Are you looking to perform Klaviyo predictive analysis? If your answer is yes, you are in just the right place! Businesses are in constant need of evolution and using modern technologies to meet the requirements of customers to have a competitive edge over others. Therefore, having the right know-how in predicting future occurrences aid enterprises in developing strategies and planning structures that will promote growth and facilitate a healthy relationship with customers. This is done by harnessing information from existing knowledge, past purchases and patterns by using models, machine learning techniques, and artificial intelligence.
Targeting an audience and trying to predict an outcome before it happens helps organizations in managing and utilizing their resources. It further improves perspective before decisions are made about a particular venture or dealings with customers to reduce risk. One such platform that offers these services is Klaviyo.
Klaviyo is a platform that gives wonderful and actionable insights from data by using a combination of data science and machine learning techniques. In this article, you will be introduced to the different types of predictive analytics data displayed in a Klaviyo account, the various ways Klaviyo computes this data, and guidelines for how you can utilize this data.
Table of Contents
What is Predictive Analytics?
Predictive analytics can be defined as the use of data mining, statistics, modeling, machine learning techniques, and artificial intelligence to predict future outcomes based on historical data. The purpose of this is to forecast future possible outcomes. Predictive models are used by businesses to better understand customers, to predict buying patterns, potential risks, and likely opportunities.
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Importance/Uses of Predictive Analytics
Predictive Analytics has become an essential tool used by organizations and businesses today because it increases their competitive advantage. It helps solve difficult problems and create new opportunities that may not be easily noticed without the prediction. It is important in the following areas:
- Optimizing marketing campaigns: Predictive Analytics helps determine customer responses to marketing campaigns and aids observation of purchasing patterns.
- Improving operations: Predictive models are used to forecast inventory and manage resources, they enable organizations to function more efficiently.
- Detecting fraud: This is done by combining multiple analytics methods that help in improving pattern detection and monitoring actions on a network in real-time to spot abnormalities that may indicate fraud.
- Reducing risk: Credit scores are an example of where predictive analytics is used to assess a buyer’s likelihood of default for purchases, this score is generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims.
Predictive analytics is used in various sectors ranging from Banking and Financial Services, Oil and Gas, Manufacturing, Health Insurance, Aviation, etc.
Working with Klaviyo Predictive Analysis
KlaviyoKlaviyo Predictive Analysis uses a combination of data science and machine learning techniques to predict outcomes on all the data in your account. This allows you to manage campaigns, set targets, and minimize risks. To be able to see the Klaviyo Predictive Analysis section on your profile, you must however meet the following criteria:
- Have at least 500 customers that have previously placed an order i.e people who have actually made an order and bought from your business and not just customers.
- Have an eCommerce integration e.g. Shopify, BigCommerce, Magento or use Klaviyo’s API to send placed orders.
- Have at least 180 days of order history and a valid order within the last 30 days.
- Have at least some customers who have placed 3 or more orders.
The Klaviyo Predictive Analysis Section of a Profile
The diagram above shows the Klaviyo Predictive Analysis section on a contact’s profile. On the image, the following fields can be seen:
- Historic Customer Lifetime Value: This shows the total value of all previous orders an individual has made, the total number of orders is also displayed below this value. For example, $1,178 is the value gotten from 17 orders as can be seen from the image above.
- Total Customer Lifetime Value: This is the sum of Historic Customer Lifetime Value and Predicted Customer Lifetime Value.
- Churn Risk Prediction: Churning is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service. On Klaviyo, the probability of a customer churning is calculated based on the number and frequency of their orders. Each time the customer makes an order, their churn probability goes down and indicates green, but as time elapses between orders, the churn probability increases and indicates red, with a medium churn risk represented in yellow.
- Average Time Between Orders: This is the average number of days between each of a customer’s orders.
- Predicted Gender: Predicted gender is a part of Klaviyo Predictive Analysis features, it is used to predict the gender of a customer. However, this does not show in a customer profile.
The order timeline is also visible in the Klaviyo Predictive Analysis section of a profile. Each tick on the timeline represents an individual order, the amount and date of the order can be viewed by hovering over each tick as shown in the picture below.
Orders that have been returned are represented by a colored tick on the order timeline.
Ticks with a diamond represent the next expected order date for a customer. In the image below, the customer was expected to make an order on November 26th, 2019.
How Customer Lifetime Value (CLV) Data is Calculated
Predictions work best when averaged over many customers and not individual customers because some individuals will spend more than their predicted CLV and some will spend less, but as a whole, they will average each other out.
The Predicted CLV value is not a definite prediction, an improbable number can be derived at times. For instance, 1.36 may be the number of predicted orders for a particular customer which is not feasible as we can’t have a fraction (0.36) of an order. This can be interpreted to mean the customer is expected to make one or two orders, though they may make more or fewer. These expectations become more strategic and make sense when multiple customers are grouped because you can easily predict the total number of orders or spend for the group. For example, If four customers with a predicted number of orders 2.17, 0.25, 1.77, and 1.97, we can predict approximately 6 orders would be expected across this group.
Klaviyo automatically builds a Customer Lifetime Value (CLV) model using this data and retrains the model at least once a week.
How Expected Order Date is Calculated
To calculate the Expected Order Date, Klaviyo takes into account specific customer’s order behavior and the general order behavior of all your customers. If a pattern is identified on a customer’s purchase, Klaviyo recognizes this and will make a prediction based on it like monthly or bi-weekly purchases e.t.c. In a situation where the customer’s orders don’t exhibit a pattern or if enough data has not been derived from the customer, Klaviyo will make a reasonable prediction based on how other customers behave. You can also check out the klaviyo to data studio Integration.
How Predicted Gender is Calculated
The first name along with census data is used by Klaviyo’s gender prediction algorithm to predict a customer’s gender to be likely male, likely female, or uncertain. Since predicted gender is still an approximation, it is always better to still include some information for both genders when using targeted communication. For example information about female clothing can still be included when sending out a targeted advert to a supposed male contact.
In this write-up, you were introduced to Klaviyo Predictive Analysis. The article showed the importance and uses of this method by businesses to maximize knowledge gotten from customer patterns to manage resources and have an inclination for future purchases. You were also exposed to how Customer Lifetime Value, Expected Order Date, and Predicted Gender are calculated. Using the analysis discussed here can help you make informed decisions and cater to the specific needs of clients.
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