As we begin this new year, we want to share some great news.
Today we’re excited to announce that Wootric is joining Pearl-Plaza, a market leader in customer and employee experience. Pearl-Plaza serves many of the largest, most sophisticated global organizations from Starbucks to Ford to VMWare.
This next step in our evolution means great things for our customers and other businesses seeking a modern approach to CX improvement.
We will continue to deliver the world-class product experience you expect from us. In addition, our pace of innovation and our ability to support our customers around the globe will accelerate as we leverage the considerable resources and expertise of Pearl-Plaza.
Our customers will also be able to tap into Pearl-Plaza’s expertise and enterprise solutions as their CX needs evolve beyond our turnkey approach.
Seven years ago, we founded Wootric with a mission to empower customer-centricity in every organization through modern, always-on CX improvement. We launched with a high-response in-app microsurvey and quickly disrupted a dated approach to gathering and responding to Net Promoter Score feedback.
Guided by input from our customers, we invested in omni-channel feedback collection, AI-driven customer journey analytics, and native integrations with the modern tech stack — all the while staying true to the flexible, lightweight, user-centric approach to CX improvement that businesses expect from Wootric.
Wootric now delivers the fastest ROI in the Experience Management category on G2. Over 1200 businesses worldwide, including DocuSign, Zoom, and Comcast, use Wootric software to improve customer lifetime value with insights and action from voice of the customer data.
We thank our customers, partners, investors, advisors, and above all members of our exceptional team for their support, and for choosing to be on the CX journey with us.
Together, with Pearl-Plaza, we will make 2021 an amazing year for customer experience!
With gratitude,
Deepa Subramanian, CEO & Jessica Pfeifer, Chief Customer Officer
The founders would also like to extend a special thank you to Steve Gurney at Viant Group for representing Wootric through this process.
Customer comments are the lifeblood of any CX program, giving you the “why” behind customers’ NPS, CES, and CSAT scores. But until recently, it’s been nearly impossible to make sense of feedback from hundreds of customers at a time. Using artificial intelligence (AI) to automate text analysis gives you the consistent and fast insights you need, at scale.
That said, automated text analysis isn’t just about technology. Humans need to put in the time upfront to teach the machine, by providing an accurately tagged set of feedback for AI to work from. The quality of that training data sets up the quality of your text analytics results, or as the old saying goes “garbage in, garbage out”.
Let’s look at what you need to be successful with automated text analytics. We’ll dig into the basics of text analytics, the inconsistencies of manual tagging, and how to create good training data and models.
A quick primer on AI training sets
Analyzing customer feedback from unstructured text can be complex. In one sentence a customer may talk about a variety of topics, offering negative, neutral, or positive feedback (sentiment) about each of them. It’s the job of machine learning to recognize what the customer is talking about and identify how they are feeling about those topics. With text analytics, it only takes an instant to:
Tag comments / categorize themes
Assign sentiment to each of the tags and the comment overall
Aggregate results to find insights
Text analyzed for sentiment and themes
Again, machine learning is only as good and accurate as the data set you (the humans) provide to train the algorithm. So you need to do it right.
At Wootric, we have a lot of experience helping teams create training datasets. While we have sets of tags that are specific to various industry verticals, we also build custom machine learning models for many customers. Custom models are helpful for companies that are in a new vertical or a unique business.
Training model process
For the most part, companies have a good feel for what their users/customers are talking about and the topic tags they need. If they’re not sure, we can analyze their data and work with them to help them think through a set of tags to get them started. Once they have a set of agreed tags, they start creating the training data.
The process for creating this training dataset goes something like this.
Decide what tags are important to your business
Create definitions for those tags so everyone knows exactly what the tags mean
Pull 100-200 customer comments
Our customer assembles a team of at least 3-5 people who independently review each comment and determine:
If the comment sentiment is overall positive, neutral, or negative
Which tags apply to that comment
That last point is where things get interesting for a data analyst like me.
Manual tagging: an inconsistent truth
Many companies still believe having human tagging and analysis is superior to AI. We’ve seen one employee hired full-time to pour over spreadsheets, organizing data and pulling insights, which takes A LOT of time. Other companies bring in a team of people (the interns!), which introduces inconsistencies. Not only is it expensive and time-intensive, manual tagging isn’t necessarily accurate.
These same inconsistencies appear when creating training datasets because the process starts with manual tagging. Customer teams creating training data are always surprised by the level of disagreements on “defined” tags. It can take a few rounds of work to iron these out.
Tag definitions vary
Not all tags carry the same level of complexity. Some tags make it easy for people to agree upon a definition, while others may be more ill-defined. Vague tags tend to invite more disagreement between human labelers who label the same dataset independently.
Let’s look at a couple of examples from the software industry:
“MOBILE” — applied to any feedback containing references to a mobile app or website functionality. This should be straightforward for a group of human labelers to apply similarly, and would most likely only result in a few disagreements between them.
“USER EXPERIENCE” — a more complex phrase with many different definitions of what could be included in a user’s experience. When a comment mentions search functionality, is that UX? How about when they say something like “While using the search bar, I found information on…“? Or even “Great product”? Because there is so little clarity on what fits in this category, the training team will surely disagree, leading to more rounds of tagging and defining.
The good news is that at the end of the process, after a few rounds of defining the tags and applying them, the team REALLY knows what is meant by a given tag. The definition is sharper and less open to interpretation. This makes the machine learning categorization more meaningful, and more actionable for your company, which leads to an improved customer experience.
Getting to a good training model
Let’s look at a real-world example of creating a training set, and the level of label agreement between the people creating those labels.
A recent enterprise customer used 8 human labelers on the same initial set of 100 comments, and we then evaluated the labeler-agreement of each tag. During this exercise, each labeler worked independently , and we charted the agreement scores.
In the following two charts, the number in each cell represents the strength of the agreement from 0 – 1 between two labelers (F1-Score calculated from Precision and Recall values).
1.0 would be the ideal labeler agreement value.
0 means no agreement.
The lower row of the chart contains the average agreement across all cells for that labeler.
In Figure 1, it’s clear that the tag is fairly well-defined, which results in an overall average labeler agreement of ~0.83. This is on the higher end of what we typically see.
Figure 1 – agreement on a well-defined tag
In other words, even a well-defined tag doesn’t garner complete agreement between labelers. Labeler 5, our most effective labeler, only scored a 0.85 average. Labeler 1 and 8, with an F-1 score of .75, didn’t apply the tag, in the same way, a significant portion of the time. But it’s still considered successful.
Now, look at Figure 2, which shows the first-round results of the team’s effort to consistently apply a more complex tag. It resulted in an overall average labeler agreement of only ~0.43.
Figure 2 – disagreement on a vague tag
For the same group of labelers tagging at the same time, two different tags demonstrate nearly 2x difference in overall agreement — showing again that even we humans aren’t as good at manually categorizing comments as we would like to think.
Even when teams agree on 1) what tags to use and 2) the definition of each tag, they don’t necessarily wind up applying tags in the same way. It takes a few rounds for teams to come close enough to consensus to be useful for machine learning.
