Oops! The input is malformed! 8 CRM Uses for Text Analytics by David Bean - BeyeNETWORK UK

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8 CRM Uses for Text Analytics

Originally published 4 August 2008

Executives in charge of customer relationship management (CRM) have it tough. Their charter – namely, to understand customers implicitly and constantly increase satisfaction to achieve retention and growth – is a never-ending process of amalgamating data from various sources, deciphering meaning and communicating with other department heads to determine actions. Unfortunately, despite the dollar amounts thrown at CRM, many companies have yet to harness the virtually limitless “first-person” customer feedback data available to them.

The reality is that every hour of every day, directly and indirectly, customers place calls (that are transcribed), send direct emails, complete surveys and talk among themselves online in blogs, forums and social networks. They share their thoughts about products and services, their likes and dislikes, and their hopes for future features. Customers tell companies about product failures. They request help. And they offer opinions about their experiences that contain insights for organizations that listen. This data is extremely valuable to customer-facing organizations as it’s in the form of first-person narrative – accounts from a single customer referring to himself or herself explicitly using words such as “I,” “me” or “we” that typically provide detailed opinions, issues, thoughts and sentiment about products and services, requirements and ideas.

The intensity of tales told in the first person can be striking, especially when the person narrating has something to say about your product or service. The “why” of an event or opinion is often revealed, as are potential marketing opportunities or even early warnings on issues. Employees, managers and executives can answer key questions such as:

  • Is our product launch going well?

  • What marketing messages resonate most with customers?

  • Is there an emerging product issue?

  • Where should the product team focus its development dollars?

  • Are there more effective methods for positioning current products?

  • Which services have the best chance of surviving a turbulent market?

  • Is someone committing fraud?

  • Is there a product defect in the market?

With Web 2.0 in full swing, there is obviously a boundless wealth of knowledge to be had by listening to one’s customers on their terms, in their forums. However, tracking this vastness is anything but a simple endeavor. Sheer volume aside, the data is unstructured and often poorly written, making it extremely difficult to marry with traditional business intelligence (BI) databases.

The market is flooded with software that attempts to tag, sort, search, organize and manage much of this unstructured data. But discovering the actual facts in this data  –  the “who,” “what,” “where,” “when,” “how,” and most importantly “why”  –  is a challenge that leaves most companies scratching their heads. Give-and-take online discussions and the constancy of customer emails simply cannot be parsed, processed or packaged into useful, actionable data without the capabilities offered by text analytics solutions.

Text analytics allows users to break down sentences linguistically to get the facts – extracting meaningful data that can then be fused with existing structured information. By properly analyzing this first-person feedback through text analytics, enterprises have the right information they need to improve their products, services, reputations and balance sheets. Once the facts are extracted, the sentiment categorized, the results structured, the data integrated and the reports delivered, only then can companies claim to have “first-person intelligence™” worthy of action.

How are leading companies employing text analytics and benefiting from first-person intelligence?  Here are eight great examples:

1.  Net Promoter™ Root Cause
One of the largest direct-to-consumer travel companies in the world employs text analytics to flesh out the actionable details behind their NetPromoter Score™ (NPS), which gauges customer loyalty and satisfaction via an NPS survey. Executives at this company are eligible for bonuses based on the attainment of consistently high NPS scores. Since a customer’s likelihood to continue purchasing from the company is directly connected to their NPS, it’s vital that the company understands the “who,” “what,” “when,” and “why” of the scores so they can take action to improve low scores.

On average, customers that were giving scores of a “1-2” were not returning to repurchase.  Leveraging verbatim feedback to drive programs actually enabled the company to increase scores to “4’s” out of a 5-point scale. Customers who gave a “4” and above were ten times more likely to continue making purchases through the company.  After implementing new programs to improve the customer experience, based on knowledge and ideas obtained using text analytics to analyze survey feedback, the company was able to materially impact their NPS score and drive increased customer loyalty.

2.  Sentiment Analysis
One of the largest financial institutions in the world uses text analytics on a daily basis to review customer complaints and sentiment shifts. The financial institution’s ability to retain and grow current customers – their veritable lifeline – is directly correlated to understanding and acting on sentiment shifts and their respective root causes. With text analytics, they monitor opinions and attitudes in order to determine where and how to spend on client initiatives. Text analytics gave them the ability to make decisions regarding expenses on marketing materials and viability of their online offerings. They were also able to produce accurate insights about customer preferences and indicators for what prompts customers to spend more.

Each day, using text analytics to analyze customer emails, complaints and call agent notes, the organization looks for answers to questions such as:

  • Did customers like our new product?

  • Did they understand our new marketing message?

  • What was their biggest issue?

  • Are loyalists angry about something?

  • Are new customers asking questions that might pose an opportunity for up-sell?

  • Are there specific features of our online banking system that are applauded or appalled?

3.  Early Warning
One consumer electronics manufacturer uses text analytics to uncover product issues early, before they turn into expensive problems. When products are high-priced and marketed as the preeminent option, customers expect not only good quality, but also rapid and competent service when something goes wrong.

