360° Single
Customer View
or a complete preview of customer
It collects, then stores and analyzes all relevant data about each customer in particular.
360° Single Customer View or a complete preview of customer within loyalty program is actually the approach to collecting, storing and analyzing data about each customer in particular with the aim of creating a wider picture of their customer behavior, preferences, buying habits and interaction with the brand. A preview like this one is actually the integration of all data available for each customer separately and from various sources, also containing demographic data as the main information about customers that may include their name, sex, age and location, marital status, etc.
This option also includes in-depth information about transactions or purchases a customer made, showing products or services they bought and the frequency of making purchases as well as the amount of money spent on them. In our loyalty platform, you can also see whether such product was discounted at moment of purchase and many other details.
This comprehensive preview of customer includes even data about their interactions and engagement. In other words, it presents all the methods of interaction between the brand and the customer, detailing how the brand has engaged with customers and how customers have interacted with the brand—whether through visiting shops, email, the website or social networks, etc.
It also encompasses the information about customer’s engagement in loyalty program, gained rewards, ways earned points are used and other special privileges. Once such data get integrated into single preview, companies have a more profound understanding of their customers so that they can make personalized offers and finally enhance customer experience. An approach like this one is a chance to recognize trends and patterns of customer behavior that is crucial for an excellent optimization of loyalty program and marketing campaigns.
360° single customer view – recognize trends and behavioral patterns!
Customer segmentation
(RFM & Customer Behavioral)
A more profound understanding of behavior and needs of customers is essential for success
Customer segmentation through RFM – Recency, Frequency, Monetary and Customer Behavioral is actually a strategy for segmenting customers in smaller groups depending on their behavior, buying habits and activity, all with the aim of better understanding their value for the brand and creating further detailed personalized marketing steps.
RFM method analyzes how frequently customers make purchases and observes the time of the last purchase or interaction made with the brand. Customers who have been active more recently are perceived as more valuable for the brand since they are either more likely to return or they are still interested in offers and promotions. This method observes frequency meaning that it analyzes how often clients buy products or they show any interaction with the brand over a certain period. Customers making purchases more frequently are more inclined to become loyal ones. It is extremely important to create offers that customers within such group find attractive to encourage them to repeat their purchases.
Monetary value is metrics used to evaluate the total amount of money customers and clients have spent. The ones who spend more are more valuable to the brand and that is the reason why companies should reward them with even more exclusive privileges within loyalty program.
Customer Behavioral Segmentation is a method focused on analyzing distinctive customer behavior in order to understand their interests, needs and habits. Analyzing is carried out using various parameters and some of them are the groups of products customers most often show interest in, products that they most commonly buy, ways of using loyalty cards, which promotions they find most attractive and many more.
Our loyalty program provides endless possibilities for setting customer segmentation criteria!
Customer segmentation is important since it assists brands in understanding behavior and needs of customers and clients. It also allows creating personalized marketing messages or offers that are more meaningful for each segment separately. It also greatly contributes to creating marketing campaigns that are more efficient. Marketing campaigns with a target for each segment of customers result in a better allocation of resources and prevent wasting any of your resources. By using these methods, we actually establish a longer-lasting loyalty and value.
These methods for customer segmentation contribute to improving customer experience, boost sales and guarantee the loyalty of customers on a long-term basis. A stronger and more meaningful connection with clients is achieved by using RFM and Customer Behavioral, so that way you keep clients and reduce the likelihood of their leaving for a competitive brand.
AI + Loyalty
Advanced version of Machine learning (ML)
This loyalty program can easily be upgraded with AI element
Advanced version of Machine learning performs segmentation of users automatically, recognizes similar individuals according to their behavior and purchase habits. Based on the analysis, Machine learning anticipates the types of offers customers are likely to be interested in and shows them such precisely tailored recommendations. This way brands offer their customers completely personalized benefits making the program flawless even in the case of the most narrowly segmented groups of customers.
Experience
Spotlight in practice.
We know that the future lies in a comprehensive loyalty program that inspires, attracts and recruits new customers while personalized benefits secure that the existing ones will return and repeat their purchases.
Do not miss this chance and entrust the profitability to a proven strategy you can rely on that certainly yields results.
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Connected integration with
Point of Sale (POS) systems
SpotLight – Customer Data, Loyalty & Engagement Platform.
© 2023 – All rights reserved.
SpotLight – Customer Data, Loyalty & Engagement Platform.
© 2023 – All rights reserved.
