- What the use case does
- How to set the use case up
- How to further customize the use case
- How to use the dashboard for evaluation
This use case is part of Engagement’s Plug&Play initiative. Plug&Play offers impactful use cases like this one, which are ready out of the box and require minimum setup on your side. Learn how you can get Plug&Play use cases or contact your Customer Success Manager for more information.
There are a number of challenges marketers struggle with when aiming to acquire new newsletter subscribers. Customers inputting their email in a wrong format or being confused about what they are actually signing up for are some examples of those challenges.
To address these kinds of challenges, a single opt-in banner provides a way to ensure that the visitors at your website enter their email addresses in the right format when subscribing to a newsletter, which should lead to increased newsletter subscriptions. Banners such as this have yielded up to a 69% improvement in leads captured for Bloomreach Engagement clients.
For this reason, we developed the Single Opt-In Subscription Banner with Automated Consent Management use case, as a ready-to-use solution. The web layer included in it ensures that only an email address with a usual format of [email protected] gets through.
The web layer also provides you with an opportunity to explain what it is that the customer is signing up for and the benefits associated with it. Furthermore, the consent management is fully automated, with Bloomreach Engagement updating a customer’s consent event and Newsletter consent attribute once the customer submits their email.
What is included in this use case:
- Custom animated single opt-in subscription banner
- Custom evaluation dashboard
- Bloomreach engagement modules:
- Customer attributes:
- Consent tracked:
- Newsletter (in case you use a different naming, feel free to change it within the UC)
Address any discrepancies if needed.
The web layer included in this use case has the format of a small dialog window and it is customizable on different levels. Locate the web layer in the use case initiative and open it for customization according to your needs.
Design > Editor > Visual (parameters) editor:
By adjusting the parameters, here you can change:
- Visual aspects - the text and the color.
- Consent Category - choose which consent category should be tracked to your customers when subscribing through the banner
Design > Editor > Code editor:
HTML code / CSS code / JS code
- Adjust the code parameters according to your preferences.
Design > Settings
Select appropriate values
- Show on - select the web pages where you want to display the banner
- Target devices - consider if you want to display it on both mobile and desktop devices
- Consent category - select an appropriate consent category
- Audience - possibility to narrow down your audience with filtering. Do not forget to exclude the customers that already have the consent.
It is highly recommended to test the use case before deploying it. To do so, follow the steps below. You can also find out more about Testing Weblayers in our documentation..
A quick preview is available directly in the web layer file:
- Design tab - Onsite preview to see the preview on different devices
- Test tab for a larger preview
To test the web layer in “real” conditions, you can use one of the following 2 approaches:
- Go to the Settings of the web layer and define ‘Show on’ to match a testing page (not accessible to the public). The banner will be shown to everybody on this webpage. You can then navigate to this web page to preview it.
- Go to the Settings and in ‘Audience’ select ‘Customers who...’. Filter only the testers from your customer database, e.g. by filtering by the cookies of testers - you and your colleagues.
Click on the ‘Start’ button to launch the web layer with the restricted settings as described above. Once the test is finished, click ‘Stop’. We recommend testing on different browsers and devices.
Once you are satisfied with the testing results, configure the web layer to match the desired display settings in the Settings tab and click on the ‘Start’ button to launch the web layer.
A/B test is necessary to evaluate whether the use case is performing, and most importantly, if it is bringing extra revenue or subscriptions. We can drive conclusions from the A/B test once it reaches appropriate significance (99% and higher).
This use case is not currently set up with an A/B test. However, we strongly recommend testing more Variants (e.g. wording, page placement etc.) to optimize the performance. More about setting up the A/B test for banners can be found here.
Read more about A/B tests and significance in our public documentation. To achieve the desired level of significance faster, you can opt rather for a 50/50 distribution.
Once the significance is reached and the use case is showing positive uplift, you can:
- Minimise the Control Group to 10% and continue running the A/B test to be able to check at any given moment that the uplifts are still positive.
- Turn off the A/B test but exercise regular check ups e.g. turn on A/B test every 3 months for a period needed to achieve significance to be sure that the use case is still bringing positive uplifts.
The use case performance can change over time. We recommend regular checking instead of proving the value only in the beginning and then letting the use case run without further evaluation.
The use case comes with a predefined evaluation dashboard. There might be some adjustments necessary in order to correctly display data in your projects.
