Personalisation
Personalisation is the practice of changing what a shopper sees on a storefront based on signals about them, such as past behaviour, location, device, or stage in the buying journey. It can adjust product order, recommendations, banners, video clips, offers, and messaging so each visitor gets a more relevant experience instead of one fixed page.
Personalisation is the practice of changing what a shopper sees on a storefront based on signals about them, such as past behaviour, location, device, or stage in the buying journey. It can adjust product order, recommendations, banners, video clips, offers, and messaging so each visitor gets a more relevant experience instead of one fixed page.
Personalisation in commerce means tailoring what each shopper sees based on what you know about them. That might be past purchases, items viewed, location, device, referral source, or simply whether they are new or returning. Instead of one storefront for everyone, each visitor gets a slightly different version.
On a product page it can show a different hero image. On a category page it can reorder products. In a video widget it can surface clips for the categories someone has browsed. In email and WhatsApp it can change the offer based on cart history.
Personalisation is not the same as segmentation. Segmentation groups people into buckets like new visitors or repeat buyers. Personalisation can go finer, down to one shopper and one session, using rules or a model that learns from behaviour.
The honest tradeoff is data and complexity. Personalisation needs clean signals and a way to act on them, and it needs to feel helpful, not creepy. Done well, it lifts how relevant the storefront feels. Done poorly, it shows the wrong thing to the wrong person at the wrong time.
On beyondRegular
With beyondRegular you can lean into a light form of personalisation without a heavy data stack. Use a reel feed or carousel to show category-specific clips on each collection page, surface festive or regional looks during sale windows, and place a floating bubble that follows the shopper with the video most relevant to the page they are on. Tags link to your existing product pages, so the catalogue, prices in INR, and your own checkout on Razorpay, Shopify Payments, or similar stay in charge. Start with simple page-level rules, watch what shoppers actually tap, then refine which clips lead with which products.
Common questions
Does personalisation need a lot of customer data to be useful?
Not at first. Useful personalisation can start with simple signals you already have, like new versus returning visitor, the product or category someone is viewing, the referral source, or the device. These rules cover most of the lift. Deeper personalisation using purchase history or model-based recommendations comes later, once you have enough orders and traffic for the patterns to be reliable rather than noisy.
Is personalisation different from product recommendations?
Product recommendations are one form of personalisation, usually the carousel that says you may also like. Personalisation is broader. It can change the homepage banner, the order of a collection, the video shown in a widget, the offer in a popup, or the WhatsApp follow-up after an abandoned cart. Recommendations sit inside personalisation, not the other way around.
Related resources
First-party data
First-party data is information a brand collects directly from its own shoppers through its website, app, checkout, widgets, or messaging. It includes orders, product views, video engagement, email, phone, and on-site behaviour. Because the brand owns the relationship, it can use this data for personalisation and remarketing without depending on third-party cookies.
Product discovery
Product discovery is how shoppers find products they did not specifically search for. On a D2C store it covers the path from landing on a page to noticing a product worth buying, through homepage modules, category pages, recommendations, search, and short shoppable videos that put products in front of a browsing visitor.