The Shortcomings of Comment-Based Surveys

Comment-based surveys can be effective for immediately gathering feedback from customers. However, there are several arenas in which brands use comment-based surveys when another survey type would yield better intelligence.

Comment-based surveys can be effective for immediately gathering feedback from customers. And when it comes to customer experience (CX), timeliness can make or break an organization’s ability to act on that feedback.

However, there are several arenas in which brands use comment-based surveys when another survey type would yield better intelligence. Today, I’d like to dive into several shortcomings that can make using comment-based surveys challenging for brands, as well as a few potential solutions for those challenges. Let’s get started.

Outlet-Level Analysis

As I discussed in my recent article on this subject, comment-based surveys are often less effective than other survey types for conducting outlet-level analysis. In other words, while brands can see how well stores, bank branches, and the like are performing generally, they usually can’t determine where individual outlets need to improve .

The reason for this has as much to do with the feedback customers leave as the survey design itself. From what I’ve seen across decades of research, customers rarely discuss more than 1-2 topics in their comments. Yes, customers may touch upon many topics as a group, but rarely are most or even a lot of those topics covered by singular comments.

What all of this ultimately means for brands using comment-based surveys to gauge outlet effectiveness is that the feedback they receive is almost always spread thin. The intelligence customers submit via this route can potentially cover many performance categories, but there’s usually not that much depth to it, making it difficult for brands to identify the deep-rooted problems or process breakages that they need to address at the unit level if they want to improve experiences.

(Un)helpful Feedback

Another reason that brands can only glean so much from comment-based surveys at the outlet level is that, much of the time, customers only provide superficial comments like:“good job”, “it was terrible”, and the immortally useless “no comment.” In other words, comment-based surveys can be where specificity goes to die.

Obviously, there’s not a whole lot that the team(s) running a brand’s experience improvement program can do with information that vague. Comments like these contain no helpful observations about what went right (or wrong) with the experience that the customer is referring to. The only solution to this problem is for brands to be more direct with their surveys and ask for feedback on one process or another directly.

How to Improve Comment-Based Surveys

These shortcomings are among the biggest reasons brands should be careful about trying to use comment-based surveys to diagnose processes, identify employee coaching opportunities, and seeing how well outlets are adhering to organization-wide policies and procedures. However, none of this means that comment-based surveys should be abandoned. In fact, there’s a solution to these surveys’ relative lack of specificity.

Brands can encourage their customers to provide better intelligence via multimedia feedback. Options like video and image feedback enable customers to express themselves in their own terms while also giving organizations much more to work with than comment-based surveys can typically yield. Multimedia feedback can thus better allow brands to see how their regional outlets are performing, diagnose processes, and provide a meaningfully improved experience for their customers.

Click here to read my Point of View article on comment-based surveys. I take a deeper dive into when they’re effective, when they’re not, and how to use them to achieve transformational success.

Text Analytics & NLP in Healthcare: Applications & Use Cases

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:

  1. You (a physician, patient or media person) call into a biotechnology or pharmaceutical company’s Medical Information Department (MID)
  2. Your call is routed to the MID contact center
  3. 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.Image Showing A Use Case of Text Analytics in Healthcare: MedInfo Search Application 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 InMoment company) 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. Whimsical image showing 2 people in bathtubs with sentiment-colored phrases above htem

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.

NLP in Healthcare: AlternativesPharma Case Study Image

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

Natural language processing NLP in healthcare graphic from McKinseyThe 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 (HIPAAobsolete. We investigated and found some alarming data privacy and ethics concerns surrounding AI in healthcare.

Read – AI in Healthcare: Data Privacy and Ethics Concerns

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.

Read – A Better Approach to AI for Regulatory Compliance

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–.”

Read – Stories of AI Failure and How to Avoid Similar AI Fails

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.

Read – Bias in AI and Machine Learning: Sources and Solutions

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.

The 7 basic functions of text analytics are:

  1. Language Identification
  2. Tokenization
  3. Sentence Breaking
  4. Part of Speech Tagging
  5. Chunking
  6. Syntax Parsing
  7. Sentence Chaining

Natural language processing features include:

Sentiment analysis

Entity recognition

Categorization (topics and themes)

Intention detection

Summarization

Chart showing Lexalytics' NLP feature stack
Lexalytics’ text analytics and NLP technology stack, showing the layers of processing each text document goes through to be transformed into structured data.

Beyond the basics, semi-structured data parsing is used to identify and extract data from medical, legal and financial documents, such as patient records and Medicaid code updates. Machine learning improves core text analytics and natural language processing functions and features. And machine learning micromodels can solve unique challenges in individual datasets while reducing the costs of sourcing and annotating training data.

The Case for Moving Your Experience Program Beyond Metrics

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.

