Machine learning uncovers a mother lode of commercially valuable insights from the often-overlooked opinions of customers and clients
copyright by www.technative.io
Strange as it may seem, there was a time when transaction data was neglected, as companies felt they lacked sufficient expertise to extract meaningful insights from it. Now every customer-facing enterprise is using analytical techniques to pound value from millions of transactions.
One important source of data that has been overlooked is customer sentiment. How you might ask, can an enterprise extract meaningful data or insights from something as nebulous as what customers feel? And where, in any case, can they find the source material?
The answer comes from artificial intelligence, which is powering customer experience platforms to provide businesses with actionable insight. Every day millions of customers leave opinions about services or products, offering a wealth of data into where enterprises are getting it right or wrong. With research finding that 94 per cent 1 of consumers use reviews or star ratings before they decide to buy any product or service, nobody can doubt that customer opinions matter.
These opinions would have not have been regarded as a serious source of business insight until now. The transaction data is already available and ready to be combined with natural language processing and machine learning (ML) technology.
ML is embedded in customer experience technology, and when applied to natural language processing (NLP), enables highly actionable insights to be extracted from the sentiment in thousands of posts, whether positive, negative, indifferent or superficially trivial.
ML will rapidly analyse the information to spot important behavioural trends as soon as they emerge. Take a national recruitment company in the UK, for example. Job candidates in Hull may register very poor levels of satisfaction, whereas their counterparts using the company’s branches elsewhere in Humberside and Lincolnshire are perfectly happy. What might be the cause? Drilling down into the data with machine learning will rapidly expose any weak on-boarding practices or inadequate, disorganised recruitment consultants causing such poor feedback.
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More broadly, customer insight offers hugely valuable information about the journeys of candidates, so that recruitment companies can establish what makes job-hunters come to them, where else they apply to and where success is most likely for particular demographics or skillsets. It means an agency can quickly adapt its procedures and tactics to optimise customer satisfaction.
Equally, two car dealers selling the same model of vehicle may generate very different levels of satisfaction in their respective sets of customers. ML and NLP can rapidly drill down into the reviews left by customers and blend all the available data to find out the likeliest causes of these problems from hundreds of possible factors.
Customer sentiment can be blended with transaction data, stocking levels and supply chain data, or details of finance deals, after-care packages, warranties, or product specifications, along with a customer’s previous purchasing history, browsing habits or inquiries. Trends will quickly emerge from the analysis, as will the causes of whatever is affecting individuals. Generated in real-time, this enables customer-care departments to respond quickly to aggrieved or ecstatic consumers, building trust in a brand as a result.[…]
read more – copyright by www.technative.io
Machine learning uncovers a mother lode of commercially valuable insights from the often-overlooked opinions of customers and clients
copyright by www.technative.io
One important source of data that has been overlooked is customer sentiment. How you might ask, can an enterprise extract meaningful data or insights from something as nebulous as what customers feel? And where, in any case, can they find the source material?
The answer comes from artificial intelligence, which is powering customer experience platforms to provide businesses with actionable insight. Every day millions of customers leave opinions about services or products, offering a wealth of data into where enterprises are getting it right or wrong. With research finding that 94 per cent 1 of consumers use reviews or star ratings before they decide to buy any product or service, nobody can doubt that customer opinions matter.
These opinions would have not have been regarded as a serious source of business insight until now. The transaction data is already available and ready to be combined with natural language processing and machine learning (ML) technology.
ML is embedded in customer experience technology, and when applied to natural language processing (NLP), enables highly actionable insights to be extracted from the sentiment in thousands of posts, whether positive, negative, indifferent or superficially trivial.
ML will rapidly analyse the information to spot important behavioural trends as soon as they emerge. Take a national recruitment company in the UK, for example. Job candidates in Hull may register very poor levels of satisfaction, whereas their counterparts using the company’s branches elsewhere in Humberside and Lincolnshire are perfectly happy. What might be the cause? Drilling down into the data with machine learning will rapidly expose any weak on-boarding practices or inadequate, disorganised recruitment consultants causing such poor feedback.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
More broadly, customer insight offers hugely valuable information about the journeys of candidates, so that recruitment companies can establish what makes job-hunters come to them, where else they apply to and where success is most likely for particular demographics or skillsets. It means an agency can quickly adapt its procedures and tactics to optimise customer satisfaction.
Equally, two car dealers selling the same model of vehicle may generate very different levels of satisfaction in their respective sets of customers. ML and NLP can rapidly drill down into the reviews left by customers and blend all the available data to find out the likeliest causes of these problems from hundreds of possible factors.
Customer sentiment can be blended with transaction data, stocking levels and supply chain data, or details of finance deals, after-care packages, warranties, or product specifications, along with a customer’s previous purchasing history, browsing habits or inquiries. Trends will quickly emerge from the analysis, as will the causes of whatever is affecting individuals. Generated in real-time, this enables customer-care departments to respond quickly to aggrieved or ecstatic consumers, building trust in a brand as a result.[…]
read more – copyright by www.technative.io
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