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Big data

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Creating a tailor-made listening experience requires time, resources and above all the right tools to reach customers and understand their needs in depth. In an ever changing business environment, new methods and processes bring with them new challenges and hurdles to overcome. Customer Relationship Management (CRM) has allowed us to become good at listening to our customers but research has shown how difficult it can be to make sense of the infinite quantity of information gathered.

 

What is unstructured customer feedback

Feedback from customers can be structured or unstructured. A clear example of structured data is data in an excel file, a voting poll, likes on a Facebook page, products purchased or personal details. All these instances are easily defined and can be placed in specific categories.

Unstructured data is found in everyday language and often noted by their incorrect spelling, lack of punctuation and so more difficult to analyse. Anyone using CRM will no doubt have encountered emails or social media comments, which have underlying emotions, thoughts and tones. These cannot be ignored.

80% of data collected is by default unstructured data. Indeed 76% of CRM users see the analysis and interpretation of this data being the greatest barrier in achieving business insight. This is the problem: we collect enormous amounts of data (Big Data) but don’t know what to do with it. The result is business managers making decisions without taking into consideration fully the information that their customers have provided for them.

 

Where will we find unstructured customer feedback?

  • Help desk: help-desk instruments can be excellent in capturing customer feedback through case descriptions, customer call-backs and notes. In order to understand repetitive behaviour and their consequences, effective analysis is essential if we are to anticipate and resolve customer problems even before they happen.
  • Emails: it’s not only the emails that are written by customers that are important but also these between product managers, sales managers, marketing, product development and sales teams. These opinions and thoughts are a wealth of information about customers and the problems they face.
  • Surveys & product reviews: surveys and product reviews are honest customer feedbacks which relay their satisfaction level about the product, the staff or the quality of service. Customer Experience methods recommend however that these surveys are kept to a minimum in length in order to understand quickly and efficiently the level of satisfaction or dissatisfaction of the customer.
  • Live chat and community discussions: these forms of communication allow a company to interact directly and live with users. Matching this form of communication up with opinions, ideas and insights could prove to be an excellent opportunity to find out if the business’s objectives are aligned with customer needs. It can help them rapidly change course and also put solutions in place.

 

How to analyse this wealth of information?

The answer is simple: text analytics. Text analytics apply natural language processing, machine learning and visualization techniques which allow us to analyse and extract information from unstructured data.

For years Text Analytics were seen as complicated analysis which only experts in the field understood. Today, the Sophia Semantic Engine has been created to be simple, adaptable and fluid to the needs of every user. Front and back offices can rapidly resolve customer problems, thus unveiling new market opportunities and ways to improve business operations. More importantly, Sophia Semantic Engine allows you to transform the mass of customer feedback into actionable insights with a view to Customer Experience Management.

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