Ready for autocategorization
Text analytics is not a perfect science. When are the label agreement results ready for prime time? Typically, we consider a model good enough to deploy once the F-1 score is around 0.6 (give or take a bit based on other factors). Like most things in life, when you invest more time upfront — in this case boosting F-1s with additional rounds of tagging/defining — you typically end up with better results.
Even if it’s just a quick trip to the grocery store, customers seek something more profound from brands than a mere product: meaningful customer experiences.
Conventional wisdom holds that customers shop the brands whose products and services best match their needs. But there’s more to the story than that. Even if it’s just a quick trip to the grocery store, customers seek something more profound from brands than a mere product: meaningful customer experiences.
There’s a lot for organizations to gain by orienting themselves around customers’ search for meaning. Experience programs can help them get there.
We’re going to go over exactly how companies can achieve that reorientation, create meaningful experiences for customers, and, ultimately, ride that heightened connectivity to the top of their respective verticals.
Right Audience, Right Problem
We touched on this in our last conversation about the importance of carefully designing your program before deploying it, but it’s worth saying again:
Some audiences are more worth brands’ time than others.
Sounds harsh, but let me explain. Some audiences offer context and solutions to problems that other groups may not even be aware of. Therefore, one of the first things brands should do to create meaning for their customers is consider the problems that can be solved by focusing on specific audiences.
This approach is vital is because it allows brands to hone in on customers’ “moment of truth.” This is the moment in which a customer finds significance in their interaction with a brand, not just a product or service.
What is preventing customers from finding their moment of truth? The answer to this question will dictate what you should design your listening program around.
Furthermore, that search will allow your company to create fundamental human relationships with customers. And those relationships will create positive buzz, build lifetime loyalty, and result in a much stronger bottom line.
Sharing the Love
Thinking how certain audiences can help solve business challenges is important, but it’s not the only step brands must take. Once a company’s experience team finds moments of truth, they absolutely must share the news across the organization! This sharing process is often called data democratization.
I really can’t say enough how important it is to share customers’ moments of truth. First, socializing that data across the organization gives every employee a glimpse of how their role affects the customer.
Second, sharing this intel makes it easier for brands to identify moments that matter out of mountains of experience program data. Ultimately, brands that intentionally democratize data from the beginning get so much more from their listening than companies who fail to design their strategy.
Listening Empathetically
The final key to creating meaningful customer experiences is on that is often overlooked: empathy. Empathy is the key to understanding moments of truth and, ultimately, business success.
Catering to customers’ search for meaning is neither a program luxury nor a saying you put on a wall sign. It’s a strategy that builds transformational brand success and the meaningful, emotional relationships that can sustain it indefinitely.
I go into greater depth about the importance of designing your experience program before listening in my article on the subject, which you can read here. Thank you!
Healthcare databases are growing exponentially. Today, healthcare providers, drug makers and others are turning this data into value by using text analytics and natural language processing to mine unstructured healthcare data and then doing something with the results. Here are some examples.
This article explores some new and emerging applications of text analytics and natural language processing (NLP) in healthcare. Each application demonstrates how HCPs and others use natural language processing to mine unstructured text-based healthcare data and then do something with the results.
Healthcare databases are growing exponentially, and text analytics and natural language processing (NLP) systems turn this data into value. Healthcare providers, pharmaceutical companies and biotechnology firms all use text analytics and NLP to improve patient outcomes, streamline operations and manage regulatory compliance.
In order, we’ll talk about:
Sources of healthcare data and how much is out there
Improving customer care while reducing Medical Information Department costs
Hearing how people really talk about and experience ADHD
Facilitating value-based care models by demonstrating real-world outcomes
Guiding communications between pharmaceutical companies and patients
Even more applications of text analytics and natural language processing in healthcare
Some more things to think about, including major ethical concerns
NLP in the Healthcare Industry: Sources of Data for Text Mining
Patient health records, order entries, and physician notes aren’t the only sources of data in healthcare. In fact, 26 million people have already added their genetic information to commercial databases through take-home kits. And wearable devices have opened new floodgates of consumer health data. All told, Emerj lists 7 healthcare data sources that, especially when taken together, form a veritable goldmine of healthcare data:
1. The Internet of Things (IoT) think FitBit data)
2. Electronic Medical Records (EMR)/Electronic Health Records (EHR) (classic)
3. Insurance Providers (claims from private and government payers)
4. Other Clinical Data (including computerized physician order entries, physician notes, medical imaging records, and more)
5. Opt-In Genome and Research Registries
6. Social Media (tweets, Facebook comments, message boards, etc.)
7. Web Knowledge (emergency care data, news feeds, and medical journals)
Just how much health data is there from these sources? More than 2,314 exabytes by 2020, says BIS Research. For reference, just 1 exabyte is 10^9 gigabytes. Or, written out, 1EB=1,000,000,000GB. That’s a lot of GB.
But adding to the ocean of healthcare data doesn’t do much if you’re not actually using it. And many experts agree that utilization of this data is… underwhelming. So let’s talk about text analytics and NLP in the health industry, particularly focusing on new and emerging applications of the technology.
Improving Customer Care While Reducing Medical Information Department Costs
Every physician knows how annoying it can be to get a drug-maker to give them a straight, clear answer. Many patients know it, too. For the rest of us, here’s how it works:
You (a physician, patient or media person) call into a biotechnology or pharmaceutical company’s Medical Information Department (MID)
Your call is routed to the MID contact center
MID operators reference all available documentation to provide an answer, or punt your question to a full clinician
Simple in theory, sure. Unfortunately, the pharma/biotech business is complicated. Biogen, for example, develops therapies for people living with serious neurological and neurodegenerative diseases. When you call into their MID to ask a question, Biogen’s operators are there to answer your inquiry. Naturally, you expect a quick, clear answer. At Biogen Japan, any call that lasts more than 1 minute is automatically escalated to an expensive second-line medical directors. Before, Biogen struggled with a high number of calls being escalated because their MID agents spent too long parsing through FAQs, product information brochures, and other resources.
Today, Biogen uses text analytics (and some other technologies) to answer these questions more quickly, thereby improving customer care while reducing their MID operating costs. When you call into their MID, operators use a Lexalytics-built search application that combines natural language processing and machine learning to immediately suggest best-fit answers and related resources to people’s inquiries. MID operators can type in keywords or exact questions and get what they need in seconds. (The system looks like this illustration.) Early testing already shows faster answers and fewer calls sent to medical directors, and the application also helps new hires work at the level of experienced operators, further reducing costs.
Hearing How People Really Talk About and Experience ADHD
The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely.
A Lexalytics (an pearl-plaza.rupany) data scientist used our text analytics and natural language processing to analyze data from Reddit, multiple ADHD blogs, news websites, and scientific papers sourced from the PubMed and HubMed databases. Based on the output, they modeled the conversations to show how people talk about ADHD in their own words.
