Contact Center Analytics

2018 Contact Center Trends: Punching Through the Barrier.

By Bright Pattern

Customer experience (CX) ran out of steam in 2017. Almost all companies have by now realized that CX is the differentiator and customers value the experience above almost everything. Enormous effort and resources have been thrown at CX, and there have been huge gains. But according to Forrester’s 2017 CX IndexTM, CX quality plateaued or declined for most industries and companies.

It’s plain to see why. CX was a classic land grab where companies found it easy to deal with obvious problems. But now the hard work begins. Customers are getting used to enhanced experiences and want better and better service. Companies will need to keep up with these expectations or fall farther behind. Forrester is predicting that in 2018, 30% of companies will see further declines in CX performance, which will mean declines in growth or worse.

So are we going to stay put or decline? Or are we going to punch through to the next level? 2018 will be the year where this is decided. So what will be the big stories? How will technology and automation advance the customer experience? Here’s what we think will be the trends in 2018.

 

Artificial Intelligence – It’s going to get real, very fast

In the years running up to 2018, AI has been the solution to almost any problem. And for good reason: chatbots, robotic process automation, and virtual assistants have transformed customer experience and expectations, and have changed the roles of customer service agents for the better. But now the rubber meets the road. The early gains were made by applying AI to existing business operations. The true growth moving forward will be to use AI to invent new ways to interact with the customer, reinvent business processes, and create whole new markets for products and services.

A Forrester survey tells us firms’ investment in AI rose 51% in 2017. But 55% of firms have not yet achieved any tangible business outcomes from AI, and 43% say it’s too soon to tell. That’s because AI is not a plug-and-play proposition. Unless firms plan, deploy, and govern it correctly, new AI tech will provide small benefits at best or, at worst, result in unexpected and undesired CX-related outcomes. If CIOs and chief data officers (CDOs) are serious about becoming insight driven, 2018 is the year they must realize that simplistic approaches will only scratch the surface of possibilities that new tech offers.

Take machine learning for example. Companies are quickly realizing that, ironically AI requires huge amounts of human input. Agents are tagging text and speech, customer interactions. Companies are using their customers to teach their AI, and sales reps are training the AI rather than relying on out-of-the-box learning. Add to this the data hygiene and knowledge management required to keep an automated system up to date, and you will see an enhanced adoption of the blended AI model for 2018 where humans play a critical role in constantly perfecting AI to improve the customer experience.

Bright Pattern is a leader in this blended AI trend and automating with a human touch. For instance, our APIs allow bots to integrate with IBM Watson, Reply.ai, and Alterra to provide human-like interactions that can be switched to a live agent at any time. The agents also have internal assistants and bots that use AI to guide them through the call, offer suggestions, track tone and sentiment using cognitive analysis technology and natural language understanding.

 

Digital Transformation Needs to Pick up Speed

There are now heightened expectations from the customer and companies need to rise to meet them. Digital transformation is the key to making this happen. But it’s not happening at a quick enough pace.

According to Forrester, up to 60% of executives feel they are lagging behind with their digital transformation initiatives. The trend for 2018 will be that digital transformation moves from just an IT or CIO issue to become the responsibility of the entire organization. Thinking will change, it will no longer be looked at as an investment that gets a return. Digital transformation will be seen as the one thing that will keep the company alive. In fact Forrester also has a sobering statistic for this: 20% of CEOs will fail to act: As a result, those firms will be acquired or begin to perish.

 

Moving to the Cloud Will Become Even Safer

Here’s some good news! The cloud is going to get even more business-friendly in 2018. We all know that moving to the cloud provides a way to avoid capital investment in volatile technology and focus on core competencies. And it enables companies like Bright Pattern to provide rapid innovation delivery, instant upgrades and provide integrations with other cloud systems.

Every tennant on the Bright Pattern Call Center solution enjoys the very latest, most advanced version of out software. This includes data, configuration, user management, and tenant individual functionality. Every company, department and user is on the same version and the latest patch level.

And, we offer the insurance of an on-premises option using exactly the same cloud software for call centers, ensuring an additional level of control. Moreover, switching to an onsite option or back to the cloud is as easy as downloading the export file of your account and uploading it into another system.

The cloud will continue to be a dominant force in the digital transformations of virtually all successful companies. With continued innovation from Bright Pattern, we do not see this trend losing steam in 2018.

