The onset of the internet has accelerated the very human act of product recommendation to the next level.
With the various platforms of information and communication that lie at our fingertips these days, anybody aiming to sell something online knows that that they’re bound to do better if they have a well-organised system where ‘one thing leads to another’. Amazon is, of course, an early and still-perfect example, where users are gently nudged to consider ‘similar items’ to what they’ve just bought, based on their shopping history on the site.
But what are some of the dynamics of such product recommendations? And what are some of the ways that it can be improved in the seller’s favour?
Catching them quick
One of the main challenges of a web-based marketer is taking full advantage of users’ attention spans and time spent on their sites. Statistics prove that the average user spends just around 7 seconds on each site, which gives the marketer a limited time window in which they can make the most out of the user’s presence.
This is why a pro-active approach to recommendations should be made a crucial part of your business strategy. And it’s perhaps why a product recommendation module might just prove to be one of the most worthwhile investments you can hope to make. But more on that later — first, let’s get into the ins and outs of how the filtering of online recommendations work.
Check out AXON Gaming and its built-in recommender functionality.
There are two main approaches to product recommendations – one is content-based, the other is collaborative. The content based approach attempts to maximise on the attributes of the product itself, so any further recommendations are based on individual features of the product in question.
The problem is that this may work well with products that have distinctive content that separates them, such as films or TV series. For example, you can assume that somebody who likes The Lord of the Rings may be willing to give Game of Thrones a try. But this wouldn’t work in the case of personal items such as clothes or makeup. Likewise, it also runs the risk of getting customers to essentially purchase the same things over and over again.
With the collaborative approach, on the other hand, we take our knowledge of the users as our starting point, to then match them to the right set of products. In this way, variation is more fluid and can take advantage of various platforms at the same time – while also being able to exploit the possibilities of ‘chance encounters’ with products that users may have – thus broadening the range of options substantially.
Memory vs model
There are two ways in which collaborative filtering comes into play – one of its models is memory-based, the other is model-based. When it comes to the former, it is all about exploiting all the data in the system to full effect, while the latter is focused on packaging certain variables together to find connections and similarities which could prove useful.
So in this way, the ‘memory’ method will take a closely focused approach – either user-to-user or item-to-item. The real work then becomes about figuring out the commercial ‘distance’ between users and items. Taking the user-to-user approach as an example, we would find similar users (call them ‘neighbours’) and help create recommendations based on what these neighbours have in common.
This ‘recommendations web’ then clearly becomes a rich resource for the business in question, as it enables them to draw on a varied pool of users to generate those all-important recommendations.
Up close and personal
The strongest solution for businesses looking to keep their clientele both regular and diverse is investing in robust product recommendation functionality, specifically one which takes full advantage of the ‘memory’ of its users.
Ensuring that customers are guided to products that they have not yet tried before, but which past purchasing experience suggests they might like – this kind of functionality will ensure that the customer is kept interested throughout their lifetime experience with your brand.
Another pro-active aspect to this kind of functionality is the ability to connect various similarities in both the product as well as its context. For example, a campaign based on a sale would not just guide users to the sale, but also fine-tune recommended items within the sale based on the individual user’s likes or dislikes.
It is this pin-sharp degree of personalisation that will really push your recommendations to the next level – effectively marking the difference from digital window-shopping to shopping-proper.
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Know ahead of time
Knowing what current and prospective users will want to buy next is the magic bullet for any web-based marketer and seller, and a product recommendation solution is the perfect way to place you on the right track. After all, would you ever say no to a product guaranteed to hook users in?