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2016

2423560905_ccd1b5b8a3_o.jpgI like to play golf, badly on most occasions, but it’s a hobby of mine and I enjoy it.

Tiger Woods also plays golf and he recently returned in the Hero Challenge after a long time out of the game with injury. From being former World number one his ranking was in the mid 800’s.

Most people would agree that Tiger has been the outstanding golfer of his generation. He wants to equal or better Jack Nicklaus record of Major Championships which stands at 18 wins but will Tiger ever win another Major?

What’s any of this got to do with Quality Management?  Well the game of golf, the Quest for Majors and Quality Management are all linked. I will explain.

Suppose you play golf, on the first hole you get a par, do you stop playing then and record a par round and retire to the clubhouse to reflect on how well you played? If you have a bad start do you accept the round is going to be bad and walk off? No. Of course you do not. So why do organisations only monitor a small percentage of their customer interactions in Quality Management, for compliance, training or other insight and accept that the results they see are the completed picture?

The information you get back through QM manual random sampling is accurate and useful, I would not dispute that, but when you are only applying it to a small percent of interactions, it can only ever be considered anecdotal.

With Contact Analytics you can automate the scoring process, you can gain real insight into the interactions, the factors driving them, the agent performance and the trends in your contact centre and business which you will not get with manual sampling. You can move from subjective to objective feedback to the agents, making them more motivated and more productive. You can improve the service you provide your customers and the experience they have in dealing with your organisation.

Easy? Not really or everybody would be doing it. Contact Analytics requires work in the planning, design, implementation and in service phases. You have to be committed to wanting to change and prepared to put the time in to make that change effective.

I would offer the following suggestions to anybody looking for improving their QM processes to Major winning standards with Analytics

  1. Plan the rollout Understand what you want to measure, where the priorities are the sequence of events – do not try and boil the ocean on day one, it could overwhelm you
  2. Do not try and automate bad processes. Change your goals and processes to take advantage of what contact analytics can do instead of trying to force the technology into your existing processes.
  3. Invest Time in the design phase. Understand what your scorecard should be reporting and what information and behaviours you would like to change and impact. Involve the agents in the planning and deployment
  4. Create a review and improvement process. Evolve and continually improve the scorecards, think about how they should be used to coach and improve agents and be prepared to listen to agent feedback to make the scorecards work for and be relevant for everybody

The destination is worth the effort. Tiger will not win another Major unless he understands where his game is at today, the areas he needs to improve and he puts the investment of time and energy into making those improvements. Your QM Major dream will not be achieved by investing in technology alone, but analytics technology combined with organisational commitment and focus will get you there. No longer will you be marking that scorecard after the 1st Hole, you will play the full round, you will understand what went wrong, where and why? You will see the great things that happened on your round and you will be a happier and better golfer for the experience.

1.jpgWhat’s the point of doing a customer satisfaction survey? Well, rather obviously, to gauge how customers perceive you, and where their expectations are being—and not being—met.

 

This requires walking in the customer’s shoes and designing your survey from the customer’s perspective. Sound simple? It is…sort of.

 

Unfortunately, organizations run a high risk of tunnel vision, becoming acutely interested in only the problems they think are most pressing. So they pack their questions into customer surveys, despite the fact that their interests have NOTHING to do with customers’ actual experiences.

 

Let’s look at an example:

 

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“Please rate the balance of graphics and text on *******.com.” (1-10, or Don’t Know).

 

The biggest problems with this question are:

  • It’s unclear: Does a bad rating indicate too much text, too many graphics, or both?
  • It’s out of touch with the customer experience: Customers don’t think, “Hmm, the graphics-to-text ratio has balance problems” They DO think, “This webpage sucks, I can’t find a simple answer.”

 

The question of graphic balance is best saved for a web design or UX team. As I often say, your customers are not your analysts. Leave questions out of the survey that do not reflect the customer’s immediate experience.

 

Examples of questions that customers CAN answer and that provide actionable insight for your customer satisfaction survey are:

  • “Who else might you buy similar merchandise from in the future?”
  • “How would you rate your call with our support team?”
  • “Did you get an answer to your question that made sense?”