The results showed stark differences in how people talk about ADHD in research papers, on the news, in Reddit comments and on ADHD blogs. Although our analysis was fairly basic, our methods show how using text analytics in this way can help healthcare organizations connect with their patients and develop personalized treatment plans.
Facilitating Value-Based Care Models by Demonstrating Real-World Outcomes
Our analysis of conversations surrounding ADHD is just one example in the large field of text analytics in healthcare. Everyone involved in the healthcare value chain, including HCPs, drug manufacturers, and insurance companies are using text analytics as part of the drive towards value-based care models.
Within the value-based care model, and outcome-based care in general, providers and payers all want to demonstrate that their patients are experiencing positive outcomes after they leave the clinical setting. To do this, more and more stakeholders are using text analytics systems to analyze social media posts, patient comments, and other sources of unstructured patient feedback. These insights help HCPs and others identify positive outcomes to highlight and negative outcomes to follow-up with.
Some HCPs even use text analytics to compare what patients say to their doctors, versus what they say to their friends, to identify how they can improve patient-clinician communication. In fact, the larger trend here almost exactly follows the push in more retail-focused industries towards data-driven Voice of Customer: using technology to understand how people talk about and experience products and services, in their own words.
Guiding Communications Between Pharmaceutical Companies and Patients
Pharmaceutical marketing teams face countless challenges. These include growing market share, demonstrating product value, increasing patient adherence and improving buy-in from healthcare professionals. Lexalytics customer AlternativesPharma helped those professionals by providing useful market insights and effective recommendations.
Before, companies like AlternativesPharma relied on basic customer surveys and some other quantitative data sources to create their recommendations. Using our text analytics and natural language processing, however, AlternativesPharma was able to categorize large quantities of qualitative, unstructured patient comments into “thematic maps.” The output of their analyses led to research publications at the 2015 Nephrology Professional Congress and in the Journal Néphrologie et Thérapeutiques.
Further, AlternativesPharma helped customers verify assumptions made by Key Opinion Leaders (KOLs) regarding the psychology of patients with schizophrenia. This theory was then documented in collateral and widely communicated to physicians. (Full case study)
More Applications of Text Analytics and Natural Language Processing in Healthcare
The above applications of text analytics in healthcare are just the tip of the iceberg. McKinsey has identified several more applications of NLP in healthcare, under the umbrellas of “Administrative cost reduction” and “Medical value creation”. Their detailed infographic is a good explainer. Click the image (or this link) to read the full infographic on McKinsey’s website.
Meanwhile, this 2018 paper in The University of Western Ontario Medical Journal titled “The promise of natural language processing in healthcare” dives into how and where NLP is improving healthcare. The authors, Rohin Attrey and Alexander Levitt, divide healthcare NLP applications into four categories. These cover NLP for:
Patients – including teletriage services, where NLP-powered chatbots could free up nurses and physicians
Physicians – where a computerized clinical decision support system using NLP has already demonstrated value in alerting clinicians to consider Kawasaki disease in emergency presentations
Researchers – where NLP helps enable, empower and accelerate qualitative studies across a number of vectors
Healthcare Management – where patient experience management is brought into the 21st-century by NLP used on qualitative data sources
Next, researchers from Sant Baba Bhag Singh University (former link) explored how healthcare groups can use sentiment analysis. The authors concluded that using sentiment analysis to examine social media data is an effective way for HCPs to improve treatments and patient services by understanding how patients talk about their Type-1 and Type-2 Diabetes treatments, drugs, and diet practices.
Finally, market research firm Emerj has written up a number of NLP applications for hospitals and other HCPs, including systems from IQVIA, 3M, Amazon and Nuance Communications. These applications include improving compliance with industry standards and regulations; accelerating and improving medical coding processes; building clinical study cohorts; and speech recognition and speech-to-text for doctors and healthcare providers.
Some More Things to Consider: Data Ethics, AI Fails, and Algorithmic Bias
If you’re thinking about building or buying any data analytics system for use in a healthcare or biopharma environment, here are some more things you should be aware of and take into account. All of these are especially relevant for text analytics in healthcare.
First: According to a study from the University of California Berkeley, advances in artificial intelligence (AI) have rendered the privacy standards set by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) obsolete. We investigated and found some alarming data privacy and ethics concerns surrounding AI in healthcare.
Second: Companies with regulatory compliance burdens are flocking to AI for time savings and cost reductions. But costly failures of large-scale AI systems are also making companies more wary of investing millions into big projects with vague promises of future returns. How can AI deliver real value in the regulatory compliance space? We wrote a white paper on this very subject.
Third: The “moonshot” attitude of big tech companies comes with huge risk for the customer. And no AI project tells the story of large-scale AI failure quite like Watson for Oncology. In 2013, IBM partnered with The University of Texas MD Anderson Cancer Center to develop a new “Oncology Expert Advisor” system. The goal? Nothing less than to cure cancer. The result? “This product is a piece of sh–.”
Fourth: “Bias in AI” refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. Algorithmic bias in healthcare AI systems manifests when data scientists building machine learning models for healthcare-related use cases train their algorithms on biased data from the start. Societal biases manifest when the output or usage of an AI-based healthcare system reinforces societal biases and discriminatory practices.
Improve Your Understanding: What Are Text Analytics and Natural Language Processing?
In order to put any tool to good use, you need to have some basic understanding of what it is and how it works. This is equally true of text analytics and natural language processing. So, what are they?
Text analytics and natural language processing are technologies for transforming unstructured data (i.e. free text) into structured data and insights (i.e. dashboards, spreadsheets and databases). Text analytics refers to breaking apart text documents into their component parts. Natural language processing then analyzes those parts to understand the entities, topics, opinions, and intentions within.
Lexalytics’ text analytics and NLP technology stack, showing the layers of processing each text document goes through to be transformed into structured data.
Experience programs can revolve around so much more than scoreboard-watching and reacting to challenges only as they arise—we’re going to go over how much more these programs can be and why brands should adjust their ambitions accordingly.
For a lot of companies, the phrase “experience programs” brings careful management and lots of metrics to mind. Both of those things are important components of any experience effort, but they can’t bring about meaningful change and improvement. Experience programs can revolve around so much more than scoreboard-watching and reacting to challenges only as they arise—we’re going to go over how much more these programs can be and why brands should adjust their ambitions accordingly.
Movement Over Metrics
Conventional wisdom holds that if an experience program is returning great measurements, that must mean it’s really working for a brand. However, this isn’t necessarily true. Metrics are effective for highlighting a brand’s high points and weak spots, but that’s about it. A true experience program’s job doesn’t end with better metrics—that’s actually where the work begins.
Companies can create a fundamentally better experience for their customers (and thus a stronger bottom line for themselves) by taking action on their program’s findings. This means sharing intelligence throughout an organization rather than leaving it siloed, as well as encouraging all stakeholders to own their part of the process. In short, taking action is what makes the difference between being really good at watching scores roll in and actually fixing problems that might be muddying up the customer journey.
Narratives Over Numbers
The phrase “program findings” from the preceding paragraph can also mean more than just numbers. It can also denote customer stories, employee reports, and other, more abstract forms of feedback. Many experience programs pick this information up as a matter of course, but it can be difficult to take action on that intel without a concrete action plan.