3 Simple Steps That Make Your CX Program Actually Move The Needle

It’s no secret that many companies’ experience programs aren’t delivering the results that those brands expect and, frankly, need. Too many customer experience (CX) initiatives are stuck solely on giving companies metrics, which by themselves cannot deliver a meaningfully improved experience and thus a stronger bottom line.

It’s no secret that many companies’ experience initiatives aren’t delivering the results that those brands expect and, frankly, need. Too many customer experience (CX) programs are stuck solely on giving companies metrics, which by themselves cannot deliver a meaningfully improved experience and thus a stronger bottom line.

However, there is a solution. Companies don’t have to stay stuck merely “managing” their experiences. We’ve put together three proven steps that companies can follow to take their program, and thus their brand, to the top:

  1. Determining Business Objectives
  2. Gathering The Right Data
  3. Taking Intelligent Action

Step #1: Determining Business Objectives

Traditionally, many firms have been in such a hurry to start listening in on their customers’ tastes and preferences. And while this eagerness is admirable, it often results in wantonly turning listening posts on everywhere and waiting for insights to roll in. Listening is important, yes, but listening passively is worlds different than listening intently. The former focuses on gathering metrics, feeding those metrics into a piece-by-piece reactive strategy, and calling it a day. The latter calls for businesses to firmly establish what they want to achieve with their experience program before turning any ears on.

There are several merits to determining business objectives before listening to customers, and they all have to do with looking before leaping. First, companies need to decide what business problems they want their experience program to solve. Foregoing this step and listening for the sake of listening is why so many programs either fail or provide ROI that’s murky at best.

Additionally, companies can take considering objectives as an opportunity to tie their experience programs to financial goals. Like we just said, it’s hard to prove a CX initiative’s ROI if it has no clear objective beyond just listening to customers. Spelling your program’s goals out in financial terms gives CX teams a hard number to work toward—then, when that number is achieved, those teams will have a much easier time using that achievement to leverage additional funding in the boardroom.

Step #2: Gathering The Right Data

There’s another reason why it pays to stop and think before turning listening posts on in every channel: some customer segments are more worth listening to than others. This idea may sound a bit callous, but think about it—a listening program geared toward evaluating a loyalty program is going to be much more useful if it hones in on long-term customers instead of casting a net all over the place.

This notion is also known as the concept of gathering the right data. It’s okay for brands to use different listening posts for different audiences—in fact, this strategy is much more likely to garner useful intelligence. Thus, it’s just as important for companies to consider their audiences as it is concrete financial goals when it comes to experience programs. The right data can yield the right intelligence, which can enable brands to take the right steps toward transformational success.

Step #3: Taking Intelligent Action

Much of the work in this step will already have been done if companies follow the previous two steps correctly. Like we said, it’s a good idea for brands to look before they leap and carefully consider what they hope to accomplish with a listening program. Yes, the goal of “listening” is all well and good, but the problem with experience management is that the buck stops there. Take your CX aspirations further than gathering metrics and decide what that listening is meant to accomplish. More customer acquisition? Retention? Lowering cost to serve? Set those goals and attach dollar amounts to them.

Then, take some time to consider which audiences you need to listen to in order to achieve those goals. Arming yourself with concrete goals and intelligence from the right audiences will enable your organization to take the meaningful action it needs to reach the top of its vertical, make a stronger bottom line, and create an emotional, connective experience for both customers and employees. Companies can use these steps to move the needle and take their program from experience management to something far more profound: experience improvement.

Want to learn more about how CX programs can move the needle and create lasting success for businesses, customers, and employees? Check out our new POV article on the subject, written by EVP Brian Clark, here.

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, Bloomberg built 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.

This post originally appeared in Forbes Technology Council.

Stop Managing Experiences—Start Improving Them

InMoment® 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.

InMoment® 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.

Four Ways to Create Emotionally Moving Experiences for Your Customers

Most brands are keenly interested in creating experiences that move their customers on an emotional level—the trick lies in figuring out which factors companies can and should wield to elicit that response from the individuals they seek to serve. 

Most brands are keenly interested in creating experiences that move their customers on an emotional level—the trick lies in figuring out which factors companies can and should wield to elicit that response from the individuals they seek to serve. 

Experience outcomes have a lot to do with all the usual elements, like brand professionalism, but they also have everything to do with how customers feel before, during, and after an experience. Companies can only do so much to manage customers’ feelings, of course, but that does include evaluating how those individuals feel as they share experiences and using that feedback to make meaningful changes.

Today we’re going to touch on four ways that companies can create more emotionally meaningful experiences:

  • Identifying Customers’ Emotional State(s) of Mind
  • Evaluating Emotions’ Impact on KPIs
  • Shifting Customer Emotions
  • Empowering Staff & Processes with New Intelligence

Method #1: Identifying Customers’ Emotional State(s) of Mind

As we mentioned, companies can’t control customers’ emotions, but they can gauge how those individuals feel before and after an experience. Whether it’s via a quick post-purchase survey, social media, or other listening tools, organizations can easily learn not just how their customers are feeling, but also how those feelings inform their decision to come to the brand for a specific need and what their impression is after the interaction.