 

When you combine customer-centric survey questions with good customer feedback research methods, you’ll get to the heart of the customer experience.  So, take a hard look at your customer satisfaction survey and make sure it’s actually relevant to your customers. Start listening to your customers now!

sentiment_full_800x500.pngSentiment analysis has become a household phrase in this “era of the consumer” that has ushered in an acute focus for organizations towards customer experience, as both a necessity in business success and a core differentiator.  Sentiment analysis in the context of customer experience refers to gaining an understanding of how your customers feel about your products, promotions, brands, or the interactions they have with your organization such as through the contact center.

 

Traditionally customer feelings have been measured through use of surveys, what Gartner refers to as Direct Voice of the Customer.  While asking for direct feedback is a critically important component of measuring customer sentiment, surveys do have several limitations, one being that they only collect feedback from the small percentage of customers that actually respond.  The small sample of respondents usually represents a dichotomy of customer groups – the very happy, or the unhappy.  The contact center or customer engagement center represents a huge repository of data, that if tapped, could give you a much broader view of your customers’ sentiment, with limitless ability to ask different questions of the data that you are already capturing every day.

 

This two-part blog will explore how to achieve sentiment analysis with the use of speech and text analytics, and how to implement a sentiment model within an engagement analytics platform such as CallMiner Eureka.

 

What is “Sentiment Analysis” and why should you care?

According to Oxford Dictionaries Sentiment Analysis is:

 

The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc., is positive, negative, or neutral.

 

Of course with the advent of speech analytics, sentiment analysis is not limited only to written text, but can also be extracted from spoken word. Contact centers record an estimated nine million hours of calls per day in the United States alone (extrapolated from Pelorus Associates Interaction Recording report).  According to The Northridge Group, 48% percent of consumers still prefer to use voice channel for their mode of engaging with organizations, more than double that of any other channel, but of course live support through text channels such as chat, email, and Facebook messenger are growing rapidly. Through all of these customer communications and the associated data collected with them, there is a treasure trove of opportunity to extract and analyze customer sentiment.

 

The best news is you don’t have to conduct surveys in order to get at this data. While surveys do allow your customers to provide you direct feedback about how they feel, there are several key limitations to relying on surveys to understand customer sentiment.

 

For starters, response rates are low.  According to the American Customer Satisfaction Index, response rates to customer surveys range between 5 and 15%, and as the volume of surveys are increasing (SurveyMonkey alone collects more than 3 million responses a day), response rates are continuing to decline (this article from survey vendor Medallia with suggestions on how to combat response issues).  This means that your analysis of customer attitude through surveys only represents a small portion of your customer base, and typically only the dissatisfied or extremely satisfied customers will take the time to respond.  This won’t give you a true measure of customer sentiment towards your brand, products, or promotions.  The short falls of sampling to collect feedback can be avoided by leveraging the already captured customer interactions to extract customer sentiment through analytics.  You already have all the “responses” in your interaction archives, in data that is just waiting to be mined.  Interaction analytics (aka customer engagement analytics) will give you a better understanding across your customer base for how they feel towards various elements within your organization.

 

Surveys are also limited largely to the questions asked within the survey.  Best practices suggest surveys should be a maximum of 10 questions according to Survey Gizmo (and find more in depth data from Survey Monkey).  To gather additional information, particularly if you need to dig into a particular reason for specific response, would require another survey, and the likelihood of getting the same respondents is slim to none. Surveys limit your ability to get context, or to be able to ask the “how” or “why” associated with the questions asked. Using interaction analytics to analyze your existing interactions allows you to return to the data over and over with additional questions.  You have the ability to filter, target and dig in deep in certain areas as desired. Such analysis can even greatly inform what questions you should be asking when you do conduct a survey.

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The last shortcoming of surveys is speed to intelligence, or lack thereof. In order to collect feedback, you have to determine the questions to ask, construct the survey, issue the survey, wait for responses, and then analyze the responses.  Yes – analytics requires some of the same “setup” in terms of configuring the queries or searches to help ask the questions of the data you desire, but once configured, “responses” or insight can be gathered in near real-time as your customer engagement center continues to have interactions with your client base.