One reason why many companies encounter this difficulty is because their programs don’t acknowledge a simple truth: some customer segments are worth more to listen to than others. It doesn’t make much sense to try to listen to every segment for feedback on a loyalty program that only long-term customers use or know about. This is why it’s important for brands to consider which audiences they want to gather feedback from before even turning any listening posts on.
Once brands have matched the audiences they want to listen to to the goals they want to achieve, that’s when they can turn their ears on and start gathering that feedback. Companies that take this approach will find feedback significantly more relevant (and helpful) than intelligence gathered through a more catchall approach. They can then perform a key driver analysis on those customers and put their feedback against a backdrop of operational and financial data for further context, which goes a long way toward the goal of all of this: meaningful improvement.
Experience Improvement Over Experience Management
Experience improvement is not a goal that can be reached just by reading metrics. It demands more than turning listening posts on and hoping that a good piece of customer intel comes down the wire. Rather, experience improvement demands action. Much like water molecules, the forces that drive customer expectations, acquisition, churn, and other factors are in constant motion, and thus demand constant action to stay on top of it all.
Desiloing intelligence, motivating stakeholders, and expanding program awareness to customer stories instead of just higher scores and stats is what makes the difference between an industry-leading experience and everyone else’s. These actions create better experiences for customers, compel employees to become more invested in providing those experiences, and creates a marketplace-changing impact for the brand.
Click here to learn more about how to take your program from simple metric-watching to meaningful improvement for all.
While the impact of artificial intelligence (AI) is a bit of a mixed bag in a number of industries, we’re seeing some exciting traction in financial services. In this month’s article, I take a look at some specific examples of where machine learning and AI are helping financial services organizations improve their services, products, and processes.
AI Helps Financial Services Reduce Non-Disclosure Risk
Financial firms and banks are taking advantage of AI to ensure that their employees are meeting complex disclosure requirements.
Generally, financial advisors must make sure that their “client advice” documents include proper disclosures to demonstrate that they’re working in their client’s best interests. These disclosures may cover conflicts of interest, commission structure, cost of credit, own-product recommendations and more. For example, advisors must clearly disclose the fact that they’re encouraging a client to purchase a position in a company that the firm represents (a potential conflict of interest).
To ensure compliance, firm auditors randomly sample these documents and spot-check them by keyword or phrase searches. But this process is clunky and unreliable, and the cost of failure is high: Some estimates put the price of non-compliance as high as $39.22 million in lost revenue, business disruption, productivity loss and penalties.
To help financial services firms ensure disclosure compliance, companies like FINRA Technology, Quantiply and my company offer AI solutions that use semi-structured data parsing to analyze client advice documents and extract all of the component pieces of the document (including disclosures). Then, using natural language processing to understand the meaning of the underlying text, the AI structures this data into an easily-reviewable form (like an Excel document) where human auditors can quickly evaluate whether all necessary disclosures were made. Where before an auditor might spend hours to review 1% of their firm’s documents, AI solutions like this empower the same person to review more documents in less time.
AI Fights Elder Financial Exploitation
$1.7 billion. That’s the value of suspicious activities targeting the elderly, as reported by financial institutions in 2017 alone. In total, the United States Consumer Financial Protection Bureau (CFPB) says that older adults have lost $6 billion to exploitation since 2013. One-third of these people were aged 80 or older, some of whom lost more than $100,000.
Thankfully, tech companies and financial institutions are fighting back. The CFPB notes that “Regularly studying the trends, patterns and issues in EFE SARs [Elder Financial Exploitation Suspicious Activity Reports] can help stakeholders enhance protections through independent and collaborative work.” This is a great opportunity for machine learning and AI, which use reams of historical data to predict what is likely to happen next.
Wells Fargo, for example, uses machine learning and AI to identify suspicious transactions that merit further investigation. Ron Long, director of elder client initiatives for Wells Fargo Advisors, told American Banker earlier this year that their data scientists are constantly working to add new unstructured and structured data sources to improve their capabilities. “While a tool can’t replace human assessment,” he said, “machine-learning capabilities play an important part in our strategy to reduce the number of matters requiring a closer look so we can focus on actual cases of financial abuse.”
One example is EverSafe, an identity protection technology company founded in 2012, which draws on multiple data sources to train its AI. EverSafe places itself at the nexus of a user’s entire financial life, analyzing behavior across multiple accounts and financial advisors. This approach dramatically improves their AI’s ability to identify erratic activity or anomalous transactions. Eversafe’s founder, Howard Tischler, says he was inspired to create the company after his aging, legally blind mother was scammed multiple times, including by someone who sold her a deluxe auto club membership.
AI Adds A Crucial Competitive Edge In High-Frequency Trading
Back in the 1980s, Bloombergbuilt the first computer system for real-time financial trading. A decade later, computer-based high-frequency trading (HFT) had transformed professional investing. Some estimates put HFT at 1,000x faster than human-human trading. But since the 2010s, when trading speeds reached nanoseconds, industry leaders have been looking for a new competitive edge.
To keep up with (and ahead of) the competition, industry leaders are turning to algorithmic trading. The sheer volume of trading information available for machines to analyze makes artificial intelligence and machine learning formidable tools in financial marketplaces. Investment firms use AI to increase the predictive power of the neural networks that determine optimal portfolio allocation for different types of securities. In simpler terms: Data scientists use reams of historical prices to train computers to predict future price fluctuations.
AI has already proven its value in HFT. Renaissance Technologies, an early adopter of AI, boasted a return of 71.8% annually from 1994 to 2014 on its Medallion Fund (paywall). Domeyard, a hedge fund, uses machine learning to parse 300 million data points in the New York Stock Exchange, just in the opening hour. And PanAgora, a Boston-based quant fund, deployed a specialized NLP algorithm to quickly decipher the cyber-slang that Chinese investors use on social media to get around government censorship. These findings give PanAgora, a firm that operates at the speed of fiber optic cables, vital insights into investor sentiment fast enough to keep up with (and influence) its trading algorithms.
Wrapping Up: Tempering Expectations For AI In Financial Services
The value of AI in financial services is clear. But don’t get lost in the hype. For every useful AI system, you can find a dozen problematic algorithms and large-scale failures. To succeed, keep a realistic perspective of what AI can and can’t do to help.
The truth is that artificial intelligence is just a tool. Alone, AI doesn’t really “do” anything. What matters is how you combine AI with other technologies to solve a specific business problem.
Pearl-Plaza® today announced its mission to challenge the customer experience industry and offer an elevated approach focused on Experience Improvement (XI)™ for the world’s customers, employees, and top brands.
Pearl-Plaza® today announced its mission to challenge the customer experience industry and offer an elevated approach focused on Experience Improvement (XI)™ for the world’s customers, employees, and top brands. This involves dramatically increasing the results from experience programs through a new class of software and services specifically designed to help leaders detect and ‘own’ the important moments in customer and employee journeys. Read more in the full press release here.
