Greetings from Sean in the scenic harbour city of Sydney. Hope all of you are doing well. And of course following us for the latest on “fixating” topics in the world of all things Digital.
As you may recall my last article focused on exploring UBA (User Behaviour Analytics) and the upside of utilising the said product to improve your insights into your customer’s online behaviors. Reviewing how Recommendation Engines work was the next logical step
What is a Recommendation Engine?
A recommendation engine is defined as an algorithm that analyses the End User behaviour to suggest items they are likely to prefer. It uses data analysis techniques to match the items and products to the users’ tastes and preferences. The primary objective of the recommendation engine is to generate demand and engage users. In short a recommendation engine can prove useful wherever there is a need to provide personalised suggestion and advice.
5 Benefits a Business can achieve by utilising Recommendation engines
With years of research, experiments and execution primarily driven by Amazon, not only is there less of a learning curve for online customers today. Many algorithms have also been explored, executed and proven to drive conversion rate v non-personalised product and service recommendations.
We often take recommendations from friends and family because we trust their opinion. As they know what we like better than anyone. This is the primary reason they are great at recommending things and this is what recommendation engines try to emulate. You can use the data accumulated indirectly to improve your website’s overall services and ensure they are suitable according to the user’s preferences. In return, the user will be in a better mood and mindset to purchase your products and services.
3. Customer Satisfaction
Customers tend to look at their product recommendation from their last browsing. Primarily because they think they will find better opportunities for good products. When they leave the site and come back later; it helps if their browsing data from the previous session is still available. This further helps and guides their online activities, in a way similar to experienced staff at Brick and Mortar stores. This sort of customer satisfaction leads to customer retention.
4. Providing Reports
Recommendation engines give the client accurate and up to the minute reporting that will allow them to make solid decisions about particular marketing campaigns. Based on these reports the clients can generate offers for slow moving products or services in order to boost and create more drive in sales.
A good example of this is the “Genius Recommendations” previously used in iTunes. Or the “Frequently Bought Together” of Amazon.com makes surprising recommendations which are similar to what we already like. People generally like to be recommended things which they like and when they use a site which can relate to their choices it’s a given they are bound to visit that site again. Frequent visitors to YouTube would be familiar with its brand of recommended videos. Although sometimes they recommend videos that I have minimal interest in. But at least it’s a valiant effort.
So what are your thoughts on Recommendation engines? Do you find value in them? Do they cause you to readily return to certain websites? Let us know your thoughts and provide feedback on this piece. Next week I’ll be looking at the top 10 Digital Marketing Trends of 2021. And next month in the leadup to the US Presidential Election I’ll be exploring the Digital Implications and Impact on the respective campaigns. Enjoy your week!