News Events And Mining Profit: Strategic Approaches For Manchester City

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News Events And Mining Profit: Strategic Approaches For Manchester City – Traditionally, businesses have used their presence in physical stores to understand how they can attract, engage and delight their customers.

However, when we move our relationships online using digital channels as the main points of interaction (especially with worldwide WFH and social distancing measures brought about by COVID19), our ability to understand customers in this distanced situation is also compromised.

News Events And Mining Profit: Strategic Approaches For Manchester City

News Events And Mining Profit: Strategic Approaches For Manchester City

Businesses are interacting with customers more than ever. Many businesses feel the pressure to be omnichannel and ubiquitous. The digital nature of the relationship also means that every click, every payment, every message can be tracked and measured.

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We struggle to truly know our customers precisely because there is so much information. Information bombardment. Analysis paralysis. We’re missing crucial insights because there’s so much going on.

Text analytics helps businesses analyze large amounts of text-based data in a scalable, consistent and unbiased way. It analyzes data and extracts valuable information without requiring excessive resources, ensuring that companies have the freedom to act on these insights.

Considering that 80% of business information is mostly unstructured textual data, this type of intelligent automation is vital for the modern business to stay ahead of the competition and continue to attract, engage and delight customers.

In this guide, you’ll find an overview of text analytics, how it’s used in business, and some tools to get you started. Feel free to jump to the sections you’re interested in:

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Text Analytics is the process of analyzing unstructured and semi-structured text data for valuable insights, trends, and patterns.

It is generally used in situations where processing large amounts of text-based data is required for insights but would otherwise be too resource- and time-consuming to analyze manually by humans.

In all these cases, text analysis methods will outperform humans. In addition to being less time and resource intensive, the final insights are more consistent with less human error or bias interfering with the process.

News Events And Mining Profit: Strategic Approaches For Manchester City

That’s why many organizations across all industries are using text analytics methods to provide quantitative and qualitative understanding of text datasets. These are then used in conjunction with data visualization tools to better transform the information into actionable insights for informed decision making.

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Venn diagram showing the intersection of Text Analysis (or Text Mining) with six related fields: statistics, artificial intelligence and machine learning, computational linguistics, library and information services, databases and data mining. Source:

Natural language processing is actually a subset of the broader field of text analysis; Therefore, it can often be difficult to understand the difference between the two.

Natural language processing (NLP), or more specifically natural language understanding (NLU), helps machines “read,” “understand,” and transcribe human speech.

Various analysis methods are used to gain insight in the text analysis process, and natural language processing is one of them. NLP is actually an interdisciplinary field between text analysis, computational linguistics, artificial intelligence and machine learning.

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Text analysis aims to gain quality insights solely from the text or words themselves. The semantics in the text are not taken into account. It answers questions such as the frequency of words, the length of the sentence, the presence or absence of words.

NLP, on the other hand, aims to understand the linguistic usage and context behind the text. Grammatical structures and semantics are analyzed here. It answers questions like the intent behind a sentence, people’s language habits, and even classifies which of your emails should go to the Primary, Social, Promotions, or Updates tabs.

So how are these used in business life? The next section will discuss use cases and applications of text analysis.

News Events And Mining Profit: Strategic Approaches For Manchester City

Whether it’s customer feedback, phone transcripts, or long feedback surveys, text analytics helps teams extract quantitative and qualitative meaning from text data with relative ease.

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Because 80% of business information is unstructured, primarily in text format, analyzing text is useful across all functions of a business.

Considering the amount of customer communications, it’s a no-brainer that text analysis methods are incredibly useful for customer support teams.

Customer service teams are often the highest-turnover, most stretched teams in any company, and they face numerous stressors dealing with high volumes of customer interactions on any given day.

By adopting text analytics, service teams can automate many of the mundane tasks such as researching, updating, routing, and reducing time spent on repetitive questions. Instead, with the support of NLP, machine learning, and AI, they will improve their ability to outperform NPS, satisfaction, and CSAT KPIs.

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As the dominant means of communication between businesses and customers, customer conversations from emails, support tickets, social media or chat provide rich information to understand customer needs and contribute to business growth.