Executives and end-users look for different things when choosing software products. An executive, for example, might be more interested in ROI and scalability, while the end-user often cares more about just getting their work done, quickly and easily.
There was a time when executives were the gatekeepers who decided which B2B software products their companies purchased while the end-user experience took a back seat—but that era has ended. Today, you’ve got to win over your end-users to gain a foothold in an organization and give your product a fighting chance.
What does this look like? Picture Sophie, an Accounting Manager who uses the free version of Zoom to chat with her brother in Spain. She prefers Zoom over Skype, so she recommends it at work. The department tries it out, likes it, and begins using the paid version. Eventually, other departments try Zoom and it gains company-wide adoption. Cut to Zoom’s IPO in 2019, and global adoption in the wake of the pandemic.
What is Product-Led Growth?
A Product-Led Growth (PLG) model focuses on the end user’s needs when developing products, crafting education and support strategies, and shaping user experience.
“Growth in a PLG business comes from consistently fine-tuning the product experience to optimize the rate at which new users activate, convert, and expand in the product. Ideally, these improvements start to compound over time, allowing PLG businesses to accelerate growth as they scale (unlike traditional SaaS businesses). Customer feedback is critical to prioritizing the areas that will make the biggest difference to your customers.” — Kyle Poyar, Market Strategist, OpenView
Where end users rule, customer experience is everything
Welcome to the end-user era, a time when users (rather than CIOs or other executives) introduce SaaS products to organizations and drive product adoption. If you want to succeed as a SaaS company in the end-user era, you need to find ways to eliminate end-user pain points and create a seamless experience.
Word of mouth drives new customer acquisition. Then viral adoption within a company increases customer lifetime value. This is a powerful combination. In recent years, PLG is how many of the most successful SaaS companies have rocketed to IPO. Think Zoom, Slack, Hubspot, and Atlassian.
If you’re at a company that takes a traditional approach to CX—tinkering around the edges, nudging the product team to “improve customer experience”—get ready for a big change. Once your C-Suite or VP of Product embraces Product-Led Growth, the spotlight will be on customer feedback in all forms. CX metrics will drive cross-functional alignment and priorities.
The relationship between CX and Product-Led Growth
Despite the name, Product-Led Growth is not solely the domain of the Product team. Customer experience is an integral part of any PLG strategy. “If there is a challenge in implementing Product-Led Growth, it is actually achieving alignment across and within teams along with monitoring the multiple digital and physical touchpoints affecting customer experience,” says Despina Exadaktylou, Director of Programs, Product-Led Growth Hub, the world’s first PLG academy. Product Teams are taking note and initiating collaboration.
“Customer Experience focuses on brand loyalty and customers’ likelihood to recommend. User Experience [within a Product team] focuses on the immediacy of user interaction with your product. But the lines between them have blurred as the role of the UX researcher and the tools in our toolkit have expanded beyond the narrow focus of the user’s engagement with the user interface, “ says Carol Barnum, Director of User Research and Founding Partner at UX Firm. She counsels product teams by saying, “If you are siloed within a UX group that isn’t engaging with CX stakeholders, seek opportunities to … collaborate with them. We all want the same thing—great user experiences and strong loyalty to brand.”
Source: UXMatters
Kieran Flanagan, VP of Marketing and Growth at Hubspot, takes this one step further. “To excel and thrive in a product-led company, you must be great at cross-functional collaboration,” says Kieran “A lot of the benefits that [PLG] has brought to companies is distilling your funnel down to these very concise metrics and the ones that actually matter.”
The importance of end user feedback
In the Product-Led Growth era, a seamless end user journey is paramount–from acquisition to advocate. As a result, product teams are hungry for data about user experience inside and outside of the product. Product managers and UX teams need to understand anything that is slowing end users down, so they can figure out how product design can alleviate that friction.
CX professionals and front line teams are skilled at using established customer experience KPIs to monitor loyalty and gather feedback. They have valuable information about end user pain at critical touchpoints in the SaaS user journey, including:
Onboarding experience
Support experience
Product or feature adoption
Creating Alignment
Product-Led Growth success demands shared accountability for metrics, so be ready to co-create a plan. Product teams benefit from the customer journey insight that CX teams (along with Success, Support, Sales and Marketing) bring to the collaboration. CX champions finally have the kind of cross-functional partnership that they’ve been seeking all along.
Learn how Wootric can help you measure and improve customer experience. Book a consultative CX demo today.
The ROI from increased referrals, cross-sells and upsells
The potential for a customer success program to become a “growth engine”
The sheer impact of returning revenue and customer lifetime value
The ‘free marketing’ of brand advocacy
And the list goes on. But, we’ve all had some paradigm shifts recently, haven’t we? So I’m not going to talk about what customer success can do for you. Because it’s not about you. It’s never been about you. It’s always been about other people.
What they need most, and what they need right now.
I predict that the companies that will grow from the Covid-19 pandemic crisis are the ones who deeply, genuinely care about their customers’ wellbeing. Not just their success.
How are your customers feeling right now? And how can you support them?
We can answer the first question ourselves — we’re all feeling isolated, lonely, cut-off, mournful, insecure, anxious. Maybe our kids/partners/dogs/cats are driving us a little crazy at this point. Maybe we’re self-isolating alone and wish we had kids/partners/dogs/cats around.
What we need most right now is to feel connected and cared about. And I’ve seen two companies step up to meet this need in vastly different ways.
[Yes, if we were to put traditional “Customer Success” verbiage around this, we’d say “what success looks like for your customer right now is to *feel* less alone. Not to get their work done faster. Not to multi-task with better focus. But to *feel* connected.]
Community Building
When these lockdowns started, many of us shared memes that read: “Check on your extravert friends… they’re NOT okay!”
As time dragged on, however, even the introverts among us started to crave human connection. Human beings thrive on community, and you may be in a unique position to give it to them.
Wootric held informal CX “office hours” via Zoom for CS and CX professionals who want to offer each other support, ask questions and compare notes on how they’re adapting (or anything else, for that matter).
“I’m part of an online community of marketing leaders. There’s something incredibly valuable about being with others who are facing the same challenges that I am, so offering that kind of forum to leaders in the CS/CX trenches became a priority for me, ” says Lisa Abbott, VP Marketing at Wootric.
On the last CX Office Hour call, a Customer Success professional at a startup shared that she was feeling overwhelmed after losing her team and being placed on the front lines, dealing directly with customers. Members of our newly formed community jumped in with advice on how to prioritize and set boundaries, helping her get through it while maintaining her sanity.
Consider creating a similar forum for your customers—a live video conference where they can come together, connect, and share their wisdom and support.
Be there for a chat
I got an email last week from one of my favorite online companies, Greetabl, a service that sends beautifully packaged thoughtful gifts. I’ve been using them for years to cheer up friends from afar or show appreciation to clients and colleagues, and I didn’t think I could love them more, until I found this in my inbox:
Hey there Greetabl Insider,
Brittany from Greetabl here (you might recognize my name from Greetabl’s marketing emails). If you saw Joe’s note on Medium over the weekend, you know that Team Greetabl has cleared our calendars of all scheduled meetings and we’re reaching out to our people to see if they want to talk. About anything.
