How to develop effective business metrics during digital transformation

Customer success is the best outcome for any digital transformation, but how to approach it from a customer-focused lens?

March 2022

This article discusses how to approach a customer-centric digital transformation, and the pivotal role that customer experience journeys and business metrics play in transforming how we function in the digital age.

• What is digital transformation?
• A cohesive approach for customer-centric digital transformation
• The pivotal role of customer experience journeys
• How to develop effective business metrics

How to develop effective business metrics during digital transformation

One of the benefits of defining Customer Experience Journeys is that it enables various business metrics to be mapped and captured directly into various phases of customer life cycles. This method is much more effective than preparing them in a downstream analytical system where it becomes difficult to correctly identify customer journey steps or their experiences.

In today’s digital environment customers will interact with the brand in traditional and digital channels to complete their transactions. Digital channels are increasing beyond the web and mobile. It now includes social media, connected devices, wearables, smart home devices, smart tv, smart cars, and a large variety of internet of things. A single channel alone can not deliver the compelling and connected experience that the digital customer expects today. A well-defined journey across all interaction channels and touchpoints will enable the brand to track and understand where the right optimization is required.

As we explained earlier, customer journeys are not linear. They can go back and forth, pause/resume, and hop between channels. The organization will need to invest in a good journey management platform and omnichannel capabilities to ensure a seamless and delightful experience. In addition, there will be many other capabilities required to track, monitor, and optimize these journeys, e.g., business metrics platform, analytics platform, and platform to generate actionable intelligence

This article will touch on business metrics primarily from a customer experience and digital transformation perspective. Business metrics are required in all areas of an organization therefore many organizations take a balanced scorecard approach to create an overall view of their organizational performance.

How are business metrics defined?

Let us get a brief understanding of business metrics. A Business Metric is defined as a quantifiable measure that is used for tracking and monitoring business performance. This may include tracking marketing campaigns, sales performances, new leads, revenue, company financials, etc.

TM Forum explains that a measure is more concrete, and a metric is a more abstract or higher level¹. Any quantity can be measured. But a good metric needs interpretative definition and a preferred progression. This progression of a metric indicates how well or badly a business is performing. For instance, when it comes to revenue metrics higher progression is better, and when it comes to customer response times lower is better. Often metrics are some sort of comparison between two values, e.g., a ratio, a frequency, a run rate. However, not all metrics need a comparison. For instance, how many days does it take to ship an order? and here lessor is better².

Figure: Measure vs Metric vs KPI

• A Benchmark is a target. Metrics are used for benchmark research or to compare performance. An effective business metric is often compared to an established industry benchmark or a business objective. This provides a valuable context to measure performance and allows business operators to act effectively.
• A Dimension is a breakdown for a metric and helps in the roll-up or roll-down view of a metric. E.g., “product sold” can be dimensioned by region, by product, by customer segment, etc.
• Key Performance Indicators (KPI) are a focused set of business metrics that monitor critical areas of business. KPIs are high-level abstract views of performance measures targeted for stakeholders that are monitoring from a distance, and not engaged in day-to-day hands-on activities, like executives, shareholders, board members. E.g., Net Profit Margin (NPM) shows net profit as a percentage of total revenue. However, lower-level KPIs are often developed in any organization to monitor departmental performances, like sales, marketing, or call center performances. E.g., Lead conversion rate, which is the percentage of web visitors that are captured as a lead. The most important aspect of KPIs is that they are actionable. It is important to develop actionable KPIs and review them consistently for their effectiveness and performance.

Why is this important? Metric names often confuse, and they overlap. These definitions help in properly naming business metrics to delineate them and being uniquely defined.

Balance scorecard

A balanced scorecard is often used in many industries to define a strategic view of performance metrics in the organization. It’s a model to organize business metrics to provide a comprehensive view of business performance. Generally, it has four or five perspectives:

Figure: Balance scorecard perspectives

1. Business Growth Perspective — represents perspective business growth and how many new customers were added or churned, or products sold, or how long it takes to launch a new product to market, etc.
2. Customer Perspective –represents perspective from customer-facing business, focuses on customer value, customer satisfaction, customer experience, customer loyalty, and retention
3. Operational Perspective — represents the efficiency of internal business process and operations, efficiency, quality.
4. Revenue or Financial Perspective — represents the perspective of financial performance, revenue, the efficiency of use for financial resources, etc.
5. Organization Perspective — represents organizational perspective, capacity, human resource, talent and skills, organization culture, infrastructure, technology.