This information is invaluable for meaningfully changing and/or improving experiences, and gives brands a real shot at better managing customers’ emotions as they interact with the organization. Of course, it should also mean a better experience for all parties involved.

Method #2: Evaluating Emotions’ Impact on KPIs

This one probably goes without saying, but it really can’t be understated how large an impact customer emotion has on KPIs. A customer who’s made to feel angry, for example, probably isn’t going to do wonders for a brand’s retention or cross-sell/upsell KPI. Brands should thus always view KPI improvement through the lens of customer emotion.

This topic connects heavily to the idea of meaningful experience improvement as well. The most transformational process changes can ripple through an entire organization from the bottom up—a better experience occurs, customers become happier, and all the best KPIs light up as a result of the positive emotions that experience improvement instills toward the brand.

Method #3: Shifting Customer Emotions

This point definitely forms a Venn diagram with our first method, but the idea of shifting customer emotions during an experience really deserves its own bullet. Brands shouldn’t restrict their emotional evals to seeing how customers feel before and after an experience—they should also evaluate what can be done to elicit positive emotions (and quash negative ones) in the midst of customer interactions with a brand.

This lens affords customer experience (CX) practitioners a chance to tweak experiences in truly meaningful ways and can be thanked for conventions such as, say, auto dealerships offering customers coffee while they wait for repairs. Likewise, every experience a brand provides should also be thoroughly evaluated for pain points, bottlenecks, and other broken touchpoints that risk upsetting customers. Brands that find and fix these areas will have shifted their customers’ emotions mid-experience, which is powerful.

Method #4: Empowering Staff & Processes with New Intelligence

To expand upon the point made at the end of the last section, knowing how customers feel only really means something if brands execute on those emotions. It also means that companies shouldn’t confine that execution to a CX or customer-facing team. In fact, why not share those learnings throughout the business? Even teams who work far from the frontlines usually have something to do with providing a great experience, and should thus be let in on new learnings.

Finally, as we already talked about, process fixes are a must once companies have learned how experiences make their customers feel. Besides, actual fixes are really the only way that brands can create emotionally moving experiences for their customers in the first place. Using these methods as an improvement taxonomy can help any brand actually reach that goal.

Check out our full report on the importance of customer emotion created by longtime CX expert Simon Fraser.

How to Craft Deliverable Brand Promises

Delivering promises is one of the most important things a brand must do for its customers. Keeping commitments is much easier said than done, but customer loyalty lives and dies by companies’ ability to follow through.
Staff meeting in restaurant

Delivering promises is one of the most important things a brand must do for its customers. Keeping commitments is much easier said than done, but customer loyalty lives and dies by companies’ ability to follow through. Succeed, and the brand generates loyalty and retention. Fail, and the organization ends up burning bridges—potentially permanently.

So, how can brands avoid breaking promises? Well, as I outline in my recent POV on this subject, one of the ways that companies can ensure that they consistently fulfill customer obligations is to create realistic brand promises in the first place. Here’s how brands can do that.

Know Your Customer

Brands should always evaluate the promises they make through a customer’s lens. That means knowing who their customers are, what they consider to be important, what they’re looking for in an experience, and why they come to you for it. This notion is sometimes referred to as the customer’s “moment of truth” and a brand has fulfilled a promise in their eyes when it delivers that moment consistently.

To many customers, the difference between failing to keep a promise and failing to deliver on a moment of truth is miniscule. In my aforementioned POV, I talk about how a colleague of mine experienced an especially brutal broken promise: an airline flight that didn’t uphold its promised anti-COVID safety measures. Not understanding the moments of truth is one thing; understanding and then failing to deliver can be a deal breaker. Additionally, depending on the severity of the problem, some customers will not give brands a second chance.

Delivering The Goods

Companies need to clearly understand what their customers want so they can both rise to the challenge and ensure that they deliver flawlessly on that desire. Brands can increase their likelihood to succeed by building a customer experience (CX) program as part of their business operation. A decent CX program can make brands aware of customers’ wants and needs—a great CX program unites customer, employee, and marketplace perspectives to give companies a continuous, 360-degree view of the experience(s) they provide.

This approach gives brands the opportunity to know what their customers value, so they can create grounded, realistic promises that can be delivered every time. If nothing else, it’s always better to underpromise and overdeliver than to overpromise and underdeliver.

Brands that take this tack will be positioned to create not just good promises for their customers, but the right promises. Companies that pick the right brand promises and deliver at the moments of truth create customer loyalty and a  stronger bottom line for themselves.

Want to learn more about the importance of creating and keeping effective brand promises? Take a look at my article on the subject here.

