Why use AI-powered product recommendations?

A humble bestseller product recommendation is an easy win. For a little effort and low data dependencies, it gives you strong revenue uplift.

Bestsellers’ high performance makes sense.

Your most popular products are popular for good reason. They exist at key intersections of value, features, desirability, quality, and trend.

Amplifying any of those signals to your audience is always going to make you money. It’s easy to understand why it’s one of the most popular types of product recommendations.

Job done, right? Why do you need AI?

Avoiding saturation

After you’ve marketed your bestsellers for a time, you may hit a couple of challenges.

First, bestsellers favor your more established products. Unless new products are immediately successful, a recommendation can become self-reinforcing. It can be hard to get different products to breakthrough.

Second, you may notice that revenue uplift starts tracking new customer growth. At this point, the recommendation is saturating. Whilst it’s smart to hit your new customers with your best stuff, it’s now underperforming for existing customers.

Both problems warrant their own detailed discussion. For now, let’s look at a more nuanced strategy and how AI can help find new revenue in your existing customer base.

Unlocking inaccessible revenue

AI-powered product recommendations will identify new and unique customer/product relationships.

Finding these relationships at scale is where machine learning comes in. It analyses your products, orders, and web behavior data, so you don’t have to. It roots around the dark corners of your data to match products to customers.

Doing this analysis manually, even if you knew what to look for, would be an impossible task. Machine learning does it continually for you. Each time it trains on new data, it learns and gets more accurate.

My argument for this kind of big data approach to marketing is simple: don’t assume your established customer personas are the only truth. Until you use machine learning, you don’t know what you don’t know.

Winning with blended recommendation strategies

Bestsellers may always be your top performing recommendation. Talking to retailers, I’ve heard cases where a small set of products accounts for over 60% of sales. AI is unlikely to outperform against such massive numbers. (Unless you’re Amazon and have an enormous and diverse catalog!)

These retailers are aware of the risks of saturation. Not marketing effectively to their wider customer bases is a long-term challenge. Historically, there are easier battles to win that deliver nice returns.

Fortunately, technology is catching up to support retailers.

We’ve built Engagement Cloud product recommendations to support a blended strategy. You can combine so-called heuristics (like bestsellers) with hyper-personalized recommendations using AI.

The theme behind this strategy starts with covering known areas with broad sets of rules. Create non-AI product recommendations to match your known customer cohorts. You might focus on product categories, price points, seasonality, trends, or any other rules you like.

Once you have those, it’s time to infuse your campaigns with AI recommendations.

Here’s how to use different classes of recommendation:

  • Set up multiple category-targeted best sellers for some big hitting recommendations;
  • Find tomorrow’s best sellers with the most viewed recommendation type;
  • Mix things up with the hybrid trending recommendation type (it blends best sellers and most viewed);
  • Match your niche customers to their perfect products with AI-powered lookalikes;
  • Use best next’s AI to let shoppers help other shoppers find products they didn’t even know they wanted.

With this approach, you’re casting the widest possible net to drive more sales. You’re building automated marketing around cohorts you know. Meantime, AI is finding new customer/product relationships you didn’t know you had.

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Reblogged 1 month ago from blog.dotdigital.com

How to improve product recommendations

Anyone who has shopped with Amazon has experienced the same frustration. After you’ve bought a shower curtain, you don’t need to be recommended more shower curtains. Especially ones cheaper, or more expensive than the one you just purchased.

That’s not what anyone wants. Let’s be honest, it feels a little bit lazy of the ecommerce giant.

The thing is, product recommendations aren’t difficult to
implement. With very little effort, you can reap the rewards that product recs
all but guarantee. According
to Barilliance
, a single recommendation can increase AOV (average
order value) by 369%.

The key is getting the right recommendations to the right customer, at the right stage of their journey. The more personalized and engaging recommendations are, the more they resound with your audience. The more resounding they are, the more you’ll benefit from larger orders.

Start the journey strong

Segmentation is essential for delivering the right content.

New subscribers should be automatically enrolled onto a welcome program. Welcome emails generate 320% more revenue than a generic campaign. Introducing them to your bestsellers as part of this is the fastest way to get them engaged with your product catalog.

Barnes & Noble welcome product recommendations

Give your audience a stir by demonstrating your range of products with category recommendations. And, Engagement Cloud’s AI-powered technology even takes care of tag generation, so each block is automatically filled for you.

Category product recommendations

By tracking what they click and where they go, you’ll also
be gathering rich, valuable data for our AI-driven product recommendation tool
to use in the future.

Make it special

Let the power of AI determine which products are likely to
get shoppers excited to shop with you. Best
next
uses machine learning to predict items customers are most likely to
purchase next.

Include this type of block at the end of your order confirmation emails or in abandon cart programs to drive them back to your site.

Ralph Lauren best next recommendations

Keep them coming back and inspire long-lasting loyalty by
demonstrating you have everything they didn’t even know they needed.

Reveal hidden gems

Engagement Cloud’s AI-powered lookalike recommendation block analyzes your product catalog,
identifies items with similar attributes and surfaces the products that go well
together.

This is the perfect accompaniment to any abandoned cart email, driving customers back and helping to increase their AOV.

Tommy Hilfiger lookalike recommendations

Using lookalikes or best next utilizes the power of AI to push
the products that will truly resonate with your audience.

Share them everywhere

Incorporate your product recommendations anywhere on your site. Pick-up underperforming products by featuring them in a custom recommendation block on a bestsellers’ page or show them something new with a what’s trending section on the homepage.

Buzzfeed trending recommendation

Let Engagement Cloud do the work

Make the most of every engagement with the help of our AI-powered product recommendation tool. Including intelligent product recommendations throughout the customer’s journey boosts sales and revenue. So much so, you’ll end up asking yourself ‘why wasn’t I doing this before?’

The post How to improve product recommendations appeared first on dotdigital blog.

Reblogged 2 months ago from blog.dotdigital.com