In the current flood of Business Intelligence and insight tools, there is a phrase causing users to abandon the fanciest tools and leading to serious self-doubt for the provider – the “so what?” question. Indeed, your high-quality analytics application might spit out accurate, statistically valid data and package them into intuitive visualisations – but if you stop there, your data has not yet become a basis for decision and action. Most users will be lost or depend on the help and expertise of a business translator, thus creating additional bumps on their journey to data-driven action.
In this article, we focus on applications of Web-based Text Analytics – not “under-the-hood” technological details, but the practical use of Text Analytics and Natural Language Processing (NLP) to answer central business questions. Equipped with this knowledge, you will be able to tap into the full power of Text Analytics and fully benefit from large-scale data coverage and machine intelligence. A real-time mastery of the oceans of data floating on the Web will allow you to make your market decisions and moves with ease and confidence.
1. The basics
Before diving into details, let’s first get an understanding of how Text Analytics works. Text Analytics starts out with raw, semi-structured data – text combined with some metadata. The metadata have a custom format, although some fields, such as dates and authors, are pretty consistent across different data sources. The first step is a one-by-one analysis of these datapoints, resulting in a structured data basis with a unified schema. Even more important than the structuring is the transformation of the data from qualitative to quantitative. This transformation enables the second step – aggregation, which condenses a huge number of structured representations into a small number of consolidated and meaningful analyses, ready for visualization and interpretation by the end user.
2. Answering questions with Text Analytics
A number of questions can be answered with Text Analytics and NLP. Let’s start with the basics – what do users talk about and how do they talk about it? We’ll be providing examples from the Chinese social media landscape on the way.
First, the what – what is relevant, popular or even hot? This question can be answered with two algorithms:
- Text categorisation classifies a text into one or multiple predefined categories. The category doesn’t need to explicitly be named in the text – instead, the algorithm takes words and their combinations as cues (so-called features) to recognise the category of the text. Text categorisation is a coarse-grained algorithm and thus well-suited for initial filtering or getting an overview over the dataset. For example, the following chart shows the categorisation of blog articles around the topic of automotive connectivity:
- Concept extraction digs more into depth and identifies concepts such as brands, companies, locations and people that are directly mentioned in the text. Thus, it can identify multiple concepts of different types, and each concept can occur multiple times in the text. For example, the following chart shows mention frequencies for the most common automotive brands in the Chinese social web in February 2018:
Using time series analysis in the aggregation, text categorisation and concept extraction can be used to identify upcoming trends and topics. Let’s look into the time development for Volkswagen, the most frequent auto brand:
Once we have identified what people talk about, it is time to dig deeper and understand howthey talk about it. Sentiment analysis allows to analyze how the topics and concepts are perceived by customers and other stakeholders. Again, sentiment analysis can be applied at different levels: whole texts can be analysed for an initial overview. At an advanced stage, sentiment analysis can be applied to specific concepts to answer more detailed questions. Thus, competitor brands can be analysed for sentiment to determine the current rank of one’s own brand. Products can be analysed to find out where to focus improvement efforts. And finally, product features are analysed for sentiment to understand how to actually make improvements. As an example, the following chart shows the most positively perceived models for Audi in the Chinese web:
3. From insights to actions
Insights from Web-based Text Analytics can be directly integrated into marketing activities, product development and competitive strategy.
By analysing the contexts in which your products are discussed, you learn the “soft” facts which are central for marketing success, such as less tangible connotations of your offering – these can be used as hints to optimise your communication. You can also understand the interest profile of your target crowd and use it to improve your story and wording. Finally, Text Analytics allows to monitor the response to your marketing efforts in terms of awareness, attitude and sentiment.
With Text Analytics, you can zoom in on awareness and attitudes about your own products and find out their most relevant aspects with concept extraction. Using sentiment analysis, you can compare the perception of different products amongst each other and focus on their fine-grained features. Once you place your products and features on a positive-negative scale, you know where to focus your efforts to maximise your strengths and neutralise your weaknesses.
Your brand doesn’t exist in a vacuum – let’s broaden our research scope. Text Analytics allows you to answer the above questions not only for your own brand, but also for your competitors. Thus, you will learn about the marketing, positioning and branding of your competitors to better differentiate yourself and present your USPs in a sharp and convincing manner. You can also analyse competitor products to learn what they did right – especially on those features where your own company went wrong. And, in a more strategic perspective, Text Analytics allows you to monitor technological trends to respond early to market developments.
How to show that your findings are not only accurate and correct, but also relevant to business success? Using continuous real-time monitoring, you can track your online KPIs and validate your actions based on the response of the market. Concept extraction can be used to measure the changes in brand awareness and relevance, whereas sentiment analysis shows how brand, product and product feature perceptions have improved based on your efforts.
With the right tools, Text Analytics can be efficiently used in stand-alone mode or as a complement to traditional field research. In the age of digitalisation, it allows you to listen to the voice of your market on the Web and turns your insight journey into an engaging path to actionable, transparent market insights.
Get in touch with Anacode’s specialists to learn how your business challenges can turn into opportunities with Text Analytics.
Just as the rest of the China’s financial system, the Chinese stock market is subject to rather strict government regulations. However, in recent years, it offers more and more opportunities to risk-tolerant foreign investors.
This report sample provides an overview over the Chinese stock market based on data from the Chinese finance portal 金融界 (http://finance.jrj.com.cn; Finance World).
Download the report sample here.
This report provides a descriptive overview of the Chinese Web 2.0 landscape for automotive feedback, focussing on BMW 7 Series and comparing it with Audi A8 and Mercedes-Benz S-Class. The feedback is analysed both from a qualitative and a quantitative perspective. The main observations and findings are as follows:
- Popular topics and concepts: We find that users are most concerned about the price and optical aspects (design, visual appearance) of the three considered series. Competitor brands that are discussed in a comparative perspective are mostly high-end or consumer-oriented foreign brands from Germany, US and Japan, whereas native Chinese brands are much less frequent. Geographically, users concentrate in the big cities and more affluent regions along the East coast.
- Temporal evolutions: The quantity of buzz grows relatively evenly for all three series before 2015, with BMW 7 and S-Class leading. In 2015 – 2016, there is a burst in the quantity of data for BMW 7, which correlates with the introduction of the new generation of the series.
- User satisfaction and sentiment: Users are generally satisfied with the frequently mentioned major product features of BMW 7. There are, however, some categories that are perceived negatively – specifically, components related to the front part of the car, the fuel consumption and aspects related to acoustic quality and insulation.
- Social influencers: Among the key influencers on WeChat, China’s leading social network, we mostly find media accounts posting on general automotive topics. There are no accounts with a wide social reach that would specialize on the BMW brand. Thus, influencer marketing is an opportunity yet to be explored by BMW’s marketing and branding strategy.
Download the social report.