Three Factors That Help Experience Programs Avoid Unanticipated Costs

Unanticipated costs can quickly become the bane of any business project, customer experience (CX) or otherwise, if they’re not carefully considered before pens have been put to paper. It’s thus imperative for CX practitioners who want to pitch their programs to anticipate and prepare for unexpected costs as much as possible.

Unanticipated costs can quickly become the bane of any business project, customer experience (CX) or otherwise, if they’re not carefully considered before pens have been put to paper. It’s thus imperative for CX practitioners who want to pitch their programs to anticipate and prepare for unexpected costs as much as possible.

We’ve listed the three most effective considerations that practitioners can use to anticipate and avoid unexpected experience program costs:

  • Vendor Scalability
  • Vendor Flexibility
  • Nonparticipation Costs

Factor #1: Vendor Scalability

This tip may seem gratuitous, but program scalability actually isn’t considered as often as it should be, and brands can end up paying extra for that mistake. Practitioners can avoid a lot of headaches with their own teams, the C-suite, and the accounting department by selecting an experience partner that can scale programs from the very beginning.

This approach enables brands to select and begin a program that grows alongside both their CX accomplishments and aspirations. It also allows organizations to reduce operating costs from the very beginning, which can result in both a much healthier program and a CX budget that always stays in the black. CX practitioners can use this method to strive for an ambitious program while still avoiding unanticipated costs.

Factor #2: Vendor Flexibility

Though picking an experience partner and implementing its capabilities is no small task, the days of rigid, prepackaged experience programs are drawing to a close. This is great news for businesses because they can now work with vendors to create a versatile experience solution instead of attempting to wrap themselves around an unflinching list of features (many of which a given company may not actually need).

Solution flexibility enables CX practitioners to avoid unanticipated costs by paying only for what they need from a vendor. For example, would your brand benefit from an analytics team or does that capability already exist within your organization? What about a self-service approach versus full management from the vendor? Once practitioners consider these questions, they should select a partner that’s flexible enough to meet their needs without showering them in unneeded extras and—you guessed it—unnecessary costs.

Factor #3: Nonparticipation Costs

There’s another element to cost consideration that often goes, well, unconsidered when brands talk about implementing an experience program, and that’s what happens when companies don’t have such an initiative in place.

Feedback collection, experience improvement, and customer centricity are all more important now than ever before. These ideas are the means by which brands can both create a better experience for customers and use that capability to plant a flag at the top of their vertical. Therefore, brands should consider the very real opportunity cost of not collecting, analyzing, and implementing feedback. An experience program isn’t a luxury anymore—it’s non-negotiable for any company that wants to succeed.

Taken together, these three methods can empower brands and the experience practitioners who work for them to avoid unanticipated costs and keep their programs viable. They can then use their programs to achieve what we just talked about: a meaningfully improved experience for customers and thus a more commanding presence in their marketplace.

Three Ways to Convince The C-Suite Your CX Program is Essential

Proving experience programs’ worth isn’t easy, but it needn’t be the bane of CX practitioners’ existence. In fact, we’ve discovered three ways to convince the C-suite that experience programs are much more than just a garnish.

It’s not uncommon for organizations to consider customer experience (CX) programs a nicety—something powerful, no doubt, but also just a luxury instead of an essential component of business success. This attitude prevails even as today’s marketplaces become more competitive and the COVID-19 pandemic changes customer wants and needs faster than many brands can keep pace with.

As we outline in our recent paper on this subject, proving experience programs’ worth isn’t easy, but it needn’t be the bane of CX practitioners’ existence. In fact, we’ve discovered three ways to convince the C-suite that experience programs are much more than just a garnish. These three methods are:

  • Aligning Capabilities With Strategic Objectives
  • Pitching Customer Centricity
  • Demonstrating The Power of Real-Time Feedback

Method #1: Aligning Capabilities With Strategic Objectives

As we just mentioned, marketplaces and industries are all becoming more competitive, which means that brands must strive to provide the best customer experience possible if they hope to stand out. The specific goals that businesses put forth to accomplish that vary wildly from industry to industry, but there’s one common denominator here: CX capabilities that enable these goals can take a company all the way to the top.

With that in mind, CX practitioners who want to prove the necessity of their programs need to select software that can enable business objectives. A lot of organizations take this to mean that CX software is useful only for measurement. Measurement is important, of course, but the best technology empowers brands to execute something much more important than measurement: action. A brand’s ability and willingness to take action on CX learnings determines whether that organization is a transformative leader, or a follower that’s content with management.

Method #2: Pitching Customer Centricity

This tip may sound too general, maybe even like it’s a Herculean task, but consider that the organizations that do best within their verticals are the ones that effectively disseminate CX learnings throughout the business rather than leave statistics siloed up with an experience team. CX practitioners can pitch their programs by pointing to this example and encouraging their organizations to follow suit. All it takes to create a culture of customer centricity is desiloing CX intelligence and handing it out to the right departments and stakeholders.

