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Introduction to Klaviyo’s ‘Expected Date of Next Order Flow’:
[UPDATED] Klaviyo's Predictive Analytics feature, specifically the Expected Date of Next Order Flow, is a powerful tool when deployed appropriately and intelligently, that can help email marketers predict with some fair degree of accuracy when their customers are most likely to make their next purchase and leverage that prediction like a psychic sales person ready to book in the next order before your customer even knows it themselves.
In this article, we'll be exploring Klaviyo's Predictive Analytics feature, specifically the Expected Date of Next Order Flow. We'll cover everything you need to know about this feature, including how it works, how to set it up, and how to use the insights it provides to improve your email marketing campaigns consistently over time.
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Alongside this will be some use cases of where we, ourselves at Magnet Monster, have won a lot of recurring revenue for clients using this very technology.
What Exactly IS Predictive Analytics?
Predictive analytics is a process that uses data mining, machine learning (AI), and statistical modelling techniques to analyse historical data and make predictions about future events. In the context of email marketing, predictive analytics can help you identify patterns and trends in your customer data to make more informed decisions about how to target and engage your audience.
In other words, Klaviyo's Predictive Analytics feature is designed to help you understand your customers better by analysing their previous and existing behaviour and then predicting their future actions before they take them. As you can imagine, this handy Klaviyo feature includes a range of different predictive models & dimensions, each of which is designed to provide insights into different aspects of customer behaviour.
One of the key benefits of predictive analytics is that it can help you, quite accurately with very little effort, personalise your marketing messages based on individual customer behaviour and preferences. By using predictive analytics, you can segment your audience into groups based on their likelihood to take certain actions, such as making a purchase or responding to a specific type of offer.
A caveat note from Klaviyo:
Please note that you will only see the Predictive Analytics section on profiles if you meet the following conditions:
- At least 500 customers have placed an order.
This does not refer to total profiles, but rather the number of people who have actually made an order with your business. If this section is on a profile but is blank, this means we don't have enough data on that individual to make a prediction.
- You have an ecommerce integration (e.g. Shopify, BigCommerce, Magento) or use our API to send placed orders.
- You have at least 180 days of order history and have orders within the last 30 days.
- You have at least some customers who have placed 3 or more orders.
The table below defines the predictive analytics fields shown above. Note that, CLV stands for Customer Lifetime Value.
Expected Date of Next Order (EDNO) Model
One of the most useful predictive models in Klaviyo is the Expected Date of Next Order (EDNO) model. This model uses historical data about customer behaviour to specifically predict when they are likely to make their next purchase.
By understanding when your customers are most likely to buy, you can time your email marketing campaigns more effectively and ensure that your messages are reaching your audience at the right time.
How does the Expected Date of Next Order Flow work?
The full breakdown of EDNO in Klaviyo works by analysing a range of different customer data points to predict when they are most likely to make their next purchase. These data points include:
Purchase history: This includes information about the customer's past purchases, such as what they bought, when they bought it, and how much they spent.
Website behaviour: This includes information about the customer's behaviour on your website, such as what pages they visited, how long they spent on each page, and whether they added items to their cart.
Email engagement: This includes information about how the customer has interacted with your email campaigns, such as whether they opened your emails, clicked on any links, or made a purchase as a result of receiving your emails.
By analysing these key data points and dimensions about your customers' habits, Klaviyo's predictive models can identify patterns and trends in customer behaviour and use this information to make predictions about when they are likely to make their next purchase. In some cases, you will know before your customer, when they are required to repurchase or even that they will repurchase. How powerful is that?!
Important to note about EDNO:
Klaviyo, and ourselves, don’t recommend counting down to the expected date of the next order as repeat customers will simply get the same sequence of emails leading up to every order which may result in unsubscribes.
The EDNO flow shouldn’t replace the use of replenishment flows if customers know the general cycles for most of their product categories. This is a creative next step to compliment foundational work.
If you have a high percentage of repeat buyers, you may want to only use this feature for customers who have purchased once to nurture them for their second purchase, deploying specific flow filters and flow architecture for your model.
Case Study - waterdrop®
We deploy an intelligent, specific usage of the predictive analytical EDNO in various formats under different conditions with different clients. Check out our recent case study with Waterdrop® USA where we are leveraging Klaviyo’s predictive analytics features, being able to stay top-of-mind for high frequency buyers and build in customer advocacy with gifting incentives.
