Kenyt was started with a goal to help buyers make quick and informed purchase decisions. To achieve this, we envisioned a knowledge platform which can process available information to extract useful knowledge and a product ranking engine which can rank products using this knowledge. Like Google gathers all the web pages and give you top 10 for any query, we wanted to rank all products to produce top 10 products for your needs. I am happy to say that we have cracked both of these pieces and now have an awesome knowledge platform and a product ranking/recommendation engine. In this post I am going to talk about factors we use in our product ranking engine.
Existing solutions provide options to sort by popularity, ratings etc. which does help a bit but still require you to do lot of work. Our engine looks at many parameters together to give you top 10 list tailored just for you. You can get “Top 10 products by brand”, “Top 10 by price”, “Top 10 by availability on a particular site” easily. Heck, we can even tell you “Top 10 Laptops with i5/i7 processor, 8+ GB ram having backlit keyboard” or “Top 10 Mobiles from Samsung, Lenovo, Honor with 8+ mp front camera in 10-20K price range“. Any Top 10 list is possible with Kenyt. Please note that this list is completely data driven and is always up to date unlike other lists which become outdated quickly.
Lets talk about the factors which Kenyt ranker uses to rank the products.
1. Feature strength: We evaluate all the features of a product and assign a spec score. To do this, we look at specs and compare it with other similar products to measure how good are its features compared to its competition. Not all features are considered equal. For example screen size is much more important than a USB port in TVs. Kenyt Spec Score for each product reflects our ranking of its specifications.
2. Value for money: If a product offers more features at lower price, it gets higher “value for money” score. This enables us to rank products offering more value for money higher.
3. Product ratings: This is typical star rating and ratings count. We assign some weight to rank products with more ratings and better star rating higher.
4. Recent popularity: Customers are influenced by what others are buying. Knowing what is popular help buyers choose more confidently. We guesstimate every product’s recent popularity using various data points like number of search queries, page views, purchase volume etc. and rank products popular in last one week/month higher. Many sites give you option to sort by popularity. At Kenyt you can even see exactly how popular is a product.
5. User Reviews Sentiment: Customers rely heavily on user reviews to get unbiased insights from existing buyers. To accommodate this, our engine go through the user reviews and automatically find aspects people talk about in a product. It then assigns scores to various aspects based on polarity of each sentence (and not use review level star rating). These aspect sentiment scores are then used in the product ranking. All aspects are not equal. Aspect weight is decided depending on its importance. For example, battery and camera reviews are given more importance than weight of a mobile.
6. Brand popularity and quality: Brand is another extremely important aspect involved in purchase decisions. Customers would choose product from a superior brand if everything else is same. To reflect this in our ranking, Kenyt calculates brand popularity and brand strength in different price bands. We use brand search volume, page views etc. to guesstimate popularity and calculates a brand score. We also look at average rating of all products and reward brands with multiple good products higher than ones which has few good but many bad products. Products from higher ranked brands are ranked higher.
All these factors are used to calculate a final Kenyt Score for each and every product. Our top 10 is list of products sorted by this score. Yes, there are other factors which are involved in decision making but even with these our rankings are fairly good. We will continue adding more signals to make our ranker even better. Further we are looking at making these recommendations personalized.
Product rankings can be only as good as the quality of the data. Many times data is incomplete, incorrect and inconsistent. Challenge is to be able to rank with all the imperfections in the data. I will talk about our data collection/extraction challenges in next post and give details of problems we solved to reach a point where we are able to do such deep analysis of hundreds of thousands of products from many sites in 52 categories with just 4 people.
You can download Kenyt Android app from here. Our iOS app is under development and will be released next month.
P.S. Interested in working on simplifying decision making with us, send me a message.