Self Service is about to get personal

Personalization will be key for companies looking to keep up with customer expectations. The empowered customer is now king, but they do not want to have every option available to them at all times. Their time is precious and they want to have a self-service experience that is hyper-relevant to them.

Companies who know what product a customer has for instance, will be able to serve up a limited set of options and disregard the irrelevant. They will learn which channels a customer prefers and route them without having to ask. Organizations that take customer experience seriously through personalization will stand out from the noise and create loyal customers.

 

The Employee Experience Will Be Enhanced, Not Just the Customer Experience (EX=CX)

The customer service employee experience is changing rapidly, so companies need to find ways to ensure that their agents are well motivated and rewarded for taking on new responsibilities. As blended AI becomes more prevalent, the role of the agent or customer service representative will change. Forrester predicts more and more agents will quit because of work overload. An example of this trend would be tagging. A live chat agent can look through a chatbot transcript to see where the chatbot didn’t understand the customer. The agent can tag an intent to that particular phrase. This additional task adds to an already complex list of responsibilities, applications, and processes that today’s agent must own, use, and follow. Without the right tools companies put employee experience at risk.

Bright Pattern provides the most effective agent desktop in an all-in-one call center app, which offers a decluttered user interface that selects and displays the most relevant information based on context. Higher levels of employee and agent engagement are known to improve the customer experience.

 

Automation Spreads From the Back Office to the Front Office

The big news in automation for 2018 will be the migration of many tried and tested robotic processes from the back office to help out in the front office. Automation will enable agents to focus on helping customers and spend less time on navigating systems or post-contact wrap up. Additionally, automation at the desktop will improve quality by decreasing errors of manual data entry, reducing rework, and decreasing complaints. Reducing manual tasks allows for a better focus on listening to the customer, empathizing, and providing a frictionless experience. In 2018, we’ll see better collaboration between the front- and back office, and see the almost immediate ROI that robotic process automation has traditionally been known for.

Channel Proliferation is a Party That Won’t Stop

It’s not news that consumers like to interact in the channel of their choice. And that channel can change on a whim and by the second. A conversation started in a messaging platform can migrate to a call that can shift to an email and back to a message. But companies need to do a better job of offering a true omni channel experience. According to Dimension Data and their 2017 Global Customer Experience Benchmarking Report, only 8% of organizations say that they have all of their channels connected and, in fact, as many as 70% say that none or very few of their channels are connected.

And new channels are coming on stream all the time. Customers are communicating with brands using just emojis. Video chat is starting to be adopted. Screen Sharing, virtual assistants, in-app messaging will all continue there rise in 2018.

The big news here is that this explosion of customer expression will not be stopping any time in 2018. So how can a company keep up, let alone stay ahead?

The simple answer is to have a simplified multichannel setup for call center managers to enable a true omni channel communication style. In practice this means a conversation must be able to be continued when switching or changing channels. It means adding a messaging or content channel to an existing communication is a must. And finally the rich context of the conversation must be maintained at all times.

To do this in 2018, you must have the agent tools to simplify multi channel interaction handling. Bright Pattern has created a web-based agent desktop to make multichannel communication seamless. It keeps all the information needed in the visible portion of the desktop, it intelligently extracts the relevant elements of context to display eliminating switching, alt-tabbing, and scrolling through long pages, and it transparently rearranging the desktop when the the conversation changes from one channel to another.

Conclusion

2018 is going to be a big year of disruption for the contact center. The technology that’s coming online and the shifting attitudes of business leaders will lead to some huge developments. At Bright Pattern we are well aware and well prepared for what’s to come. Because just like you, the expectations of our customers will not stop growing.

Customer Journey KPIs Every Contact Center Should Track

 

The customer journey can be a difficult thing to map and understand. With so many touchpoints along the journey, the map isn’t predictable and linear, yet it’s still necessary to monitor and analyze. These Key Performance Indicators (KPIs) will help you gain insight from the customer journey and move on to improve it.

Customer Effort Score (CES)

Even if a customer prefers self-service to live agent support, they don’t necessarily want to put a ton of effort into solving their own issue. Self-service shouldn’t be a difficult-to-implement alternative to normal customer support. Instead, it should meet the needs of the type of customer who seeks out self-service via quick, easy-to-find answers and the ability to make changes sans agent assistance.