This approach has another advantage in that it can help CX practitioners create grassroots support for their initiatives. Creating customer centricity can help employees become more invested in their work and more strongly feel that it matters. Their own insights and feedback is also an invaluable component of any CX initiative, and collecting it can make them feel heard. With this approach, practitioners can ride a groundswell of bottom-to-top support all the way to the boardroom.

Method #3: Demonstrating The Power of Real-Time Feedback

This tip overlaps somewhat with the first method we talked about, but the power of real-time feedback truly deserves its own special mention. Real-time feedback is the only truly effective way for brands to know which customers are promoters and which are detractors, enabling them to both save at-risk customers and identify the themes common to both groups’ view of the business.

Real-time feedback also empowers brands to achieve four business goals that practitioners can use to further assert their programs’ value. These goals include acquiring customers, retaining existing ones, cross-selling or upselling to established customers, and lowering cost to serve. Practitioners who pitch real-time feedback through this paradigm can both better tie it to financial goals and give the C-suite something more specific to chew on than, say, “becoming more customer-centric.”

These three strategies are effective means of introducing or ensuring the longevity of CX programs at any brand, and can help CX teams make the case that experience initiatives are no mere flight of fancy but rather the key to transformational success in today’s business world.

4 Ways to Measure (and Prove) B2B CX Program Results

Tying experience program results to improved outcomes often proves to be the most challenging aspect of running an experience program, especially since stakeholders usually express at least a little skepticism alongside all the buy-in. Luckily, there are several tried-and-true metrics that practitioners can track to justify ROI.

If you’re a practitioner who won support for your B2B experience program and have since implemented it across your organization, congratulations! Garnering sponsorship for experience programs is not easy, but doing so means that you’ve built trust at and received investment from both the frontline and executive levels.

Now comes the hard part: proving results and justifying ROI.

Tying experience program results to improved outcomes often proves to be the most challenging aspect of running an experience program, especially since stakeholders usually express at least a little skepticism alongside all the buy-in. Luckily, there are several tried-and-true metrics that practitioners can track to justify ROI, and we’re going to hit them all right now:

  • Customer Acquisition Growth
  • Customer Retention & Recovery
  • Upselling Established Customers
  • Profitability From Lowered Costs

Metric #1: Customer Acquisition Growth

This is one of the biggest goals that most brands set for their experience programs, which makes it a vital metric for practitioners to keep track of as their initiatives take off. Acquiring new customers is neither simple nor cheap, and if there’s one group of stakeholders who constantly bears this fact in mind, it’s the C-suite.

Tracking customer acquisition is thus a must for any B2B experience program. Doing so demonstrates an experience program’s merit to all stakeholders involved, especially since, as we just mentioned, acquiring new customers is no small task. Experience platforms that can track changes and monitor new growth are especially useful here since they make proving acquisition relatively straightforward.

Metric #2: Customer Retention & Recovery

There are two big reasons why customer retention is a powerful and pertinent B2B experience metric: first, retaining customers is far cheaper than acquiring new ones, and second, most brands begin experience programs to, well, improve experiences for their existing customer base. Experience practitioners can prove their programs’ effectiveness at hitting both goals by tracking customer retention and recovery.

There’s a myriad of ways to demonstrate experience initiatives’ value when it comes to customer recovery. For example, these programs often make it simple for practitioners to survey customers who reach out to contact centers, garner feedback, and turn it into actionable intelligence. That intelligence can then be used to meaningfully improve both call center processes and customer retention along with it. All of those metrics can be tied directly to experience programs.

Method #3: Upselling Established Customers

Retaining existing business is great, but many B2B brands set their sights on a more ambitious goal: increasing their share of wallet with their established customer pool. The tools, improvements, and processes afforded by experience programs make this goal possible, and practitioners can and should tether the improvements brought about by their efforts to any increase in share of wallet.

Practitioners commonly use experience programs to upsell existing customers by honing in on those individuals’ needs and wants. They can also use these programs to call upon a backdrop of operational and financial data, which grants B2B organizations a 360-degree view of who these individuals are. Practitioners and customer experience (CX) teams can then identify and act upon upsell opportunities.

Method #4: Profitability from Lowered Costs

This ROI metric can be less flashy than sporting new customers or increased share of wallet from existing ones, but the C-suite loves it no less.

In addition to providing meaningful experience improvement opportunities, a well-run experience program can help brands identify ways to eliminate waste and save costs. Experience practitioners can establish their programs’ value by showing stakeholders such opportunities as their initiatives reveal them, giving B2B organizations the chance to save money while also being empowered to improve experiences. To put it candidly, nothing screams “value” to an organization quite like increased profitability.

