Improving Revenue and Customer Experience Through Recommender Systems

Posted 2020-07-22 Posted by Tom O'Connell


Recommendation engines have quickly become a way for businesses to target their customers effectively, particularly when the volume of content or products can be otherwise overwhelming. Consequently, companies provide richer, tailored, personalised experiences for their users, and measurable success, such as KPIs and conversions can be achieved or optimised.

How do they work?

Recommender systems work through a variety of methods, looking at a user’s activity, comparing it with other user’s activity, or looking at the query or customer needs to make a recommendation. Pivigo have built recommender systems for companies to better understand their customers, such as analysing customer base through segmentation in order to influence future marketing strategies and retargeting. We’ve built a movie recommender for a subscription-based mobile app, which recommends content in a similar manner to Netflix.

Some recommenders look solely at one single user’s behaviour and interactions with a platform. When there is enough data, it establishes patterns within the behaviour and its recurrence; for example, a customer who purchases multiple pairs of shoes is likely to recommend a shoe rack to put them on. Pivigo used this method to build a recipe recommendation system from the analysis of 7 million rows of data for Mindful Chef and to recommend fitness classes to users on an online fitness app, which is essential given the recent increase in at-home exercise.

The pitfall of this system alone is what’s known as the cold-start problem, which is where the engine does not have enough data to ascertain any distinctive behaviour trend. The accuracy of this system is dependent largely on the amount of data fed to it. The more often the user interacts with the app, the more likely the recommendation will be correct and suited to the users needs. This type of recommendation engine can only be used once a user has interacted with the platform multiple times.

A different type of recommender is the knowledge-based recommendation and can be used in cases where data is lacking. Rules are defined within the context of a user or customer’s needs. Once this criterion is met, such as a search query is typed, the algorithm will search the domain knowledge for the relevant rules, and the recommendation is made. This is particularly useful when there is not a wealth of data from the user’s activity. E-commerce stores such as Ali Express use this to make product pairings or alternative buys.

You can also utilise data from other users in addition to the one being given the recommendation. It will look at the trends and opinions of other users in order to infer product matches and next-buy recommendations. Amazon, in particular, leverages this method when recommending similar products once an initial purchase is complete and Pivigo developed one similar for a computer hardware giant to recommend products.

How can they help?

Recommendation systems are the driving force in personalised customer experience, helping you to create granular marketing and retargeting campaigns to accelerate customer acquisition and retention. They boost sales without the risk of alienating a customer through irrelevant marketing and thus enhance their interaction with your products and services.

They can be used across a variety of sectors, including healthcare; Pivigo built a diabetic nurse for a leading diabetic charity, which you can read about here, or check out our other case studies from our 230+ data science projects.

To discover what is required to build your own recommender system, you can watch our webinar on our recipe recommender system for Mindful Chef here or get in contact with one of our team here.


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