From Insights to Impact: Data-Driven Personalization in Loyalty Programs
Key Takeaways:
- Personalized offers and communications are vital to drive customer loyalty
- Data analytics and AI can uncover new insights at a granular level
- Concerns around data use and privacy must be addressed
- Third-party expertise can help drive cost-effective innovation in this area
It’s never been easier for customers to shop around online, which means making efforts to secure and maintain customer loyalty are as important as they’ve ever been.
From retail to travel, loyalty programs combine a clear goal for the retailer, well-defined actions that help customers unlock rewards, and incentives that encourage greater participation. But while ideas like membership tiers and reward redemption options are well-established, consumers are increasingly expecting personalized offerings, backed up by data analysis and communication strategies.
In this blog, we'll explore how retailers are revolutionizing customer loyalty through personalization. We'll answer ‘what is data-driven personalization’ and examine its impact on retail loyalty programs, while addressing the critical challenges of data management and consumer privacy.
Data Types and Personalisation Techniques in Retail
Firstly, it’s important to understand the types of customers that retailers deal with. There are four main categories:
Analytical
The customer who does plenty of research before making a purchase or contacting support, and expects similar accuracy from a retailer.
Expressive
The customer that wants to engage in a conversation and build a relationship with the retailer, and hopes that they will respond in kind.
Amiable
The customer who seeks out the honest, expert opinion of a retailer, so that they can make informed decisions.
Direct
The customer that knows what they want and wants to get it as quickly as possible, without any deviation or delay.
With these customer types in mind, retailers are then in a better position to understand individual customers, and deliver personalization based on the data that these customers generate. Data-driven personalization techniques can include (and are not necessarily limited to):
- Tailoring rewards and offers based on individual preferences
- Tiered loyalty levels with escalating benefits and greater choice of rewards
- Targeted emails and messaging with personalized offers and content
- Personalized multi-channel experiences, driven by data analytics into customer behavior
Learn more about how hyper-personalization works in this blog: The importance of hyper-personalized user experience in eCommerce
Benefits of Data-Driven Personalisation in Loyalty Programs
According to Forrester, 63% of US adults are willing to share some of their personal information with businesses in exchange for rewards. But at the same time, Deloitte has found that 40% of consumers aren’t satisfied with the level of personalization within loyalty programs, which means there’s still work to be done to meet their expectations.
By successfully deploying those data-driven personalization tactics, a retailer is far better-placed to monetize data, and to:
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Boost customer engagement, interaction and satisfaction, as retailers better engage with individuals’ desires and preferences |
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Encourage long-term customer retention, through loyalty structures that make it more cost-effective and rewarding to stick with the same retailer |
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Maximize conversion rates through personalized offers and omnichannel engagement that better resonates with each customer and makes them more likely to buy |
These three benefits can all be maximized by taking a hyper-personalized approach. For example, customers could be sent discounts and offers on items that they regularly purchase, which demonstrates that a retailer understands their needs and is responding appropriately. This can be powerful in building a positive emotional connection, making customers feel valued and making them more likely to shop with that retailer over competitors.
Challenges in Retail Customer Data Usage
There are two major barriers to progress with data-driven personalization, and the first lies in how data is utilized. Modern data technology is therefore essential for helping retailers avoid these pitfalls:
- Disconnected and siloed systems that prevent a unified customer view, and by extension, prevent coordinated personalization
- Data that is inaccurate or out of date, leading to recommendations that are irrelevant or frustrating to customers
- Over-personalization that customers feel is invasive and is more likely to deter them or proceed with more caution
The second barrier relates to data privacy and ethics, and how retailers strike the balance between reaching out to customers on a personal level, and ensuring that the use of data in the process is both legal and ethical.
The same principle applies to the responsible use of AI, but it’s just as important here. According to Forbes, more than 70% of customers will stop buying from a company if they feel their data isn’t being protected properly. Because of the extent of data collection involved, this means that transparency around data usage, alongside strong security measures, are vital to retain trust and ensure that personalization is effective without being intrusive.
Measures that retailers can take to build that trust include:
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Security and privacy best practice, such as multi-factor authentication (MFA), single sign-on (SSO), and password managers, all of which build confidence among customers that they and their data are safe throughout their buying journey |
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Encrypting customer data so that only authorized users can access it, helping minimize the risk of a damaging data breach |
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Data masking tools that limit the range of people within a retailer’s workforce that can see customer data, so that data is only accessed and viewed on a need-to-know basis |
Notable Success Stories of Data-Driven Personalisation in Customer Loyalty Programs
Explore the Ciklum Approach to Data-Driven Personalisation
The reason that Starbucks and others have had so much success with data-driven personalization is thanks to the use of advanced technology. This starts with ensuring the quality and security of data, and extends to the use of AI and machine learning, but it’s clear that having the technological building blocks in place is essential to connect with individual customers.
Some retailers will have the resources and skills to achieve this in-house, but in the age of highly specialized innovations like AI, many won’t. And that’s where the expertise of experienced retail technology partners like Ciklum comes into play.
Through our Experience Engineering approach, we can help retailers like you harness AI, predict and respond to users needs, and provide the customized experiences that your consumers are looking for. By leveraging advanced algorithms, optimizing data flows, and getting support in addressing key data and privacy challenges, we can ensure you can maximize your hyper-personalization efforts in the most efficient and cost-effective way possible.
Get one-to-one advice on how we can help your data-driven personalization efforts by contacting the Ciklum team today.
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