Acquiring sponsorship for an experience program isn’t easy. Harder still is proving that program’s worth. However, practitioners who focus on proving their programs’ worth through these four lenses will have a markedly easier time actually doing so. They can then secure additional resources to expand their programs’ scope and reap additional success for themselves, the B2B brands they serve, and the customers who sustain those organizations.

Learn more about B2B experience programs, proving ROI, and creating continued success here.

What Is Customer Lifetime Value?

The technical definition of Customer Lifetime Value (CLV) is the revenue earned from a single customer over time. It’s an equation that subtracts the cost to acquire a new customer (CAC) from the total revenue from that customer. The goal is to make the revenue-over-time from each individual customer as high as possible.

But the technical definition doesn’t cover the magic that’s actually in customer lifetime value – as a metric and as a mission for a digital marketplace,  an e-commerce site, and SaaS businesses. Because when you go after customer lifetime value with intention, making it one of your “North Star” metrics, you’ll find that the cost-to-acquire actually shrinks. It becomes less expensive to acquire new customers, and the revenue pours in exponentially. 

We are also at an inflection point with SaaS. While many SaaS companies are still largely concentrated on acquisition-based growth through demos and trials, we’re seeing a shift to focus on the end-user and the metrics that capture how happy they are, because those end-users lead growth. And that’s where customer lifetime value comes in as a business case. It is the ideal way to tie customer loyalty to revenue.

Those end users who are sticking with you are buying more from you (cross-sells and upsells) and they’re telling their friends and colleagues how great you are (referrals). In a sense, they become your virtual sales army. They’re out there warming up leads and sending them to you, so you don’t have to pay to find them

This is the magic we’re going to unlock for you in this comprehensive article. If you want to know how to maximize your bottomline, then improving Customer Lifetime Value is key. And we’re going to explain how it all works, and how you can start using it to get better ROI for your business right now.

Part 1: Making the case for Customer Lifetime Value as the key metric for your customer experience strategy

I don’t know a single company that hasn’t pondered these questions:

  • What resource investment will have the most impact on customer health and revenue growth? 
  • What can (or should) I automate?
  • Should I invest more money into customer experience (CX), customer support, or customer success right now?
  • Should we focus on building this new feature or should we focus on infrastructure improvements that might make our platform more secure or faster, etc.?
  • Should we invest in self-service onboarding to improve the journey for the end user?

The answers to all of these questions lie in Customer Lifetime Value. 

If your business thrives on high-volume sales and high turnover, then you’re probably not a subscription-based business – but you also don’t need to worry so much about customer lifetime value. 

But, if your business would benefit from high-volume sales AND returning customers AND lower acquisition costs, then customer lifetime value is your metric, and you’ve probably got your answers to the above questions. The more you invest in both user experience and customer experience, the less you have to invest in customer support, leading to organic growth and a higher customer lifetime value.

Customer lifetime value isn’t a passive metric – a numerical pat on the back for when you’ve done a “good job.” It’s an active, actionable metric that can be used in a few different ways.

Let’s look at a few different ways to use the Customer Lifetime Value metric:

CLV as Profit Metric

Traditionally, customer lifetime value has been used as a benchmark for whether your business is going well or going under. You look at your CLV/CAC ratio, and if it works out to at least 3 or higher (for every $1 dollar you spend acquiring a customer, you earn at least $3 dollars) you’re in the clear. You could then calculate the CLV/CAC ratio across your marketing channels to determine which are creating the most lifetime value (invest in those more) and which aren’t.

CLV as Customer Persona Builder

Once you start parsing out which clients have the highest customer lifetime value, you can look for what they have in common in terms of demographics, psychographics, user behavior, how they found you, and other characteristics. You can then use those commonalities to create better customer personas so you can go after higher CLV clients with intention.

Predictive CLV

Customer lifetime value can be used to predict the lifetime value of new customers when you examine current behavior and purchase patterns, and then base projected behavior and patterns based on those early indicators. You might already know how to predict churn based on “red flag” customer actions, and this concept is the same but in the opposite direction. You look for retention and upsell-predictive behaviors by reverse engineering what your best customers did at the beginning, middle, and ends of their journeys with you (if they’ve ended!). 

CLV as Key Performance Indicator

Customer lifetime value is a broad KPI of how well you’re serving clients, how valuable your product or service is to them, and how well you’re delivering your solution with the appropriate customer experience. It’s a great North Star metric. You know you’re headed in the right direction as CLV rises. But, you’ll also need metrics that tell you, more granularly, what’s going on and why at each stage of the customer journey. So we also use Customer Journey Metrics like Net Promoter Score, Customer Effort Score, Customer Satisfaction, etc.

Once you start tracking customer lifetime value, you can a lot with it to improve your business – which we’ll get to in Part 3. But for now, let’s look at customer lifetime value as an equation – or really, several equations.

