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Tech Outages and Customer Feedback: How a Leading Bank Leveraged Pearl-Plaza’s Platform

The CrowdStrike outage shows the need to be prepared when crises happen, as they don't just impact operations—they shake customer confidence and loyalty.
Three business people sitting in a large room while typing

Did you know that 77% of customers expect to interact with someone immediately when they contact a company during a crisis? 

In our hyper-connected world, tech outages and cybersecurity incidents have become an unfortunate reality. The recent global outage affecting major service providers like Microsoft and CrowdStrike has highlighted the need for businesses to be prepared. When such disruptions occur, they don’t just impact operations; they shake customer confidence and loyalty. For enterprise companies, the stakes are even higher. The key to navigating these turbulent times lies in capturing and responding to customer feedback as quickly as you can. 

Recognising the urgency, Pearl-Plaza experts have quickly put together a framework on best practices to help businesses navigate these disruptions effectively.

The Significance of Real-Time Feedback During Outages

When a tech outage hits, customers immediately feel the impact. Whether it’s a supermarket where transactions are delayed, a bank with disrupted online services, or an airport where flight information systems go down, the frustration is real—and customers have little bandwidth for the inconvenience.

Real-time feedback during these moments is more important than ever before. It allows businesses to understand customer pain points as they happen and to respond as quickly as possible.

Capturing feedback in real time isn’t just about damage control—it’s about gaining insights into the customer experience during a crisis. This immediate understanding helps businesses prioritize issues, allocate resources effectively, and maintain a proactive stance rather than a reactive one.

What Sources Should You Be Capturing? 

During a crisis, feedback floods in from various channels—social media, emails, call centers, in-app messages, and more. Manually sorting through this avalanche of information is just not possible. 

Your CX platform should be aggregating feedback from all these sources, providing a holistic view of customers—what they’re feeling, what they’re saying, what they need. Whether a customer is calling about a delayed service, emailing about an inaccessible account, or leaving a message through your app, your CX platform should be capturing all of it. This omnichannel customer experience approach makes sure that no feedback is overlooked, and enables your businesses to respond effectively to the most pressing issues.

How a Leading Bank Used Pearl-Plaza’s Platform to Navigate a Major Tech Outage

When the recent tech outage disrupted services across multiple industries, a leading Australian bank found itself at the epicenter of the crisis. With online banking services down and customers unable to access their accounts, the potential for a significant loss of trust and satisfaction was high. But, by leveraging Pearl-Plaza’s Advanced AI and Workflow capabilities, the bank was able to turn a potential disaster into a proof point that highlights its commitment to customer experience.

Identifying and Analyzing Feedback with Advanced AI

As soon as the outage hit, the bank saw a surge in customer inquiries and complaints across various channels, including emails, call centers, social media, and their mobile app. Sorting through this massive influx of feedback manually would have been in impossible. Instead, the bank utilized Pearl-Plaza’s advanced natural language processing (NLP) to aggregate and analyze the feedback in real time.

The AI-powered text analysis software swiftly categorized the feedback based on urgency and topic, identifying the most affected services, such as online transactions, account access, and customer support. By using NLP, the system was able to understand the underlying sentiment and priority level of each piece of feedback. This allowed the bank to quickly understand the most critical pain points for their customers.

Proactive Communication with Targeted Updates

Using these insights, the bank implemented a proactive communication strategy. They used Pearl-Plaza’s workflow capabilities to automate and personalize their responses, ensuring that each customer received timely and relevant updates. Here are some examples:

  • Emails and Notifications: Customers who prefer using online banking received detailed emails explaining the nature of the outage, expected resolution times, and alternative ways to manage their accounts during the downtime.
  • Social Media Responses: The bank’s social media team was equipped with data-driven insights to address widespread concerns and provide real-time updates on platforms like Twitter and Facebook.
  • Call Center Scripts: Pearl-Plaza’s platform helped create dynamic call center scripts that guided agents in addressing the most common issues and providing accurate information to anxious customers.

Ensuring Transparency and Maintaining Customer Satisfaction

The bank’s commitment to transparency was evident through their consistent and honest communication. They didn’t shy away from acknowledging the inconvenience caused by the outage and re-assured customers by detailing the steps being taken to resolve the issues. This transparency helped in maintaining customer trust and satisfaction during a challenging time.

Strengthening Customer Relationships with AI-Driven Insights

Beyond managing the immediate crisis, the bank used the incident as an opportunity to strengthen their customer relationships. Pearl-Plaza’s Advanced AI tool provides deep insights into the specific needs and preferences of their customers. For example, they identified a segment of customers who preferred SMS updates over email, and they can adjust their communication strategy accordingly.

By analyzing the feedback and outcomes, the bank can now implement several improvements for a stronger future:

  • Enhance their digital infrastructure to prevent similar outages in the future.
  • Develop more robust contingency plans and customer communication protocols.
  • Personalize customer service strategies based on the preferences identified during the crisis.