Business metrics are rolled-up for top management in these balanced scorecards to provide a more concise viewpoint for them to focus on key measures that are most critical.

Align business metrics to customer journeys

Business metrics related to customer interactions generally all align well with customer journeys. However, not all metrics can be associated with journeys. Hence, business metrics are of two natures:

1. Metrics that are strongly associated with customer journeys and allow tracking by journey stages for both macro and micro journeys, e.g., quotes, orders, or payments occur during considering or purchase journey stage. They can be captured during customer interactions.
2. Metrics that are weakly associated with customer journeys and may not directly associate with journeys, e.g., revenue, fraud, product counts, etc. They are generally generated by downstream fulfillment or processing systems e.g., by a billing system.

While developing customer journeys and business processes, the designer must think through all aspects to support five perspectives (from balanced scorecard) i.e., metrics required to measure the customer experience, metrics to support business operations and channels, metrics for report financials, and metrics to monitor organizational activities.

A strawman business metrics canvas will enable organizations to place and align their desired metrics to customer journeys. They can evolve strawman canvas iteratively over time. Each industry or organization may develop a very different view of its business metrics canvas. It will depend on products or services they sell, the industry they are in, the focus of their leadership, how they define their customer journeys, and the maturity level of their digital transformation. Some industry verticals are much more mature. They have forums that published standards and reference models. For instance, the telecommunication industry has published business metrics models available from TM-Forum. Organizations can leverage these industry references to develop their strawman models.

I would like to emphasize that the quality of business metrics will depend on how well-organized the upstream customer journeys and business processes are. A well-defined customer journey with build-in tracking will provide accurate measures for both customer experience and business operations. Many organizations develop their business metrics in a downstream funnel in their data warehouse, as an after-thought. This not only makes it extremely difficult to interpret which journey step the customer is in, but also the poor quality of the data doesn’t provide enough references to accurately gauge customer experience. This in turn will impact the organization’s ability to properly address customers’ concerns or optimize their experience.

A digital transformation can provide a rare opportunity in large complex organizations to rethink their customer journeys and re-engineer their business processes to build it right this time (as Michael Holmes will say!).

Figure: Sample business metrics mapped to customer journeys

Above, I took a stab at placing some of the key business metrics on a sample canvas, aligning them to an appropriate customer journey. This includes approx. 47 business metric domains. Each of these domains will hold many lower-level business metrics. For instance, the “Customer Order” domain itself may contain dozens of business metrics like “% of Orders abandoned”, “% of Orders required manual handling”, “# of orders required updated quote”, etc.

Business metrics are complex, especially those that need to support an actionable KPI. Hence, my suggestion is that don’t seek perfection, rather start with a basic strawman. It is an iterative exercise that will mature over time. What’s important is that customer journeys are designed with built-in tracking in mind.

The above model has eight swim lanes, representing five balanced scorecard concerns:

1. Channels & Operations Perspective provides overall channel performance of all interaction channels (e.g., digital web/mobile, call center, etc.), marketing, & sales
2. Customers, Channels & Operations Perspective, includes end-to-end experience journeys. Experience journey metrics are intertwined with customers, channels, and business operations.
3. Business Growth Perspective includes Products and Customers.
4. Financial Perspective includes Revenue and Fraud.
5. Organizational Perspective includes Employee, Security, and Process. For Digital Transformation, technology and transformation metrics can be added here.

These swim lanes are aligned with the balanced scorecard approach. Additional KPIs are developed to roll up business metrics to balance scorecard(s), where each KPI may use one or more business metrics. For instance, “Average shipment time” is a single metric that with preferred progression to be lower. On the other hand, “Customer acquisition cost” uses multiple metrics to calculate itself, i.e., (sales costs + marketing costs) / count (new customer), here again, lower is better.

A business metric may be associated with more than one balance scorecard perspective, and that’s normal. In this canvas, I have placed them based on my preferred primary location for that metric.

Define business metrics as Products

Just like data products, each of the business metric domains can also be defined as an independent “product”, or related business metric domains can be aggregated as a “product”. Business metrics products can then be supported by their respective product teams, allowing them flexibility for scaling with the rest of organizational activity during transformation.

