Personalisation. It's a hot topic at the moment as ecommerce businesses fight to attract customers and then keep their attention. Rightly, it has become an important strategy for businesses looking to cater to the unique needs and preferences of their customers. After all, it's never been more accurate to say that it costs more to acquire a good customer than it does to retain them.
The ability to deliver intelligent, informed and stylish (and not creepy) personalised experiences is a combination of strategy and technology. Delivered effectively, they can significantly impact customer satisfaction, loyalty, and ultimately, revenue.
One of the most valuable tools for achieving this level of personalisation is first- party data. First-party data is the information collected directly from your customers or users and given with their permission. It includes various types of data such as name, email address and product preferences, which are essential for tailoring experiences.
This data is a crucial business asset, as much as an inventory or tech stack. It's going to be what makes or breaks ecommerce brands in the future as we move in towards a cookie-less environment.
What type of data is needed for personalisation?
- Demographic data: This includes basic information like age, gender, location, and sometimes income. It provides a foundational understanding of who your customers are and can be used to segment your audience for more targeted marketing and personalization efforts.
- Behavioural data: This data reveals how customers interact with your brand's digital properties. It encompasses website engagement, app usage, purchase history, product preferences and click-through rates. Analysing behavioural data helps you understand your customers' interests and buying patterns, enabling you to provide relevant content and product recommendations.
- Contextual data: Information about the current context of your customer, such as their location, device, or time of day, can be leveraged to deliver real-time personalisation. For instance, a store might use location data to push weather-related promotions when a customer is in an area where the seasons are changing.
- Preference data: Understanding customers' explicit preferences, such as their favourite product categories, brands, or communication channels, enables you to create highly targeted and relevant experiences.
Where can you get first-party data?
- Websites and apps: Data can be collected through website analytics, user registrations, and account information on your app.
- Email marketing: Subscriber data can provide valuable insights into user preferences and behaviours.
- In-Store purchases: For brick-and-mortar businesses, point-of-sale systems can capture purchase data and loyalty program information.
- Surveys and feedback Forms: Directly asking customers for their opinions and preferences can yield valuable data.
How to make your first party data work for you:
- Segment your customers: Use demographic and behavioural data to group customers with similar characteristics or behaviours. This enables you to target specific segments with tailored messages and offers. It allows you to optimise your marketing spend in the most profitable areas and deliver the right message to the best performing customer segments.
- Create personalised content: Use preference and behavioural data to build a recommendation engine that allows you to suggest products or content tailored to each customers' interests. This applies not only to the on-site experience but any marketing channels.
- A/B Testing: Continuously experiment and refine your personalisation strategies to optimize results. What works for one customer segment, may not apply to another.
- Offer unique, personalised experiences: First party-data is how you create moments that drive brand loyalty. Perhaps your customer segment of high-value, long standing customers can be broken down into those who would like to be rewarded with a discount for their loyalty, versus those who would like a limited edition of a new product line to try out.
How do you ensure your data is reliable and accurate?
Reliability and accuracy of first-party data are paramount for effective personalisation. There's nothing worse than getting someone's name wrong in real life - it's the same in your marketing. To ensure the reliability of your data:
- Regularly update your data: Keep customer profiles current by encouraging users to update their information and verifying data accuracy.
- Data security: Protect customer data with robust security measures to maintain trust and comply with data protection regulations.
- Data hygiene: Implement data quality processes to remove duplicate entries, correct errors, and ensure consistency.
- Privacy compliance: Respect privacy laws, like GDPR or CCPA, to maintain trust and avoid legal complications.
One of the main challenges to be successful in ecommerce is that businesses will likely have data on lots of different platforms/places, and not all of these will integrate directly, which means that a true single customer view cannot be achieved. But don't worry, there is a solution. With a Customer Data Platform, all customer data can be pulled together and unified to give a true view of customers, both at a group level and individual level.
What is a customer data platform?
A Customer Data Platform (CDP) is a software platform designed to collect, integrate, and manage customer data from various sources to create a unified, comprehensive customer profile. CDPs are crucial in helping businesses understand their customers better, personalise their interactions, and make data- driven decisions.
The key features and functions of a CDP are:
- Data Collection
- Data Integration
- Creating Customer Profiles
- Segmentation
- Real-Time Data Processing
- Personalisation
- Cross-Channel Consistency
- Analytics and Reporting
- Compliance and Privacy
- Data Export
How can a Customer Data Platform facilitate A/B Testing?
- Centralised data management: by serving as a centralised repository for all customer data, it collects and integrates data from various sources to provide a comprehensive data pool and a complete and accurate view of customer behaviour and preferences
- Segmentation and targeting: Segmenting customers based on demographic and behavioural data is invaluable for A/B testing. Test groups can be precisely defined, ensuring that each test is conducted on a relevant subset of your audience. This enables you to compare how different groups of customers respond to variations, helping you refine your personalisation efforts.
- Real-time data availability: Capture and process data in real-time. This means that A/B testers can access the most up-to-date data when designing and running tests. Real-time data ensures that tests are conducted with the latest information, resulting in more accurate and personalisation efforts.
- Personalisation at scale: By processing large volumes of data quickly, businesses can personalise experiences at scale. A/B tests can be conducted on subsets of your customer base, and the results can be rapidly incorporated into personalised content, recommendations and marketing efforts.
- Historical data and trend analysis: A CDP not only provides real-time data but also stores historical data. This feature is particularly useful for A/B testers to analyse trends over time and assess the long-term impact of personalisation efforts. It enables you to understand how personalisation strategies have evolved and the results they have generated.
- Iterative testing: Supporting iterative A/B testing by allowing businesses to refine personalisation strategies based on previous test results. By analysing what works and what doesn't, you can continually optimise customer experiences for better results.
- Cross-channel consistency: A consistent and cohesive approach to A/B testing across various channels, whether it's website, mobile app, email marketing, or in-store experiences ensures that personalisation efforts remain coherent and effective in all customer touchpoints.
A Customer Data Platform is an indispensable tool for businesses looking to conduct more effective A/B testing for personalisation. By providing centralised data management, advanced segmentation, real-time data availability, and the ability to personalise at scale, a CDP empowers companies to deliver more relevant and engaging experiences to their customers. Moreover, the historical data analysis, iterative testing, and cross-channel consistency features offered by a CDP make it an essential component of a successful personalisation strategy in the digital age.
