Understanding Smarter Personalisation in Retail
I Think are you sick of seeing âpeople like you also boughtâŚâ recommendations when shopping online. Yes, those buttons were fascinating at first. But itâs all starting to get a bit stale, isnât it.
In the age of AI, retailers are rethinking how they build personalisation into their customer journeys. It starts with understanding what customers want and need. Of course. This is possible because customers are giving brands more information than ever about themselves.
Most are willing to share personal info if they see clear value in doing so - usually in the form of deals and loyalty programs. But the most valuable insights come from collecting large volumes of data about customer behaviour - what they browse, buy, and wish-list; which reviews they read; even how long they spend looking at a product photo or video. So what do brands do with all this data.
Well, some use it to create detailed profiles for each customer and segment them into different categories to tailor their messaging. Some even invest in AI-powered tools that automate everything from emails to SMSes to social media posts with copy that is likely to convert a certain audience group. They tweak content for website banners and home pages based on known preferences. Some go as far as offering immersive digital experiences that enhance shopping through AR (augmented reality), VR (virtual reality), chatbots, and virtual try-on tools.
But thereâs a fine line between personalisation and privacy violation. While most shoppers have gotten used to being tracked online, theyâre still worried about how much brands know about them and how much information theyâre willing to part with.
There have been instances of eCommerce businesses using data for manipulation and price gouging. So as smarter personalisation takes hold in the retail industry, it seems quite important that brands balance their pursuit of sales with ethical data practices that respect privacy concerns.
AI Algorithms: The Backbone of Personalised Shopping
Do you ever feel like your favourite online shopping website gets you. You could go on the app and start browsing. Next thing you know, everything you see is so in tune with what you like.
Itâs like the shopâs been reading your mind. But thereâs no mind reading involved. Instead, itâs an AI-driven algorithm that analyses all your browsing habits - from your preferred price point, chosen brands and colours to how often you shop. The algorithm picks up on patterns in your browsing history and then lays out recommendations for you.
The more you interact with the website, the better the recommendations get. Whether itâs about suggesting a shirt to go with a pair of jeans that caught your eye or pointing out other things that people who bought those jeans also got - algorithms make shopping easier and help shoppers make more informed decisions. AI does this for everyone who visits the store, each time picking up on cues unique to individual shoppers.
And it isnât just about shopping either. Several content streaming websites use algorithms to suggest TV shows and movies based on what users have previously watched or even looked up but not watched. Social media platforms use them to decide which content to show users and which ones not to show them based on their likes, shares and follows.
All of this is possible thanks to AI learning from our behaviour and then tailoring its suggestions accordingly.
Personalized Product Recommendations: Enhancing Customer Experience
How does it feel when you walk into your favourite boutique and they already know what you want. Itâs that feeling of being cared for, and seen. Thatâs exactly what AI can do for brands - itâs like a digital concierge. Personalising recommendations so perfectly, it feels like youâre their only customer.
Or at least, one of their top ones. AI has come pretty far in its ability to analyse data quickly and understand real-time customer behaviour better than the human mind could. And it uses this ability to personalise product recommendations based on several different factors at once.
These might be customer preferences, past purchases, browsing history, and current location - all working together to showcase the most relevant options to a customer in real-time. This is something that can truly enhance the retail experience for customers and improve loyalty by building trust. It might appear a bit tricky at first but it really is the best use of technology for personalisation. Smart AI-driven retail features can sort through vast amounts of data to suggest products that are more likely to convert.
This means not only getting more sales but also selling better, bringing up your average order value and reducing returns - both massive wins in any retail business. Itâs clear then that personalising product recommendations creates better, more meaningful interactions with customers and makes them feel valued.
This is ultimately what builds trust in a brand and sets retailers up for long-term success and sustainable growth.
Dynamic Pricing Strategies: Tailoring Costs to Individual Shoppers
How would you feel knowing that the price you see online isnât the price your neighbour sees. Dynamic pricing is this odd, divisive thing that some of us will absolutely hate, but can become a great tool for retailers to see improved profits. Itâs not about whether you can take a bargain off customers - itâs about leveraging AI to display the right prices for every unique user.
