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Live shopping is becoming increasingly popular for brands to directly engage with customers in real time. By broadcasting live video streams and interacting with viewers through chat and polls, brands can provide personalized shopping experiences that feel more authentic and interactive than traditional e-commerce.
Integrating artificial intelligence (AI) into your live shopping strategy can take personalization to the next level. AI tools allow you to understand your customers individually and dynamically customize content for each viewer.
Live shopping, also known as livestream shopping or streaming e-commerce, lets brands directly showcase and sell products to viewers through interactive video broadcasts. It originated in China, where it has become a multibillion-dollar industry led by Alibaba’s Taobao Live platform.
Now livestreaming is gaining momentum in the West, with social platforms like Instagram, Facebook, YouTube, and TikTok rolling out live shopping capabilities. US live shopping spending is forecast to reach $25 billion in 2023, up from just $6 billion in 2019 .
For brands, live shopping provides a more engaging alternative to traditional e-commerce by incorporating TV shopping and influencer marketing elements. Real-time video allows brands to demonstrate products and field questions from viewers dynamically.
However, simply broadcasting the same content to all viewers misses a huge opportunity for personalization. This is where artificial intelligence comes in.
AI tools can analyze individual viewer behaviour and preferences to customize multiple aspects of the live shopping experience:
- Product recommendations
- Deals and promotions
- Chatbot interactions
- Camera filters
- Graphics and overlays
- Host recommendations
- And much more
Personalized live shopping powered by AI helps brands provide tailored experiences that feel like a one-on-one shopping session. This leads to higher satisfaction, engagement, and sales conversions.
This comprehensive guide’ll explore creative ways brands can leverage AI and machine learning to boost results through personalized live shopping.
Collect Customer Data to Build Detailed Profiles
The key to effective personalization is understanding your customers. AI algorithms collect and analyse large volumes of customer data to identify trends, patterns, and preferences for each individual.
Here are some essential types of data to collect about your live shopping viewers:
Demographics. Fundamental info like age, gender, location, income level, education, occupation, and household status.
Psychographics. Lifestyle factors, values, personalities, and interests provide a fuller picture of the person.
Past purchases. Transaction history across all channels provides insight into product preferences.
Past live shopping behaviour. Info on which broadcasts they’ve viewed, products seen/purchased, comments made, etc.
Content engagement. How they engage with your brand content across channels reveals preferences.
Social media activity. Interests and connections on social platforms offer additional data.
External data. Data brokers can provide additional demographic and interest info.
Work to collect as much customer data as possible from multiple sources. Pull together data from your CRM, marketing automation platform, analytics, third-party data partners, and integrations with live shopping platforms.
Feed this data into customer intelligence platforms that use machine learning to identify patterns and clusters. These AI tools create detailed customer profiles that your live shopping personalization engine can leverage in real-time.
Recommend Relevant Products for Each Viewer
One of the most significant opportunities for personalization in live shopping is recommending products tailored to each viewer.
AI algorithms can analyze the customer data points listed above to determine which types of products a given viewer is most likely to be interested in. The system can then dynamically recommend relevant products in real-time throughout the live shopping broadcast.
Start with General Segment Matching
Dividing your product catalogue into segments or categories is a primary way to get started. For example:
- Category: makeup, skincare, fragrance, hair care
- Style: trendy, classic, edgy, bohemian
- Use case: travel, fitness, work, nightlife
- Brand prestige: luxury, premium, mass market
- Product benefits: hydrating, brightening, smoothing, volumizing
- Price tiers: budget, moderate, premium, luxury
- Ingredients: organic, vegan, antioxidant-rich
You can then use customer data to determine which segments each viewer likely fits into based on their demographics, past purchases, interests, and other attributes.
Your system can highlight products from the viewer’s matched segments during the live stream. For example, if a 25-year-old female viewer with athletic interests signs on, your AI could showcase products catered to young active females.
Take It Further with Advanced Recommendations
Even better, you can use collaborative filtering or machine learning algorithms to analyze patterns across your customer base. These advanced systems can generate more granular predictions about which products each viewer is most likely to purchase.
The recommendations become dynamic and continually optimize based on real-time data like:
- Which products a viewer clicks on or adds to the cart
- Questions they ask about specific products
- Comments they post reacting to products
- Polls they respond to
- Live reactions like thumb ups/downs
- Whether they ultimately purchase or not
Continuously monitor these signals to train your algorithms over time, iteratively refining each viewer’s recommended products list.
Tailor Deals and Promotions
Another impactful personalization tactic is to offer custom deals and promotions tailored to each viewer.
For example, you could dynamically offer:
- A discount code or promo for their most likely product categories
- Free shipping if they seem unlikely to purchase without it
- A discount matching their last purchase amount
- A “new customer” offer if they haven’t purchased before
- A bundle discount on complementary products
- Free samples of products they’ve shown interest in
- Stepped discounts based on total cart value thresholds
You can even integrate loyalty program levels, allowing Gold members to see 20% off while Silver members see 15% off.
