The Retention analysis dashboard gathers all key metrics you need to improve your retention rate and transform one-time and repeat customers into loyal customers. From there, you have an insight into your customers' behavior and the average revenue they generate for each group: Leads, One-time customers, Repeat customers, Loyal customers. You can also analyze your Coupons, Products, Months, or Sources revenue to know which ones generate and retain loyal customers the most.
🧲 Retention analysis
Retention represents how many times and how often your customers place an order on your online store. The higher the retention rate is, the best your revenue will be. A customer who makes more purchases from your online store is always preferable to a one-time customer because loyal customers spend a lot on your business in the long-term compared to one-time customers.
The example above clearly shows the difference in terms of revenue between one-time customers and loyal customers. In the long term, we can see that the customers who spend the most on your website are often loyal customers. The goal is then to move your leads from the left category to the right categories until they become loyal customers. To do so, you must analyze your actual loyal customers' behavior and habits to learn what makes them stay. Analyze the different cohorts to see which ones contain the most returning and loyal customers and put effort into what matters to them ⬇️.
👥 Cohorts analysis
A cohort is a group of people who share the same characteristics. Analyze your loyal customers' behavior to find what they have in common. It could be a social network or product for example. If you have a large number of leads or one-time customers, you can transform them into loyal customers and make a lot of revenue with them by predicting their future behavior and put effort in your marketing where it really matters: best-time to sell, best product ...
The marker below each line indicates the overall average for the data. It makes it easier to see where your data is standing according to the average. For each cohort, you can sort your results by increasing or decreasing number by clicking Contacts, or by alphabetical order by clicking a data (Coupons, Products, Months, Sources). For example, this could be useful to sort your most purchased products or less purchased products.
With the cohort analysis dashboard, you can easily see which products or initiatives you should focus on to increase your Customer Lifetime value and revenue. Compare the different cohorts to see which factors impact your retention rate and where you should put effort into your marketing strategy:
This cohort groups all your contacts that made their first purchase with a specific coupon code. For example, you could analyze the customer retention for contacts who made their first purchase using the coupon FLASH20.
🔬 Analysis
In the example above, more than half the contacts (54.03%) who used the FLASH20 coupon came back to your website and placed another order.
In addition to this, they spend more than the average when placing an order ($207.59), and their Customer Lifetime Value ($1,303.52), which is the average amount each contact has spent on your website, is also above the average.
With this analysis, we could say that the FLASH20 coupon works pretty well since your customers usually come back after using it, and they spend much more than other contacts who used other coupons.
✔️ How to use this data in my marketing strategy?
- Reveal which promotions work best and encourage your customers to buy again on your website. You can thus repeat the coupons that work best to gain loyal customers.
- Reveal which coupons don't work or don't make your contacts come back to your website. You can drop them and only use the ones that generate the most revenue and loyal customers.
- You can also mix the cohort analysis and also analyze the month during which you used a specific coupon, for example, a coupon for Christmas or Valentine's Day, and see if they were successful.
This cohort groups all your customers based on the product they purchased when they first became your customers. For example, you could analyze the group of people who made their first purchase by buying the Grey cardigan with a bear print.
🔬 Analysis
In the example above, for the Grey cardigan with a bear print, the total revenue is $3,860. Among the 202 contacts who bought this product, only 6.06% returned to your website to place another order, which is a low percentage.
In addition to this, they spend less than the average when placing an order ($36.42), and their Customer Lifetime Value ($38.99), which is the average amount each contact has spent on your website, is also under the average.
With this analysis, it looks like the grey cardigan with the bear print doesn't have much success. Even worse, the contacts who bought this product didn't place another order on your website, which could mean its quality is disappointing.
✔️ How to use this data in my marketing strategy?
- Reveal which products don't bring loyal customers and drop them as the quality might be disappointing.
- On the contrary, reveal the products bought the most by your loyal customers and promote them on your website, as they might be the best products you sell.
- Replicate your customer's journey to show a related products category, or promote similar products in campaigns.
This cohort groups all your customers that made their first purchase within a specific month. For example, the December 2021 cohort includes all your customers who made their first purchase between the 1st and the 31st of December 2021.
🔬 Analysis
In the example above, the months of November and December are potentially the months during which your contacts bought their Christmas gifts. The overall revenue for these months is higher than the average.
However, only a tiny percentage of your contacts returned to your website to place another order. The average order and Customer Lifetime Value for this cohort is low compared to the average.
This could mean that a majority of the contacts who bought on your website during this month are one-time customers who bought your product to make gifts.
✔️ How to use this data in my marketing strategy?
- To increase the number of repeat customers and maybe transform them into loyal customers, you could target those customers during other gift seasons.
- See if month-long campaigns, website changes, or seasonality (holiday coming up, new season item, Black Friday, or holidays such as Christmas or Valentine's Day) have a negative or positive impact in some months and target customers during those specific periods.
This cohort groups all your customers that used the same medium to buy your products on their first purchase. It could be a social media, website, referral, organic search, etc. For example, you could analyze the group of people who placed their first order on your website after clicking the sponsor link provided by John Smith.
🔬 Analysis
In the example above, many contacts placed their first order after clicking a referral link shared by one of your loyal customers, John Smith.
The revenue generated from this source is high compared to the average ($1.02M), and more than half of those contacts placed another order on your website. The average order for those contacts ($96.48) is slightly above the average, just like their Customer Lifetime Value ($282.18).
This could mean that people trusted John Smith, who sponsored them, and they now also became loyal customers.
✔️ How to use this data in my marketing strategy?
- Learn how the various medium you use allow repeat sales and whether it makes sense to put effort into all of them.
- Drop channels that don't bring loyal customers.
- Put marketing money on the proper channels by using the same social media as the one from where loyal customers bought their first product.
⏭ What's next?
- Discover our eCommerce Dashboard
- eCommerce settings
- Check your product performance with our eCommerce dashboard
🤔 Have a question?
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