Adjustments to consider:
- [WSOI] Consent target - add the corresponding consent category collected with this UC and optionally add ‘data_source’ attribute equals ‘single opt-in web layer’ if you are collecting it
- [WSOI] Web layer target - check if the ’banner_id’ corresponds to the scenario
- Metrics in the dashboard - possibility to adjust the display e.g. comparison to last 14 days
Check the evaluation dashboard regularly to spot any need for improvements as soon as possible. All needed metrics definitions are included directly in the dashboard.
If you decide to modify the scenario (e.g. rename the banner), some reports and metrics in this initiative need to be adjusted to show correct data.
While this use case is preset for the above specification, you are able to modify it further to extract even more value out of it. We suggest the following modifications, but do not refrain from being creative and thinking of your own improvements:
- Try different A/B tests - possibility to run more variants at the same time and test different banner designs and website placements to maximize the conversion rate.
- Use this subscription method alongside a discount to drive up your newsletter subscription rates as well as increase the revenue.
The dictionary below is helpful for understanding metrics in the evaluation dashboard and their calculation. The most important metrics are marked in bold.
Key metrics calculations
The attribution model used for revenue attribution takes into consideration all the purchases made within:
- 48h since email open or click
This time frame is called the attribution window.
- Impressions - count of all actions that translates into customer being impacted by a marketing campaign e.g. web layer show or click actions / emails opened or clicked/ sms delivered/ push notifications delivered or clicked
- Visitors - count of all customers that have been impacted by the marketing campaign (weblayers = show or clicked, emails = open or click)
Revenue - total value of all purchases made by customers impacted by the campaign (e.g. opened or clicked on the email, show or click on the web layer etc) that occured within the attribution window.
Purchases - all purchases made by customers impacted by the campaign (e.g. opened or clicked on the email, were shown or clicked on the web layer, etc.) that occured within the attribution window.
Buyers - all customers impacted by the campaign (e.g. opened or clicked on the email, show or click on the web layer etc) who made a purchase within the attribution window.
Conversion rate (CR) - Percentage of impressions that were converted into purchase within the attribution window
- Conversion rate = count all purchases / count of all campaign impressions
Unique Conversion rate (UCR) - The proportion of customers who have seen the campaign and were converted into a purchase within the attribution window
- Unique Conversion rate = count of all purchases / unique customers with impressions
Average Order Value (AOV) - Average revenue from one purchase/order
- AOV = total revenue / total number of purchases
Revenue Per Visitor (RPV) - Average revenue per customer that has an impression (e.g. open email, show banner etc.)
- RPV = total revenue / all visitors
Uplift represents the difference in performance between Variant A and the Control Group. If the uplift value is positive, Variant A is the winner and the use case should be maintained. If the uplift is negative, it means that the Control Group is performing better than Variant and the use case hypothesis should be adjusted.
Uplift results should be taken into consideration together with the statistical significance.
The results are significant if they reach more than 98%. The significance value can be found as part of the Evaluation dashboard, more specifically Conversion funnel > Confidence.
Revenue Uplift - Uplift determines the absolute financial outcome of your Exponea campaigns. It is defined as extra revenue brought by Variant compared to Control Group.
- Revenue Uplift = [ RPV(Variant A) - RPV(Control Group) ] x Visitors(Variant A)
Revenue Uplift Potential - Potential Uplift determines the theoretical financial outcome of your Exponea campaign if the Variant A would be deployed to all the customers (Variant A and Control Group). This outcome is extrapolation of known data, not a guaranteed number.
- Revenue Uplift Potential = [ RPV(Variant A) - RPV(Control Group) ] x Visitors(Variant A + Control Group)
Unique Conversion rate Uplift % - Percentage difference between UCR (Variant A) and UCR (Control Group).
- UCR uplift = [ UCR(Variant A) - UCR(Control Group) ] / UCR(Control Group) x 100
AOV Uplift % - Percentage difference between AOV (Variant A) and AOV (Control Group).
- AOV uplift = [ AOV(Variant A) - AOV(Control Group) ] / AOV(Control Group) x 100
RPV Uplift % - Percentage difference between RPV (Variant A) and RPV (Control Group).
- RPV uplift = [ RPV(Variant A) - RPV(Control Group) ] / RPV(Control Group) x 100
There are two types of metrics in the evaluation: non unique and unique ones. The non unique ones are counting the number of events that have occurred and the unique ones count the number of customers that have made the action. Example: one customer will open the email three times. Non unique open metrics = 3, unique open metric = 1.
Updated 1 day ago