Name: Using Smart Insight Customer Lifecycle Dashboard in SAP Engagement Cloud, Emarsys Edition
Uploaded: 2026-6-5
Duration: 378 s
Description: Explore how Smart Insight helps marketers increase Customer Lifetime Value (CLTV) by analyzing customer behavior and purchase data. Learn how lifecycle stages are defined, conversion rates are influenced by marketing, and discover new opportunities with predictive analytics and machine learning.

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<div><div>Explore how Smart Insight helps marketers increase Customer Lifetime Value (CLTV) by analyzing customer behavior and purchase data. Learn how lifecycle stages are defined, conversion rates are influenced by marketing, and discover new opportunities with predictive analytics and machine learning.</div></div>

Using Smart Insight Customer Lifecycle Dashboard in SAP Engagement Cloud, Emarsys Edition

Hello, this is a video about Smart Insight. What is your goal as a Marketer? We believe that your ultimate goal is to increase Customer Lifetime Value or CLTV, which means maximize the value of the customers already in your database. Smart Insight uses your customers’ Behavior and Purchase data to empower you to do exactly that with actionable analytics. Do you already have a system that breaks down your customers by lifecycle stage? Do you know how their lifetime value changes during the lifecycle journey? With Smart Insight, all your customers are now broken down by lifecycle stage. It even shows you how the lifetime value grows as contacts move from Leads to First-time buyers. How the value maximizes when customers are active, and how it starts to drop off when they defect, until they become inactive. That is the whole concept of Lifecycle Marketing: growing and keeping customers active, in order to maximize lifetime value. So, let’s dive into the Smart Insight functionality and see how it helps you to do exactly that. Smart Insight is our Customer Intelligence Platform. It analyzes your data and allows you to understand your customer groups, and the impact your marketing activities have on them. Now, when I say customer groups, what I’m talking about is how you organize the different types of customers in your Contact database into groups that you can understand and target. We believe every business has its own groups. Think about what your most important customer groups are, and how you would go about measuring the effectiveness of your marketing activities for them. We identify these lifecycle stages using engagement, recency, frequency and monetary modelling on your Purchase and Behavior data all out of the box. This is also known as eRFM data. A Lead is someone who has never bought before, whereas a First-time buyer made only one purchase, and an Active buyer has made multiple purchases. But to qualify as either a First-time or an Active buyer, they had to have made the purchase within a certain time period. In this example, we use 90 days, and anything outside that timeframe classifies them as a Defecting buyer, and eventually Inactive. This is the usual customer lifecycle journey. However, the parameters used to to identify and classify the groups are unique to your individual business case, as it is based on the data profiling we will do with you during your data onboarding project. For each group you can see how many contacts there are at any given time, and how well you perform when it comes to conversions. In our example, you can see that there are almost 4,000 Leads, and 9% of them are converting to First-time buyers. You can also see how many converted in the last 30 days and how many were lost. Similarly, you can see that from over 3,000 First-time buyers, 13% of them converted to Active buyers, whereas the rest became defecting. This gives you a separate conversion rate score for each group. You might be doing OK for one group, but not so well for the other… Customers are different, so you need to be able to engage with them differently and measure the outcomes. Now, let’s see how many of these conversions are influenced by your marketing activities. Each Conversion rate also shows you how many are converting due to email campaigns, and how many converted “natively”, or rather without any help. This breakdown lets you truly understand the contribution of your marketing activities to the conversions for each group. You can see that while for the Active buyers, 17 of the 47% conversion rate is driven by marketing activity, but for Defecting buyers only 1 of the 25% conversion rate is influenced by marketing, so you have an opportunity for improvement here. You also have a summary of how many effective campaigns you have for each group, that you can drill down into and analyze further. Now, let’s see how Smart Insight discovers new marketing opportunities for you, while helping you to understand your customers better. With all the marketing opportunities hidden in your data, it is crucial to invest in the areas that will bring you the highest return. Smart Insight uses machine learning to predict and recommend what your next best move is, and where you are likely to get the highest return on marketing investment. In this case, the Defecting buyers group is flagged, which means that of all the groups, this is where you will get the highest return for each additional 1% conversion that you achieve. These recommendations evolve over time, as the smart metrics constantly react to changes in your data. Smart Insight lets you understand your customer groups better, by letting you drill down into the smart metrics for each group. The Predictive Revenue metric gives you an overview of how much revenue you received in the last 30 days from each group and then goes on to show how much revenue is predicted to be received in the future, from conversions, as customers progress along the lifecycle. This helps to show that a conversion has both a short-term and a long-term value. You can see the Average lifetime value for the customers in each group, broken down by historical and future revenue. And you can also look at Purchase Patterns to find out more. Like, how many days are between purchases, what the popular categories are, what the average order values are, and much more. With these meaningful parameters you can truly start to understand your customer groups better, and you can drill down even further for more detailed information.