As customers, buying from an online shop where your preferences are stored can help simplify your purchase and find what you need a lot faster, creating a more gratifying shopping experience. But, as shown by our recent Retail Trust Index, customers are not willing to compromise their personal data that easily; only and only if brands are transparent when displaying safe management consumer benefits of such data sharing.
So, how can brands deliver extraordinary shopping experiences while safeguarding their information? Let’s explore some practical and effective ways to achieve this delicate balance.
01 LIMIT YOUR DATA COLLECTION
Think what data you really need! What is really essential for your business to thrive and offer invaluable experiences for your customers? Collecting excessive information not only poses privacy concerns, but also hinders your own ability to manage it all, as more data requires more resources, both digitally and physically. Eventually, more data processing will also increase your carbon emissions and have a negative impact on your efforts towards a greener outlook.
Minimising the use of personal data will still allow you to offer a meaningful and personalised customer experience. At the same time, your business can excel that invaluable brand-customer connection based on trust. In many cases, this brings us back to the personalisation dilemma: how much data do you need to offer personalised journeys to your customers?
Learn more about this topic by rewatching our LinkedIn Live with experienced consultants Rhiannon Hanger and Stuart McMillan, as they discuss how brands can grow by rethinking their data collection strategies.
02 CHOOSE FOR LOCAL OR SESSION STORAGE
Storing customer data locally, as opposed to relying on remote servers or cloud services, offers significant advantages for enhancing data privacy. It provides users with greater control over their data, reduces the risk of large-scale data breaches, and aligns with privacy regulations like GDPR. Session storage keeps data in the client’s device or browser during their shopping experience. The moment they decide to leave the website, that data is not kept.
In this way, you are giving your customers the shopper experience they deserve with their own data.
03 GENERALISE THE DATA
Generalisation can be compared to segmentation. By grouping your data into similar-patterned categories, offering tailored and more suitable products for your audience becomes much easier. These data categories can be divided according to age (25-30, 30-35, 35-40…), purchase location (nationally or internationally), or gender, among others.
This ‘wisdom of the crowd’ solution respects the need for data protection and privacy, as employed by our founding partner Empathy.co. By contextualising queries without gathering personal information, this approach extracts only essential details to create affinity models for each query. By tackling collective preferences and products, brands can still offer personalised shopping experiences while maintaining data privacy.
A prime illustration of this synergy comes from B/S/H, which partnered with Empathy.co to craft tailored shopping experiences without compromising personal data.
04 FIND ALTERNATIVE DATA SETS
Building an effective and broad database comes with security risks. Synthetic or artificial data is created from your original, real index of data, through an algorithm, preserving the same parameters and information, but making up a completely different set of values. Instead of having real data, you would be handling a machine-made simulation of your data, which can be useful for various purposes like testing, analysis, and research.
Let’s see it clearer with an example: a global clothing retailer, "FashionABC," wants to enhance its customer personalisation efforts while safeguarding customer privacy. They have a massive database of customer purchase history, but they are concerned about using real customer data due to privacy regulations and the risk of data breaches.
FashionABC decides to generate or get open source synthetic customer profiles and data based on their existing customer data. They employ advanced algorithms to create synthetic customer records that preserve the statistical properties and patterns of their real customer data while ensuring that no Personally Identifiable Information (PII) is included.
Something to take into account when handling these data sets is that they are linked to AI and might be subject to biases.
05 (PSEUDO)ANONYMISE THE COLLECTED DATA
Do you really have to collect data? Then, make sure it’s as anonymised as possible and stored in line with GDPR regulations.
Pseudonymisation changes some details of your clients’ data so that they cannot be easily identified. Instead of saving “John White” in your database, the name would be changed to a string of letters, a number, or a fictional name such as “Harry Potter”. Pseudonymisation can also be used on emails or other personal information.
With pseudonymisation, businesses can keep information about their clients’ motivations, likes and purchasing preferences without knowing precisely who they are.
Remember data pseudonymisation is not the same as anonymisation. It changes names and other details, but does not remove all the identifying information completely. This is extremely important for companies processing detailed information, such as payment processes.
CONCLUSION
ETHICAL DATA USE IN YOUR ONLINE SHOP
Ensuring customer trust through ethical data use in retail is not just a responsibility, but a strategic advantage for businesses in today's digital landscape. By following these easy tips, brands can strike a harmonious balance between delivering personalised shopping experiences and safeguarding customer information.
KEY TAKEAWAYS
Limiting data collection to what is truly essential not only respects privacy but also reduces your carbon footprint.
Choosing local or session storage empowers customers with greater control over their data while interacting with your brand.
Generalising data allows for tailored offerings without compromising personal information.
Exploring alternative data sets, such as synthetic data, can mitigate security risks and privacy concerns while aiding in testing and analysis.
Pseudonymising collected data ensures a level of anonymity that still allows businesses to understand customer motivations and preferences without exposing personal information.
