Data analytics and card-linked offers (CLOs) have become essential tools for businesses aiming to enhance customer engagement and drive sales.
Leveraging data analytics allows companies to understand customer behaviour, and optimize their CLOs for better engagement.
This article explores the top practices in data analytics and card-linked offers that businesses should adopt to maximize the effectiveness of their card-linked offers.
Understand your customers with a data-driven approach:
Define key objectives: Before looking at any data, it is important to ask yourself, what are you hoping to understand? How much does a product category resonate with customers? How often do customers make repeat purchases? Which locations have the most traction? These are all valuable insights to understand in order to optimize your card-linked offers effectively.
Collect Comprehensive Data: To harness the full potential of data analytics, businesses must collect comprehensive data from various touchpoints. This includes transactional data, social media engagement, customer feedback and more. Ensuring that the data is clean, accurate, and up-to-date is crucial for generating reliable insights.
Using Advanced Analytics Techniques: Once the data has been cleaned up, it can be analyzed through various methods to gain powerful predictive insights. Predictive modelling, machine learning, and artificial intelligence are all great tools for extracting valuable insights from data. For instance, predictive analytics can help determine which customers are most likely to respond to specific card-linked offers.
Data Analysis: Once the data has been analyzed, it is essential to complete the useful insights into a clean and concise presentation. It should include the following:
- Descriptive analysis to summarize data (e.g., average sales per product)
- Frequency analysis to determine how often products are bought.
- Segmentation to group customers by purchasing behaviour.
- Trend analysis to see changes over time.
Once your business has a better understanding of their customer data they are in a better position to create more relevant and engaging card-linked offers.
Using card-linked offers to increase engagement
Now that the data has been organized, businesses can better understand their customers and make more robust card-linking offers.
Card-linked offers can be an excellent tool for influencing consumer spending. Some of the key goals card-linked offers address include the following:
- Attract new customers
- Increase retention
- Boost sales
Businesses can leverage data insights and design offers that are relevant and meaningful to their customers.
Relevant offers are key to keeping customers around for the long term. Data shows that 22% of former users do not return, mainly because there are no relevant offers. Businesses can leverage the predictive lengths they have gained from data analysis to optimize their offers and ensure relevancy.
Data analytics provides useful insights and can make offers feel more personalized. Effective personalization begins with segmenting the customer base into distinct groups based on demographics, purchasing behaviour, and preferences. This segmentation allows businesses to tailor offers that resonate with each group, increasing the likelihood of engagement and redemption.
How card-linking enables real-time data flow
Businesses can track the performance of card-linked offers in real-time, gaining useful insights to improve their promotional strategies continually. By analyzing real-time transactional data, businesses can provide offers that align with customers' current shopping habits. Card-linked offers can be strategically used to boost purchases in less popular or unwanted categories.
Olive's card-linking platform benefits both customers and businesses. Do you want to offer cashback based on a percentage of sales? No problem. Do you want to offer a reward based on a frequent purchase? Even better. Olive's platform allows businesses to design flexible card-linked offers with reward criteria that suit their needs.