Tweet
Facebook
Copy
If you’re looking for a way to boost sales during peak periods (and beyond), offering bundles is an excellent way to do this.
What are shopping bundles?
Bundles are shopping packages of related items that shoppers can buy at a cheaper price point than buying each item individually. These can be items from the same range, or items which can be used together to complete a set – or even just items that you think your customers will want to buy together (for example, a sofa, armchair and matching coffee table).
Examples of tailored shopping bundles :
Fashion Retailers: Offer bundles based on seasonal trends or customer styles, e.g., “Winter Wardrobe Essentials.”
Tech Stores: Group gadgets with necessary accessories, like a smartphone with a charger and screen protector.
Food Delivery Services: Suggest meal kits with complementary ingredients based on past orders.
Tailored bundles are one key way to increase your average order value. Another way to do this is to suggest additional items customers might like to purchase, based on their or others’ previous shopping habits.
But to do this, you’ll need to know whether or not your shopping experience is delivering the right outcome for your customers. That’s where conjoint analysis comes in.
What is conjoint analysis?
Conjoint analysis is a popular research method for understanding which product attributes are most important to your customers when they’re comparing options during a purchase decision.
This is done by using a survey measuring how important customers think different attributes (for example, price, brand, or features) are when comparing products in a purchase decision.
How does conjoint analysis work?
Every conjoint survey is made up of four components:
1. Question
This gives the respondent context about the product decision they are simulating.
2. Profile
This is a complete set of attribute values that make up a potential product offering.
3. Attributes
There are characteristics relevant to a customer’s purchase decision (aka “categories”).
4. Levels
These are the range of values or possibilities you include within each attribute (aka “options”).
You might wonder how this is different from a standard customer survey. But conjoint analysis goes much deeper and is much more specific. To see a detailed example of a conjoint analysis survey, click here.
How can I use data to create shopping bundles?
Customers want shopping experiences that feel tailored to their needs and preferences. Here’s how you can use software to collect customer data and design personalized bundle suggestions effectively.
Outside of using conjoint analysis, there are lots of other ways you can gather data about your customers and audience.
1. Leverage machine learning
Modern software solutions use machine learning to predict customer needs and preferences. Here’s how you can take advantage of this technology:
– Product recommendation engines:
Tools like Dynamic Yield, Nosto, or Recombee analyse customer data and automatically suggest products that are often bought together, or that match a user’s preferences.
– Segmentation and clustering:
Machine learning algorithms can group your customers based on similar behaviours, like frequent purchases of specific categories or high engagement with certain product lines. You can then create bundles specifically for each segment.
2. Automate bundle suggestions
Once you’ve collected and analysed your data, the next step is creating and showcasing bundles.
– Dynamic bundles:
Platforms like Shopify and BigCommerce support apps (e.g., Bold Bundles, PickyStory) that dynamically suggest bundles based on the customer’s browsing or purchase history.
– Personalised landing pages:
Use software like Optimizely or Unbounce to create landing pages that automatically adjust content and bundle offers based on user data.
– Email campaigns:
Email marketing platforms like Mailchimp or Klaviyo can send personalised bundle suggestions based on your customer’s previous purchases or abandoned carts.
3. Analyse and optimise
Data collection and personalization don’t stop after the bundle is suggested. You should continuously analyse what works and refine your strategy:
– A/B testing:
Test different bundle configurations, pricing strategies, and marketing tactics using tools like Optimizely or Google Optimize.
– Customer feedback:
Gather direct feedback on bundle suggestions via post-purchase surveys or reviews.
– Performance tracking:
Monitor metrics like conversion rates, AOV, and bundle profitability to gauge success.
Using software to collect customer data and create tailored shopping bundles isn’t just about technology; it’s about building a deeper connection with your customers, finding out what they’re looking for – and figuring out how to give it to them.
By leveraging tools that analyse behaviour, predict preferences, and deliver seamless experiences, you can boost sales and cultivate loyalty in a competitive eCommerce environment.
To find out about how Zopa is giving merchants insights for growth, get in touch with us today.