Customer Satisfaction (CSAT)

Some of the most important customer journey touchpoints will occur when the customer interacts with a support agent. CSAT is the measure of the customer’s satisfaction before, during and after they contact customer service. If CSAT scores are dropping, it may be time to look closely at agent productivity, ticket management and self-service options.

Net Promoter Score (NPS)

The NPS will tell you if your customers are going to recommend your products and services to others. You have to go deeper here, though – why will your customers recommend your products and services, or what it is that’s keeping them from doing so?

Customer Churn / Retention Rate

Customer support teams for subscription-based products and services have to pay special attention to retention rate. If you see a lot of customers leaving around renewal time, it’s necessary to figure out why you lost them. What part of the customer journey is causing customers to change their mind? There’s a snag somewhere.

Customer Success

Customer Success isn’t a single KPI, but instead a customized KPI program based on your specific business, customers and goals. A Customer Success strategy may include Up- and Cross-Sell Rates; Average Revenue per Customer; or Rate of Adoption, which starts with defining beginner, intermediate and advanced customers or users. You may also want to include Retention Rate, NPS and CES in your customer success KPIs. Think of Customer Success as an overarching customer journey strategy based on what success means for you.

Customer journey KPIs may be difficult to track, but they come with a big benefit – often, improving one will have a positive impact on another.

Don’t Make These Mistakes When Buying Speech Analytics Software

Speech analytics software is a major and important investment for the contact center. Your speech analytics software should help with compliance and customer service while getting you the highest ROI possible. Avoid these mistakes when searching for new speech analytics software.

  1. Assuming speech analytics will do everything for you.

Speech analytics software isn’t a set-it-and-forget-it solution, no matter how smart the technology may be. The software gathers data that you then have to review, make sense of and act on in order to improve your contact center. Only then is it truly powerful; otherwise, it’s simply a data collector.

  1. Not taking advantage of the software’s potential.

Speech analytics software has a number of standard benefits, like agent training and quality assurance. When you purchase modern software, though, you have access to a host of other features you may not even know are there. New speech analytics software may include escalation language and objective compliance, for example.

  1. Choosing software with poor recording quality.

To truly reap the benefits of speech analytics software, it has to be able to record clearly and transcribe accurately. If it can’t, you won’t get a dependable analytics report. Remember, you can’t improve audio quality after a call has been recorded.

  1. Purchasing software for executives who don’t listen to calls.

Software brands know how to dazzle customers to get more sales. However, if contact center management isn’t currently listening to and analyzing calls, this may not change even after pricey software is purchased. It’s better to get in the habit of analyzing calls so that you know the software will actually be used (and also so you’ll have a clearer view of your needs).

  1. Relying on software that doesn’t account for conversational language.

Your agents have to say certain things on each call, like “thank you” when signing off. Your speech analytics software has to detect that these keywords are mentioned in each conversation. However, if your software only detects exact words instead of conversational language, a version of “thank you” will go ignored, and the agent could get marked for not following procedure, even if they did.

In Conclusion

When buying software, identify your contact center needs, then find a solution that checks those boxes. Make sure you’re learning from your analytics, too, instead of just letting it auto-run in the background.

 

3 Contact Center Metrics Improved by Predictive Analytics

Predictive analytics predict future events by combining various techniques that analyze historical and current patterns. Predictive voice analytics can have a major positive affect on integral contact center metrics, including customer retention, follow-up call success and quality assurance.

Customer Retention

One of the customer service industry’s main goals is customer retention, and experts believe that it costs more to acquire a brand new customer than to keep an existing customer. Predictive voice analytics, which analyze the customer’s voice during customer-agent interactions, can determine if the customer is at high risk for ending their relationship with the company altogether. It can then inform the agent that they need to put more focus on retaining the customer. On the flip side, predictive voice analytics can also tell which agents aren’t doing enough to keep the customer coming back. This is more effective than random checking for quality assurance, which can take a long time to identify poor-performing agents.

Follow-up Call Success

Often, the first contact with a customer isn’t the one that has a positive outcome (i.e. a sale); it’s the follow-up call that proves to be more advantageous. However, it’s difficult to know which customers are a priority for follow-up contact. Instead of leaving it up to your agents to determine which customers are worth a follow-up call, predictive analytics can analyze past interactions and study voice features to determine if the customer’s tone and behavior predicts a favorable outcome during the next interaction (like making a payment or finalizing a sale). Predictive analytics can create a ranked list of customers, organized by their likelihood to say “yes.”