By aggregating and analyzing feedback in real time, automating personalized responses, and maintaining transparent communication, the bank was able to manage the crisis effectively and even strengthen their customer relationships.

For CX Leaders, this case study underscores the importance of leveraging advanced technology to handle crises. Pearl-Plaza’s integrated customer experience platform provides the tools necessary to not only respond to immediate challenges but also to build a more resilient and customer-centric organization.

Improve Your Crisis Management with Pearl-Plaza

Ready to transform your crisis management strategy? Learn how Pearl-Plaza can help you capture real-time feedback and enhance customer loyalty during tech outages. Talk with an expert today for more information.

References 

Salesforce. State of the Connected Customer Report. (https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/). Accessed 7/19/2024.

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Unstructured Data is the Key to Improving the Customer Experience: Here’s Why

unstructured data analytics

Any successful business knows that understanding their customers is key to success. The best way to do that is by being able to understand the vast amounts of unstructured data that come with customer interactions.

What is Unstructured Data?

Unstructured data refers to information that doesn’t have a predefined data model or isn’t organized in a structured manner like traditional databases. Unlike structured data, which fits neatly into rows and columns, unstructured data lacks a clear format, making it more challenging to analyze using traditional data processing techniques.

What Are the Characteristics of Unstructured Data?

Unstructured data is characterized by its lack of organization. It doesn’t adhere to a predefined schema or format, which makes it difficult to organize and categorize. Unstructured data often comprises a significant portion of the total data generated by organizations and individuals. Analyzing unstructured data requires more advanced techniques than standard data analysis. 

Where Does Unstructured Data Come From?

Unstructured data can come from various sources. Anytime data is qualitative, like how different customers felt they were treated by your business, it is most likely unstructured data. Other examples of unstructured data sources include social media posts, call transcriptions, and customer reviews. 

Why Is Unstructured Data Important?

To put it simply, it is estimated that close to 90% of all data is unstructured. Unstructured data is so important because it represents such a large portion of the total amount of data you will interact with. If you do not have ways of dealing with this data, you will fall behind your competitors. 

Furthermore, the most important customer data is unstructured. Normal data analysis won’t be able to tell you about a customer’s feelings related to your brand, and how those feelings will affect their interactions with your brand in the future. 

Structured Data vs Unstructured Data

Structured data and unstructured data differ primarily in their organization, format, and ease of analysis. Structured data is organized neatly into rows and columns within a database or spreadsheet, following a predefined schema. Unstructured data doesn’t adhere to a specific format or structure, which makes it more challenging to categorize and organize.

Similarly, structured data typically exists in a structured format such as databases (SQL, NoSQL), spreadsheets (Excel), or other tabular formats. Unstructured data doesn’t follow a standardized structure and can exist in forms from audio files to customer reviews. 

Overall, structured data typically represents a smaller portion of the overall data compared to unstructured data, and is relatively easier to analyze using traditional data analysis techniques. 

Examples of Unstructured Data

The best example of unstructured data is customer reviews. Online reviews don’t usually hold much quantitative value, but that doesn’t mean their impact is any less significant. Customer reviews can either elevate your brand by increasing consumer trust and brand reputation, or they can deter potential customers away from your business.

Another example of unstructured data is a call transcript. Customers who speak with contact center agents often provide key pain points that they need to be able to identify. Analyzing these transcripts with solutions such as conversation intelligence can reveal valuable insights into customer preferences, concerns, and issues, which can inform business strategies and improve customer service.

How is Unstructured Data Used?

Unstructured data, despite its inherent complexity, holds immense potential for various applications across industries. By leveraging advanced unstructured data analytics techniques, organizations can extract valuable insights and derive actionable intelligence from unstructured data. 

When customer data comes in the form of social media posts, reviews, or survey responses, it can be analyzed to gauge public sentiment toward products, services, brands, or events. Sentiment analysis algorithms classify text data as positive, negative, or neutral, which provides valuable feedback for businesses to understand customer perceptions and sentiment trends.

Consider a retail company that monitors social media platforms to analyze customer feedback about its new product release. By conducting sentiment analysis on tweets and comments, the company identifies areas of improvement, addresses customer concerns promptly, and adjusts its marketing strategies to enhance customer satisfaction down the road.

Advantages and Disadvantages of Unstructured Data

Unstructured data offers organizations rich insights and real-time feedback from diverse sources like social media and customer interactions, driving innovation and flexibility in decision-making. However, its inherent complexity, large volume, and potential quality and security challenges can pose significant hurdles in analysis, storage, and privacy protection. Here is an overview of the advantages and disadvantages of unstructured data:

Advantages of Unstructured Data:

  • Rich Insights: Unstructured data often contains rich, diverse information that can provide valuable insights into customer behavior, market trends, and business operations. By analyzing unstructured data, organizations can uncover hidden patterns, correlations, and opportunities that may not be apparent from structured data alone.
  • Real-Time Feedback: Unstructured data sources such as social media, customer reviews, and online forums provide real-time feedback and insights into customer sentiment, preferences, and opinions. This enables organizations to respond quickly to customer needs, address concerns promptly, and adapt their strategies in real-time to meet changing market demands.
  • Flexibility: Unstructured data is inherently flexible and adaptable, allowing organizations to capture and analyze a wide range of data types and formats, including text, images, videos, and audio recordings. This flexibility enables businesses to gain a comprehensive understanding of their customers and operations, driving innovation and competitive advantage.
  • Innovation: Unstructured data fuels innovation by providing new sources of inspiration, creativity, and discovery. By exploring unstructured data sets, organizations can uncover novel insights, ideas, and solutions that lead to breakthrough innovations, product enhancements, and business opportunities.