Figure: Sample business metrics as a product model

Below, we will look at some of these business metrics products and break them down to understand:

1. How can we capture data to produce these metrics?
2. How do they support digital transformation and customer journeys? and
3. What business metrics are produced in it?

Customer experience journey metrics

How to capture customer journeys metrics upstream?

Earlier, we mentioned that customer journeys should be designed with built-in tracking. These build-in tracking will support various functions of customer journey management to visualize, track, monitor, personalize, measure, and optimize them.

This involves various platforms that support these functions, including journey manager, business metrics, personalization, real-time interaction manager, etc. These platforms are often intertwined. Hence, I will briefly explain them below to establish a context and to ensure we keep them de-coupled in our architecture. Following are key components of the architecture:

• The journey management platform is responsible for defining journeys, steps, milestones, and actions.
• The business metrics platform is responsible for analytics, metrics, KPIs, balanced scorecards.
• The personalization AI platform provides real-time listeners aided with artificial intelligence to support the personalization features.
• The interaction Management platform is responsible for delivering digital assistance (to journey application) during a live interaction with customers.
• The actual journey application delivers a particular customer experience during their journey, or more precisely a micro journey.
• The Event hub is a messaging queue (a technology platform) that supports streamed messages between consumers and producers. In this case between the above five architectural components.
• Customer Data is a central repository of customer information accessible to various platforms either directly or through local caches.

Figure: Simplified view of tracking journey and business metrics

In a simplistic view, each experience journey application (delivering micro journey experience), will communicate with the journey management platform to record customer journey progression. Ideally, this communication will be asynchronous through the event hub, so that it will not have a performance bearing on customer experience.

Here I will recap from our previous conversations that our First design principle is to pre-define customer experience journeys in the journey management platform. The platform provides capabilities to define customer journeys, related meta-structure and taxonomy, its milestones, and associated actions.

Actions are an important part of journey design. It provides the ability for journey designers to activate an “action” at certain key moments, e.g., a call to action, a to-do activity, or even to consider a personalization feature like the next best action.

The business user will monitor their customer experience metrics which will enable them to accurate identity optimization points. They can then use these “actions” to help improve customer experience as one of their optimization tools.

The second design principle is that each journey application will be developed with built-in tracking. This is to ensure business metrics and CX measurement data are captured upstream. They will send a few datasets to corresponding subsystems (ideally in real-time), i.e., Journey Management, Business Metrics, Personalization AI, and Interaction Manager. Each will serve a different purpose. These includes:

• Send updates to Journey milestones
• Send information regarding Customer interaction
• Send additional Customer experience measures, where needed.

As customers progress through their journeys, the journey application will communicate the updates (via event hub) and feed this information to the journey manager and other platforms.

1. The journey manager will record this information to update milestone statuses in the instance of the customer journey. This journey view will be available to both customers and agents alike across all channels, and it will reflect the progress customer has made during a particular journey.
   
   The business user can also leverage this information for their reporting and business metrics to track various stages customers are in their journey (e.g., track dropout points) and where optimizations are needed. This tracking will be much more accurate than any downstream analytics.
2. The business metrics platform will record this information to update the metrics and KPI, ideally in near-real-time. This will provide business just-in-time information and enable them to monitor, track and remediate customer journeys.
3. The journey application will get “actions” from journey maps, and one of these actions will direct it to seek a personalization feature, e.g., get the next best action. It will asynchronously communicate with the interaction manager to invoke a personalization response. The interaction manager will also asynchronously respond (back) to trigger the personalization feature in the journey application at an appropriate moment.
4. Where applicable, an artificial intelligence (AI) platform will provide a personalization AI feature to support some of the more complex use cases. It can either work independently to respond to the journey application directly or in alliance with the interaction manager, whichever is the case in a particular implementation.

The third design principle is to communicate and feed information leveraging event-driven architecture. Event-driven is an architecture pattern that allows passive communication between two applications without impacting customer or user experience. Since the communication happens behind the scenes and is reactive (leveraging reactive design patterns), it provides a much better and more delightful user experience without the hanging hourglass. It also implies many other design principles will be used for user interface design. Many organizations use a reactive framework for their user interface experiences.

In addition, it enables many consumers to receive the same event concurrently. As you can see in this case, the same event can be consumed by 4 or 5 participating platforms. Each platform can process it according to its priority, in alignment with customer experience. It significantly reduces integration mess and removes point-to-point integrations between two applications.