Quality Assurance

Predictive analytics are a richer way of assessing quality assurance than traditional methods. Routine QA testing often ignores customer patterns, and it is also unable to learn in real-time. Predictive analytics, however, can analyze all types of data, both structured and unstructured, to give a well-rounded view of agent behavior and how it impacts the customer. All customer-agent communication is assessed in-the-moment, allowing the contact center to get an accurate view of agent performance immediately instead of having to wait several weeks.

Contact centers can’t just gather metrics to assess their current performance and then call it a day. They must also use what they’ve learned from the past to create goals for the future. Predictive analytics can help shape those goals realistically.

 

 

5 Tips for Root Cause Analysis in the Contact Center

The best way to solve a problem is to dig deep and find out where it started in the first place. Often, what you see of a problem is a symptom, not the cause. Here are five steps you can take to improve your contact center’s root cause analysis.

  1. Consider acoustic issues.

Root-cause analysis should take acoustic factors into account. For example, if the call has long periods of silence, this could point to a problem with the system. If the contact center agent can’t access data quickly enough or if there are problems with IVR, a slow system may be the problem.

  1. Flag conversations that are abnormally long.

Speech analytics will let you sort through calls based on parameters like duration and repeated calls. You can also find calls where specific keywords are mentioned, like those that are normally associated with a complaint. This will let you know which calls need the most attention.

  1. Monitor data in real time.

Accessing real time data can help you spot and stop issues early. If a new sales or marketing strategy launches and then phone calls start coming in within an hour or two, you’ll know that there’s a problem with the launch that must be fixed. Real time data lets you identify trends as they emerge, giving you the opportunity to stop a problem in its tracks.

  1. Sort problems into categories.

As you start to uncover the main problems customers are having, you can segment them into categories, such as product defects, customer education and marketing communication. Then, you can meet with specific teams to come up with targeted strategies to solve the problems.

  1. Understand the context of the situation.

Relying on word count frequency isn’t enough – the terms and phrases that are being used have to be understood contextually, too. Knowing the context of a problem instead of just the hard data will allow you to pinpoint the situation that caused or contributed to it.

Knowing the average number of complaints your contact center receives on a weekly basis is just a start. You have to figure out the root cause of the complaints in order to effectively tackle them and prevent them in the future. Root cause analysis is a way to solve prominent issues instead of merely putting a Band Aid on them.

Learn from this Sample Customer Journey: Booking a Flight to Boarding the Plane

Today’s customer journey considers the beginning-to-end experience that the user follows to complete a task. Often, the journey involves numerous channels and devices that all must interact with the customer wherever, whenever and however they want.

Air travel can be exhausting, both physically and mentally, especially if the many plans that have to be in place don’t come together. Delayed or canceled flights, difficulty scheduling backup flights, lost luggage and missed connections are just the beginning of the travel headache. Done correctly, the customer journey of a person who’s traveling can be greatly eased with intuitive messaging and thoughtful touch points. Consider this modern customer journey for the traveler:

• Book your flight online well in advance to secure the best ticket price.

• Receive a push notification from the airline’s mobile app that allows you to check-in the night before your flight.

• Choose the way you’d like to receive your boarding pass (saving it to your phone, via email, etc.).

• At the airport, visit a kiosk to scan the boarding pass on your phone and then print your baggage ticket.

• Show security your digital boarding pass.

• Receive immediate flight status updates through your preferred contact method (text message, email, app push notification, etc.).

• While on the flight, go to the airline’s website on your phone, tablet or laptop to watch movies.

Traveling of the past was often rife with long lines to get to an agent at the airport, paper boarding passes that can get easily lost and difficulty keeping up with the latest flight changes. The reason the new, digitally-enhanced customer journey flows so well is because the airline (or booking service) the traveler uses offers online and mobile access; remembers personal information, allowing the company to send customized alerts to individual travelers; has multiple digital options for doing necessary travel tasks, then syncs those options (saving the boarding pass to your phone then scanning it at the luggage tag kiosk); and generally keeps travelers in-the-know regarding their trip. Once on the flight, the company is further able to keep the traveler happy and entertained by offering in-flight Internet service and other types of free entertainment.

This type of customer journey takes into account the cornerstones that customers need: consistent and proactive service, optimized features, collaborative options and seamless transitions.