Disadvantages of Unstructured Data:

  • Complexity: Unstructured data is inherently complex and challenging to manage, analyze, and interpret. Unlike structured data, which follows a predefined schema and format, unstructured data lacks organization and consistency, making it difficult to extract meaningful insights without advanced analytics tools and techniques.
  • Volume: Unstructured data often constitutes a significant portion of the total data generated by organizations, resulting in data overload and scalability issues. Managing and storing large volumes of unstructured data can strain IT infrastructure, increase storage costs, and impact performance.
  • Quality: Unstructured data may vary widely in quality, accuracy, and reliability, leading to potential inaccuracies and biases in analysis and decision-making. Cleaning, preprocessing, and validating unstructured data can be time-consuming and resource-intensive, requiring careful attention to ensure data quality and integrity.
  • Privacy and Security Risks: Unstructured data may contain sensitive or confidential information, such as personal data, intellectual property, or trade secrets, which pose privacy and security risks if not adequately protected. Unauthorized access, data breaches, and regulatory compliance issues are significant concerns associated with unstructured data, requiring robust security measures and data governance frameworks to mitigate risks.

Overall, there are various pros and cons to the use of unstructured data. But, if businesses are diligent in setting up the proper unstructured data analysis processes, it can provide a wealth of useful information to your business. 

How Unstructured Data Relates to the Customer Experience

Harnessing the power of unstructured data will allow you to create the best customer experience for your business. By properly analyzing unstructured data, you will not only be able to identify what your customers are currently liking or disliking, you’ll be able to predict their expectations in the future utilizing predictive customer analytics. Here are some ways that unstructured data can help you improve the customer experience:

Understanding Customer Sentiment

Unstructured data, such as social media posts, customer reviews, and feedback emails, contains valuable insights into customer sentiment. By analyzing the language, tone, and context of customer interactions, you can gain a deeper understanding of customer attitudes towards your products, services, and brand. This knowledge enables organizations like yours to identify areas for improvement, address customer concerns proactively, and enhance overall satisfaction.

Personalizing Customer Interactions

Unstructured data allows businesses to personalize customer interactions and tailor their offerings to individual preferences. By analyzing customer data from various sources, such as call transcripts and purchase histories, organizations can identify patterns and trends that inform personalized marketing campaigns, product recommendations, and customer service interactions. This personalized approach can also be a part of larger AI customer experience initiatives that enhance the customer experience, foster loyalty, and drive customer engagement and retention.

Monitoring Brand Reputation

Unstructured data allows businesses to monitor and focus on their brand reputation management in real-time. By tracking mentions, reviews, and conversations about their brand on social media, news sites, and online forums, organizations can quickly identify and address potential reputation issues or crises. This proactive approach helps safeguard brand integrity, maintain customer trust, and mitigate the impact of negative publicity on the customer experience.

Harness Your Unstructured Data with Pearl-Plaza

Ready to unlock the full potential of your unstructured data with Pearl-Plaza? Schedule a demo today and discover how our platform can drive actionable insights and elevate your customer experience strategy!

References 

Research World. Possibilities and limitations, of unstructured data. (https://researchworld.com/articles/possibilities-and-limitations-of-unstructured-data) Accessed 2/29/24.

Watch the Video – See Pearl-Plaza in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Special Feature: NLPs, LLMs and the Future of AI

Read the Interview: Pearl-Plaza Chief Scientist Paul Barbara shares insights and predictions about major topics leaders should consider regarding NLP and AI.

Read the Guide Now: Pearl-Plaza Advanced AI – Supercharging CX

Unlock the full potential of your customer and employee feedback – read the comprehensive guide now.

Pearl-Plaza Advanced AI: Supercharging CX

Close up of businessman using a laptop with graphs and charts on a laptop computer.

Data is gold. Data is truth… but data is useless if you can’t rely on it. 

Understanding customer and employee sentiment is more than just a competitive edge—it’s essential, with companies in every industry and sector focusing resources on comprehending it. 

We have a revolutionary tool that we’d like to share, one that has helped businesses large and small navigate this space. Pearl-Plaza Advanced AI turns diverse data streams into valuable insights companies can use for their strategy. It’s been the change clients in various fields have relied on. So for starters…

What is Pearl-Plaza Advanced AI??

