Your Voice Analytics Strategy: Can it Answer the All-Important “Now What?” Test
Posted: August 9, 2011 by Erich Dietz - Sr. Director, Contact Center Solutions
When creating your Voice Analytics strategy, make sure your solution can answer the all-important “now what?” text.
Let’s put this test to work against the most typical Voice Analytics solutions in EFM/VoC.
Here are a few common, yet less useful Voice Analytics scenarios:
- “I know the top ten keywords used by my customers in phone survey feedback.” (Now what?)
- “I am told I need to have a word cloud.” (Now what?)
- “My competitors were mentioned 143 times by customers!” (Now what?)
See? It’s that simple. The above scenarios cannot easily and usefully answer the “now what?” question. While they are all very interesting factoids, what practical action steps do they drive? If you cannot take action with your Voice Analytics, why bother with it at all?
Contrast those with the following useful cases:
- “My least satisfied customers complain about long hold times.” (Now what? Hire more contact center agents and/or reduce talk times.)
- “When dissatisfied with order accuracy, my drive-thru customers most often use the phrase ‘missing … toy’.” (Now what? Change the assembly process and train employees to double check that toys are included in every bag.)
These Voice Analytics scenarios easily pass the test because they lead to actions that produce measurable operational improvements. When creating your Voice Analytics strategy, make sure your solution can answer the all-important “now what?” test.
Mindshare’s focus: Insights and Action
If you’re going to collect feedback, you need to act on it. Like Text Analytics, your Voice Analytics results should be actionable and drive decisions that result in operational improvements. All analytics must pass the “now what?” test. When you see Voice Analytics results, ask yourself, “now what?” The answer should be an action that drives measurable results for a Return on Investment (ROI).
All Text Analytics Systems are NOT the Same
Posted: August 1, 2011 by Kurt Williams - CTO
Claiming that all Text Analytics solutions are the same is like saying all forms of transportation are the same. They’re not.
Research your Text Analytics options. Ask your vendor, even Mindshare, all of these important questions. You’ll be happy you did.
- Does the vendor’s text analytics platform categorize comments with similar-themed keywords and phrases for comparing and trending? (For example, would the comments “lukewarm cheeseburger” and “the hamburger patty was cold” both be classified under “Hamburger” and “Temperature”?) Or would you have to mentally group terms such as “cold” and “lukewarm” to understand the scope of a problem.
- Does the vendor’s text analytics accurately identify the key topics in each comment individually as they come in? Or does it require a large sample size and batch process?
- Does the vendor’s product ensure the highest quality results by using custom-tuned Natural Language Processing (NLP) semantic rules? Or is it based on simple keyword extraction or statistical probabilities?
- Is their insight tagging at least 90% accurate overall? Or is it only slightly better than flipping a coin?
- Does the vendor’s solution minimize setup time with pre-tuned, industry-specific models? Or is the vendor’s solution generic, lacking industry domain knowledge and requiring extensive time and effort?
- Does their text analytics product seamlessly integrate with your current EFM system (Enterprise Feedback Management) or will you have to manage two separate systems? (E.g. Will you benefit from the cost savings of a fully integrated EFM and text analytics platform?)
- Does the product automatically highlight correlations between structured survey data (like location, time of visit, and satisfaction ratings) and unstructured survey data (open-ended comments and social media)? For example, would your product be able to find that the phrase “on … cell phone” is highly correlated to poor customer service scores? Or will you have to connect the dots yourself?
- Does their system send instant alerts when a key issue is detected in a customer comment? For example, if a customer reports “slipping and falling” on a wet floor, is an alert email sent immediately to your corporate office? Or will you find out much later?
- Are their text analytics results reported in real-time and available 24/7 to managers at all levels of your organization (permission-based)? Or will the results be stale? Can the reports be pre-scheduled to email to managers or must you always log in every time to get what you need?
- Do they have a turnkey product that can identify the central themes and sentiment in customer comments? For example, could it identify that the phrases “rude cashier” and “check engine light” are showing up frequently in negative context? Or do they require significant training, setup time, and resources to reach that insight?
- Can the vendor customize their solution to tag comments in categories unique to your business, such as products, programs, competitors, etc.? Or is it limited to measuring generic insights only?
- Can you navigate directly to source comments about key performance areas or are their results limited to high-level statistics? For example, assume that several feedback comments mention the cleanliness of floors in a store; can you instantly pull up those specific cleanliness comments to identify the root of the problem?
- Do they support more than seven major languages? Or are they limited to English and not much else?
- Is their solution fully up and running (already developed) and ready to deploy within just weeks, or are they selling something you’ll have to wait months and years to ever see.
- Does a team of professional, full-time text analytics experts guide their text analytics solution? Or is their text analytics package just a piece of software with no support?
- Are they confident enough to show you an unscripted live demo of their solution? Will they allow you to test it with your own comments to see real results using a sample of your own data? Or are they hiding behind a staged presentation?
Call us at Mindshare today. Let us show you what the best
Voice of the Customer Text Analytics Solution can do for you.