On a side note, it’s worthy of mentioning that it also enables easier development and deployment of independent journey applications without unnecessary integration dependencies of point-to-point integration. Organizations often handicap themselves with many intertwined point-to-point interfaces making them difficult to release, upgrade or enhance their capabilities.

What data is passed down from journeys?

Each journey application should include three types of measures to enable tracking, monitoring, and orchestration of the journey experience:

1. Journey milestones
2. Customer interaction
3. Customer experience measures, where needed.

Journey milestone includes very simple data that a journey application communicates down to information journey management platform as to where about of customer in their relevant journeys. It simply says, “I am customer John”, “I initiated my Check-in journey at 12:21 pm”. “I printed boarding pass”. That’s all. Hence, it points to the journey step customer is in, and informs the central journey manager about it. This information is massively useful for tracking and monitoring journeys, generating business metrics, identifying dropout points, failures, etc. As you may be able to visualize now, we can track every customer experience journey this way, regardless of where or in which channel this experience is delivered.

Customer interaction on the other hand supplements lots more information to the experience. It includes more details of the actual interaction customer is experiencing with the brand, e.g., a customer called the call center to make payment, or customer-generated a quote on a website, or they send an email message, etc. Interaction can occur directly with the brand on its digital channel, or indirectly via an agent, or a partner. It can also be through product use, e.g., through mobile or smart devices. Connected IoT devices transmit a lot of sensory data, and some of that sensor data can also act as customer interactions.

During the customer experience journey, customer interaction data is sent out to an event hub where the relevant platform can consume this information. The data includes information about the type of interaction, info about customer or user, reason for interaction, type of journey it’s in, interaction channel, agent serving the customer (if involved), product or service involved, state of interaction, and some basic information on this interaction useful to journey and interaction management.

Session-based customer interactions: Each customer interaction can also initiate a session with the customer (or user). The customer performs various activities during that interaction, and finally, the session is closed. Hence, some interactions may have a state. The state helps the interaction manager understand if the interaction is in-process or if it has been completed. Interactions can be sent at the beginning of a session, during a session, or end of a session. It will all depend on how much real-time help is required during the interaction to assist the digital journey from the interaction manager.

As you will notice, we earlier mentioned that the journey map has pre-defined actions, and one of those actions could be personalization features. Here personalization features work in two ways:

1. Personalization features that are pre-defined “actions” in journey maps.
2. Personalization features that are dynamically added by the interaction manager.

Hence, these session-based customer interactions act as communication for interaction managers to dynamically interject a personalization feature into a customer experience journey. Personalization is a much bigger topic that requires a separate discussion. The purpose to mention here is only for context setting.

Here is the sample structure of the interaction record:

Figure: Sample of Customer Interaction Record

Most interactions may only send a single record at the end of an interaction, especially where it may not require additional digital assistance or personalization features like next best action etc. Interaction records will feed all downstream sub-systems including journey management, business metrics, interaction manager, or relevant personalization engines.

For our conversation here, it will update journey management and business metrics with relevant information required to prepare journey visualization and business metrics to track and monitor journeys.

Customer experience measures are supplemental data that may be required for targeted journeys, where business users may be monitoring that activity much more closely. For instance, a simple Customer Satisfaction (CSAT) captured during an interaction can be included in CX experience measures.

Customer Experience Metrics Domain

The organization must establish a good measurement experience program. Customer experience metrics are KPIs that measure how customers are interacting with the brand. The measurements will come from all three experience domains to achieve customer and employee advocacy levels:

• Customer Experience (CX) measurements are taken customer-facing experiences at every touchpoint or channel, like digital or call center, through customer inquiries or complaints, or customer surveys conducted after an experience, etc. In addition, CX measures are captured through customer effort scores, customer satisfaction surveys, NPS, sentiment and behavior analysis, etc.
• User Experience (UX) measurements are taken from journey metrics and various logs (journey logs, weblogs, call logs, session logs, etc.) and include success rates, error rates, abandonment rate, completion times, number of clicks to complete a micro journey, etc.
• Employee Experience (EX) measurements are taken from employees or agent-facing experiences, like sales agent portals, call centers, back-office fulfillment, etc. It’s not just about making everything better for the customer and then eventually bringing the EX up to the same level. This becomes more critical when digital interactive experiences are handed off from robotic to human support. This turns employees more customer-centric through digital solutions where they can solve critical issues during live customer interaction in a digital channel.

