Getting to Grips With Recommendation Engines
Recommendation engines are now an essential piece of the marketing toolkit, particularly when considering efforts to personalize the customer experience and instill customer loyalty. They can improve the way individuals discover areas of your website or products in your store. They can also help you discover the real objectives of online and offline experiences.
A recommendation engine is a composition of layered algorithms whose interplay determines the profiling of a customer, and therefore products most suitable for them. Similarly, the algorithms can recommend products based on what comparable profiles have purchased.
Traditionally, textbook recommendation engines have utilized some form of market basket analysis. In other words, recommending products to you based on the products sitting in your shopping cart. Let’s say you are shopping for eggs — the engine will recommend you buy bread or bacon. The assumption is these cart contents appear with such frequency that they are what most customers are ‘looking for’. However, these recommendations are very generalist and can fail to understand your objective.
The most effective recommendation engines will attempt to capture the context of your objective when you are shopping. For instance, one person may be looking to purchase ingredients for their dinner whereas another is shopping as a hobby. In response, the series of algorithms will change their recommendation weights: The engine leverages the data you create to understand your purpose.
Personalized services from recommendation engines should not be regarded as a privilege of organizations with the deepest pockets. Both small and large companies can benefit from knowing the customers’ desires. Capturing a customer’s intent will prove equally valuable to any business, as long as it is translated to a deserving recommendation.
Technically speaking, the best recommendation engines will be those built on the most varied data sets. In this case, access to the largest sets of behavioral, temporal and contextual data is increasingly crucial. Recommendations can become predicated on previously unknown insights, surfacing from unsupervised algorithms crunching through data and finding patterns on their own.
This is a point worth stressing. The volume of data you have access to will make or break your engine. “Data is the new oil”, and every drop of it helps you understand and profile your customers. Your customers create tons of data when surfing your websites, browsing your apps, talking to your chatbots, interacting with your social media accounts and walking your stores.
Linking these touchpoints together is as essential as fine-tuning the interplay of each algorithm. Collectively, the different types of data formulate the steps that, like episodes of a television series, detail a journey from one point to another. For instance, scraped social data shows what customers may have seen their friends or idols buying, captured website traffic illustrates the path a customer took on your website and transaction data shows how much they spent.
These data types can be fed into a recommendation engine that then pieces a profile together. They can even capture customer rage or praise on social media or customer service phone lines. Even if this data is not necessarily positive, it is still extremely valuable.
Ultimately, people want to be pleasantly surprised. They want to receive recommendations that seem genuinely catered to them. They are open to companies trying to understand them if it generates a better customer experience. Even if you recommend a t-shirt to your customer and they do not like the design, a dismissal will allow the algorithms to adapt to provide better recommendations in the future.
This application is not just restricted to the t-shirts. Collected behavioral data will filter characteristics about individual products in order to understand how the attributes are relevant to various profiles: This is content-based filtering. Eventually, you may even receive entire outfit recommendations, based on social data and your image and budget, as inferred from previous transactions.
If you are struggling to compile a perfect customer profile, there is always the option of creating feedback loops in which your customers list their preferences before they begin browsing. Subscription streaming services like Netflix have done this, though the company spent $1 million perfecting their preference-fed algorithms. With this strategy, marketers must make sure they have the right tools to neatly unpack consumers’ answers and optimize the information.
If marketers can use recommendation engines to understand what a specific individual wants, a recommendation will likely be more accurate than if considering aggregate data. Customers will realize your efforts and you will be rewarded with customer loyalty and higher transaction rates. Put simply, recommendation engines present a win-win situation for marketers and consumers by increasing profitability, building loyalty and improving efficiency.