 

This is not to say you shouldn’t be surveying and collecting that direct feedback, but if you are only relying on surveys to understand your customers’ sentiment, you’re missing a big opportunity and may be making decisions based on inaccurate data.

 

How Interaction Analytics measures sentiment (and why it’s better than traditional approaches)

In order to measure your customers’ sentiment towards various topics with Interaction Analytics, you need a sentiment model. Most sentiment models are black boxes, that look at your conversation and give you a rating.  But let’s breakdown the anatomy of one approach to measuring sentiment.

 

First off you need to look for your customers’ expressions of positive or negative sentiment.  You might also be able to look “neutral” sentiment or for simplicity, the absence of expressions of positive or negative sentiment is indicative of neutral sentiment.  There are all kinds of sources of expressions of sentiment online, but simply put, positive expressions of sentiment would include various positive adjectives such as good, wonderful, awesome. Similarly, expressions of negative sentiment would be defined by list of negative descriptors such as bad, terrible, or awful.

 

Within the presence of what we’ll call either “intensifiers” or “inverters”, such expressions of sentiment can have varying degrees of positive or negative sentiment, or they may even have the opposite meaning. To state something is “bad” is not as condemning as it is to state something is “very bad”.  And to state something is “not bad”, means the inverse, but likely doesn’t mean it is as good as saying it is “good”.  If you think of sentiment as a score ranging from -10 to 10 you can see how various combinations of expressions of sentiment can range from having very high negative sentiment, through to neutral, through to very high positive sentiment, depending on the language used.

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Interaction analytics is purpose built for looking for your customers’ use of certain language within their conversations or interactions with you. And some interaction analytics systems (such as CallMiner Eureka) support sophisticated scoring models to allow for the creation of indices as described in the previous paragraph.

 

Having a sentiment model is only the start of the process. The next step is to not just get sentiment “as a whole”, but rather to apply that sentiment model to different parts of the conversation.  For example – what is the overall or trend in sentiment of a specific segment of your customers.  How does it compare to other segments of your customer base?  More importantly, what is the “sentiment score” near references to specific products, product features, policies, or promotions? Powerful analytics solutions allow you to conduct this type of query or questions of your data by essentially stitching together building blocks of queries that identify references to products or features, and positive or negative sentiment.

 

To learn more about how you can leverage CallMiner Eureka in sentiment analysis, click here to reach out to a Customer Engagement Analytics specialist at CallMiner, and stay tuned for part 2 of this blog in our product user form.

 

For CallMiner customers and partners, find part 2 of this blog here -

Sentiment Analysis Part 2 - Building a Sentiment Score.

customer-experience-data.jpgIn today’s fast-paced, consumer-driven marketplace, customer communications channels abound.  Gone are the days of simply picking up the phone and contacting customer service.  Instead, customers communicate with companies through numerous channels, including social media, email, live chat, and more.

 

So what does this mean for companies looking to provide optimal customer experiences?

 

In order to develop a full and complete picture of the customer journey, as well as truly understand their customers, companies must find ways to leverage the wealth of customer data at their disposal.

 

Here’s a look at how companies can best leverage customer data.

 

[FULL BLOG POST]

 

This post originally appeared on CallMiner.

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Here come the Millennials, also known as Next Gen and the Baby-On-board Generation. Regardless what call you them they are nearly 98 million young adults born between 1980 and 2000 and by 2020 they will represent nearly 50% of the workforce.

 

Millennials are quite different then any generation before them.

They view the world differently and have redefined the meaning of success, both personally and professionally.

 

A growing trend with employers is modifying their traditional approach with how they are managing their workforce. Business leaders today are forced to get the most out of the millennials unique competencies and perspectives.  Call Center’s are incorporating innovated technology and modifying their cultures to encompass agent training programs and daily operations.

 

Millennials have practically grown up with technology. They are in fact the first generation to have grown up 100% with video games. They have always been able to open multiple tabs in an internet browser to conduct research and search for music while simultaneously playing Candy Crush. They are tech savvy multi-taskers because that is all they have ever known. This the very reason why Companies need to ensure their technological appetite is satiated in this new, professional environment.