Pearl-Plaza Advanced AI is a comprehensive data analytics tool that integrates and analyzes structured and unstructured data using advanced Natural Language Processing (NLP) and AI. It offers a deep understanding of customer and employee feedback, transforming complex data into clear and actionable insights. 

Central to Pearl-Plaza Advanced AI’s functionality are predictive analytics and customizable dashboards, which enable businesses to understand current data trends and anticipate future customer patterns and behaviors across these data sets. 

Pearl-Plaza Advanced AI’s power lies in its ability to analyze both historical customer experience data and real-time data sources like social media and reviews. This dual capability offers businesses an advantage over competitors who may excel in historical data analysis or current data interpretation, but struggle to integrate both into timely insights. Pearl-Plaza Advanced AI’s integrated approach provides a comprehensive view, turning past and present data into powerful, actionable insights for immediate strategic impact.

Pearl-Plaza Advanced AI enables businesses to process virtually any type of content, enrich and understand that content, and visualize it through a powerful set of dashboarding tools. The engine that enables this enrichment uses AI and NLP to understand the content and derive valuable metadata, including: intent prediction, effort signals, and emotion detection. 

Let’s go over what these are and their broader implications.

Intent Prediction

Intent prediction is a crucial component of data analysis, focusing on deciphering the underlying intentions behind customer interactions. This technology uses deep learning models to predict a customer’s future actions or needs. 

For example, in customer service interactions, intent prediction can determine whether a customer is likely to purchase, seek support, or churn. By understanding these intentions, businesses can proactively address customer needs, enhancing the overall customer experience and increasing sales and customer satisfaction.

Effort Signals

Effort signals involve analyzing customer interactions to gauge the degree of effort a customer exerts in their journey. This metric is key in understanding customer satisfaction and loyalty, as higher effort levels correlate with negative customer experiences. 

By analyzing data such as the length and complexity of customer service interactions, businesses can identify areas where customers face difficulties. Addressing these high-effort points can significantly improve the customer experience, increasing satisfaction and loyalty.

Emotion Detection

Emotion detection is identifying and analyzing emotional states in customer interactions. This aspect of sentiment analysis uses a BERT deep learning model to assign an emotion to the speaker or subject of a sentence or thought. 

This technology can distinguish between emotions like happiness, frustration, or disappointment. Emotion detection helps businesses tailor their responses and strategies to align with customer emotions, enhancing personalized customer experiences and building stronger emotional connections with the brand.

Types of Data

Structured: The Backbone of Predictability

Structured data is the cornerstone of conventional data analysis, representing the world of quantifiable and measurable information. Characterized by its specific, organized format, structured data neatly aligns in rows and columns, reminiscent of spreadsheets or relational databases. This meticulous arrangement makes it well-suited for quantitative analysis, offering clear, objective, and mathematical insights into various aspects of business and customer behavior.

It is the language of logic and mathematics, offering a clear, structured view of the world that is easily interpreted by computers. Its strength lies in its straightforward aggregation and manipulation, allowing businesses to accurately quantify and measure trends, performance metrics, and other key indicators.

This data type is the foundation of data-driven decision-making, enabling enterprises to translate complex phenomena into understandable metrics. While it might lack the nuanced storytelling of unstructured data (we’ll get there in a second), structured data offers the definitive “what” in the story of customer and business interactions—the concrete, quantifiable facts that are essential for informed strategy and planning.

Unstructured: The Streaming Thoughts of Your Everyday Life

Unstructured data, the most raw and unrefined form, is abundant and profoundly human by nature. Emerging from sources rich in personal expression like open-ended survey questions, reviews, social media, and SMS messages, this data type offers a window into the authentic human experience. 

According to IDC, The Digital Source, 85% of customer data is unstructured and it’s growing at 55% per year, highlighting the vast and rapidly expanding landscape of human communication that structured data cannot capture. Tools like Pearl-Plaza’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

What sets unstructured data apart is its embodiment of language. It directly reflects our unfiltered and unstructured thoughts in their most natural state. While structured data can be seen as the mathematics of human behavior, unstructured data is pure, unadulterated human communication.

This richness, however, presents a challenge: unstructured data is the hardest for computers to decipher, as it requires understanding nuances, context, and the subtleties of human language. Despite this complexity, our deepest and most meaningful insights lie in these unstructured narratives. Tools like Pearl-Plaza’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

Bringing Them Together: The Full Story

Integrating structured and unstructured data is a key aspect of Pearl-Plaza Advanced AI and, arguably, its strongest feature. Structured data provides precise, quantifiable insights, such as the exact factors contributing to customer churn. 

While structured data gives you the numbers, unstructured data provides the “why” behind these figures. It’s found in customer verbatims and feedback, revealing the customers’ personal stories, opinions, and suggestions. It’s the narrative that puts context and meaning behind the numbers. But on its own, unstructured data can be overwhelming and hard to navigate to find the most impactful insights.

