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What to consider before starting the Development
Recommendation systems are a crucial part of every digital business model. This blog post concisely answers two foundational questions:
- Who should care about recommendation systems and why?
- What are the primary flavors of recommendation systems? How much work is it to implement them?
In this article I focus on a solid overview. Details are described by my colleague Josef Bauer in parts two and three of this blog series.
What does a recommendation system deliver?
A recommendation system targets the specific interests of consumers. Phrased differently, it prevents a premature end of a customer journey due to overload or frustration. That is why recommendation systems are crucial for enterprises with a broad range of goods or services.
It is also essential to keep in mind the customers’ usage behavior. Whereas some pages encourage extensive browsing, others are designed to identify the optimal product as quickly as possible
Would your company benefit from introducing a recommendation system?
Digital B2C-business models benefit most from recommendation systems since such systems directly impact their most important KPIs. An introduction of a recommendation system hugely improves the customer experience. Besides, valuable recommendations are expected by users as a standard feature. The optimization of an existing recommendation system also helps to tap into the unused potential.
In addition to these main effects, recommendation systems also have several side-effects:
- The development team increases its data know-how and is incentivized to dive into the actual user behavior and preferences.
- Enterprises can use the created insights for further improvements both of the range of offers and the user experience.
- Implemented recommendation systems can also be used as a testing tool since they can be used to channel traffic.
What are the flavors of recommendation systems?
There are many details involved, but there are mainly two flavors when it comes to recommendation systems: item-based and consumer-based.
Option 1: Item-based approachOption 1 covers available products. The consumer sees products that are similar to those being sought. However, similarity can have many meanings in this context: e.g., product categories, price, or possibly the manufacturer. Naturally, a combination of various dimensions is also feasible. | Option 2: Consumer-based approachOption 2 does not aim for similarities in products, but instead among consumers. The resulting recommendations may strongly reflect those under option 1, or not at all. This approach strives to model preferences and reasoning behind the consumer’s buying decisions, and less so the objective similarities between products. |
Which option best suits my situation?
These days, legal restrictions on data access under the EU’s General Data Protection Regulation (GDPR) significantly affect one’s choices. Nevertheless, the item-based approach is an option, since one can set it up as a table and update it regularly. This makes integration in mobile websites and apps much easier. The consumer-based approach offers greater potential, depending markedly on whether you have a reliable streaming infrastructure.
What are the first steps?
There are three areas to evaluate before recommendation systems can be implemented:
- How strong is the connection between a recommendation system and the business model? Is it a central component or a nice-to-have? How large is the added value for customers?
- How much effort is needed to integrate a recommendation system into the current infrastructure? Is the necessary data available and is it legal to use it for this purpose?
- Does your company have the necessary data science and machine learning engineering skills? Is there a team ready to take on these tasks? Is it necessary to establish new organizational structures?
If these three questions are answered, the actual work on a recommendation system can start. Josef Bauer describes the details in his next article