Configure rules and use live data flow to determine the optimal promotion for each customer for maximum conversion lift. Make sure to follow a strategic approach — don’t devalue your brand or overuse discounts. Find the right balance and combinations for your business.
Leverage Chatbots for Personal Conversations
AI-powered chatbots is a fantastic tool to engage viewers in personalized conversations during live shopping events. Each chat can be tailored based on the individual’s interests, past interactions, and broadcast behaviour.
Chatbots can have natural conversations at scale to:
- Welcome new viewers and offer an intro promo code
- Provide personalized product suggestions
- Answer questions specific to a viewer’s needs
- Offer tech support and sizing guidance
- Inquire if they need help with anything
- Provide order status updates
- Cross-sell complementary products
- Provide discounts or incentives as needed
- Collect feedback on products or the live stream
Integrate chatbot systems like ManyChat or Chatfuel with your live shopping platform. Give viewers an easy way to opt-in for a personalized chat. Train the bot with a large dataset of potential conversations, product info, and customer data. Use clear messaging so viewers understand they are chatting with a bot.
With enough data and training, you can create amazingly natural chatbot experiences during live shopping events to boost engagement and sales.
Customize Graphics Overlays Based on Viewer Profile
Displaying relevant graphics overlays during your live shopping broadcasts offers another opportunity to personalize for each viewer. Your AI system can determine overlay content in real-time based on the viewer’s interests and habits.
Examples include displaying:
- Their name or loyalty status
- A fun avatar or bitmoji image
- Their astrological sign or other attributes
- Custom product recommendations
- Deals or promotions tailored for them
- Polls and questions targeted to their profile
- Interesting stats or trivia relating to their preferences
- Countdown deal timers based on past purchase behaviour
- Captions in their preferred language
- Navigation links to particular products or categories
- Interactive augmented reality effects
Viewers seeing graphics, deals, questions, and info explicitly tailored for them creates a more engaging and memorable experience. The cost of custom overlays is scalable if you utilize a digital asset management platform and automation. Get creative with overlays to add personalized interactive elements throughout your live shopping broadcasts.
Curate Hosts and Influencers Based on Viewer Personas
An often overlooked aspect of live shopping personalization is matching the on-camera host to each viewer. Certain presenter styles, personalities, demographics, and aesthetics naturally appeal to different shopper types.
For example, a bohemian 30-something female viewer likely wants to see different hosts than a conservative retired man. Younger Gen Z viewers may be drawn to hosts they see as authentic and relatable.
Consider developing designated host profiles and personas that appeal to your core customer segments. Then have your AI system assign hosts tailored to each viewer using prediction algorithms.
You can dynamically rotate hosts throughout the broadcast based on who resonates best with the live audience. This data then refine your host-viewer matching over time.
Evaluate metrics like purchase conversion rate by the host, engagement levels, poll responses, and live reactions to optimize your host recommendations.
Curating hosts tailored to your target viewers helps keep the broadcast entertaining, engaging, and shippable.
Apply Filters and Effects Based on Viewer Preferences
Live filters and augmented reality (AR) effects are popular for viewers to engage with live shopping hosts in real-time. You can personalize which filters and effects each viewer sees based on their habits and behaviour.
For example, prompt female viewers to apply a virtual makeup filter, allowing them to digitally “try on” products. Show viewers who frequently use AR filters fun effects like virtual sunglasses or hats to encourage engagement.
Display subtle sparkle or shine effects to viewers tagged as interested in jewellery and accessories. Augment viewers who regularly ask about skin products with virtual foundation or colour correction to visualize the products on themselves.
The possibilities to creatively implement personalized filters and effects are endless. Analyze usage data to identify which products resonate best with different customer segments. Then tailor the available effects lineup for each viewer.
Make your live shopping events more interactive and fun by serving effects aligned to each viewer’s interests and preferences.
Conclusion: The Power of Personalized Live Shopping
Personalizing the live shopping experience is crucial for driving higher satisfaction, engagement, and sales. AI and machine learning provide excellent tools to understand your customers and tailor interactions for each one.
Follow the strategies outlined in this guide to:
- Collect expansive customer data and build enriched profiles
- Make tailored product recommendations
- Provide customized deals and promotions
- Have personalized conversations via chatbots
- Display graphics and overlays unique to each viewer
- Curate hosts that resonate with shopper segments
- Unlock filters and effects suited to each individual
When you dynamically customize every aspect of live shopping using AI, it feels like an individualized experience at scale. Shoppers enjoy the interactive format, even more, when it caters to their needs and preferences.
Investing in personalized live shopping powered by artificial intelligence provides an unmatched ability for brands to engage and convert shoppers. As livestream commerce accelerates, personalization will separate the most successful brands.
Start exploring how AI tools can help tailor and individualize your next live shopping event to create better experiences for each shopper. The opportunity to drive results through dynamic personalization is vast.