Combining structured and unstructured data tells the full story. This integration allows businesses to quantify aspects of the customer experience and understand the underlying reasons behind these metrics. With Pearl-Plaza Advanced AI, companies can sift through the rich, detailed narratives in unstructured data, guided by clear, actionable insights from structured data. This holistic approach enables a deeper understanding of customer needs and preferences, leading to more informed and effective business decisions.

Pearl-Plaza Advanced AI bridges the gap. 

Spotlight Addresses Key Business Challenges

Understanding and Predicting Customer Behavior

We mentioned this earlier, but we’d like to go more in-depth—this one’s important. One of the paramount challenges businesses face today is their inability to predict future customer behaviors. Pearl-Plaza Advanced AI  excels in this area using AI-powered, advanced analytics and machine learning algorithms. 

According to Gartner, by 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%, underscoring the efficiency gains possible with advanced AI solutions. This capability enables businesses to move beyond surface-level insights, delving into predictive analysis that anticipates future customer actions and preferences.

By understanding these predictive patterns, companies can tailor their strategies proactively, ensuring they are always one step ahead in meeting customer needs and expectations. This forward-looking approach is vital for maintaining competitive advantage and fostering customer loyalty.

Data Unification and Analyzation: A Single Source of Truth

Data silos are a significant barrier to effective decision-making in many organizations. 

Tyler Saxey, Director of CX at Foot Locker, states, “Pearl-Plaza now ticks all of the boxes. Pearl-Plaza AI solves for any previous text analytics issues. Analyzing call transcripts and getting to the root cause brings a big ROI.” Pearl-Plaza Advanced AI addresses this issue head-on by offering data unification capabilities, consolidating data from various sources and providing a comprehensive and unified view of customer information. This holistic approach is vital for creating consistent and effective customer experiences across all touchpoints.

By breaking down these silos, Pearl-Plaza Advanced AI ensures that all decisions involve a complete and accurate picture of customer data—no decisions are made in isolation. This unified view is invaluable for creating consistent and effective customer experiences across all touchpoints.

Regulatory Compliance: Ensuring Communication Standards

We live in a time with increased scrutiny of companies’ regulatory compliance. Pearl-Plaza Advanced AI is essential in ensuring that customer communications meet the necessary standards. This aspect is crucial for highly-regulated businesses in industries like finance, healthcare, and telecommunications. 

Pearl-Plaza Advanced AI can help monitor and analyze customer communications, ensuring they adhere to industry regulations and standards. This compliance monitoring not only helps avoid potential legal issues but instills trust among customers, who are increasingly concerned about how their data is handled and used. With nearly 65% of the world’s population expected to have its personal data covered under modern privacy regulations by 2023, up from 10% today, according to Gartner, the importance of incorporating advanced AI for regulatory compliance cannot be overstated.

Why Spotlight is Essential for All Businesses 

Enhancing Experiences: Tailoring Strategies for Satisfaction and Loyalty

Pearl-Plaza Advanced AI significantly enhances customer and employee experiences. 

Tony Darden, COO of Jack in the Box, shares, “The use of the Pearl-Plaza AI solution will allow us to easily analyze feedback in all its forms to receive more detailed and immediate insight from a wider variety of guest experiences. Our team is focused on using the additional insight to make business decisions without delay—having a faster time to guest improvement that will positively influence their experience with our brand leading to increased loyalty.” 

By leveraging advanced analytics to understand sentiment and feedback, businesses can tailor their strategies and offerings to better meet their customers’ and employees’ needs and expectations.

Reducing Churn: Anticipating and Addressing Customer Needs

Customer and employee churn is a major challenge for businesses, resulting in lost revenue and increased recruitment and training costs. Pearl-Plaza Advanced AI’s predictive analytics capabilities play a vital role in identifying the early signs of dissatisfaction or disengagement. By anticipating these factors, businesses can proactively address issues before they lead to churn. This proactive approach helps retain customers and ensures that employees feel valued and engaged, reducing the likelihood of them seeking opportunities elsewhere.

Strategic Decision-Making: Prioritizing Initiatives for Maximum Impact

Data-driven decision-making is at the heart of modern business strategies. Pearl-Plaza Advanced AI provides comprehensive insights that help businesses prioritize their initiatives, focusing on areas yielding the greatest cost savings or revenue increases. These insights guide businesses in allocating resources effectively, whether it’s refining marketing strategies, optimizing operational processes, or enhancing customer service. By basing decisions on solid data, businesses can maximize their ROI and align their strategies with their overall goals.

The Takeaway: A Holistic Approach for a Winning Strategy

Pearl-Plaza Advanced AI’s ability to integrate data across multiple channels is a game-changer, providing a unified view of information from various sources. This cross-platform integration is crucial for strategic planning and executive decision-making. It allows businesses to make informed decisions based on a comprehensive understanding of their operations, market trends, and customer behaviors. 

By breaking down data silos, Pearl-Plaza Advanced AI ensures that a complete and accurate picture of the business landscape backs every decision. A study by McKinsey & Company found that companies that utilize customer analytics comprehensively are 23 times more likely to outperform competitors in terms of new-customer acquisition and nine times more likely to surpass them in customer loyalty.

