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3 common problems with choosing products for recommenders

Posted by Peter Brooksbank | June 22, 2023

Manually picking products for a recommender might seem like a breeze, right? Just choose the ones you think will do the trick, and voila! Is it really that simple though? We all know there's more to it, so, let's uncover 3 common problems caused by manually selecting products for a recommender.

Time Intensive

Manual product selection chews through your time. You need to determine a strategy to choose products, select some products that match the strategy, and do this for every single product page. Then you need to do the same for other pages on the site that benefit from recommenders, but each one of these needs a separate strategy. Let's do a quick example:

If we assume a store with 100 products, and a recommender with 4 products, we would need to strategise and select 400 products, just for the product pages. Then you need to do it again and again, updating them over time as new products or new data becomes available.

Constant Analysis

Ok, so you've chosen products. Are they the right ones? How do you know?

If you're choosing products manually, you need to constantly analyse and verify that the products chosen are contributing to revenue. That means checking the data to see how the strategies and chosen products are performing, for every recommender on the site.

Of course, you'll then need to update the products and strategies based on the data, and just like that even more time is getting consumed. At this point you're either losing significant amounts of your day to handling it, or you're letting it languish whilst you get other tasks done.

Lack of personalisation

Users now expect recommendations to be tailored to their interests, using their preferences, past behaviour and other data. They want to be offered suggestions that really hit the mark. If the recommendations are generic and don't align with the user's interests, it's a recipe for a poor user experience.

Of course, tailoring the recommendations for each individual user, based on constantly changing data, is simply not possible for a business to do manually. This means users convert less, and have smaller baskets resulting in a decreased AOV.

So what's the solution?

By now, you'll realise that choosing products manually consumes your time, leaves money on the table, and provides a lacklustre customer experience. However, the fact that you've made it this far shows you care about those problems, and are looking for a solution.

The good news is that recommendation systems have never been better and, here at PureClarity, merchants trust us to take care of it for them. Our product recommenders are backed by machine learning and personalise their results in real time for each user, using their preferences, current and past behaviour and other relevant information.

The result is increased conversion, and higher AOV. Plus, you get your time back, letting you focus on other things. So if you're ready to boost sales and reclaim your time, book a demo, find out more, or even jump straight in with a free trial.

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