 

By example, this new frontier of employee demands why most of the call centers’ agent onboarding and ongoing training curriculum needs to be modulated accordingly.

 

Here are some suggestions to be considered when training Millennials.

 

1. Let them touch it!

Millennials are very analytical and practical when it comes to learning and implementing their skill set. Agents learn more if they can get acquainted with what they are expected to do, interactive training with technology or peers is a great way to optimize the training session.

 

2. Make yourself sexy!

When trying to attract millennials to join your organization as a call center agent, you need to make it sexy, i.e innovative. Do you look like an Archie Bunker show of the 1970’s or Modern Family 2016? Let them know about the organization’s culture, how you are employee centric, communication flows in all direction, flexible working conditions, etc. Many organizations are increasingly using technology to deliver this information, which can be via video streaming or social media. Having fresh, innovative technology says a lot about where you are going. Do you want them to go with you?

 

3. Pump up the juice.

They are not only tech savvy; they are tight with their money as well. Often millennials are living on credit cards. Compensating them slightly above regional averages can provide organizations a recruitment edge with this generation.  Aside from this, finance department’s can work out creative ways to provide options to earn extra money, which can include performance based incentives, education allowance etc. which will really help in developing that hire into a valuable Call Center asset.

4. Promote innovation and diversity

Encourage leaders to show and live an appreciation for diversity in your organization. It starts at the top. This will help all generations avoid stereotyping that gets in the way of valuing the skill sets of each employee. This can be a major deciding factor for Millennials to choose your business over any other. They love innovation and experimenting. Millennials are often fun loving and want a less formal atmosphere.  Such activities can boost their inclination towards innovative learning and engagement at work. Which ultimately leads to a more effective agent.

 

So basically it has happened, Millennials are here and a lot more are coming, that is for sure. Noah built his arc before the rain came. Is your business ready? 

It seems that we’re asked to take a customer satisfaction survey with nearly every purchase. But do you ever wonder…do they really care what I have to say?

 

Our 2016 Customer Listening Study, the first of its kind, evaluated the customer satisfaction surveys of 51 top US retailers. The main finding: retailers like Lowe’s and Wal-Mart waste customers’ time—and their own—with critically flawed surveys. No company was completely scientific in its approach; nor did any company fully connect with customers in a thoughtful, compelling way.

 

Retailers issue millions of customer satisfaction surveys each day, raising the question of whether these surveys are worth the paper they're written on.

 

The top two problems: retailers collect inaccurate data, and they fail to show active customer listening. Based on an objective evaluation of 15 elements, the surveys scored 43 out of 100 points, an F grade.

 

We also found that:

  • With 23 questions on average, the surveys were excessively long.
  • 32% of all questions lead customers to give answers that companies want to hear.
  • 7-Eleven had the best survey—it was 13 questions, none of which were leading or used biased wording.
  • Family Dollar had the worst survey—it had 69 questions, 29 of which were leading.
  • Nordstrom, the retailer most known for customer service, stated its survey would take 2 minutes—but with 25 questions, it took 4-5 minutes.

 

This study highlights how easy it is to produce a flawed survey. The findings should be considered by any company with a customer listening program.

 

If retailers want to get more value from their customer satisfaction surveys, they should apply a scientific methodology, and be sure to connect with customers to show they’re listening.

 

The retailers selected for the 2016 Customer Listening Study were the National Retail Federation’s (NRF) top retailers, omitting supermarkets and membership stores. Surveys were collected from June 23 to July 27, 2016. Download the Study Report, or watch the short video.

 

Have a question about the Customer Listening Study, or want to learn about designing an intelligent customer satisfaction survey? Drop us a line.

 

About Interaction Metrics

Interaction Metrics is a customer experience agency that maximizes the value of experience planning, satisfaction surveys, mystery shopping, customer interviews, and customer service evaluations.  Only Interaction Metrics Findings Reports combine actionable customer experience metrics with specific recommendations for how to improve.