Figure: Types of CX Metrics

Customer Experience metrics can be divided into three groups:

1. Customer Interaction metrics: represents what is happening during a customer interaction. This helps contextualize what activity customer is performing during their journey. E.g., making a payment. This can help understand things about this interaction. E.g., How long it took to make payment. Did the customer get any error or customer dropped off during the experience out of frustration, etc?
2. Customer Perception metrics: represents how customers felt about their experience. Its customer’s perception of how good or bad the experience was. This can be captured either at the end of interaction by asking a simple customer satisfaction question, or through a survey, sentiment analysis, customer experience index, etc.
3. Customer Outcome metrics: represents what customers did after a poor experience. This helps understand the quality perception of the brand itself. E.g., a customer churned after a poor experience. This can be captured using NPS scores, churn metrics, subscription metrics, return metrics, etc.

Customer Experience Index

It helps measure CX quality and identify areas that have the biggest influence on brand loyalty or advocacy, areas for improvements, and optimizations. Organizations leverage industry benchmarks published by many consulting companies and compare them with their own KPIs. These indexes use a variety of customer experience drivers, some very industry-specific and many common drivers applicable across other industry verticals. Organizations create their Customer Experience Index and metrics compare them to other industry metrics.

Various metrics are used in the customer experience index, like Net promoter score (NPS), Customer satisfaction (CSAT), Customer effort score (CES), Customer churn rate, Customer lifetime value (CLV), Remediation service like Average handling time, Average response time, and Employee engagements, etc.

• Net Promoter Score (NPS): is a commonly used score. Though it’s easy to capture and track it’s not the strongest measure. It shows the percentage of customers who would be happy to recommend the brand. It’s a simple survey score (0–10) on customer perception of the brand: Detractors (0–6), Passive (7–8), and Promoters (9–10). A brand can compare this to other industry benchmarks to see how it’s performing.

Figure: Net promotor score

• Customer Behavior: is a decision-making process, influences, social factors, or action that customer performs when interacting with a brand, e.g., buying a product, making payment, etc. A good customer behavior analysis helps the brand to target or market much more effectively, and in our context can increase uptakes during the experience journey by personalizing it. Customer behavior can be, for instance, knowing buying habits of customers, which place or location they purchase certain things, type, or quantity of products they purchase, social trends, frequency or time of purchase, market basket analysis (if they buy x, they also buy y), impulse buyer vs informed buyer, brand fans, budget shoppers, etc. Customer behavior is influenced by social circles or trends, YouTubers, marketing campaigns or promotions, situational like current circumstances, economic conditions, preferences.
  
  Market data is available through industry consultants. A brand can capture its customer data during interactions and by performing machine learning on customers and social data. For instance, shopping cart data or product and feature sold can be used to perform market basket analysis.

Figure: Behavior Analysis

• Customer satisfaction (CSAT): How happy and satisfied customer is with brand interaction. Traditionally, it’s based on surveys after an interaction with the customer, product, service ratings, etc. In modern user experiences, it can be captured during the interaction as additional “customer experience measures”, similar to social media’s “like” or “comment”, where like can provide a range of emotion emojis.
  
  Machine learning can be used to scrape off the conversation interactions from Alexa or Google, chatbots, call center sessions and disposition codes, social media. Data gathered can help develop customer sentiment analysis for the brand. Predictive analytics with propensity models can also help understand the sentiments, propensity to buy, or propensity to churn.

Figure: Simple CSAT embedded within experience journey

• Customer Effort Score (CES): represents how much effort a customer must do when interacting with a brand e.g., to get a quote, or buy a product. This information is generally gathered through a survey where a customer is asked questions regarding recent interaction if it was difficult or easy etc. In my model, this measurement can also be calculated based on journey effort score (see Journey measurements in a later section).
• Customer Engagement: represents how active customers are with the brand. It is a major revenue predictor in recurring revenue and customer retention. For instance, first month engagements of new customers, daily vs monthly user activity, session time, stickiness or frequently returning users. Other CX measures like NPS or CSAT also represent customer engagement.
• Voice of the Customer (VoC): describes customers’ feedback about their experiences and expectations for the brand’s products or services. It focuses on customer needs, expectations, understandings, and product improvement. It uses similar metrics like surveys, CSAT, NPS, focused groups, etc. to measure.
• Many other metrics generated from Journeys domains, product domains, customer domains can also be included in the index. For instance, Customer Acquisition Cost (CAC), Customer lifetime value (CLV), customer retention rates, conversion rates, etc.