There’s a lot of uncertainty right now and social distancing can get lonely FAST, so I just wanted to let you know I’m here to talk. No sales pitch, no agenda; just a virtual coffee meeting to talk about whatever’s on your mind. Drop some time on my calendar if you want to chat.
Best,
Brittany
Director of Marketing
Joe Fischer, Greetabl’s CEO, had everyone clear their calendars of their regularly scheduled meetings and instead, reach out to talk to people. Brittany, their Director of Marketing, sent out this charming email, and my favorite part is “No sales pitch, no agenda; just a virtual coffee meeting to talk about whatever’s on your mind.”
My friend, copywriter Lauren Van Mullem, took her up on this offer and says “Chatting with Brittany was the highlight of last week for me. We just hopped on a video chat, and we were both in our comfy sweaters, and just talked about life, these weird times, and some of the best and worst things we’ve seen from companies right now. It felt like talking to a friend, but almost better in a way. Our social circles are sort of confined right now. You don’t get a chance to talk with a stranger very often these days. So having a chance to connect with someone I didn’t know was really special. I can’t wait for a chance to repay that kindness by sending Greetabls, especially since I know that wasn’t the point of the call at all.”
SaaS companies are uniquely positioned to help
SaaS companies of all sizes have something to offer that even the big companies don’t have: Many of us are used to working remotely and using online tools to stay focused and connected at scale. We’re agile by nature, able to navigate quickly-changing environments. We’re adept at creative problem-solving, finding opportunities in challenges, and listening – really listening – to what our clients need.
Those are life skills not everyone has right now.
So now, more than ever, listen to your customers and your community.
Create the solutions they need right now.
Give them the frictionless customer journeys that get them where they need to go under the current, world-upside-down, circumstances. And don’t assume their “ideal outcome” this month is the same as it was just a few weeks ago. Everything has changed.
And above all: Reach out. Genuinely. Meaningfully. Human to human. Because generosity and human connection are what’s really going to get us all through this.
What can you do as a CSM right now?
Back to Customer Success – what can you do as a Customer Success Manager to support your customers during COVID-19?
Focus on empathizing with your customers and doubling-down on retention.
Because in times of crisis, existing customers are the lifeblood of your SaaS company.
Four questions to ask your customers
Be proactive. Reach out and get the conversation started. How to begin? Recently, on a CX Office Hour call, customer experience thought leader Melinda Gonzales suggested that CSMs ask every customer these questions:
How are you doing, personally?
What is the impact of the pandemic on your business?
How do you think it will impact your plans for 2020?
How can we help?
And, many clients right now are ranking their spending to decide what gets cut. Where your company lands on that list may depend on…
Empathy
How can you show empathy for your clients? Both personally, individually, and for their businesses? What solutions might greater empathy lead to? Here are some options to consider:
Being open to negotiating contract terms – especially payment terms.
Offering a short-term discount.
Show your customer how they can get more value from your product without spending another dollar. Are there features they are paying for but not using? Can you share a best practice that will help them see more success?
Presenting downgrade options from a Customer Success standpoint (give them what they need to succeed right now, with the awareness that this may mean reducing spend).
Sure, will a few opportunists try to use COVID-19 as an excuse to negotiate a better deal? Maybe. And if you get one of those, present the “downgrade” option and make it very clear what that means in terms of reduction of services and reduction of results.
But for most customers, give them the benefit of the doubt. So many industries and individuals are struggling right now. And the long-term ROI of empathy is worth some short-term sacrifices.
What you choose to do right now can ignite and cultivate long-term, lucrative relationships in the future. And for our CSM friends and clients, can we just say: We understand how hard this time is for you too.
You may not be able to do your best work right now, or afford the best tools to support your work. You may have dogs/kids/spouses/cats interrupting your client calls. You may be feeling what we’re all feeling — frustration, helplessness, fatigue, fear for the future.
Take it easy on yourself if you can. Upsells aren’t likely to happen right now, and that’s okay.
But your core goal remains the same: Helping your customers reach the results they need, by whatever means necessary.
Measure and improve customer experience. Get Net Promoter Score, CSAT or Customer Effort Score microsurvey feedback with Pearl-Plaza.
In many important ways, healthcare organizations and consumer businesses are fundamentally different. And yet, there is no question that today’s patients bring a distinctly consumer mindset to their healthcare experiences. That means patients are better informed about their healthcare choices. They have easier access to information and reviews about providers and facilities. And they are much more willing to walk away from providers that can’t deliver both quality care and good overall experiences.
This dynamic raises an intriguing question: If patients are increasingly bringing consumer expectations to their healthcare experiences, what (if anything) can the healthcare industry learn from leading consumer companies about improving those experiences?
The answer, as it turns out, has important implications. A growing number of healthcare providers are discovering new solutions to long-entrenched challenges and limitations by exploring, adapting, and applying proven customer experience (CX) best practices to their patient experience (PX) efforts. There are many examples, but to begin the conversation, here are six proven and broadly accepted CX best practices that are especially relevant and useful for healthcare organizations looking to breathe new life into their patient experience programs.
Best Practice #1: Build a Winning Patient Experience Strategy
Today, 90% of healthcare organizations say improving patient experiences is a high priority. But only 8% of those organizations have managed to put a successful patient experience strategy in place. [1] This huge gap highlights the challenges of actually creating a balanced and complete patient experience strategy that defines who your patients are, clearly outlines what kinds of experiences you want to provide, and describes how you want patients to feel after they receive care from your organization.
There are obviously no easy, one-size-fits-all prescriptions for developing a strong, effective PX strategy, but there are some core ideas from the consumer world that can help guide your efforts:
Create a more patient centric culture. Cultural changes are never easy. But many leading consumer organizations have proved that with consistent, ongoing effort, you can successfully define what “patient centricity” means to your organization, communicate that definition and get buy-in across every level of the organization, and ultimately shift your core culture to focus more on delivering complete, world-class patient experiences.
Align your patient experience strategy with your core brand and business strategies. The world’s best consumer businesses understand that a successful CX strategy has to be closely connected to and aligned with the organization’s brand and business strategies. The same is true in the healthcare world. With the proper alignment in place, you can make clear promises about what patients should expect from your organization (brand strategy), consistently deliver on those promises (PX strategy), and then connect those experiences back to your organization’s overall goals (business strategy).
Find and engage with a dedicated customer experience executive. Getting organizational buy-in for patient experience improvements that impact multiple departments always requires strong leadership from the top. Smart consumer businesses often assign a dedicated executive to provide the leadership, influence, and continuity needed to develop and execute on a successful CX strategy. The same approach will help drive the success of your PX program.
Building and implementing a successful patient experience strategy takes time and a lot of persistent effort. But with the right strategy in place, you’ll reach a point where all the people, data, technology and processes you put in place start to yield results that are clear to everyone—from employees who are now empowered to deliver better experiences to patients who experience the results first hand.