Here, we are also tapping into Klaviyo's data science features, our goal with the ‘WINBACK FLOW | EXPECTED NEXT ORDER DATE | REPEAT PURCHASERS’ flow was to prevent churn and keep the customer disciplined on their hydration journey.
Setting up the Expected Date of Next Order Flow
To set up the Expected Date of Next Order Flow in Klaviyo, you'll need to follow a few simple steps:
Step 1: Set up your data tracking
To use the EDNO model in Klaviyo, you'll need to make sure that you are tracking all the relevant customer data points. This includes setting up tracking for purchases, website behaviour, and email engagement.
Step 2: Create your predictive model
Once you have your data tracking set up, you can create your predictive model in Klaviyo. This involves selecting the EDNO model and configuring it to use the data points that are most relevant to your business.
Step 3: Set up your email campaigns
Once your predictive model is set up, you can start using it to time your email campaigns
The Expected Date of Next Order Flow can be a powerful tool for email marketers, helping them to deliver more personalised and targeted messages to their audience. Here are some real-world examples of how businesses have used the EDNO model to improve their email marketing campaigns:
Example 1: E-commerce companies
E-commerce companies can use the EDNO model to predict when their customers are likely to make their next purchase and schedule their email campaigns accordingly.
For example, if a customer typically makes a purchase every three months, the company can send them a reminder email a few weeks before that expected date, offering a irresistible offer, or promoting new products, or added value incentives to upsell - all increasing AOV and customer satisfaction/lifespan. This can help to increase the chances of the customer making a repeat purchase and boost customer loyalty time and time again, having a major compounding impact on revenue.
Example 2: Subscription services
Subscription-based businesses can use the EDNO model to predict when their customers are likely to renew their subscriptions and send them targeted emails reminding them to renew. By sending these emails at the right time, companies can reduce churn rates and retain more customers over time. For some subscription based businesses this literally can be a golden ticket opportunity!
Example 3: Service-based businesses
Service-based businesses can use the EDNO model to predict when their customers are likely to need their services again and send them timely reminders. For example, a car repair shop could use the EDNO model to predict when a customer is likely to need their next oil change and send them a reminder email a few weeks before that expected date. This can help to increase customer retention and encourage repeat business & even referrals.
Example 4: Event-based businesses
Event-based businesses can use the EDNO model to predict when their customers are likely to attend their next event and send them targeted emails with event reminders and promotions. For example, a concert venue could use the EDNO model to predict when a customer is likely to attend their next concert and send them a personalised email with information about upcoming shows and ticket discounts.
Considerations
Whether you're an e-commerce company, a subscription service, a service-based business, or an event-based business, the EDNO model can help you improve customer retention and boost your bottom line. So if you're looking for a way to take your email marketing campaigns to the next level, consider using the Expected Date of Next Order Flow in Klaviyo.
For many brands you will have exceptionally good results, although there are some limitations. For instance you may want to be able to display the product details dynamically on replenishment flows, and you can't necessarily always do that. So for brands where the product visual and dynamic information is more important you may look at other ways to achieve your outcome than the standard EDNO flow setup.
Remember, every instance and every client is unique - the beauty of email marketing strategy is that there really isn’t a one-size fits all solution to all scenarios that you just rinse and repeat. It truly depends on what product/s or services your business or clients sell. EDNO can work alongside specific replenishment flows harmoniously and creatively.
We have also implemented the standard foundational setup with certain brands with some top selling SKUs on specific product replenishment flows, calculating replenishment time based on how long the product lasts, and then a generic replenishment prompt with expected date of next order. It simply comes down to taking the theory and adjusting it to meet your business needs.
You can add some filters to control the cadence of those flows, including, last order placed date - as you don't want to bombard customers with those reminders, to people who buy very frequently or have just received a product specific flow, as you might be unintentionally creating a less than fun customer experience. The point here is that you should always try to zoom out and look at the bigger picture of all that is going on.
Conclusion
Klaviyo's Predictive Analytics feature, the Expected Date of Next Order Flow, can help businesses better understand their customers' behaviour by analysing historical data and predicting future actions. By using this feature, businesses can personalise their email marketing campaigns, time their email messages more effectively, and improve customer retention. With real-world examples of how businesses have used the EDNO model to improve their email marketing campaigns, this article provides valuable insights for businesses looking to take their email marketing to the next level.
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