Back to cases
DELTA Fiber

DELTA Fiber's experimentation adventure: the seamless connection between online and offline

01

About DELTA Fiber

DELTA Fiber is connecting more and more people to the fastest network in the Netherlands, with which they want to make fiber internet accessible to as many people and companies as possible. In addition, they offer all internet providers the opportunity to provide services via DELTA Fiber's fiber optic network. They do this with their open fiber network, to which more than 15 providers are already connected.

02

Our cooperation

In recent years, DELTA Fiber's experimentation program has taken off considerably. Every month, we carry out new CRO and personalization tests. After these tests, we dive deep into the data. We do this to draw valuable conclusions that help us improve our services and products.

The collaboration between Yellowgrape and the Delta team is essential to success. The joint CRO (Conversion Rate Optimization) and CDP (Customer Data Platform) program can therefore be successfully executed. In doing so, we not only pay attention to the online channels, but we also work closely with the sales department, which is active both online and offline. And that is reflected in the results!

03

The delivery street: The beating heart of the DELTA website

After checking the availability of DELTA's fiber optic internet with a zip code checker, you will end up in the order line. Here, users can compare prices and put together their desired internet and TV package. The delivery line plays a crucial role in the customer's decision-making process, so you'll come into contact with it at various points in your customer journey.

User tests and interviews showed that users in the awareness or orientation phase of the customer journey use the order line to check DELTA's availability, explore possible packages and compare prices. During the decision phase, users come back to the order line to determine whether they actually want to place the order.

04

The challenge: The failure rate in the delivery line

After applying various quantitative research methods, we discovered a high failure rate in the delivery line. We used Hotjar to create a funnel of the order line steps and found a significant failure rate between step 1 and step 2. Even considering those still in the consideration phase, this high failure rate was unacceptable.

Hotjar surveys: our detective on the delivery street
To explain the high drop-off, we conducted several Hotjar surveys within the delivery line. These surveys were triggered when you left the website on desktop devices, asking, “We notice you're leaving the site. Can you tell us why?” The survey found that 25% of the visitors felt that information was missing. This got us thinking and led to the follow-up question: “What information are you missing?” The analysis showed that many aspects of the packages, prices and availability of the fiber network were unclear. Despite the fact that this information was available on the website, people were still uncertain.

05

Our response: A/B tests and personal advice

In response, we came up with multiple A/B tests to show clearer information in the order line so that informed and independent users can complete their orders online. However, we also wanted to listen to the needs of the (very) insecure visitors.

After extensive brainstorming sessions with the sales team, we decided to offer users in the orientation or decision phase the opportunity to receive a personalized offer via a call me back pop-up and form. This would be followed by a personal call from the telesales department, which offers a more customized and supportive service. Due to the 1-on-1 contact, uncertainties could be removed immediately.

“However, we also wanted to listen to the needs of the (very) uncertain visitors.”

06

Our Experiment: An A/B Test in the Customer Data Platform Squeezely

We set up an experiment with a control group and a variant. The test was only shown to a specific segment, namely returning visitors in the order line who have not placed an order yet. We only included returning visitors in the test for various reasons: ‍

  • First, we only wanted to include visitors who were in the consideration of decision phase of the customer journey. Previous research showed that users take longer to make their decision before placing an order for this specific product.
  • We also didn't want to interrupt the flow of new visitors too much by showing them a call me back exit intent pop-up.
07

Why did we choose an exit intent pop-up?

We opted for an exit intent pop-up because we wanted to give returning visitors the opportunity to go through the flow independently and not offer personal advice too quickly. This could also lead to extra work for offline sales and reduce (less expensive) online sales. We worked towards a test setup that would benefit both online and offline (i.e. DELTA in general). We weren't just looking at sales, we really needed to find the right balance between online and offline so that they could reinforce each other. The call me back pop-up focused on the first step in the order flow, where we found that the dropout rate was highest and users experienced many issues before making their decision.

08

The evaluation of the experiment

The experiment was evaluated based on various Key Performance Indicators. The test's primary KPI was online and offline sales. The CTR to the lead form and the number of leads collected in the lead form were also secondary KPIs. To measure CTR and leads on the website, we had to add additional events.

In the results, we see that there is a CTR of 4.36% from the pop-up to the call me back form. This has led to an increase in the number of call leads on the call me back form of no less than 76.8%. The exit intent pop-up has also not led to fewer online sales. Looking at the total telesales data, the percentage of potential leads increased from 19% to 29%. The total number of call me back has also doubled, leading to more total sales.

The value of the experiment
The experiment showed DELTA the crucial importance of bringing online and offline channels together. Using segmentation within Squeezely, DELTA was able to find the perfect balance between an improved customer experience and preventing an increase in the workload or costs for the telesales team. The leads generated were of good quality for the telesales team, as we had already collected important data about potential customers via the form. In addition, the telesales team knew that the individuals they called were more motivated to become customers, which simplified the process of closing the deal. All in all, a great case where online and offline find and reinforce each other, with a nice reward for work by being named Experimentation Heroes 2023 winners in the Omnichannel category.

4.36
%
CTR pop-up to form
76.8
%
Increase in call leads
52.63
%
Increase in leads from exit pop-up
51
%
Increase in total number of call me back
09

These experts know everything about this case

Leonie Eckhardt

Customer Journey Strategist

Nick Schaperkotter

Team Lead CRO & Design

Martijn Versteeg

Customer Journey Consultant