Part 2: Customer Lifetime Value as Equation – how to crack the code of calculating this complicated metric

If you are not mathematically-inclined, I’ll make this as straightforward as possible. 

Customer lifetime value is revenue you expect to receive from a customer over time, less the cost of acquiring and keeping that customer. 

Here it is in equation form:
CLV = (ARPU X average # of months or years retained) – (CAC + CRC) 

People have been refining ways to calculate more accurate CLV ratios for years. What’s so hard? So. many. variables. Here are the basic numbers you’ll need for the CLV calculation for a SaaS business:

Average Monthly Revenue Per Customer (ARPU)

Here are all the different ways customers bring in value in a subscription software business model.

  • Original revenue
  • Renewal revenue
  • Upsell revenue
  • Cross-Sell revenue
  • Referral Revenue

Most calculations only deal with original revenue and renewal revenue, but that doesn’t cover the whole picture. When calculating Average Monthly Revenue Per Customer (ARPU) for our customer lifetime value equation, just remember to account for upsells and cross-sells, not just original revenue and renewal revenue. Referrals take care of themselves — they’ll show up in the customer acquisition cost (CAC) calculation because you’ll see that you’re getting more new customers without spending more on sales & marketing.

You’ll also want to know your CAC because the two are intertwined. Your CLV will increase if you are able to increase revenue from customers while maintaining or lowering your acquisition cost. 

Customer Acquisition Cost (CAC)

How much you spent on sales & marketing in a given time period (learn more about this here)

Divided by…

How many new customers you gained in the same given time period

Customer Retention Cost (CRC)

The cost of serving the customer is often overlooked in CLV calculation. And if your onboarding customer success and/or customer service programs are significant, you definitely want to factor in Customer Retention Cost. Totango, a Wootric integration partner, wrote a whole book on calculating CRC, but a quick estimate looks like this: 

How much you spend to onboard, train and support customers in a given period

Divided by…

How many new customers you gained in the same given time period

Customer lifetime value calculation in non-subscription models

One more way to calculate CLV is through a predictive model that can be highly accurate. This method is common in consumer businesses such as e-commerce. That equation looks like this:

CLV = (Average monthly transactions X Average order value) X Average gross margin X Average Customer Lifespan*

*The average customer lifespan is calculated in months.

Segment Customer Lifetime Value to make it more actionable

Calculating customer lifetime value for your company can be revealing and is a great start to working with this metric.  Like measuring NPS though, it really isn’t actionable until you start segmenting the metric. To make customer lifetime value more actionable and predictive, you’ll want to separate these numbers by customer segment and acquisition channels. That’s when you’ll be able to optimize your acquisition strategies to raise your CLV rates even higher.

Start by looking at customer lifetime value by pricing tier or persona. For example, you may discover that the CLV for enterprise customers is no higher than self-service customers once you factor in the high cost of acquiring and supporting “the big fish.” 

Part 3: The 4 Most Powerful Ways to use Customer Lifetime Value to grow your business

To use CLV as an actionable, predictive, productive metric, you have to segment your users and rank them by their CLV. Then you can look at the data you’ve collected on them – which acquisition channels they came from, what their first interaction was on your website, what their customer journey looked like through onboarding and beyond – to optimize each stage of the customer journey.

And then you can return to customer lifetime value as a ‘big picture’ measurement of your optimization progress. 

Here are three primary ways to use customer lifetime value to optimize acquisition and retention.

1. Optimize your acquisition strategies for CLV – and use CLV to optimize your acquisition strategies.

Your CRM platform should tell you which channels customers came through to find you, and you may notice that your high-CLV customers tend to come from one of those channels over the others. 

One of the most clear-cut stories of how a big company used customer lifetime value to increase profit is IBM. IBM used customer lifetime value to determine the effectiveness of their marketing channels to attract high-spending customers – direct mail, telesales, email, and catalogs per customer (yes, this is an old story – way back in 2008). When they reallocated resources to the best-performing channels, they 10Xed their revenue. 

It’s low-hanging fruit to decide to spend more marketing money on the channels yielding the highest CLV clients. But we can go one step further.

2. You can use your Customer Lifetime Value to create better buyer personas.

Yes, this requires a platform that can gather all of the available information on each customer. But use whatever information you’ve got. You will find that your high CLV customers have a lot in common (though you may need to form segments for the commonalities to clearly emerge). 

Once you have your high CLV buyer personas, you can use them to form marketing, outreach, and retention strategies based on their specific acquisition channels and user behavior through onboarding and retention. 

For example, let’s say that you find that your high CLV clients come to you through G2 or Capterra. And once they reach your site, they don’t just “buy now” – they have at least one interaction with your live chat helpline. Your high CLV customers need a conversation before converting, which means if you tweak your G2 listing or website content answer their questions without having to reach out, you’ll likely see higher conversions from customers who’ll stick around.

