Online businesses aim to fulfill customer expectations while handling the competition in the market. Advanced technologies help them achieve these goals with cloud services, customer data platforms (CDP), and machine learning (ML) technologies. This also brings the benefit of analyzing a considerable amount of raw data and turning it into meaningful insights for businesses to serve their customers better in real time.
In this sense, online businesses either build in-house solutions or get third-party solutions to understand their customers and create a better experience to build and maintain loyalty. Retaining customers requires understanding their needs, behaviors, and motivations with various tools.
This guide aims to answer the following questions:
How can you understand customer behavior?
Customer data platforms (CDP) collect events, user, and product attributes on a website or application and store them in their databases. This data is used to identify new or returning users and their interactions with categories, products, and services. Segmentation of these users is a great way to understand the intent and behavior of a group of people following their paths that lead to purchases, cart abandonment, churn, or loyalty.
Although segmenting a group of users is helpful for marketing promotions and showing recommended content and products, the identity factor might be missing. Segments are high-level targeting, and personas are low-level targeting, like a micro-segment that drives better results in marketing campaigns.
Additionally, data is required for the algorithms to work. The more data there is, the better the algorithms will make predictions. Therefore, if the data is not sent after the customer starts using a predictive segment, the algorithms will not work.
How does Insider use data in algorithms?
Insider uses online and offline data in predictive algorithms.
Likelihood to Purchase can only be calculated with real-time data.
User Engagement can be calculated with online data to assess the customer's overall online engagement with your platform.
Customer Lifecycle Status (CLS) works directly with both online and offline data for purchase event data.
Both online and offline data can be used for Discount Affinity algorithms. While product page view event data should be collected via online and offline channels, purchase event data can flow from online or offline channels.
Attribute Affinity can be calculated with the customer's online product page view event.
How does Insider help with the Predictive Segments?
Insider analyzes user data and historical trends to define their interests and affinities, predicting future intents using machine learning methods. For example, if a user interacts with sweatshirts in a session and views the red-colored ones most of the time, they are likely interested in the red color. The vendor could display the red-colored clothing items at the top of the catalog on the next visit.
The machine learning algorithms can be trained to understand the differences between a shirt, shoes, or a jacket and their attributes, like colors or brands. In addition to category visits, engagement levels with messages or emails, discounts and promotions, visit densities and durations, cart adding, purchase frequencies, and other data points can also help identify micro-segments.
What kind of algorithms are available in Predictive Segments?
Insider offers the following algorithms:
Reach out to the Insider One team to enable the respective algorithm(s) that you would like to use.