Segmented Email Campaigns: Maximise Your Sales With Customer Database Analysis
Consistent monitoring enables you to change tactics and stay agile, no matter what happens.
During the last week of May 2023, we noticed a trend. Sales for one of our eCommerce clients were slowing down. If sales continued the way they were, our client would miss their sales target for the month.
After our weekly strategy session, we built email campaigns to drive as many sales as possible later in the month. Here’s how we did it:
Customer Database Analysis
We analysed our client’s customer database to determine the value of each product they sell. Brand X products were identified as the most profitable. Using this data, we decided to create two segmented email lists. One for Brand X products only, and one for all other products listed on the website.
These 2 groups were:
- 1: People that purchased a Brand X product.
- 2: People that purchased any other product on the site, but not a Brand X product.
Using this data, we then created 2 email subscriber lists that we were able to use in the email campaigns:
Email list 1 - Purchased Brand X products during payday window.
Email list 2 - Purchased any other product during the payday window.
*The payday window is from the 25th day of the month, until the last day of the month.
Why did we do all of this work, just to send an email? We sought to include subscribers that we knew would be more likely to purchase. This makes more sense than sending emails to the default subscriber list that contains more than 25,000 subscribers.
The Campaign Results
Campaign 1: Brand X products only
Within the customer database, we identified customers who had previously purchased Brand X products during the payday window. We identified these customers as “high-value” and more likely to purchase products within the same window. A total of 2,908 subscribers were “high-value” customers and added to the Campaign 1 subscriber list.
We crafted a sales driver email, giving these subscribers a 15% discount code for Brand X products on our clients' website.
Campaign 1 results: Estimated revenue generated from this campaign was R15,500.
Key insights for Campaign 1:
- Subscriber list size of 2908.
- Deliverability of 96%.
- Open rate of 35%.
- Click rate of 10%.
- Total purchases of 13.
- Average order value of R1,191.
Campaign 2: All other products excluding Brand X
We were able to identify customers that had purchased other products, but not Brand X, during the payday window. A total of 851 subscribers fell into this category. We added these subscribers to the Campaign 2 subscriber list.
We then crafted a different sales driver email, giving these subscribers a 10% sitewide discount.
Campaign 2 results: Estimated revenue generated from this campaign was R0.
Key insights for Campaign 2:
- Subscriber list size of 851.
- Deliverability of 96%.
- Open rate of 33%.
- Click rate of 5%.
- Total purchases of 0.
- Average order value of R0.
The final verdict.
Through customer data analysis, we were able to create segmented email campaigns. Campaign 1 successfully generated approximately R15,500 in revenue. This helped us reach our client’s sales target for the month.
From our campaign results, we also learnt that:
Customers who had not bought Brand X products within the payday window were less likely to buy any products during this period. Discount, or no discount, they were not interested. This means that there is more value in targeting those that have purchased Brand X products in our campaigns going forward.
Using the results from this campaign, we were able to determine that it is best for us to focus our marketing efforts on Brand X products.
At Katana, we leverage data science and machine learning to identify and target our clients' ideal customers.
By utilising data analysis techniques, we gain valuable insights into your customer behaviour. Data analysis helps us understand the needs, motivations, and pain points of your customers. With this deep understanding, we craft campaigns that target specific customer segments.
Our data analysis models help us make data-driven decisions to improve your sales. With data as a foundation, we help our clients achieve the best results possible.
