Predictive Analytics: Unveiling the Future with Data

Predictive analytics analyzes data to predict the likelihood of certain events happening in the future. Through predictive analytics software, businesses across all industries can understand their customers better and make more informed business decisions.
What Is Predictive Analytics

Organizations should take a closer look at predictive analytics to discover the myriad of ways that data and artificial intelligence (AI) can power more personalized customer experiences and enhance brand loyalty and customer retention. From a cost and ROI perspective, the impact and benefits of predictive analytics in customer experience management cannot be ignored. 

It’s an opportunity that your company can capitalize on today. According to Forrester, fewer than 10% of enterprises are advanced in their insights-driven capabilities. By equipping your organization with predictive analytics tools, you can gain rich insights into customer behavior, make data-driven decisions, and optimize business operations.

What is Predictive Analytics?

Predictive analytics is a category of data analytics and the process of using data, statistical algorithms, AI, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Put simply: it involves analyzing current and historical data to make predictions about future events or trends.

Advancements in computing power, storage, and algorithms, along with the rise of AI, have made predictive analytics more feasible and accessible to businesses of all sizes. Machine learning algorithms can analyze large datasets quickly and efficiently, enabling businesses to derive insights in real time.

For example, predictive analytics can examine text reviews from customers and predict what steps they are likely to take. Predictive models trained on large datasets of similar text inputs can learn to recognize such patterns and predict future behavior, such as making a purchase or churning.

A conversation between a customer and a representative from the company. Predictive analytics predicts the customer wants to buy

Predictive Analytics vs Prescriptive Analytics

It can be easy to confuse predictive analytics and prescriptive analytics. While they sound similar, they also go hand in hand with each other in practice. These two types of analytics are both designed to provide a comprehensive approach to data-driven decision-making. 

As mentioned earlier, predictive analytics is focused on forecasting future events, trends, or behaviors based on historical data. Conversely, prescriptive analytics goes a step further by not only predicting future outcomes but also recommending actions to achieve desired results. 

Prescriptive analytics combines predictive models with optimization algorithms and business rules, employing techniques such as simulation, optimization models, and decision analysis. These methods evaluate various possible actions and their outcomes to suggest the best course of action. 

Why is Predictive Analytics Important?

Predictive analytics is important because it empowers businesses to make informed decisions that enhance strategic planning and operational efficiency. By analyzing historical data to identify patterns and predict future outcomes, predictive analytics helps organizations anticipate trends, behaviors, and potential risks. This foresight enables businesses to proactively address issues before they become problems, optimize resource allocation, and improve overall performance.

For example, predictive analytics in healthcare enhances patient care by anticipating readmissions and improving diagnostic accuracy. This allows healthcare organizations to proactively manage patient outcomes, allocate resources more efficiently, and implement targeted interventions that reduce hospital stays and associated costs. By identifying at-risk patients early and providing personalized treatment plans, healthcare providers can improve overall patient health and satisfaction, ultimately leading to better clinical outcomes and a more sustainable healthcare system.

Benefits of Predictive Analytics in CX

Predictive analytics is also making an impact on the way companies manage the customer experience. By leveraging data-driven insights from predictive analytics, your company can foster meaningful connections with customers and achieve differentiation in today’s competitive marketplace. The wide-ranging benefits of predictive analytics applications in customer experience management include:

  • Enhanced customer loyalty and satisfaction. By predicting what customers want before they even ask for it, your company can provide a proactive and personalized experience that increases satisfaction and fosters loyalty. 
  • Improve customer lifetime value. Predictive analytics helps identify the most valuable customers and understand their behavior, allowing you to implement strategies that maximize the value these customers bring over their lifetime. 
  • Reduce customer churn. By identifying patterns that indicate a customer is at risk of leaving, you can take proactive measures to retain them, thereby reducing customer churn. 
  • Enhance cross-selling and up-selling opportunities. With predictive analytics, marketers can identify which customers are most likely to be interested in additional products or services, creating more opportunities for successful cross-selling and up-selling. 
  • Accelerate operational improvement. By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

What is a Downside of Predictive Analytics?

While predictive analytics can be a powerful tool, organizations need to be aware of the potential downsides and take the proper steps to mitigate or eliminate them. Some of the possible  downsides of predictive analytics include:

  • Incorrect predictions: Predictive analytics relies heavily on the quality and completeness of the data. Inaccurate, outdated, or incomplete data can lead to wrong predictions, which may result in misguided decisions. 
  • Ethical and privacy concerns: Using personal data for predictive analytics raises significant ethical and privacy issues. Misuse or mishandling of sensitive information can lead to privacy violations and loss of customer trust. 
  • False positives and negatives: Predictive models are not perfect and can produce false positives (incorrectly predicting an event will happen) and false negatives (failing to predict an event that does happen). These inaccuracies can lead to inappropriate actions, such as unnecessary interventions or missed opportunities.

These downsides can often be handled and resolved through proper planning, implementation, and maintenance of predictive models. While organizations should be aware of these happenings, they should not deter them from utilizing predictive analytics in their operations. 

Examples of Predictive Analytics

Several predictive analytics examples show how the process is being applied by companies looking to better understand their customers, anticipate their needs, and deliver personalized and proactive experiences that drive satisfaction, loyalty, and ultimately, business success.

Predict Behavior and CLV

More and more retail brands are deploying predictive analytics software to forecast customer behavior and monitor market trends. 

Retailers can personalize the retail customer experience and increase sales by analyzing information such as past purchase history, browsing behavior, and demographic data. Brands can also leverage predictive analytics algorithms to analyze historical data and market trends, helping predict the optimal price points for products in order to maximize revenue while remaining competitive.