Best Practice #2: View Your Patients’ Experiences Through Multiple Lenses
Many healthcare organizations depend on standardized survey programs as their main (or only) source of patient experience data. But the best consumer organizations have learned that meaningful improvement comes from collecting information from the widest possible range of sources along every step of the customer journey. For healthcare organizations, this involves combining and complementing standardized surveys with more targeted and personalized information gathering tools. It also includes finding ways to unify and tap into all of the incredibly rich sources of patient information that exist in your point-of-care, safety and quality, operations, and other healthcare systems. Surveys ask patients to look back at their experiences after they’re over, but these other tools often measure reactions and responses in real time at specific points. They also make it possible to incorporate and share (with permission) the perspectives and experiences of family members who are involved in caring for their loved ones.
Of course, this “multiple lens” approach requires a technology platform that’s capable of normalizing all these different sources of data, analyzing them, and converting them into cohesive and useful patient experience insights. But when this platform is in place and working properly—and all of your different patient systems are connected to it—you gain an incredibly rich and unified view of the complete patient journey.
Best Practice #3: Use Predictive Analytics to Prioritize Your PX Efforts
In addition to combining and analyzing customer experience data from different sources, smart consumer organizations leverage advanced predictive analytics to accurately identify what matters most to their customers and pinpoint what types of CX changes will have the biggest positive impact.
By adding this additional intelligence to your patient experience technology platform, you gain the confidence of knowing that your efforts are making the largest possible contribution to increased loyalty and improved patient experiences.
Best Practice #4: Empower Employees to Make Smarter, Faster Decisions
For consumer businesses, survival often depends on making smart decisions faster than the competition. In the CX realm, this typically takes the form of dashboards and reports that quickly synthesize multiple performance measures and data sources into clear, simple, and actionable insights—and then makes them available to everyone who needs them in nearly real time.
In most cases, healthcare organizations have been much slower to adopt these types of dynamic, customizable tools. But a technology platform that combines and unifies different sources of patient data also lays the groundwork for the types of near-real-time dashboards that can drive smart, informed, and relevant patient experience decisions across every layer of your organization.
Best Practice #5: Take Advantage of the Net Promoter Score
The Net Promoter Score (NPS) uses a single, standard question to measure how likely a customer is to recommend a product, service, or brand, and it has been nearly universally adopted by companies in the consumer world. NPS serves a uniquely valuable purpose, because it uses a single numeric score to consistently measure satisfaction and brand loyalty across nearly every market and industry.
Today, the healthcare industry rarely uses NPS, but it presents an interesting opportunity for forward-looking healthcare organizations. By adding NPS to your patient experience program, you can gain a perspective that goes beyond the healthcare industry—and measures your performance against the larger consumer landscape. This becomes especially valuable as patients increasingly bring consumer expectations to their healthcare experiences. Of course, with NPS—as with any other metric—it’s important to focus on meaningful action and improvement, rather than simply “chasing the score.”
Best Practice #6: Focus on Actions and Results
Nearly every consumer organization collects customer experience data and documents the results. But the true CX leaders also know how to translate those efforts into meaningful, systematic changes and improvements, and they know how to do it quickly. This is an especially relevant area for healthcare organizations, because there is a strong tendency to focus more on collecting patient experience data than actually driving and managing change.
That’s not surprising. Gathering survey data, generating reports, and documenting scores are focused, self-contained activities that fit neatly into familiar, well-defined boxes. Effective change management, on the other hand, requires the buy-in and active participation of virtually everyone, across all roles, levels, and departments. As a result, many healthcare organizations dedicate resources to the part of the process they can more easily understand and measure—and hope that the information somehow leads to improvements.
For consumer businesses and healthcare organizations alike, closing this gap between measurement and action means investing equally in the information gathering and change management sides of the equation. If you’re collecting more complete and relevant information about your patients’ journeys in real time and from more sources, turning that data into actionable insights in near real-time, and then feeding it into a unified and effective change management framework, you can quickly identify, prioritize, and implement changes that will make the biggest difference for your patients.
Start Applying CX Best Practice to Your Patient Experience Program Today
The world’s biggest and most successful consumer businesses have been obsessed with improving their customers’ experiences for decades. And despite the important differences between healthcare organizations and consumer businesses, there is a very long list of techniques, tools, and best practices you can adapt and apply to breathe new life into—and create new possibilities for—your patient experience program.
Find out how MaritzCX can help you apply best practices from the consumer world to enhance every part of your patient experience program and meet the rising expectations of your patients.
Call 385.695.2800 or visit maritzcx.com/patient-experience to talk to a representative and schedule a demo.
[1] Kaufman, Hall & Associates report 2017 State of Consumerism in Health Care: Slow Progress in Fast Times.
The sales gong is a motivational technique used on sales floors around the world. Every time someone closes a deal, they bang a gong or ring a bell to celebrate their success. And when the office gets quiet? Everyone knows it’s time to hustle.
In other words, there’s never any question about how the team is doing at any given moment, and the constant feedback gets the entire sales force aligned in their mission to sell, sell, sell.
Now… imagine doing the same thing, for your entire organization, regarding Customer Experience (CX)? Rather than a sales gong, picture a TV monitor that broadcasts real-time customer feedback and Net Promoter Scores (NPS), getting everyone from accounting to operations aligned in the mission to turn customers into raving fans.
Broadcasting real-time NPS data will help you build a customer-centric culture, which ultimately leads to greater customer loyalty and powerful returns.
What is a Net Promoter Score (NPS)?
Measuring your Net Promoter Score is relatively straightforward. An NPS survey asks your customers to rate, on a scale of 0-10, how likely they are to recommend your company, products, or services to a friend or colleague.
Responses are then grouped into:
Detractors (those who responded 0-6)
Neutrals (7-8)
Promoters (9-10)
To determine your Net Promoter Score, subtract your percentage of promoters from your percentage of detractors.
NPS = (% Promoters – % Detractors)
Example: You survey 500 customers, asking how likely they are to recommend your company to friends or colleagues. 50 respondents (10%) answered 0-6, another 100 (20%) answered 7-8, and 350 (70%) answered 9 or 10.
Your Company’s NPS is 70% – 10% = 60.
The Net Promoter Survey is then followed by one of the following open-ended questions (depending on their answer):
“What can we do to improve?” (those who rates you 0-8)
“What did you love about your experience? (those who rated you 9-10)
Why does NPS matter?
The correlation between revenue and CX is solid. And, NPS is the foundational metric that can serve as a north star on the journey to customer-centricity and the growth that comes with it. However… simply gathering feedback and measuring NPS gets your nowhere.
The real power of NPS comes from the system you build around it. This means:
Closing the loop with customers (i.e., fixing the problem, thanking them for their feedback).
Analyzing responses to prioritize improvements to products and services.
Using NPS to create a more customer-centric culture.
Only then does NPS help you retain more customers and drive growth.
In a custom-centric company, the Success and Support teams works diligently to close the loop with customers, the Product team analyzes the data to create superior products, and Marketing crafts their messaging to educate and answer objections before they arise.
