An introduction to retail personalisation

Retail personalisation is driven by excellent data

Customer data is crucial for effective retail personalisation because, rather than assuming the preferences of the customer based on gut feeling, businesses are able to deliver informed experiences that are informed by information.

There are many types of customer data that can be used for retail and eCommerce personalisation, including:

Behavioural data - Information about customers' browsing and shopping behaviour, such as which products they clicked on, how long they spent on a page, and whether they abandoned their cart. Businesses can gather this data through website’s analytics tools and management systems, tracking pixels and third-party products like Hot Jar.

Purchase history - Data about the products customers have bought in the past, when they have bought them, how frequently they have bought them and more can be gathered through integrated order management systems loyalty programs and retail EPOS systems - all of which are offered through our eCommerce platform, Venditan Commerce.

Demographic data - Information about customers' age, gender, location and more can be gathered through the customer sign-up process, surveys and social media profile connections. Customers can also be surveyed on their personal interests, hobbies and tastes. Customers tend to be more inclined to part with their demographic information when incentivised with discounts or prizes.

Communication data - Data about customers' interactions with the business, such as email opens and clicks, chatbot conversations, and social media engagement. Businesses can gather this data through email marketing platforms, chatbot software, and social media analytics.

Retail personalisation for customer acquisition

Data can be leveraged to improve the experience for first-time eCommerce website users by serving a more personalised experience to them.

Personalised product recommendations

eCommerce websites can be designed to help users find the products that are relevant to them. With quiz funnels, users can answer a series of questions based on their product preferences and generate personalised product recommendations tailored to them.

Rather than browsing through hundreds of products, quiz funnels can quickly and easily narrow down the choices for the user, saving them time and effort, and forming a strong first impression of the eCommerce brand.

These functions also serve to educate the user about the different products and services available, helping them make more informed decisions, which can be particularly useful if the user has arrived at the website for the first time.

It’s a smart way of encouraging a more personalised website UX for users that you don’t currently have any hard behavioural or order data on. Over time, the quiz funnel responses can be stored and used to improve product offerings, marketing campaigns, and customer service.

Personalisation based on location

An eCommerce website can introduce personalisation based on the user's geographical location in several ways.

Here are some examples:

Location-based product recommendations - The website can use the user's location to recommend products that are popular or relevant in their area. For example, a retailer with several warehouses can suggest products based on their availability at the warehouse closest to the user’s location.

Currency and language customisation - The website can automatically detect the user's location and display prices in their local currency and language. This improves the user experience and can increase conversions by eroding a usability barrier.

Serving localised content - The website can display content that is specific to the user's location, such as shipping information, local events, or regional promotions.

Location-based search results - The website can prioritise search results based on the user's location. For example, products that are available for in-store collection only.

To implement these personalisation features the eCommerce website needs to collect the user's location data. This can be done through IP address tracking, GPS location, or user input. However, it's important to ensure that the website follows privacy laws and regulations when collecting and using user data.

Personalised customer support

Support can also be personalised by leveraging data and AI to deliver an experience that is more relevant and consistent with the problems the potential new customer is having.

Websites can use AI-powered chatbots to provide customer support around the clock. As we discussed in last month’s piece around ChatGPT and eCommerce, these chatbots can deliver more specific and targeted support to customers.

For example, if a customer is browsing the PDP of a relatively technical product, then the chatbot can be given that context and prompt the customer after a period of inactivity, as that inactivity could be driven by a desire to understand more about the product they're looking at. The support would therefore be tailored to the product the user is browsing, delivering a more relevant experience.

Aside from that, AI can also be used for predictive analytics, helping to anticipate the new customer’s needs and provide personalised recommendations. For example, if a user has opened up the same product several times, the website could prompt them with further support on the assumption they are looking for a particular piece of information.

But how does personalisation actually help acquire new customers?

Personalising the experience a website can provide to new users leads to an increase in conversion rate, because:

• Personalised shopping experiences can create a positive first impression for new customers, increasing the chances of repeat purchases and positive word-of-mouth recommendations to friends and family.
• Relevant product recommendations appeal more to new customers as they are aligned with their requirements.
• Personalised experiences encourage new customers to spend more time on the website and explore additional products, increasing their engagement and potentially increasing their basket total.
• Personalisation can help reduce cart abandonment rates by improving on irrelevant product recommendations and poor website navigation.

Overall, personalisation can play a critical role in attracting new customers - yet, arguably, it is even more important when it comes to retaining those customers over the long term.

Retail personalisation for customer retention

Unless your customers feel valued and understood there is every chance that they will engage with one of the undoubtedly many other retailers vying for their attention.

Delivering customers with an engaging, personal and easy experience will go a long way towards cementing them as advocats for your business. Here are some examples of personalisation with the aim of retention.

Regular, personalised offers

If you are able to use a customer management tool to segment your customers based on key variables, you are able to begin rolling out offers and discounts that are much more relevant to them.

Rather than running generic discount campaigns, consider what each individual customer is most likely to engage with. For example, a sports retailer could specifically target regular purchasers of Adidas with a limited discount code for that brand.

The personalisation comes in the way that the offer is presented. Direct to the customer’s inbox, addressed personally with clear reference to the fact that you understand what they like.

Hi [First Name], we know you’re a big fan of Adidas products. As a token of our appreciation for your custom, we would like to offer you 20% off all Adidas items over the course of the weekend. Just use code…

This campaign could go out to thousands of regular Adidas purchasers, but the personalisation makes the customer feel like they’re being addressed directly.

Personalised product recommendations

By analysing customer data, including past purchases, browsing history, and demographic information, retail and eCommerce companies can recommend products that are relevant to the customer's interests and preferences.

Retailers can use a variety of techniques to provide personalised recommendations. Some of the most common techniques include collaborative, content-based and hybrid filtering.

Collaborative filtering involves recommending products based on the behaviour of similar customers. If a customer purchases a particular product, collaborative filtering can recommend other products that were also purchased by customers who bought the common one.

Content-based filtering allows the online retailer to provide recommendations based on the characteristics of the product and the customer's preferences. For example, if a customer has shown an interest in a particular brand or type of product, content-based filtering can recommend similar products based on those characteristics.

Hybrid filtering combines both of the above to provide more accurate and relevant product recommendations.

Personalised marketing communication

No one likes to feel like they’re being talked at by a brand, rather than to by a person.

Online retailers can personalise their communication with customers by addressing them by name and tailoring their messaging to the customer's preferences.

When customers receive personalised communication they are more likely to feel valued and appreciated, which can lead to increased engagement and stronger relationships with customers, which in turn can translate into loyalty and repeat business.

Website customisation

Customers can be given the freedom to organise the website based on their preferences, creating custom categories of their go-to products for ease of use.

Allowing website users to customise categories to show specific products that they are interested in can provide several benefits. A personalised shopping experience means the customer is able to find products they are interested in faster and more efficiently, which can improve their overall satisfaction with the website.

Customised categories can help online retailers collect more data about their users' preferences and shopping habits. This information can then be used to improve product offerings, website design, marketing strategies and further personalisation of the customer’s experience.

A personalised checkout experience

The checkout experience can also be tailored to the repeat customer’s needs and preferences. Users appreciate a simple and straightforward checkout process. By removing unnecessary steps, such as asking for information that the business already has from the customer, the checkout process can be completed quickly and easily.

If a user has already provided their information, such as their name and shipping address, on a previous visit, pre-filling this information during the checkout process can save time.