How Real-Time Analytics Monitoring Improves the Contact Center

Real-time data helps businesses run smoothly. Being able to see the truth about how your company is performing moment-to-moment lets you understand the reality of your business. When you know where you’re losing as well as where you’re winning, it’s easier to appropriately adjust and monitor the contact center’s daily functions. When day-to-day functions are made more efficient, productivity can increase. Here are six benefits of real-time analytics monitoring

1. The quantity and quality of calls can be tracked.

Real-time monitoring allows the contact center to know exactly how many calls are being handled by agents, plus how many calls are currently in queue. The supervisor can see how many calls are being worked on and resolved by specific agents, and conversations can be listened in on to find out how they’re being handled. At any point, the contact center can track a call in real-time and then step in if the agent seems to be struggling. By closely monitoring calls, you can determine where a specific agent or a group of agents need more work.

2. Assess the changing value of a customer.

Customers will change their value to a company as they continue to purchase items or as they become dissatisfied with the products or level of service they receive. When a customer changes how frequently they purchase, real-time analytics can immediately update the customer’s status. The next time the customer contacts an agent, the agent will know how valuable the customer is. If the customer has been purchasing more frequently, they can be moved to VIP status. If they haven’t been purchasing as frequently as in the past, they may need an incentive in order to trust the company more.

3. Analyze waiting and idle time.

The amount of time a customer has to wait to have their problem resolved is a huge part of the customer experience. The longer the wait time, the more upset the customer may get, getting the customer-agent conversation off to a bad start. While an agent may need extra time to resolve a problem, the customer only cares about how long the process is taking. With real-time analytics, the wait and idle time for each agent can be assessed.

4. Quickly manage long queues.

At times, the contact center will be understaffed or inundated with calls, live chat requests, and emails. During these times, real-time monitoring can show you which agents are idle, allowing you to redirect calls that are currently in queue to those agents. Team members can quickly be reallocated in order to meet a surge in demand.

5. Find out how long it takes for agents to handle queries.

Contact center agents are tasked with resolving issues in the quickest way possible without lowering service quality. With real-time analytics, you can see how long agents are spending on each customer. You can then work with the agents who regularly take a long time with customers in order to lower their average resolution time.

6. Take advantage of cross-sell and up-sell opportunities.

When a customer has recently bought a product or service, the contact center agent has an opportunity to cross-sell or up-sell. Real-time data can track what the customer has recently purchased and then automatically populate other products or services that they may be interested in. The system may also prompt the agent to offer the customer a better version of the product they’re ready to purchase at a higher price point.

3 Speech Analytics Myths You Should Stop Believing

The number of misconceptions about speech analytics (SA) are surprising, especially considering that even people who currently use SA technology believe the myths. While the learning curve for SA is steep, understanding that the following notions are false will be a big help. When you know how SA does and does not work, you’ll be able to better utilize to its potential.

Myth #1: It’s Not Necessary to Listen to Actual Calls

It’s not possible to simply guess at the voice of the customer and you won’t be able to set useful SA settings if you don’t first listen to real recorded calls. The only way to figure out which business intelligence will be most useful to your company is to listen to a random selection of recordings from a specific queue. As you dissect recordings, you should do the following:

  • Map call types for specific objectives.
  • Listen to how things are said, not just what is said.
  • Create a ranking of keyword categories.
  • Identify issues that could cause trouble with the software (transfers, hold music, pre-recorded messages).
  • Interpret dialects, accents and colloquialisms.

While listening to numerous calls from beginning to end is tedious, it’s the best way to figure out which words and phrases are most meaningful when it comes to customer intent and outcomes.

Myth #2: Speech Analytics Are a Set-and-Forget Solution

SA make it easy to locate keywords and phrases, but that ability alone isn’t going to give you valuable insight. SA software has to be optimized in order to provide useful and actionable intelligence, taking into account things like the relationship between content and context or specialized language for certain industries. Furthermore, the data you glean from SA won’t be helpful if it isn’t accurate. In order to make sure that the software is at its optimal level of accuracy detection, the speech engine has to be calibrated, tested and tuned until perfect. To do this, search results have to be audited several times over until the speech engine is in harmony with your business objectives. The setup and interpretation phases may be time-consuming and even challenging, but they’re necessary.