3. Use Customer Lifetime Value with Customer Success for higher retention rates & referral revenue

Customer lifetime value and customer success are so intertwined as to be inseparable. Why? Successful customers don’t leave. So, when you want to improve your customer lifetime value, having a customer success program in place is one of the best ways to do it. Customer success asks, at each stage of the customer journey: What is the customer’s ideal outcome, and how can we best move them towards it? Then the customer success team can create strategies around supporting customers at pivotal moments – like places in the onboarding process where customers tend to get frustrated and leave (Customer Effort Score surveys are ideal for flagging these points of friction) or using churn-predictive behaviors to ‘red flag’ certain interactions to receive Customer Support pop-up chats.

4. Use Customer Lifetime Value to obtain more referrals from customers.

Your long-term high CLV customers are your brand ambassadors and influencers, and once you identify them, you can start to leverage that by rewarding and strengthening their connection to your brand. That could be something as simple as inviting them to be part of a free Beta testing group, so they can give you their insights into the next iterations of your product or service, or even just asking them to write an online review. Some businesses host online communities for their best clients, or offer them priority support.

Want an even easier way to identify the customers most likely to refer you to others? Learn how to use NPS surveys to not only find your promoters, but encourage them to promote you more.

5. Use Customer Lifetime Value to guide product design and validate product development decisions at the business level.

Product teams may be removed from revenue goals on the day-to-day, but strategic decisions about where to expend engineering resources should be made with business impact in mind. Product can use CLV to inform what customer segments the product should be designed for.  Building CLV-related goals into user stories or feature specifications can help prioritize the roadmap and provide a success metric for retrospective once the product is out the door. 

Part 4: 10 Ways to Increase & Optimize Your Customer Lifetime Value

  1. Prioritize customer experience above everything else. And don’t just say it; measure it with metrics like Net Promoter Score, Customer Satisfaction, and Customer Effort Score. Calculate and track churn rates and engagement metrics.
  2. Invest in customer success. Customer success drives acquisition, retention, and customer spending (upsells and cross-sells), raising customer lifetime value by helping customers achieve their ideal outcomes.
  3. Invest in UX testing. The data you get from UX testing makes your product easier to use, reducing friction, and making it a must-have tool for your users. 
  4. Pay special attention to onboarding. Churn happens most frequently during or shortly after onboarding, so paying attention to churn-predictive behavior patterns (often identified by a Customer Effort Score survey) in the onboarding process can help you form a strategy to smooth those friction points and find easier ways to move your client towards meaningful success milestones.
  5. Bring product management, customer success, customer support, and marketing together in shared responsibility for metrics. Collaboration between product and customer success is common, but it is a good idea to expand the team because they have so much to gain from working together. For example, with onboarding, product managers need to understand how their tech decisions affect adoption and retention metrics; and customer success teams need to have access to onboarding user data that helps them identify upsell opportunities. Some shared metrics for success include NPS, churn rate, trial conversion rate, adoption rate, and, of course, customer lifetime value.
  6. Use CLV as a segmentation tool.  This allows you to deliver appropriate experiences to customers who are high-value, and who have the potential to become high-value. The appropriate experience might be the level of customer support each segment receives, or the messaging they get throughout their buyer’s journeys. You may also find that each CLV segment has different pain points and needs, which you can target for even higher acquisition and retention rates.
  7. Ask your most loyal customers for support. Following up a positive NPS survey response with an automated request for an online review is simply asking happy customers to follow through on what they just said they’d be willing to do. They’ve already said yes – so make it easy for them to act on promoting you. This won’t directly affect your CLV score, but it will drive down your CAC as the referrals come in.
  8. Keep customers engaged by adding value to your product or service, or through high-value content. If you don’t have substantial updates/improvements/expansions planned for your product, you can keep customers engaged with educational materials–i.e. content that helps them reach their ideal outcomes faster and easier. This has the added benefit of being useful for top-of-funnel marketing as well.
  9. Listen to your customers and act on their feedback.  Voice of customer data is so important for improving products and reducing friction. The only problem is that sentiment analysis at scale can be difficult without the right tools.
  10. Don’t “acquire customers” – build relationships. The customers who stay with you the longest feel like they know you. They feel like you know them. You’ve become an integral part of their daily lives, and they’d miss you if you went away. So consider changing the way you think of acquiring customers. You’re building relationships. And the more personalized and personal you make your customer interactions, the more likely your customers will feel connected to you and committed to your brand.

Maximizing Customer Lifetime Value is really a whole-company effort, requiring a great product, great service, and a deep understanding of your customers’ needs, frustrations, and desires. It’s a ‘big picture’ metric; a North Star number to guide you towards creating better customer experiences. But this one metric can also shed light on valuable segments and strategies that can profitably impact your business. customer lifetime value is a number you can’t afford to ignore.

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