Even better are personalized product recommendations. Modern shoppers are crying out for individualized shopping experiences. In fact, we know that an astonishing 77% of consumers will actually pay more for products when brands provide personalized shopping experiences.
But, how do you ensure your personalized recommendations are hitting the mark?
What happens when product recs go wrong?
Have you ever received emails packed with content based on a one-time purchase? Perhaps you helped a friend out and let them place an order through your account. Now you’re receiving endless suggestions based on something you didn’t even want to buy.
How about when you receive a recommendation that has nothing to do with anything you have browsed or bought from the site? This is because the brand you’re shopping from hasn’t set an appropriate fallback recommendation.
Customers switch off and unengage from your emails when they receive content that is irrelevant to them. So, product recommendations that don’t include exclusion rules are likely to fall into this category. After all, how many people out there are buying more than one set of bedside tables on a regular basis?
Smarter product recommendations
As with every other element of the personalized shopping experience, the success of product recommendations is reliant on the data you use. Or, more specifically, how you use the data you have.
Naturally, as consumers, we’d rather received recommendations and upsells based on items we’ve already purchased. To not use this data would guarantee irrelevance. But, using it intelligently is crucial.
Getting product recommendations right
AI-powered product recommendations are brilliant to save time and generate money, but that doesn’t mean you should rely on them entirely.
You need to consider product recommendations as part of a wider strategy. How should they be built in order to maximize their impact? What inclusion and exclusion rules are you going to set? What is and isn’t going to be included in your fallback recommendation? In which campaigns are you going to use them?
These questions have to be carefully considered as you build your product recommendation blocks.
Where to place them
We know product recommendations work, but that doesn’t mean you should use them in every email you send. Testing is key. This will help you gather the data you need to inform your strategy. Customers are unpredictable so what works for some, may not engage others.
Choosing where to include your product recs can be difficult, but we’d suggest trying with the suggestions below and following what the data is telling you.
- Abandoned cart/abandoned browse program
We can’t know for sure what has caused shoppers to abandon their browsing or shopping on your website. Whether it’s because another task has demanded their attention, or they’re indecisive about the item, a product recommendation can be the driver for conversion. Try using categories such as ‘best next’ and ‘lookalikes’ to offer customers alternatives if they’ve still indecisive about their purchase.
- Order confirmation email
It’s never too late – or early – to get your customers thinking about their next purchase. Predictive recommendations based on the shopper’s purchase and browsing behavior are a powerful way to keep them engaged with your brand. These types of predictive recommendations are a great way to introduce AI into your daily marketing tactics.
- Post-purchase review programs
After giving shoppers time to get used to their new product, you should be enrolling them into a post-purchase program. In this, you should be asking them to leave a review of your product, preferably through a trusted third-party site such as Trustpilot and Feefo. But, if customers aren’t ready to review, this is another opportunity cross-sell or upsell. AI-powered recommendations like those used in your order confirmation email or bought together blocks are great for this.
Whatever the campaign, adding product recommendations to landing pages and websites is a guaranteed win. Showcase your bestsellers or trending products across your website and on product pages to drive recipients to convert.
What to call them
How you title your recommendation block is half the battle. An engaging header teamed attractive product images will do most of the work for you.
Depending on the type of recommendation block, you’ll need to name each one differently. After all, what’s the point of having an AI-powered block if the shopper doesn’t recognize it’s specifically tailored to them. Here are some of our suggestions:
- Pick up where you left off (AI based on browsing behavior)
- Caught you browsing (AI based on browsing behavior)
- We thought you’d like (AI based on previous purchases)
- Don’t leave us hanging (AI based on browsing behavior)
- What’s hot (based on trending/most viewed)
- Going fast (based on bestsellers)
- Don’t miss out (based on bestsellers)
- Complete the look/Get the set (based on bought together/also bought/best next)
Setting them up right
Finally, and possibly the most important tip we can give you is this: take your time and set them upright.
1. Set your parameters
No one wants to receive a recommendation for something they’ve already bought. Make sure this never happens by setting exclusion rules based your segments and catalog data.
2. Make sure data is synced
Don’t accidentally hook a reader with an out-of-stock item by ensuring your catalogs are being synced with your ecommerce platforms. This way, you’ll be able to set rules to exclude items low in stock and avoid disappointing customers.
3. Create a fallback
For those instances where technology fails you, make sure you arm all your product recommendations with fallback products. That way, if products are sold out or don’t match your set criteria you’ll always have a functioning and relevant product recommendation for shoppers to see.
Product recommendations offer ecommerce retailers endless opportunities. Don’t miss out on the chance to boost your revenue with one simple tactic.