalog
• Inconspicuous CTA button

If buyers find it difficult to navigate your website, search for products, find information on delivery / guarantees / payment methods, and so on, then most likely you’ll lose most visitors.

Dividing users into segments is necessary to display special offers for each segment. For example, for the registered users segment, it makes sense to provide information on additional discounts or special offers, while you might show unregistered users content on product benefits.

After segmenting their audience, our client reduced their advertising expenses and extended the life cycle of customers and the LTV of the customer base as a whole.

Get to know your customers better, visualize their steps on the path to purchase, and optimize this path, making it more convenient. Raise your business to a new level with the help of your sales funnel.

If you still have questions, ask us in the comments below :)