Right, so last week I was chatting with Logan about how ridiculously ineffective most email marketing feels these days. I mean, who actually wants another generic discount code clogging up their inbox? We were brainstorming ideas on how to make email feel… well, less like spam and more like a genuinely helpful conversation. That’s when we really dove into the potential of AI, particularly when it comes to contextual product recommendations. Logan’s insights were particularly enlightening.
Forget Batch & Blast: Personalisation is King
The starting point is admitting the old ‘batch and blast’ approach is dead. Sending the same email, at the same time, to everyone on your list? That’s a recipe for unopened emails and unsubscribes. Instead, we need to be thinking about hyper-personalisation, and that’s where AI becomes your best friend. What we’re talking about goes way beyond just using their name in the greeting, this is about fundamentally changing what products you advertise to that particular person on that particular send of an email.
Contextual Recommendations: It’s All About the Now
Logan highlighted the core principle: relevance. Instead of pushing products you think someone might like, AI can analyse their recent behaviour and serve up recommendations directly related to what they’re actively doing. Think about it: someone just bought a new camera from you. What’s more relevant than suggesting compatible lenses, a carrying case, or an online photography course? That’s the power of contextual recommendations.
Diving into the Data: How the AI Works
So, how does this AI magic actually work? Well, it’s all about data. The AI algorithms are trained on a wealth of information about your customers, and importantly, you can feed the information from many different sources into the AI system to provide a highly detailed output. This includes:
- Purchase History: What have they bought before? What products do they frequently purchase? This is the bedrock of understanding their needs.
- Browsing History: What pages have they visited on your website? What products have they viewed? This reveals their current interests.
- Cart Abandonment: Did they add items to their cart but not complete the purchase? This is a prime opportunity to remind them and offer assistance (or maybe a small incentive). This data needs to be real time as older items will not be as high priority.
- Email Engagement: Which emails have they opened and clicked on in the past? This helps refine future content and offers.
- Social Media Engagement (Optional): If ethically and legally permissible, social media activity can provide additional insights into their interests and preferences. For example, have they recently followed some brands of watches or clothing that may give insight.
By analysing these factors, the AI can build a detailed profile of each customer and predict what products they’re most likely to be interested in right now. The key, as Logan pointed out, is the ‘right now’ part. It’s about anticipating their needs and offering solutions before they even fully realise they need them.
The Technical Stuff: Building Your AI-Powered Email System
Okay, so this might sound complicated, but there are several ways to implement this. You don’t necessarily need to build your own AI from scratch. Many email marketing platforms now offer built-in AI features for product recommendations. Look for platforms that allow you to:
- Integrate your CRM and e-commerce data: This is crucial for feeding the AI with the necessary information.
- Set up rules and triggers: Define the specific actions that trigger product recommendations (e.g., browsing a particular product category, abandoning a cart).
- A/B test different recommendation strategies: Experiment with different algorithms and layouts to see what works best for your audience.
Another approach is to use a third-party AI recommendation engine that integrates with your existing email marketing platform. These engines typically offer more advanced features and customisation options.
The Upselling Angle: A Gentle Nudge in the Right Direction
Beyond basic product recommendations, AI is incredibly effective for upselling. Logan emphasised the importance of being subtle and relevant. For example, if someone buys a basic laptop, you could recommend a premium version with more RAM and storage, highlighting the benefits for their specific use case (e.g., video editing, gaming). The key is to frame it as a helpful upgrade, not a pushy sales tactic. Show the additional features and explain why they will improve the particular customers experience using the product.
Real-World Examples: Bringing it to Life
We discussed a few real-world examples. Consider a clothing retailer:
- Recent Purchase: Someone buys a pair of hiking boots. The email recommends hiking socks, a waterproof jacket, and a trail map.
- Cart Abandonment: Someone leaves a designer dress in their cart. The email shows the dress again with a limited-time discount and suggests complementary accessories.
- Weather-Based Recommendation: It’s a rainy day. The email promotes waterproof umbrellas and raincoats.
Timing and Placement: Maximising Impact
It’s not just about what you recommend, but also when and where you place those recommendations within the email. AI can analyse user behaviour to determine the optimal send time for each individual customer, as well as the best location within the email body to display the product suggestions. For example, if a customer typically browses your website in the evenings, sending the email in the late afternoon might increase the chances of them seeing it at the right time.
By gathering all the information and inputting to the AI engine the key points that a successful email needs, the impact on conversion and brand loyalty will greatly improve.
