In recent years, market research conducted by various software vendors and consulting firms has attempted to quantify the relative percentage split between structured and unstructured data in the average user organization. Most estimates name unstructured data the unqualified winner at 80–85%, leaving structured data in a distant second place at 15–20%.
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. 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.I have seen very few organizations who have started leveraging this information for decision making
Unstructured information can be used to answer the following questions effectively
- Is our product launch going well?
- Is there an emerging product issue?
- Where should the product team focus its development dollars?
- Is someone committing fraud?
- Is my customer happy? Can I sell more to the same customer?
- Is my customer unhappy? Will he stop using our services?
- Is there a product defect in the market?
There are several applications of text analytics in various industries.
One of the large multinational bank is using text analytics on a daily basis to review customer service requests, complaints and sentiment shifts. The bank's ability to retain and grow current customers 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?
- What was their biggest issue?
- Are my loyal customers angry about something?
- Are new customers asking questions that might pose an opportunity for up-sell?
- Is my most profitable customer unhappy about something?
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.
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.
One of the large multinational bank is using text analytics to sort and route web customerservice request forms. This saves huge amount of manual efforts involved in manually routing the service requests. 30-40% of web customer service requests are routed to wrong customer service group by call center auto routing systems due to incorrect subject line. This leads to delay in response to customer. This bank has started using text analytics to auto route emails based in text in body of email & web forms.
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.
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.
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.
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!
There are several goverment agencies who are using text analytics to detect fraud and monitor terrorist movements. There are several voice to text converters are available in market today. These tools help govt agencies convert voice into text and mine the text to detect terrorist movements.