As an Ecommerce business owner, you already recognize the importance of staying in touch with your customers via email marketing and reaching out to the right person at the right time.
However, it can be difficult to determine when to email your prospects, one-time buyers, or loyal repeat customers and what sort of information the email should contain – especially if you have thousands of them. That’s where Klaviyo’s predictive analytics comes in.
You can use predictive analytics to increase customer retention by targeting existing customers with tailored emails. This guide covers predictive analytics, what it is, how to use it, key benefits, and specific use cases. This is something we’re often asked about in our Klaviyo marketing agency, so we hope you enjoy this guide.
Here’s what we’ll cover:
What Is Klaviyo Predictive Analytics?
Klaviyo relies on data science techniques and machine learning to identify patterns in your customer data and provide actionable insights. Klaviyo uses historical data to predict future customer behavior.
This combination of technologies and techniques like artificial intelligence, machine learning, and Bayesian statistical analysis is used to forecast future outcomes.
What Klaviyo aims to achieve with these predictive techniques is to come up with personalized differentiators for your recurring customers, making it easier to provide a unique experience for them, and find more people who are likely to continue buying from you in the future.
Klaviyo Predictive Analytics Requirements
Data is powerful; in fact, the only way predictive analytics can work is if you have the necessary amount of data. In order for Klaviyo to give clear, customized insights, your business needs to meet a few basic requirements:
500+ customers with prior orders
Your business needs to have at least five hundred customers who have made an order to generate concrete data. Remember this does not refer to active profiles but actual paying customers. You may notice the predictive analytics section on your customer profiles appearing blank – this likely indicates that Klaviyo not have enough data on your overall customer base to make predictions yet; this is especially the case with first-time customers.
Existing Ecommerce integration
Klaviyo received placed order data from your Ecommerce platform – Shopify, Magento, or something else. Make sure you have the full deep integrations setup in order to receive precise predictions.
Customers who have placed a minimum of three orders
In order for Klaviyo to make correct predictions, it needs to analyze your customers” buying habits, so your online store must have at least some customers who have placed a minimum of three orders over their lifetime of activity with your brand.
A minimum of 180 days of order history
Future predictions are most likely to be precise if the data used is recent, which is why Klaviyo requires that you have a minimum of 180 days of order history. Your store should also have recorded orders from the last 30 days.
Those are the basic requirements you’ll need to fulfill if you want to utilize the Klaviyo predictive analytics suite.
How To Use Klaviyo Predictive Analytics
To use Klaviyo predictive analytics, you need to login into your Klaviyo account and access the profiles tab. Clicking on any of the profiles will open a new window with lists and segments populated with metrics.
Predictive Analytics Fields In A Klaviyo Profile
Historic customer lifetime value (HCLV): This is the value of all the historical orders an individual customer has made within your store. Klaviyo also displays the number of all previous orders below the HCLV.
Predicted customer lifetime value (PCLV): This is a prediction generated by Klaviyo’s predictive analytics for each customer, indicating the average amount of money the particular customer is likely to spend in the next one year in your store. The algorithms also predict the total number of orders the customers will make, and the value is displayed below the PCLV.
Total customer lifetime value (TCLV): This represents the total of HCLV plus PCLV.
Churn risk prediction (CRP): the churn rate refers to the probability of a paying customer not returning to purchase again in the future. Essentially the chances of them becoming a lost customer. In Klaviyo, a low churn rate is represented by the green color on the individual profile, while yellow indicates a medium churn risk represented in red when the churn probability rises beyond the average. Learn more about churn risk in Klaviyo here.
The average time between orders: Klaviyo calculates the average number of days each customer takes before making another order based on their prior order dates.
Predicted gender: Klaviyo’s gender prediction algorithm utilizes various pieces of information to make a prediction of each subscriber’s most likely gender. This comes in handy while using targeted communication.
How Does Klaviyo Analytics Calculate Customer Lifetime Value (Clv)?
Every week Klaviyo analytics uses your store’s data to retrain the customer lifetime value (CLV) model. The predictions are based on patterns the predictive model identifies in historical data, so they are not absolutely certain, and they work best when averaged across multiple customers.
CLVs are not exact, so it’s common to see predictions with decimal digits, for example, 1.44, in which the analytics predicts that the customer will make one or two orders within the year. These decimal points, however, do add up when you calculate the total number of anticipated orders which is also an approximation to guide you in predicting the expected order or making decisions on how to spend capital in preparation for the expected future demands.
