How to Master the Customer Data to Urge Customers to “Buy More”?

How a marketing campaign based on the customer data can help you cross-sell and upsell?

Imagine this: You’ve just bought a smart camera for your home security, and a few weeks later, you receive a personalised recommendation for a smart doorbell or a robot vacuum cleaner. It feels like the brand knows you, right?

That’s the power of using the right customer data, in the right context, to recommend the right product to the right person. This approach not only enhances customer value but also boosts revenue, especially when launching new products.

Let me walk you through a real-life example and break down how to master this strategy.

The Smart Home Case: Selling More by Recommending the Right Product, at the Right Time

Last year, I worked with a smart home brand that wanted to sell their robot vacuum cleaners and smart locks to customers who had already purchased their home security cameras. They had tried to run some mass marketing campaigns, but the results were disappointing. Why? Because expensive but optional purchases require customers to have more trust to the brand and longer time for consideration. Generic ads will not work in this case.

The key was to use the right customer data, in the right context, to recommend the right product to the right person.

So, we took a different approach. We analysed the following data sets:

• purchase history
• frequency
• spending patterns
• gender and age
• and customer engagement levels.

Then, we segmented the customers and tailored our messaging and product recommendations accordingly.

For instance, we noticed that customers who had bought cameras were likely concerned about home security. So, we first recommended the smart lock to them, as it was cheaper than the robot vacuum cleaner and a complement the security camera they already have. For loyal customers who had made multiple purchases, we introduced the higher−priced robot vacuum cleaner.

The result? A 59% increase in conversion rates compared to their previous mass marketing strategy. This success was all about using the right data, in the right context, to recommend the right product to the right person.

The 4-Step Framework for Mastering the Customer Data

Step 1: Understand the Core Logic and Set Clear Goals

The foundation of effective product recommendations lies in understanding your customers’ specific needs, their trust to your brand, and timing too. Here’s how to approach it:

• Identify Customer Needs: For example, if someone bought a security camera, they likely value home safety. Recommending a smart doorbell with a built-in camera feels like a natural next step. This is about using the right customer data to understand the right context and then recommending the right product.
• Cross-Selling: This is about recommending complementary products. Think of how Apple suggests AirPods when you buy an iPhone. It’s about recommending the right product to the right person based on their purchase history.
• Upselling: This involves encouraging customers to upgrade or buy a more premium version of what they’re already purchasing. For instance, when buying a coffee machine, you might be offered a deluxe model with more features. Again, this is about using the right data to recommend the right product at the right time.

Step 2: Use Data to Gain Customer Insights

Data is your best friend here. It helps you understand who your customers are and what they might want next. Here’s how to do it:

• Segment Your Customers: Use tools like the RFM model (Recency, Frequency, Monetary) to identify high-value customers. For example, a tech enthusiast who frequently buys gadgets might be more open to trying out new smart home devices. This is about using the right data to identify the right person.
• Analyze Purchase Behavior: Look for patterns. Do customers who buy printers also tend to buy ink cartridges? If so, you can bundle these items together. This is about using the right data to understand the right context.
• Predict Future Purchases: Use machine learning to predict what a customer might want next. For instance, if someone bought a fitness tracker, they might be interested in a smart scale or workout accessories. This is about using the right data to recommend the right product.

Step 3: Design High-Converting Product Combinations

The key here is not to focus solely on high-margin items but to create combinations that customers actually want. Here are three strategies:

• Complementary Products: Think of a printer and ink cartridges, or a smartphone and a protective case. These are items that naturally go together. This is about recommending the right product in the right context.
• Price Anchoring: Offer a basic version and a premium version. For example, a streaming service might offer a standard plan and a premium plan with extra features. This is about using the right data to recommend the right product to the right person.
• Dynamic Recommendations: Use real-time data to suggest products. If a customer adds a tent to their cart, you might recommend a camping stove or a sleeping bag. This is about using the right data to recommend the right product at the right time.

Step 4: Execute with the Right Channels and Tactics

Now that you’ve got the strategy, it’s time to put it into action. Here are some effective ways to do that:

• Online Channels: Use personalised recommendations on your website or app. Amazon does this brilliantly with its “Frequently Bought Together” section.
• Offline Channels: In physical stores, place related products next to each other. For example, display coffee capsules next to coffee machines.
• Promotions: Offer discounts for bundling products. For instance, “Buy a laptop and get 20% off on a laptop bag.”
• Membership Programs: Reward loyal customers with exclusive deals. Costco, for example, offers special member-only bundles.
• Content Marketing: Show how products work together. A video demonstrating how a smart lock and a security camera enhance home safety can be very persuasive.

Pitfalls to Avoid When using Customer Data

1. Don’t Overdo It: Customers can tell when they’re being pushed to buy something they don’t need. Always align recommendations with their actual needs. This is about recommending the right product, not just any product.
2. Test and Iterate: Use A/B testing to see what works best. For example, test whether a discount or a free gift drives more conversions. This is about using the right data to refine your recommendations.
3. Keep Your Supply Chain in Check: Make sure you have enough stock of the recommended products. Nothing kills a sale faster than an “out of stock” message. This is about ensuring the right product is available at the right time.

The Bottom Line

The secret to successful cross-selling and upselling is all about using the right data, in the right context, to recommend the right product to the right person. When done well, the recommendations will seamlessly appear in front of the customers. And that’s how you turn one-time buyers into loyal, high-value customers.