Interaction Channel Metrics

Figure: Interaction Channel Metrics

Experience Journey Metrics

Experience journeys can be measured for a good design and if they deliver the optimal experience, in the following ways

• Track journeys at both levels (micro or macro) for all the steps and interactions it has with customers.
• Journeys rated higher for fewer steps, and if customers complete those steps. If the journey has too many steps, and customers abandon it then it is rated negatively. If customers complete the journey despite it being long, it can still be rated positively but lessor rating.
• If the customer hops through the call center or through manual handling to complete their journey, it can also be tracked for improvements and optimization areas.

For each customer experience journey, this can track many cx measures, e.g., the time it takes to complete, the complexity of the experience, efforts require to complete, points of failure where customer abandoned it, numbers of channel hops, if journeys completed with/without manual handling, etc.

Figure: Common Experience Journey Metrics

Following are a few use cases that can be supported from the common journey metrics domain:

• How many customers are active in their journeys and their % completed?
• Which journeys include more than 2 human interactions?
• What is the average time it takes to complete journeys with an effort score > 8, and how many were abandoned?

Sales Order & Service Request Metrics Domain

It’s the most tracked metric domain. The organizations with CRM platforms to support sales order & service request capabilities can generally track these metrics with lessor difficulty. Where they lack these capabilities, it is challenging to produce these metrics, especially in the siloed line of businesses there are many orders and service intake processes. For sales, they rely on sales reporting to track sales, but they do not have a good handle on order fulfillment. The service request suffers from poor quality of service and customer complaints of lack of transparency in providing status visibility.
Digital transformation must acquire these core capabilities as a priority. For digital organizations, it is of utmost importance to track them not only to support the Omnichannel experience but also to improve their customer satisfaction.

Service requests can support a variety of maintenance journeys, for instance, customer complaints, inquiries, profile changes, warranties, installations, repairs, refunds, returns, etc.

For both sales and service, two highlighted metrics to track are the time it takes to fulfill customer requests, and how many of these fulfillments are straight-through and do not require manual handling. A CRM platform should record this information and send updates to the business metrics platform through the event hub in near real-time

Figure: Sales Order & Service Request Metrics

Product & Quotation Metrics

The quality of reporting these metrics will largely depend on product cataloging and capabilities. Siloed line of businesses generally has three issues around product management:

1. They don’t have a centralized catalog for their product offerings, associated features, discounting, promotional campaigns, and pricing eligibilities for different market segments across all LOBs.
2. The products and rates are maintained in billing platforms or another admin platform, and they are not built to support omnichannel or digital experiences.
3. The quotations are manually provided to customers, and they are not tracked in a digital-friendly quotation platform.

This makes it very difficult to develop the consistent experience to provide product browsing or quotation across channels to digitally engage with customers

Figure: Product & Quotation Metrics

Product and quotation are different sets of capabilities. I mentioned them together here as they correlate to provide a continuous digital experience for customers browsing through cataloged offers and lead them into a quotation journey. The two platforms generally have good harmony and integration between them. The metrics above will support many aspects of tracking awareness, consideration, and purchase journeys. For instance,

• How much lead time is needed to launch a product?
• How many leads were converted to opportunities and were presented with a quote?
• How complex is the brand’s product offering and which offers are trending?
• For how many offers brand can provide a personalized quote to their customers?
• How many quotes were converted into an actual order?

Lead & Customer Metrics

Figure: Lead & Customer Metrics

Footnotes

1. US National Institute of Standards and Technology (NIST) — https://www.nist.gov/itl/ssd/software-quality-group/metrics-and-measures, and Structured Metrics Metamodel (SMM), V1.0 — https://www.omg.org/spec/SMM/1.0/PDF/
2. TM Forum Metrics Frameworks v19.0

The views expressed are my own and do not represent any organization. I aim to have respectful discussions that further positive change as we navigate unprecedented technological transformation. Change is constant, so my perspective may evolve over time through learning, testing, and adapting to new information.