All businesses that communicate with their customers have access to this information. However, not many people use it, let alone manage to extract it.

With text analytics tools and techniques, customer contact data can be summarized and analyzed at scale to find data-driven insights for customer service teams to outperform their KPIs.

News Events And Mining Profit: Strategic Approaches For Manchester City

Want to know what high-performing agents have to say about retaining high-value customers? Instead of filtering by high-value customers and high CSAT scores and then going through thousands of their conversation recordings, text analysis does the heavy lifting for you.

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Information extraction techniques identify products, services, keywords, and phrases that top-performing reps use effectively in these conversations. Micro-classification techniques uncover details that allow you to find the order or pattern in which these phrases or keywords are used to produce customer-satisfying results.

Without extensive resources or time, customer service teams can find winning phrases, keywords, language, tone of voice, and combination of these to produce excellent results.

The ability to identify prospects or customers who are similar to your best customers is incredibly important for any business that wants to be successful. Equally useful is the ability to quickly eliminate potential problems that may grow.

As the intermediary between customers and the company, customer service teams are in the best position to pre-empt valuable customers and customer issues.

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Identifying potential customer issues before they become major problems is a common use case for text analytics in customer support. Simple clustering and issue modeling techniques can help teams address these issues in real time.

Traditionally, identification of ideal customers, customer lifetime value, frequency of purchase, advocacy, etc. made according to measurements. But these are not the only traits they share.

With a combination of text analysis techniques, you can find patterns of pre-purchase paths, communication preferences, and even similar sequences in word and phrase combinations in their communications.

News Events And Mining Profit: Strategic Approaches For Manchester City

Using micro-categorization, subtle meaning is added to small sections of text, allowing customer service teams to add flexible yet detailed interpretations to data for comprehensive analysis results. Clustering and topic modeling techniques then use these broad categories to find trends and relationships and uncover data-backed insights to uncover valuable customer identification patterns.

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What remains are patterns and defining characteristics that help target and prioritize high-value customers to achieve team KPIs.

This process would be terribly resource-intensive, but with text analysis, diving deep into already available data to find insights that break down KPIs is not only faster, but also more accurate and scalable.

It’s no secret that having conversations delegated is frustrating for customers, but many overlook just how hair-raising it is for customer service teams.

Inefficient or completely incorrect routing and prioritization of tickets creates unhappy customers who lash out at employees. The necessary emphasis on expediting as many tickets as possible does not support the quality post-interaction work (wrap time) that also helps conversation analysis.

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Automatic labeling helps reduce time-consuming packaging time after customer interactions. The global average winding time is 6 minutes. This translates into 6 minutes of valuable employee time spent completing data entry and acting as human facilitators after each call.

More importantly, to label an important basis for deriving value during speech analysis. The resulting analysis is meaningless without quality and correct labeling. Would you rely on an inaccurate and incomplete dataset to provide you with revenue-generating and customer-satisfying information? I never thought so!

Naturally, as the company’s customer frontline team, support teams are well positioned to be the company’s Voice of the Customer champions.

News Events And Mining Profit: Strategic Approaches For Manchester City

But the day-to-day management of customer service processes and employees is already challenging enough. Conversations may not always have enough time or resources to devote to finding the insights that affect the results.

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Text analysis tools like Intent Manager mostly handle the analysis for you. After a human-informed training to customize the value you want to see for your team or company, forward-looking insights will automatically be generated.

Want to report what customers are willing to see and what they will pay for next quarter? Text analysis techniques such as extraction, categorization, and topic modeling can be used together to find trending topics, measure their frustration, and estimate the value of solving the problem.

Looking to increase service team productivity by offering an automated chatbot for tier 1 queries? Text analysis methods can give you data-backed insights into how many times these questions have been asked, how often they are asked, and how many interactions are required to solve them. Even better, it gives you data on what to start training your chatbots on, exactly what training data to use, and all the information it needs to provide automated responses.

This information can then be exported to data visualization platforms

A Guide: Text Analysis, Text Analytics & Text Mining

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