As for shifting company culture? That’s a job for the CEO or a C-Suite sponsor of your NPS program (often with the support of the VP or Director of Customer Experience).
Creating a more customer-centric culture with NPS
Culture change is a multifaceted undertaking, but you can anchor it with your NPS program. Evangelize NPS as a Key Performance Indicator (KPI), right alongside revenue, churn, and other important figures.
What does evangelizing NPS look like?
Educate everyone (from new hires to the board) about why NPS is important to customer experience management.
Share the results of NPS surveys in company newsletters and quarterly board decks.
Unfortunately, in between those quarterly reports, NPS can get lost. Unlike the Sales gong that keeps everyone on the floor tuned into customer acquisition as a Key Performance Indicator, NPS can be out of sight and out of mind.
And that’s where a real-time display of NPS feedback comes in. You can bridge that gap between reporting cycles when you display your evolving NPS live, on a monitor, along with raw customer feedback.
Real-time NPS, trends, topics, and comments on a Wootric TV display.
4 Reasons to broadcast real-time NPS and feedback in the office
If your goal is to create a more customer centric company and reap the benefits of a high NPS, an “NPS TV” can help unite everyone on that mission in the following ways.
1. Reinforce awareness of NPS as a KPI.
Certain KPI’s are more obvious than others. Revenue, profit, and conversions get a lot of airtime, but NPS is easy to ignore unless you put it front and center.
In reality, everyone should be thinking about NPS if you want to drive growth!
2. Build empathy with the customer.
There is nothing quite like seeing the latest comments from active customers appearing on screen. Overjoyed, frustrated, curt, complimentary—it’s the emotions in the verbatim comments that humanize your customers and creates a connection to their experience. For more tips, check out these ways to build customer empathy.
3. Reach departments that don’t normally engage with customers.
The customer success team lives and breathes NPS because it’s an early indicator of churn. But the finance team? Not so much. At customer-centric companies, everyone understands their responsibility for customer loyalty.
Set up the monitor in a prominent place. It could be on a wall everyone sees as they come in or out of the office, opposite the coffee machine, or in any hub in the workplace.
That way more people are likely to read customer comments and tune into NPS trends. 4. Invest in an easy, low-cost enhancement to your NPS program.
For the cost of a computer and a monitor, you’ll be in business in minutes—and that’s a small price to pay if you are after customer experience transformation. As far as NPS tools go, it’s one of the cheapest and most effective ones on the market.
Your customers are the lifeblood of your business, but unless an employee’s job is customer-facing, it’s easy to lose sight of what your customers are going through. Making everyone aware of NPS brings the customer experience into stark relief, and it unites everybody in a single, all-important mission.
Remember: Net Promoter Score has a solid impact on revenue, and it’s the single best metric for predicting long-term growth and success—when you build a customer-centric culture around it. If you can involve everyone with creating a positive, seamless customer experience, your NPS score will rise and your revenues will soar.
To view the first part of this blog series, click here.
The Important First Day of the Employee Journey
In the last blog on the Employee Experience in the Automotive industry, we looked at the strategic importance and economic benefit of an effective onboarding process and focused on what should happen prior to the employee’s start date.
In this post, we’ll look at what happens when employees arrive on their first day. As before, we are focusing on the automotive industry, but the principles equally apply to other industries as well.
Creating a Welcome Kit
Once the day has arrived, you want to make it special and the best way to do that is to create an exceptional first impression. Have your receptionist be aware of the start date and ensure that the new employee is welcomed appropriately.
In fact, consider creating a “Welcome Kit” that contains numerous positive first impression opportunities such as branded assessories. Have a welcome letter from the Dealer Principal, or even from the OEM President, prepared and left at the new employee’s desk.
Often items like these are used daily and a new hire will feel an immediate attachment, so much so that they will often continue to use them for years all the while linking back to that first day.
Lastly, provide any desktop resources and in this case, the term desktop is in the literal sense. Any print materials such as dealership newsletters, upcoming community involvement notices, employee recognition programs -anything that conveys positive dealership activity will help to make a new employee feel good about their decision to join the team.
From an online standpoint, consider adding a dedicated Welcome page to your intranet or LMS. Creating a specific Welcome starting point will be engaging and will direct a new hire to specific curriculum best suited for their role.
Be sure to include a Welcome video or a step-by-step tutorial of where and when to access available training resources which, again, builds on that important first impression and helps to ease the potential training concerns people face with any new job.
As the day continues, ensure a dealership tour takes place and introduce the new hire to the various departments and team members. This is just as important for the existing team as for the new hire as positive introductions will help break the ice and hopefully lead to productive working relationships.
Engaging the New Employee Beyond the First Day
After the tour, review any administrative processes and outline not only the orientation for the remainder of the day, but also for the week ahead. For example, if this is a sales role, you may want to suggest the new hire learn as much as possible about one specific model per day.
Encourage them to drive the vehicle and speak with other salespeople. Have them talk to the service personnel to better understand the maintenance requirements of the vehicles they’ll be selling. Learning all the details of an entire product lineup can be daunting, so focus on small daily or weekly goals that are attainable.
To sustain this positive feeling past the first day, OEMs or even large dealer groups should consider conducting monthly webinar sessions for new hires. This would be a great way to meet others, online at least, who are in a similar situation and allows for the moderator to run through the onboarding process once again to promote upcoming events, answer outstanding questions, and receive important feedback.
This also could be a great opportunity for a short, anonymous employee survey to uncover any opportunities for improvement in the onboarding process.
Who Should Lead the Onboarding?
In terms of leading the onboarding, often this is left up to a Sales or Service Manager and while this is optimal, typically these managers are busy and other responsibilities may interfere with the full attention they can bring.
As an alternative, consider creating a role for an onboarding Champion, an individual whose responsibility it is to see that new employees are thoroughly walked through the onboarding process and are there to help answer additional questions in the upcoming days and weeks ahead.
This role would not take the place of a manager, as it would likely be a secondary role for a peer in the new hire’s respective department and as such, is designed to be another level of support.
When developing this role, consider making it a possible precursor to a management position as it will involve people skills, accountability, and guidance – all valuable traits in any future manager.
Onboarding is an Essential Part of the Employee Experience
To recap, onboarding is an essential part of the employee experience. Onboarding any new hire will be most effective when done in a consistent process. Include it in the hiring stage, allowing you to demonstrate your commitment to their success, the level of support available, and necessary accountability to complete the required curriculum.
Turnover is costly and leads to lower employee and customer satisfaction so ensure you take onboarding seriously and allocate the necessary resources to make a new hire feel comfortable, valued and a welcome part of your dealership family.
Onboarding is one of the most important processes a dealership can have, as it often predicates the likelihood of a new hire actually staying long term and starting a successful career. Not only will the implementation make a difference in the company, but it will also help individual employees to feel valued and achieve their career goals.
When you call into their MID, operators use a 
The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely.
The above applications of text analytics in healthcare are just the tip of the iceberg. 