Pearl-Plaza Advanced AI’s ability to transform this unified data into actionable strategies makes it indispensable. Its benefits are wide-ranging and impactful, from enhancing experiences and reducing churn to aiding in strategic decision-making and facilitating cross-platform data integration. Adopting Pearl-Plaza Advanced AI is not just a step towards better data analysis, but a leap towards a more informed, customer-centric, and efficient business model.

For businesses considering Spotlight:

  • How are you currently gathering and interpreting customer and employee feedback?
  • What tools are in use for understanding customer and employee experience?
  • How is this data being used to drive experience initiatives?

A Final Word

Pearl-Plaza’s Pearl-Plaza Advanced AI stands out in the realm of customer experience management. Its ability to harness structured and unstructured data, combined with advanced analytics, positions it as an indispensable tool for businesses aiming to enhance customer engagement and make data-driven decisions. 

Adopting Pearl-Plaza Advanced AI translates into not just collecting feedback but transforming it into a strategic roadmap for business success. Stay ahead of the pack and contact us to learn more about how Pearl-Plaza Advanced AI can directly impact your business.

Watch the Video – See Pearl-Plaza in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Special Feature: NLPs, LLMs and the Future of AI

Read the Interview: Pearl-Plaza Chief Scientist Paul Barbara shares insights and predictions about major topics leaders should consider regarding NLP and AI.

Read the Guide Now: Pearl-Plaza Advanced AI – Supercharging CX

Unlock the full potential of your customer and employee feedback – read the comprehensive guide now.

Customer Success Analyst: When to Hire Someone Dedicated to the Data

The Customer Success Analyst has evolved to be the go-to person for all the data – or as Marketo put it in their Linkedin job ad, “the primary deliverable of the Customer Success Decision Analyst is to convert our Customer Success operation at Marketo into a highly data-driven business where we can measure, analyze and optimize every aspect of our engagement with our customers.”

This includes data like:

  • Feature usage patterns
  • Maturity scores
  • NPS results
  • Voice of customer qualitative feedback
  • Customer journey mapping
  • Customer experience metrics
  • Capacity models

Among all of the hats that CSM’s wear, the number-crunching, data-heavy, quantitative analyst hat is one of the most time-consuming. But because of the data-savviness this role demands, CS analysts also hold the keys to unlocking incredible potential when your business is scaling up.

The CS analyst role isn’t *just* about collecting data for dashboards and reports (and basing recommendations on that data) though. It complements the Success Operations role, which builds new tools and processes to scale CSM’s everyday activities. As the person navigating multiple platforms for data on a day-to-day business, CS Analysts know how information flows and who needs what information.

For one of Wootric’s customers, Chorus.ai, CS Analysts also take ownership of the technical onboarding process for new or upgrading customers, ensuring “a smooth implementation, including initial and ongoing training for customers.”

It’s a prime position from which to watch for opportunities to make big impacts on the success of customers – and the success of the company. That’s the subtextual expectation: By being in charge of the data, the CS Analyst knows how to use it to find untapped value.

What does a CS Analyst need to know?

Experience working with large amounts of data (SQL, Python or R) and with survey and analysis tools (Wootric, Tableau, etc.) are must-haves, but the most important qualification is having taken that data and used it to produce actionable insights.

Analysts are data story-tellers. They work with the numbers and provide context for them, creating reports to recommend strategic options and solutions. A listing for a CS Analyst position at Salesforce described one of the responsibilities as “assist in developing and delivering presentations for senior executives”, which requires strong public speaking and presentation skills.

If a company struggles with data silos, CS Analysts must bridge the gap between teams. Not only must analysts overcome the technical issues of compatibility, but they need to possess strong internal communication skills to overcome any organizational walls that may be contributing to the data isolation.

While CS Analysts own the quantitative facet of Success, a customer-centric mindset and empathy for the humans behind the numbers distinguish a great analyst from a good one. These soft skills help analysts frame their analysis to produce long-term, customer-centric solutions that support CSMs to retain customers.

What does this role look like in real life?

For some companies, the CS Analyst position can be a foot-in-the-door to Customer Success.

Anthony Enrico, VP of Customer Success at Emailage, created the Analyst position because his “CSMs were being asked to spend enormous amounts of time compiling reports and the opportunity cost of spending time deepening relationships and loyalty with customers was too great.” As a leader within the organization, Anthony was also doing a lot of work with these reports, when his time was clearly better spent working on strategy, escalations with his CSMs, and focusing on new business opportunities with the company.

So Anthony hired Bryan Mehrmann, now a CSM at Emailage. Bryan was originally brought on as the first CS Analyst to support the CSM team. Bryan compiled and sent out daily reports on customer usage trends to identify and correct anomalies as early as possible. He took detailed revenue projection reports and distilled them for the C-suite for their weekly use. Bryan took on more responsibilities as time went on.