Myth #3: Anybody Can Manage Speech Analytics

SA may run automatically, but the software has to be managed on a regular basis, too. There are a range of tasks that must be completed in order to keep SA performing well. While you may have a staff member who’s qualified to take on this role, it’s important to know what’s expected of the person in charge:

  • Software setup
  • Server and database management
  • Compilation, analysis and delivery of key findings
  • Content review

One or more people can take on this role. Since the various steps call for different skill sets, some companies opt to split up the role into three parts

  1. Administrator for setup and management.
  2. Business Analyst for choosing and delivering key findings.
  3. Interactions Monitoring Analyst to review content.

Whoever is in charge of SA has to analyze and communicate the findings, explaining why and how changes should be made.

In order to get the most out of SA, it’s important to control your expectations. SA software doesn’t rely on omniscience. Instead, it’s a smart, calibrated filter that shows you the parts of calls that relate to your specific business issues. SA software provides the tools you need to uncover actionable business intelligence. Without it, it would be nearly impossible to find these subtle points of a conversation. With it, you can apply sophisticated strategies to work toward your business objective.

The Customer Journey from Beginning to End

Just as contact centers evaluate analytics, customers evaluate the experience support agents deliver. Most importantly, purchase decisions are often made off of those evaluations.

Assuming your customers are happy isn’t enough. You need to know the realities of the experience you’re providing. Often, customers have a different experience with a product or service than the brand expects.

Understanding the beginning-to-end customer journey provides the opportunity to develop a meaningful and intuitive customer journey map.

The True Beginning of the Customer Journey

Figure out exactly where the customer journey begins. For example, an airline may assume that the majority of the customer journey is the same as the physical journey, i.e. when they’re in the air. However, customers know that the journey starts much earlier and ends much later than the flight itself. The customer journey for travelers begins when they’re thinking about where they should go. During the brainstorming and planning stages, they search for information about destinations and travel deals. Airlines, in turn, can provide this information on their blog and in their newsletters. While data may not be able to capture the exact moment when a potential customer is thinking about travel, the airline can make sure that helpful information is there when a person goes looking for it.

Discovering Your Most Important Customers

Who is your brand most appealing to? What do they need in order to meet their immediate goals and then to be satisfied and impressed beyond that? For example, if an airline’s main customer base is made up of business travelers, it’s a given that they need WiFi access and a business lounge. While business travelers may be on a work trip, though, they’re not robots who eat, sleep and breathe their job. Treating business travelers to relaxation (massage chairs in the lounge) and niceties (gourmet in-flight meals) that they may not get once they arrive at their destination makes for an enjoyable and memorable trip.

Customer personas specify the customers that need your focus. Job title alone won’t help with categorization, though. Instead, consider their role as it relates to your brand. For example, if you sell Software-as-a-Service (SaaS), you’ll have several types of customers for each transaction:

  • The marketing manager, who understands why your product is so important.
  • The person in charge of the budget, who makes the ultimate decision about whether or not to spend the money on your product.
  • The head of the IT department, who knows how to use the software.
  • The person who’s going to sign the contract and finalize the purchase.

While each of these people are your customers, they need to be handled differently.

Outlining Customer Stages

Depending on the complexity of your brand, you may have as few as two customer stages or more than twenty. For most brands, the customer journey map follows a basic formula: research, compare, choose and purchase. These four simple stages can be built upon as the map is fine-tuned and expounded.

While the full spectrum of the stages should add up to one complete journey, each customer will not fit neatly into every stage. Some personas will skip entire stages, starting later or ending earlier along the journey than a different type of customer. For example, the marketing manager may be the only person to research SaaS, while the person in charge of the budget may come into the journey toward the end.

Setting Goals Throughout the Customer Journey

In order to create an empathetic customer journey map, brands have to understand what the customer wants to do. For example, when travelers fly, getting to their destination is just one of their goals. They want to get to their destination without changing flights multiple times, without killing time during a long layover and without losing luggage. Beyond that, they would probably also love to travel without being hungry, thirsty or bored. Certain goals are functional (getting to where they’re going as quickly as possible), while other goals are emotional (enjoying the flight instead of wishing for it to end).

Each customer persona and step of the journey has its own goals. Some will overlap and others will differ entirely. The focus should be on meeting the needs of the customer while staying on course to meet the overall goal of the brand. Each goal can be broken down into two aspects:

  1. What does the customer want to accomplish in this stage?
  2. What will help move the customer along to the next stage?