How Klaviyo Analytics Calculates Expected Date Of Next Order
Klaviyo’s Machine Learning relies on each customer’s order behavior and the average order behavior of all your customers to calculate the expected date of the next order. With the expected date of the next order, you can then send a customized message to your customer via email one week ahead of the expected date. It’s recommended that you use a dynamic product feed to vary the sort of content you send the customer and personalized suggestions based on their prior purchases.
For customers with more than three orders, Klaviyo’s predictive analytics can determine a pattern in terms of the average number of days between orders, and the expected date of the next order is based on the prediction.
In cases where the customer’s profile lacks enough historical data, the average expected date of the next order from all your customers is used instead.
Suppose the customer only has one prior order. In that case, the prediction analytics uses the median value of the expected date of the next order for all of the store’s orders, eliminating frequent and infrequent orders that usually affect the average.
In some customer profiles, you may notice that the next expected order date is in the past and is usually accompanied by a high churn rate. This indicates that it’s highly probable that based on all of your customer’s order behavior and this customer’s most recent order date, they are unlikely to order again because the model assumes if they were going to – it would of happened already. Hence the past date.
Benefits Of Predictive Analytics for Ecommerce Brands
These are the primary reasons we feel the tool is a value-add for online retailers.
Improves customer retention
With Klaviyo, you can predict each customer’s churn risk and take decisive steps to retain them. With these predictions, you can also identify discontentment among your customer base and which customer segments of your business are more likely to leave before they do. Remember, it’s cheaper for you in the long run to improve customer satisfaction and retain existing customers than it is to acquire new paying customers.
Provides insights on how to run ongoing nurture campaigns
The best way to boost the performance of ongoing campaigns is by ensuring that they target the right segment of your overall emailing list. With predictive analytics, you can improve customer segmentation based on the criteria that matter to your business, allowing you to focus on the right target audience.
With Klaviyo, you can gain more precise insights into the performance of your business and your customer’s future probable purchases. This improves every Ecommerce entrepreneur’s decision-making, such as planning for a future increase in stock demand and identifying the proper channels to engage your customers and the best time to do so.
Identifying profitable customers
With predictive analysis, you can identify which customers are most profitable depending on their average order count and the expected date of the next order. With this data, you can then focus most of the marketing expenditure on growing the segment that generates the most profits.
How Klaviyo Predictive Analytics Calculator The Churn Risk Prediction (CRP)
Klaviyo calculates each customer’s churn risk prediction based on their order data analysis. To get the most precise predictions, the AI tool also relies on Ecommerce data from other sources such as Shopify to differentiate between high churn risk customers and low churn risk customers.
Every week the predictive model retrains on new data, and over time, it can tell apart one-time purchase customers and those more likely to make a repeat purchase. Every time customers buy from your site, their risk for churn decreases. Suppose the customer takes longer than their average time between orders to return to make their next order; their churn probability increases gradually.
You can learn more about the software’s more advanced functionality in our detailed review of the Klaviyo platform.
Frequently Asked Questions (FAQs)
Where is Klaviyo predictive analytics data displayed?
Data from Klaviyo’s predictive analytics is displayed on each contact’s profile. Navigate to the individual contact and click on the predictive analytics section of the customer profile.
What are some Klaviyo predictive analytics examples?
A common predicament most store owners face is how to nurture customers who have previously made a single purchase and get them to make their second purchase. With predictive analytics, you can estimate the expected date of the next order and use targeted communication.
Repeat buyers can use predictive analytics to set up repeat purchase nurture flows that contact the customers at an appropriate time based on their purchase behavior. You can also use their purchasing patterns to suggest another purchase for each replenishment cycle. You should avoid sending countdown emails to the expected date of the next order as it may lead repeat customers to unsubscribe after receiving the same email sequence over and over again.
What is CLV in Klaviyo?
Klaviyo calculates CLV as the total amount of past and predicted purchases a customer will make over their lifetime before they churn.
Does Klaviyo use AI?
Yes, Klaviyo uses artificial intelligence (AI) tools and machine learning to analyze customer data and provide deep insights that make it easy to personalize your contacts at scale. The AI predictive models retrain every week, tracking every customer’s journey and forecasting their future decisions.
With the insights from predictive analytics, you can transform your email marketing game and gain the upper edge over your competitors. From learning the perfect time to send emails to automation that saves you time and money, there’s no end to how useful the AI predictive models predictions are to your Shopify store.
Klaviyo simplifies data science and machine learning to valuable segments that are easy to interact with, providing actionable insights and reasonable predictions, which boosts your marketing strategy immensely.
Get In touch with us today to learn more about how our Ecommerce email marketing agency services can transform your targeted communication and bring in more sales.