800.645-5407
Employee Loyalty = Customer Loyalty = Financial Success
Posted: July 22, 2011 by Jon Sanderson - VP of Marketing
We’ve all been to that coffee shop. Every week there’s a different overly-pierced twenty-something behind the counter. You wish your experience was more like an episode from “Cheers” where you walk in and everyone says “Norm!” (even if that’s not really your name) and the employee already knows what you want and has it ready by the time you reach the counter. Or if you don’t have a “usual,” you at least wish the employees stayed long enough to learn your name and greet you with sincerity.
The biggest key to creating everlasting sales is to build customer loyalty. The first step to customer loyalty is employee loyalty. Customer loyalty is all about an emotional connection – relationships between customers and employees/products/services. High employee turnover kills credible relationships with customers.
Every business with high employee turnover seems to have some sort of excuse for it. “We’re a college town so kids go home for the summer,” or “It’s a stepping stone for the next best job,” or “We don’t have the money to pay annual bonuses and raises.”
But none of those excuses require high turnover rates. Employees, no matter their age or career level, will find a reason to stay with your company … if they love their jobs. Even cashiers, servers, and call center agents can indeed love their jobs for reasons other than money.
How can you use this to your advantage? Focus on how your managers treat employees. Make each employee a manager of their own destiny and of their customers’ happiness. Give them a little bit of freedom and watch it go a long way. Employee satisfaction isn’t about monthly pizza parties and employee discounts (though those don’t hurt). It’s about genuine respect. Just like every customer, if an employee feels important, they’ll stay loyal.
The longer an employee stays with a company, the better they become at their job, the better they understand their products, and the better they will be at satisfying customer needs. As they stay loyal, turnover is reduced. As employee turnover is reduced, costs decline.
The result: loyal employees, better served customers, lower costs. Plus, with loyal employees, you create loyal customers, which produce higher long-term revenue.
Voice Analytics Should Be Equal to Text Analytics
Posted: July 15, 2011 by Erich Dietz - Sr. Director, Contact Center Solutions
Your Voice Analytics Should Be Equal to Your Text Analytics
Text Analytics is a hot topic right now. Companies with successful customer feedback programs are using Text Analytics. At its very basic form, Text Analytics provides keyword search and a word cloud of topic frequencies so businesses know what’s important to their customers. But many Text Analytics engines do much more than that: trending, root-cause analysis, automatic comment categorization, and much more.
What about Text Analytics’ up-and-coming brother, Voice Analytics? Only a handful of VoC (Voice of the Customer, or EFM – Enterprise Feedback Management) vendors even offer Voice Analytics. And most of those Voice Analytics engines only provide the basics: keyword search and word cloud topics. Why? Because Voice Analytics engines don’t transcribe the whole comment, they listen for keywords within the comment predetermined by the user. For example, if a fast-food manager wants to stay on top of his location’s French fry quality, his Voice Analytics will be tuned to flag comments that mention “fries.” Then, the manager has to listen to that comment to find out what it says.
Frankly, that’s pretty weak technology. But that’s the state of current Voice Analytics engines.
The answer is yes, they should have equal importance. Mindshare believes that the best way to utilize Voice Analytics and Text Analytics is to transcribe your audible comments into text and then feed them through your Text Analytics engine. The two methods become equal in the quality and quantity of their results. Valuable, actionable insights are extracted from both. Plus, transcribed comments make for easy referencing and provide retainable data for use over and over again.
Just remember, transcribed audible comments provide potential insights. Transcription is near-worthless if you don’t analyze the comments to find usable information.
Analyzing “Analytics”
Posted: July 8, 2011 by Kurt Williams - CTO
It seems you can’t swing a baseball bat without knocking over three or four people asking for analytics: Text Analytics, Predictive Analytics, Social Media Analytics, Decisiolytics, whatever (Mindshare’s CEO recently coined the term “Decisiolytics.” Your guess is as good as mine). The list seems never ending.
What exactly does everyone mean by “analytics” anyway? For most, I think it refers to the application of arcane mathematical and statistical techniques to mass quantities of numbers. It’s when data geeks and quant freaks in white lab coats pour over reams of data, invoke some sort of mystical incantation, kill a chicken or two, and out pops some kind of interesting “insight” – whatever that is.
Well, that’s all changing. More and more the term “analytics” refers to a packaged-up application that hides the complexity and does the analytics for you, providing the uninitiated workers the ability to take action based on the data. Mindshare’s CoachTM product is a perfect example. Store managers don’t have to know anything about ordinal-logistic-dogeewhatsists. They are directed what to do and they just take action. There was a great article last month that discussed this topic in Information Week:
http://www.informationweek.com/news/software/bi/229700319
“It’s official. The term “analytics” no longer refers only to advanced statistical methods and operational research. It’s now shorthand for what people really want from business intelligence: concise, actionable insight that lets them (1) respond to what’s happening now, and (2) anticipate what will happen in the future, rather than just react to the events of last week or last month. Enter prebuilt analytic applications. As the name suggests, these are off-the-shelf apps, ready-made for specific industries …”
Small, pre-built, easy to consume nuggets of analytic goodness.
Yep. Analytics. Mindshare has it.