Pricing is comparatively an important factor for consumers who are coming to your website. Retailers might want to encourage loyalty and increase their LTV by giving frequent buyers lower prices and encourage one-time shoppers to convert by giving them a first-time discount. Instead of putting these in the hands of customers through some UX efforts like a pop-up or banner, retailers use AI-powered dynamic pricing tools to do this automatically for them.
These tools help monitor real-time demand, monitor competitor prices, give product recommendations and build flexible pricing models - all with algorithms. Most people are against dynamic pricing because it seems to suggest predatory behaviour from corporations. Weâve seen this in the airline industry where ticket prices fluctuate too often (and often quite dramatically), making the industry seem volatile and unpredictable.
But if implemented properly, dynamic pricing can be extremely effective for both customers and businesses. It helps set appropriate prices so brands can compete with each other while still offering good deals to customers without compromising too much on profits or quality. With AI-powered insights, brands can now track user preferences and habits along with data like purchase history and browsing behaviour to develop personalised pricing strategies. This is evidently turning into an industry in its own right as more retailers move online or try experimenting with omnichannel retail.
Pricing algorithms are fairly customisable so brands have more control over what they show customers - based on their intent, behaviour and even willingness-to-pay. While it might not be the most popular option among shoppers, itâs slowly becoming an essential tool for growth in retail (especially online).
Chatbots and Virtual Assistants: Elevating Customer Engagement
You know how it feels when you have a question about an online shop at 2am. Not really a time to call the customer care number. So, you scroll through the website with little hope and your sleepiness for company. Then, you find a chat window and out spills your inquiry like confessions at a friendship circle.
These chatbots often fill in the shoes of a store staff and are ready to take you through every step of your shopping journey. From browsing products to making payments, theyâre here to help so that even those who may find online shopping daunting can manage it fairly well. For instance, think about the Ikea chatbot named Billie.
Give them a question about price, order status, cancellations or refunds, ask them for assembly instructions or some design tips - Billie comes back with answers much faster than humans probably could. Itâs an interesting dynamic - shoppers engage with these chatbots out of curiosity and brands keep up with their spirit by making their chatbots more exciting. And over time, they learn how to create more meaningful conversations too. Now, instead of just automated answers based on a set of prompts, they go to great lengths to create realistic dialogue based on analysis from previous interactions.
A shopperâs questions can then be tackled with context and anticipation - not something many people expect from programmed bots. Youâll also find virtual assistants with speech recognition capabilities that interact via voice commands. These are powered by smart AI like Alexa or Siri - we all know how casual chatting with them can be.
If you have a simple question or need information quickly while driving or cooking without having access to your phone screen or laptop, this is ideal for you. Being able to get information quickly no matter where and when is reason enough to see why people love virtual assistants so much.
Predictive Analytics: Anticipating Customer Needs and Trends
Ever noticed how streaming platforms know exactly what you want to watch next. Or that online shops always seem to recommend what you didnât realise you needed. Thatâs predictive analytics working behind the scenes - and it has been for some time now. It seems like although, the tech powering this phenomenon is fairly advanced now compared to say five or ten years ago.
If youâre an online retailer, this data-driven approach can be a game changer for staying ahead of trends. The way I see it, with ai-powered predictive analytics, stores can identify patterns in customer behaviour, see when something is trending even before it peaks on social media, and offer personalised recommendations that make customers wonder if their phones are being bugged (the answer is no, but their search history says otherwise). This means retailers can kind of optimise inventory and marketing - while providing fairly tailored products and experiences.
But before you start worrying about your privacy, know this: Most brands donât have access to as much information as Facebook or Google do - which is allegedly what personalisation is based on. If anything, most brands use this intelligence about consumer behaviour responsibly because they want to keep their customers returning with trust in them. This involves using predictive analytics for things like product launches or exclusive discounts.
That said, predictive analytics can be a fairly affordable investment for small businesses too. All thanks to ecommerce tools powered by AI that are getting smarter every year. Todayâs biggest advantage is the ability to create hyper-personalised shopping experiences that keep customers engaged with your brand.