Working together, Anthony and Bryan shaped the role as it is today. As for how the position fits into the CS team, Analysts can be promoted to full CSMs after they’ve achieved a comprehensive understanding of the product, metric drivers, and relationships with Emailage’s customers.

On the other hand, CSMs may choose to specialize in VoC data analysis like Customer Success Analyst Tim Dressel at Qualer. For him, there’s the usual collection and analysis of customer data in spreadsheets, but also a lot of room for innovation and collaboration. If he sees a red flag in the metrics, he leads investigations into those customer issues, working with his cross-functional team (and collaborating, at times, with Qualer’s Head of Technology) to make sure customers’ needs make it into the software they develop.

How do you know if you need a Customer Success Analyst on your team?

Customer Success Managers are often being pulled in five different directions at once, and when that happens, they sacrifice time on one task for another. Not only does Customer Success provides data and insight crucial to their own day-to-day, but they are the go-to team for reports for the C-suite.

Customer Success needs data. Data is at its core. So if your Customer Success team doesn’t have time to live and breathe data, you may be at the tipping point to bring in an analyst who can parse the numbers for you. This is especially important for scaling processes when anecdotal experiences have to give way to metrics.

For some companies, bringing on an Analyst to Customer Success may happen by incorporating a company-wide business analyst into the team and transitioning them into a full-time Success Analyst. Depending on the company, Customer Success may not need an Analyst until their team is four or five CSMs.

The most common theme among companies looking to hire a CS Analyst is major growth.

For example, a Wootric customer that recently started trading publicly, DocuSign, decided to add the Customer Success Analyst position as they accelerated their growth.

Analysts (& their data) are a CSM’s best friend

For Customer Success, the best way to prove value, whether it’s to senior management or to a customer, is with numbers and context. Having a role dedicated to creating robust reports to highlight value and propose inventive, data-backed solutions is an excellent way to help your current CSMs be the best that they can be at scale.

Automatically send customer feedback to Salesforce, Gainsight and Slack for quick action. Learn about Pearl-Plaza’s integrations.

Watch the Video – See Pearl-Plaza in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Not Your Average Trends Report – Read it Now!

Read it Now – The 2024 Consumer Trends Report – lead the charge into a new era of integrated CX!

85% of your CX data is unstructured – Pearl-Plaza unlocks 100% of it.

A simple 10-step guide to building your team’s integrated CX strategy.

There is a Correlation between CX and Revenue Growth – and Here’s the Data to Back It Up

Shot of a group of young business people having a brainstorming session in a modern office

“Our conclusion: superior CX drives superior revenue growth.”
– Harley Manning, Forrester

“Customers who had the best past experiences spend 140% more compared to those who had the poorest past experiences”
– Peter Kriss, Harvard Business Review

There is a lot of chatter happening in business circles about customer experience (CX) as a growth engine. It’s almost intuitive – you and I both understand how having a great experience affects us as customers. We all have businesses we love, products we’ll follow to the ends of the earth (in hopes they’ll finally go on sale), and websites we follow with almost religious fervor.

As CMO, VP of Success, or Head of Customer Support, you are constantly advocating for customer experience within your company. After all, from the very first moment the second blacksmith’s shop appeared in the village, creating competition for the first blacksmith’s shop, customer experience has been a deciding vote for who gets the business – just as much as price and quality. But as a business owner, or a professional marketer, you can’t afford to go with your gut. To win resources you need data to back up your argument that CX is the future (you know it is).

There is a correlation between CX and revenue growth, and we’ve compiled the research to back it up.

Why the effects of CX have been tricky to track

Customer experience has been treated as a ‘soft’ discipline, and I have a theory as to why. 

We’ve grown up with it. Whether watching Santa send Macy’s store shoppers to competitors in Miracle on 34th Street, or walking into Nordstrom’s shoe department to be followed around by suited young men carrying piles of boxes to the nearest padded chair. We recognize great CX when we experience it ourselves.

However, it’s inherently subjective. Subjective issues – anything based on opinion or emotion – tend to be hard to track. One person’s “helpful” is another person’s “pushy.” Your “attentive,” might be my “stalker.”

Modern tools now quantify CX

But online buyers’ journeys are different than the sales experiences most of us grew up with. With modern tracking and customer surveys, you can tell (often in real-time) whether your efforts are coming off as too much, or too little. You can identify problems and preferences, which allows you to fine tune the end experience for your target customer.

Most importantly, for the first time in human history, we have the tools to track the actual, absolute effect that positive customer experience has on a business’s bottom line. This is transforming the discipline of customer service into the science of CX.

The science of CX starts with measurement. Read the article, A Primer on the 3 Most Important CX Metrics – NPS, CSAT and CES, and start measuring CX today.

It’s no longer just “the right thing to do,” it’s an engine for measurable growth.

