Market Research: Find What Consumers Value Most

There are plenty of stats that can tell you consumers are interested in other's opinions and experiences. These statistics reveal that at least 90% of us are influenced by what we read. Even more so if it's a negative review - the sentiment resonates. In recent years, multiple sites have collected reviews for local eateries, vacation destinations, and, of course, consumer products.

A positive review is a form of social proof.

If your company is considering entering a new market or needs to research product ideas, why not start with online reviews from real users? This was the very idea that drove a major Amazon case study on pricing by a young analyst team.

They wanted to understand the best speakers available to purchase at the $150 price point. They theorized that if you're going to develop and market a new product, it is useful to understand what features are most valued. What a great business use case and reasonable example of analytics!

The team extracted data for five speakers based on popular brands that Amazon customers had reviewed. The data contained consumer ranking, price, and all customer reviews.

This data was a mix of structured data (ratings, price) and unstructured data (review text).

Using the customer rating, these junior data scientists wanted to learn which product characteristics influenced scores. The following figure shows the products with the final text topic extraction analysis.

It makes sense that a speaker's most outstanding quality should be its sound quality. There are many choices in this market space. Within the target price range, consumers must choose the most valuable features.

By reviewing low-rating topics and considering the reviewers' sentiment toward that topic, you learn which features are essential. Its battery life, speaker material, and a charge port.

The business analysis paid off! But more importantly, the business is able to understand the customer experience.

When starting your speaker re-design or even a marketing campaign, you understand what features are essential to consumers. This market research from this data could have been expanded to include multiple sites or even all products. It is vital to understand what features drive purchasing decisions and leads to the most product dissatisfaction.

Questions:

1.The analysis shown in this case study is typical descriptive analysis, as it identifies what has happened. Based on this scenario, please give an example of predictive analysis and an example of prescriptive analysis, as well as discuss the needed input data to each in the context of the case study.

2.To identity which features influence customers most, you may need to use association rule mining to find which features go with high ratings or low ratings more frequently. If we find a rule {charger}=>{low rating}, [35%, 65%], what are the meanings of 35% and 65%?

3.Following Question 2, if we have found another rule {noise}->{low rating}, [35%, 75%]. How will you decide which feature is more influential to customers, and why?