Analyzing product reviews for more holistic insight

The data is flowing. Your customers are taking action - making purchases, using products, and telling others about their experiences. Marrying this information with market research data, like survey questionnaire results, will help you gain a holistic understanding of your customer. These data sources are often very different and need to be carefully harmonized to be usable together.

With so much data available, it can be helpful to talk about specific data streams and discuss how that fits into the bigger picture. One kind of data we wrote about recently was social listening data, and how to get insights from these enormous, complex data sets quickly. Another specific kind of data that is important to customer understanding is product reviews. 

Product reviews are a critical part of your company’s data sets. Customer feedback can tell you exactly what customers like or do not like about your products, and give you valuable insights into sentiment that can help with product development and marketing. It can also provide a basis for you to make competitor comparisons. This data often comes from multiple places, like customer review sites, eCommerce sites, a company’s own online properties, and more. It is often unstructured and can be challenging to process and manage cohesively.  Getting a big dump of all your product review data is far from helpful; it’s what you do with it after you have it that counts. 

As with the social listening data, the only way to integrate product review data is to use the right technology platform. The right platform will be set up for multi-level analysis and allows AI-driven text analytics to mine product reviews, drilling down into multiple different segments.  It will find not just a theme per review, but themes and sentiments within sentences and phrases as well.

For example, a customer may provide a product review that reads: “I loved this sleeping bag - it kept me warm, but it was a bit heavy for my backpacking trip.” You want your analysis tool to pick up on both the review’s sentiments: loved and warm (positive) as well as heavy (negative). You then want to add other data sources to enrich the data further - such as a product description or specification.  In this example, the specs may indicate that the sleeping bag was unsuitable for backpacking.  

You and your team can then look at all the data tied together from different angles to build the bigger picture and inspire decision-makers to action.

Harmoni makes it easy to gain customer insight from product review data, immediately connecting variables from multiple levels and data sources. It brings the most relevant results to the surface fast, so you and your team can make informed decisions.