“CX is no longer just a discipline; it is the basic ingredient for growth”
– Winning on the Battleground of CX, Forrester

Data that ties CX to Revenue

Transaction-based v. Subscription-based CX

“What we found: not only is it possible to quantify the impact of customer experience – but the effects are huge.” – “The Value of Customer Experience, Quantified,” Harvard Business Review

Harvard Business Review looked at the revenue data from two global $1B+ businesses – one was a transaction-based business, the other was a relationship-based subscription business.

“We looked at two companies with different revenue models — one transactional, the other subscription-based — using two common elements that are relevant to all industries: customer feedback, and future spending by individual customers. To see the effect of experience on future spending, we looked at experience data from individual customers at a point in time, and then looked at those individual customers’ spending behaviors over the subsequent year.”

Transactional business models rely on frequency of customer return and how much they spend per visit. Modcloth would be a good example – they want you to come back every day and buy (or at least Save to Wishlist), and come up with ingenious ways to incentivize that behavior.

Subscription-based businesses include Software-as-a-Service (SaaS), or even those recipe kits from Blue Apron. No matter what they’re selling, the model is the same. It relies on retention, cross-sells and upsells.

The results?

After controlling for other factors that drive repeat purchases…

  • Transaction-based: Customers with the best past experiences spend140% more than those with the poorest past experiences.
  • Subscription-based: Customers with the best past experiences have a 74% chance of remaining a member for at least another year; customers with the worst experiences have a 43% chance of being a member one year later. In fact, those who gave the highest CX scores were likely to remain members for another six years.

CX Effects Across Multiple Industries

On Harley Manning’s Blog at Forrester, Manning (Forrester VP and research director) discusses two studies, conducted one year apart, that compared five pairs of publicly traded companies “where one company in each of the pairs had a significantly higher score than the other in Forrester’s Customer Experience Index during the period 2010 to 2015.”

The Customer Experience Index measures each brand on a scale from “Very Poor” to “Excellent” in these six categories:

  • Effectiveness
  • Ease of use
  • Emotion
  • Retention
  • Enrichment
  • Advocacy

Then, Forrester looked at the businesses’ revenue data and built models to calculate the compound annual growth rates for each of the ten companies over those five years.

The results:

The publicly traded companies studied ran the gamut of industry types, from cable to retail to airlines. But in terms of the CX effect, industry didn’t seem to matter as much as the reported CX scores each company received.

“In two industries, cable and retail, leaders outperformed laggards by 24 percentage and 26 percentage points, respectively. Even in the industry with the smallest spread, airlines, the CX leader enjoyed a healthy 5 percentage point advantage in global revenue. And when we compared the total growth rate of all CX leaders to that of all CX laggards we saw that the leaders collectively had a 14 percentage point advantage.” – Harley Manning, Forrester

Unlike the Harvard Business Review’s study, Forrester did not control for outside influences that could have driven revenue growth. But, they did conclusively determine that “customers who have a better experience with a company say they’re less likely to stop doing business with the company and more likely to recommend it.” They also observed that companies with superior CX saw increased growth in customers.

And, as Harley Manning points out, “Both of those factors should drive increased growth in customers and, in turn, increased growth of customer revenue.”

Essentially, as CX rises, so does revenue growth.

But there’s another interesting correlation that Forrester’s Customer Experience Index research uncovered. The top performing brands, including USAA, Barnes & Noble, Etsy, QVC and Zappos.com, “achieved a 17% compound average growth between 2010 and 2015 – which is no small feat with many of them already in the top revenue percentiles in their respective industries.” (Salemove.com)

Compared with the brands at the bottom, who only saw a compound average growth of 3%, that is a very wide gap.

To put a possible dollar amount on this, consider: “a one-point score improvement in the CX Index can lead to an increase of $65 million in revenue in the upscale hotel industry,” according to Forrester’s Harley Manning.  

CX spending is on the rise

You may think companies still seem to feel more comfortable spending money on things that do not have a direct impact on customer experience, or that Support and Customer Success teams can still be the last area to receive investment. Think again. Per Forrester research, 71% of business and technology decision-makers reported that improving CX will be a high priority for spending in the next year.

Ready to join the CX revolution?

Now with modern survey platforms, companies of all sizes can measure and improve customer experience at scale.  Forrester’s CX Index measured six attributes of experience and probably took months to collect, analyze and report. However, a lightweight approach to CX improvement using metrics such as Net Promoter Score (NPS) can get you 90% of the way there and not break the bank. 

The key is to start small. Determine your “north star” metric. Get customer feedback, take action, repeat.  Consistently repeat this process. As your company’s customer experience improves, so will your bottomline. 

Start measuring Net Promoter Score for free with Pearl-Plaza

Watch the Video – See Pearl-Plaza in Action

Check out a quick demo video to learn more about how our solutions can help your team grow your business.

Not Your Average Trends Report – Read it Now!

Read it Now – The 2024 Consumer Trends Report – lead the charge into a new era of integrated CX!

85% of your CX data is unstructured – Pearl-Plaza unlocks 100% of it.

A simple 10-step guide to building your team’s integrated CX strategy.

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