To meet that expectation, customer loyalty managers at this company set up automatic alerts through their text analytics engine so they would know immediately when new product issues occurred. Once identified, proactive measures are taken to mitigate the issue and customer satisfaction is monitored and acted on. In one example, a product defect was found before the product came out of limited release, giving the company time to fix the issue and greatly reduce potential recall costs, not to mention customer satisfaction issues.

4.  Call Center Optimization
A large cell phone carrier uses text analytics to stay on top of customer issues as they are being discussed online in web forums and blogs. In doing so, they’re able to leverage that knowledge to prepare their call center, proactively handle the customer issues, and possibly even deflect calls.

In one instance, this company found a serious issue being discussed in web forums two weeks prior to it actually emerging in inbound calls and chats. Once the issue was identified (on a product that was released that same week), the call center took immediate action, posting remedies in an online FAQ, routing customers to agents who had been trained to handle the specific issue, and even proactively notifying customers about the problem. The company noticed a marked increase in customer satisfaction for the customers involved in this early action, which mitigated both a potential public relations problem and an influx of hard-to-manage inbound calls. 
5.  Launch Monitoring
An industry-leading mobile phone manufacturer uses text analytics to keep a sharp eye on customer sentiment and any potential issues with products they release into the market. As new product introductions in the cell phone industry are frequent and expensive, and cell phones are some of the most discussed consumer electronic products on the Web, the company is committed to listening to, understanding and acting on feedback. Text analytics enables the manufacturer to identify issues early, improve quality, and increase customer satisfaction with each new product.

In one example, this company identified a software flaw with a newly introduced phone within the first 24 hours of the product’s release. Discussion about the issue immediately hit online community forums and their text analytics engine discovered and summarized all of the data. The company was able to take immediate action: sending emails to customers with the solution, fixing new products in the queue for shipment, putting an FAQ on their site and notifying their partner carrier to fix new products sold. These steps turned a potential launch failure into a remarkable success.

6.  Product Innovation and Quality
The largest appliance manufacturer in the world uses text analytics daily across its customer service, marketing, quality, engineering and development groups to identify product quality issues and to uncover new opportunities for innovation. This manufacturer uses the insights and ideas derived from customer feedback to drive product innovation.

The company has also experienced “hundreds of millions of dollars” in cost savings resulting from early warning on issues. Had text analytics not identified some of these issues, immediate attention would not have been possible. The company has greatly benefited from the ability to understand the root cause behind product issues and respond quickly to manufacturing defects, as well as customer interactions and repair situations rather than having to react via expensive recalls.

In one example, this company was able to mitigate an expensive product replacement support protocol after identifying the root cause of the product issue, which had been reported by customers to service agents. The company was able to determine that only a single part of the product need be replaced, rather than the entire product. A full product replacement would have cost the company an estimated $3,100 per unit (not including installation fees), whereas the part replacement only ended up costing approximately $15 per product.

7.  Market Research Analysis
Do you regularly survey your customers? If the answer is yes, then you are among the best companies out there. But the real question is, do you take full advantage of that valuable research and augment it with what customers are telling you through “unaided” interactions? A large consumer high-tech company does this every day using text analytics. They go beyond scores and analyze the verbatims in their market research to get to the “why” behind their scores. Now they know what action to take. 

One of the things the company recently discovered using text analytics was a large disparity between scores and verbatims. Although customers reported that agents were courteous and provided good service, they explained in the verbatim that the issue wasn’t with the agent being nice, “I just couldn’t understand them.” In fact, the company found that for certain problem types their outsourced call center got good scores, but were actually generating call backs because language barriers prevented the agents from resolving the problem. For those call types, the company re-routed the calls to agents in a different locale and with different skills and were able to measure a material increase in their scores. Without the verbatim analysis the company wouldn’t have known what to do.

8.  Competitive Analysis
One major airline uses text analytics not only to understand exactly why their customers are loyal and some are not, but to garner knowledge about their competitors as well. In an industry where fixed costs have risen dramatically and competitive data is transparent, staying on top of customers and their opinions is paramount.

The airline analyzes survey responses and call center notes, but they also “harvest” the Internet for customer conversations about themselves and the competition –  topics include everything from services, issues, products and prices to specific customer desires. In doing so, the airline is able to make better decisions such as where to invest to beat the competition, what marketing messages will resonate with customers, and what specific competitive differentiators should be promoted. Such insights enable the airline to truly understand how it compares to its fierce competitors, but more importantly, how it will win!

SOURCE: 8 CRM Uses for Text Analytics

  • David Bean
    Dr. David Bean founded Attensity, a text analytics software and solutions company, and serves as its chief technology officer. Dr. Bean holds six patents covering Attensity’s award-winning Exhaustive Extraction™ technology, which automatically extracts facts from unstructured data and puts it into a relational form for analysis. He received a B.A. degree in French and Arabic from the University of Utah, and an M.S. in Management Information Systems from the University of Arizona School of Business. He has a doctorate in computer science from the University of Utah and now is the school’s adjunct assistant professor of applied computational linguistics.


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