Here are general goals that most customers and brands have during the four main phases of the customer journey:

Research: The customer’s goal is to find interesting and up-to-date solutions that will meet their needs. The brand’s goal is to showcase their thought leadership.

Compare: The customer’s goal is to compare the solutions they find during the research phase in order to narrow the list down to a few front runners. The brand’s goal is to stand out from competitors.

Choose: The customer’s goal is to figure out the brand they most want to work with. The brand’s goal is to be chosen by the customer.

Purchase: The customer’s goal is to make the final decision and select the brand they’ve chosen to work with. The brand’s goal is to have the invoice paid, the contract signed or whatever it takes to close the deal, and to make it easy for the customer to take this step.

Setting Up Touch Points

Today’s customers are setting the trends when it comes to how they’re shopping. From mobile apps to “Buy Now” buttons on social media, it’s important to know the new ways for your company to connect with customers during each stage.

Once you’ve determined the personas, stages and goals to add to the customer journey map, specify the tools that should be available along the way. Here are common touch points that are used during the four main phases of the customer journey:

Research

  • Blog posts
  • Case studies
  • Conferences
  • Newsletters
  • Seminars
  • Webinars

Compare

  • Demos
  • Reviews
  • General pricing sheets
  • Product descriptions
  • Testimonials

Choose

  • Email support
  • Live chat
  • Meetings
  • Phone calls

Purchase

  • Contract explanation
  • Detailed pricing sheet
  • Guide with implementation steps
  • Supporting documentation

Analyze Important Data

Making decisions about the customer journey map shouldn’t be based on hunches and guesswork. Analytics can help you identify stages and touch points you may not have noticed yet as well as show you if what you’ve been doing is working. You’ll also determine how long it takes for customers to pass through each stage of the journey. For example, if the customer is spending more time than expected in the research phase, is this because they can’t find the information they need? Is there additional content you can offer or a different way to deliver that content?

When further developing the customer journey map, figure out the data that’s most important for the customer experience. Long-term goals are more important than short-term successes. Creating a good relationship with customers will pay off down the road. Forcing a sale may bring more cash flow at the moment, but could be a turn-off to customers and prevent them from returning.

The Benefits of Speech Analytics in the Contact Center

Analytics are used to leverage contact center data in order to resolve problems and improve business processes. Specifically, speech analytics refer to software that can go through millions of hours of recorded text (like e-mails and web chats) and audio to analyze and gather information.

Speech Analytics to Improve Quality Assurance

It’s impossible to evaluate every single agent interaction and it can even be difficult to evaluate a significant sample of conversations. However, since the main goal of analysis is to improve overall performance, taking random samples of customer-agent interaction would be a waste of time. Instead, purposeful targeting allows for only specific, valuable interactions to be assigned to evaluators.

Creating Buckets to Segment Conversations

Creating pre-defined categories, sometimes called buckets, allows contact centers to choose the telltale phrases that are most likely to identify a high-value conversation. One bucket may be for repeat calls and another may be for customer complaints. A bucket may contain just one word, like “ridiculous,” or a series of words and phrases. According to KPMG, in the insurance field, when a customer uses the word “ridiculous” on a customer service call, they are 80% more likely to switch insurers within 90 days, regardless of whether or not the customer service agent is sympathetic. Additional types of buckets may include:

• Repeat calls with the customer indicating they’ve called before. Possible phrases include “called earlier,” “spoke to someone yesterday,” “had to call back” and “called twice already.”

• Repeat calls with the customer indicating their issue is unresolved. Possible phrases include “keeps happening,” “hasn’t resolved it,” “having the same issue” and “never heard back.”

• Calls with complaining or dissatisfied customers. Possible phrases include “frustrating,” “annoying” and “you people.”

• Calls with agents who are not being helpful to the customer. Possible phrases include “that’s our policy” and “can’t give that information out.”

• Calls with happy customers who show their appreciation. Possible phrases include “you’ve been so helpful” and “thank you so much.”

Speech Analytics vs. Post-Call Surveys

Contact centers that have invested in advanced speech analytics software find that the process of identifying and measuring repeat calls is now quicker and more accurate than before. Post-calls surveys have long been a method of analyzing customer-agent conversations, but they’re known to have low participation rates. With speech analytics tools, contact centers can now be proactive when it comes to exploring problems and commonalties across conversations.