Finn Flare’s Automated Campaigns Increased Revenue by 128% in the Space of Four Months — Here’s How

The following case study is from Mindbox, the original brand behind Maestra’s technology
Finn Flare grew its revenue by 128% in the space of four months, with 28% of that increase occurring in the first month. In this story, we’ll go into detail on how they achieved this result and explore the campaigns they launched.
The Finn Flare brand has been around for more than 50 years. Its first retail outlet opened more than 40 years ago in a small town called Salo in Finland.
Finn Flare uses emails for the following types of personalized communications:
  • Discounts and promotional campaigns
  • Newsletters with fashion recommendations and announcements of new collections and arrivals
Finn Flare’s marketing department given the task of increasing revenue across all channels, including email. Achieving email revenue growth simply by sending more emails and “spamming” customers was out of the question.
Instead of this, the team decided to focus on automating emails and making them more personalized, both in terms of content and delivery logic. They hoped that by automating their email marketing they would not only improve relationships with customers through personalized and useful communications, but also reduce the workload on the marketing department by eliminating a huge range of routine tasks.
And that’s why they turned to Mindbox for a solution.
Finn Flare’s email revenue relative to total revenue

Data cleanup

Before their integration with Mindbox, Finn Flare used one platform for triggered campaigns and another for manual campaigns. All in all, customer data was fragmented over four separate systems:
  • Their website
  • A platform for manual campaigns
  • A platform for triggered campaigns
  • A platform for pop-ups
With Mindbox we consolidated all channels and communications into one system.
Now all customer interactions are recorded in real time in a unified customer profile in Mindbox. This allows Finn Flare’s marketing team to understand who their customers are, their prefrences, and how they respond to marketing campaigns.
The consolidated information is used to create segments for bulk emails to ensure that customers receive the most relevant content.
Customers in this segment receive a discount code for kids’ clothing.
Example of an email with a discount code for a customer segment interested in kids’ clothing
With centralised data storage, it’s much easier for us to track and limit how many emails each individual subscriber receives. For example, we can configure triggered campaigns in a way that ensures no customer receives more than one email per day. This helps to keep our communications unobtrusive.
A filter in the trigger mechanism ensures that emails are only sent to recipients who have not received any other triggered emails within the past 24 hours

Maintaining data integrity

Information flow is monitored both on Finn Flare’s end and by Mindbox system managers. If any errors arise, they’ll be easy to spot in our integration dashboard.
The “All good” status means that all customer data was successfully received by Mindbox with no errors. Data integrity is vital to ensure all operations within the system use information that’s up-to-date

Adding more automated targeted campaigns

We were anxious to see results. In the whole first month of collaboration, Finn Flare’s marketing and their Mindbox manager kept a close eye on things, checking almost daily on how emails were doing and what results they were getting. We wanted to be sure that these emails were correctly matched to customer activity and that they weren’t being sent out too often.
Given the sixfold growth in revenue from triggered campaigns, it’s safe to say our customers appreciated our efforts.
Triggered campaign revenue share relative to all revenue-generating emails at Finn Flare
We began with the highest-converting campaigns from the Mindbox best practices library:
  • Welcome feed
  • 2-stage abandoned cart sequence
  • Abandoned category view
  • Abandoned item view
  • Favorites
  • Discount on items in favorites list
  • Discount on items in cart
  • Discount on next order
  • Reactivation sequence
Trigger operating logic can be monitored transparently in Mindbox. This is what an abandoned cart event looks like in a customer’s profile:
The system responds to an abandoned cart by sending an email:
And the email looks like this:
The email achieved the goal we had in mind — Tatiana came back and completed her purchase
Every email is generated according to rules and segmentation configured in Mindbox prior to sending, so every recipient receives a unique and personally-tailored email.
If a customer has been dormant for a long time, they’ll receive a reactivation email with a personal discount code.
An example of a reactivation campaign email. The customer receives a discount code after reconfirming their email address
You can create virtually any campaign using Mindbox — if you can conceive it, Mindbox can run it.
We set up a campaign for Finn Flare that alerts customers when sizes are in stock.
If the customer wants to purchase an item but their size isn’t in stock, they can sign up for a notification that will let them know when it becomes available via a pop-up. The system automatically tracks inventory in Finn Flare warehouses and sends the customer a message when the requested item is back in stock.
Here’s what the pop-up looks like:
And the email notification:

Better subscription management

Before our Mindbox integration, the only option customers had to manage their incoming email communications from Finn Flare was to unsubscribe from all emails. After the integration we added a basic subscription management section into customers’ online accounts on the Finn Flare website.
In due course we’ll add a separate page where customers can have full control over their subscriptions for different topics and communication channels.
Here’s what the subscription opt-in looks like in the customer’s account:
And here’s what a subscription/unsubscription event looks like in Mindbox:

Testing product recommendations

We deployed a number of product recommendation algorithms:
  • Based on similarity of the selected parameters, e.g. seasonal collections
  • Purchase and viewing history
  • Number of times an item was viewed by other customers
If a customer is interested in menswear, the system will automatically create recommendations based on their viewing and purchase history.
Here’s an example of product recommendations in an email:
Recommendations are based on behavior, viewing and purchase history
Sadly, we haven’t yet found the right recommendation algorithm. Almost a dozen different A/B tests to date have shown no additional effect:
Nonetheless, we’re convinced that product recommendations can be effective with the right algorithm, which is why we intend to keep testing and tweaking different approaches.