{"id":25082,"date":"2020-10-08T06:00:00","date_gmt":"2020-10-08T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=25082"},"modified":"2020-10-09T09:14:31","modified_gmt":"2020-10-09T16:14:31","slug":"how-can-brands-can-leverage-text-analytics-for-better-consumer-insights","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/10\/08\/how-can-brands-can-leverage-text-analytics-for-better-consumer-insights\/","title":{"rendered":"How Can Brands Can Leverage Text Analytics for Better Consumer Insights"},"content":{"rendered":"\n

Retail and CPG brands face an incredibly complex sales and marketing landscape, with channels ranging from traditional brick and mortar stores to e-commerce – and the latter growing fast. Every day more consumers turn to eCommerce channels to purchase their products or at least to capture other consumers\u2019 opinions before going to a store – 94% of users today read reviews before deciding to buy.<\/p>\n\n\n\n

As a consequence, data is being generated at an incredible speed and its importance is growing. Although getting access to what consumers are saying is not that hard anymore, finding the time to read, analyze, understand, and categorize that data is almost impossible – especially when companies try to do that with content from multiple data sources. That\u2019s exactly where text analytics comes in.<\/p>\n\n\n\n

What is Text Analytics and why you should care about it<\/strong><\/p>\n\n\n\n

Text Analytics is the process of interpreting and categorizing content, extracting context out of words. This process involves the automatic extraction and classification of written information according to relevant aspects of a specific situation. It can be applied to several different areas, from contracts to sales and marketing, and some of the traditional types of text analysis include sentiment analysis and keywords or topic detection.<\/p>\n\n\n\n

In a Consumer Insights context, Text Analytics can be applied to any consumer data, from consumer-generated content like product reviews and conversations to content that relates to consumers, whether it\u2019s something about them or something that influences them – like expert reviews and blog posts. From there, a company can discover patterns that highlight behaviors and attributes or predict trends.<\/p>\n\n\n\n

This type of analysis can be performed manually as long as a process is established, but it becomes extremely complicated to do it when larger subsets of data are included, especially from different structures and types. Taking the time to look at each text in detail and relating it to the context until finding patterns becomes inefficient and prone to errors. Therefore, Text Analytics as it\u2019s known today has become key – considering that 2.5 quintillion bytes are generated every day – to extract meaning from content in a scalable way, using text mining and NLP – Natural Language Processing to find context in huge amounts of text.<\/p>\n\n\n\n

Companies who do not leverage Text Analytics in their Customer Experience and Consumer Insights processes will leave untapped data on the side, missing relevant insights not only about what their current customers think about them but also about how it is impacting other consumers. They will also miss the chance to discover what they could do differently to mitigate future problems and to potentialize the good aspects of the brand while reducing the impact of the bad aspects.<\/p>\n\n\n\n

How to choose the right Text Analytics tool – and get the best out of it<\/strong><\/p>\n\n\n\n

There are several text analytics tools and it is important to know how to differentiate one from another considering your use case. Many of the analytics tools available in the market are general-purpose tools that must be configured appropriately to give you the information and the granularity you want. So the first thing you should consider is what are your goals with Text Analytics and what specific insights and patterns you expect to find out.<\/p>\n\n\n\n

To keep the example within the Consumer Insights space, let\u2019s consider you want to structure data from consumer opinions (written in natural language) that come from different sources. There are some important things you need to know before choosing the correct tool, such as:<\/p>\n\n\n\n

1. It\u2019s out of the box ability to extract metadata from the opinions<\/strong><\/p>\n\n\n\n

Extracting keywords out of conversations is the basic feature of a Text Analytics tool. Connecting each of these keywords to relevant business aspects that make sense to your company\u2019s reality is a bit more complicated. Although some tools can still deliver some relevant metadata using queries and topics, there is still a lot of manual work left to do in order to relate them to your business. And that\u2019s when having this ability out of the box can be crucial to save you time and ensure you get value out of the tool.<\/p>\n\n\n\n

What is it about<\/em><\/p>\n\n\n\n

What is the opinion about? Is it the whole company, a specific product, a place, an experience, or a service? That is important because your analysis must be segmented enough to give you the ability to filter those opinions that are not your focus right now. Wrong segmentations might give you wrong information, conclusion and actions.<\/p>\n\n\n\n

If you want to know about aspects related to a product, <\/strong>for instance, <\/strong>granular product segmentation is important to you in order to know exactly to which product (or series\/model) the consumer is given his\/her opinion. You might consider tools that enable you to extract information such as:<\/p>\n\n\n\n