By recommending relevant products, delivering personalized content, and identifying cross-selling and up-selling opportunities based on individual customer profiles and purchase history, brands can create highly personalized retail experiences that drive customer lifetime value (CLV).

A review of a product where the words "renewal" and "impressed" are highlighted.

The key is to connect customer experience data from every touchpoint and channel for a complete view of the customer journey. Jim Katzman, Principal of CX Strategy & Enablement for InMoment, suggests that companies should “expand the data sources that you use to understand what your customers are saying and how they perceive you. While surveys will continue to be important, they only give you part of the picture. Expanding your data repertoire to such sources as purchasing data, location-tracking data, web searches, social media, and online reviews is a must.”

The next step is to take a long view when looking at customer relationships. Adds Katzman, “You’ll be surprised at how many brands get caught up in the lure of ‘What can I sell you today?’ without considering what seeds to plant for even more success tomorrow.”

“Equally important is to understand how your competitors view this dynamic and what, if anything, they’re also doing to be proactive when it comes to building lifetime value.

Score Leads by Analyzing Customer Data

Another great application example of predictive is lead-scoring marketers leveraging historical data and machine learning algorithms to predict the likelihood of leads converting into customers. Today more than ever, marketers are empowered to make data-driven decisions when scoring and prioritizing leads, resulting in more effective lead management, higher conversion rates, and improved overall sales and marketing performance.

  • Identify Ideal Customer Profiles (ICPs). Predictive customer analytics tools can analyze historical data to identify patterns and characteristics common among high-value customers. By identifying these attributes, marketers can create an ideal customer profile (ICP) that serves as a benchmark for scoring leads based on their similarity to the ICP.
  • Assign predictive lead scores. Marketers are also utilizing statistical algorithms to analyze various data points such as demographics, firmographics, online behaviors, engagement with marketing content, and past purchase history to assign a predictive score to each lead. This score indicates the likelihood of a lead becoming a customer based on similarities to past successful conversions.
  • Prioritize sales efforts. Marketers can use predictive analytics to prioritize leads based on their likelihood to convert. Leads with higher predictive scores can be routed to sales teams for immediate follow-up, while leads with lower scores can be nurtured through targeted marketing campaigns until they demonstrate stronger buying signals.
  • Reduce sales cycle length. Predictive lead scoring enables marketers to identify leads that are further along in the buying process and more likely to make a purchase. By prioritizing these leads for immediate engagement, marketers can accelerate the sales cycle and shorten the time to conversion, leading to faster revenue generation and increased productivity for sales teams.

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Predictive analytics can also have a significant impact on brand reputation management efforts, helping companies anticipate, monitor, and respond to potential reputation threats more effectively. 

Algorithms, for example, can analyze large volumes of data from various sources such as social media and online reviews to gauge customer sentiment toward the brand. By identifying patterns and trends in sentiment data, teams can proactively address emerging issues or negative perceptions before they escalate into major reputation crises.

These analytical techniques help crystallize information contained in reviews into insights — helping companies achieve a more accurate, complete, and unified view of the customer.

With online reputation management software, companies can also analyze customer feedback and sentiment data to identify areas for improvement and proactively address customer concerns. By identifying recurring themes or issues in customer feedback, brands can take corrective actions to improve products, services, and overall customer experience, which in turn enhances brand reputation. 

InMoment’s approach is based on machine learning, a method of data analysis that allows companies to find patterns and unlock insights as it is exposed to new review and feedback data. This approach is fast, consistent, and programmable, helping teams quickly understand — at a glance and at scale —  exactly what customers are saying. Proprietary relevancy scores for sentiment analysis also provide measurement of positive and negative language, with unparalleled accuracy.

Use AI to Improve Personalization

Predictive analytics empowers companies to better understand their customers, anticipate their needs, and deliver personalized experiences. It’s a particularly powerful tool for curating content based on historical customer data. 

One of the best predictive analytics examples comes from streaming giant Netflix, which has a powerful personalized content recommendation engine. The company analyzes user data, including viewing history, ratings, and browsing behavior, to make predictions about what users might want to watch next. This is all reflected as soon as viewers land on Netflix’s home page, which displays content tailored to individual users, improving user engagement and satisfaction.

With predictive analytics, teams can dynamically customize website content, email marketing campaigns, and other communication channels based on individual customer preferences and behaviors. By delivering content that is relevant and timely, businesses can improve personalization, create more engaging customer experiences, and drive higher conversion rates.

Extract Insights from Reviews and Social Media Data

Online reviews and social media data provide a wealth of insights for a business but can be labor-intensive to read through and digest. There are many ways to try to automate this task. Currently, the leading approaches use deep learning models that extract many different kinds of keywords, predict their sentiment, and classify them into relevant categories. This allows companies to improve operations, make better decisions, and elevate the customer experience with data.

Using AI and advanced machine learning techniques, predictive analytics tools can read through thousands of reviews, comments, and other forms of customer feedback in the time it would take a human to read through just a few. The right technology will provide valuable insights, summaries, trends, and statistics that can be applied to support data-driven decision-making and customer-centric innovations.

Rural King, a family-owned farm supply store with 128 stores across 13 states, is no stranger to leveraging predictive analytics in order to create memorable customer experiences. The company regularly analyzes massive amounts of unsolicited feedback to unlock the potential of all its stores’ review data.

“We are hearing directly from customers about the store experience as well as pricing and product challenges,” says Kirk Waidelich, VP of Marketing for Rural King. “This allows us to narrow in on the stores that are experiencing issues — and to target and understand these issues versus simply guessing.”

What to Look for in Predictive Analytics Software

Predictive analytics software allows users to complete predictive analysis. This software can be used by different professionals across many different industries.  Predictive analytics software will come with different features, and which specific features will work best for you depends on the goal of your business. However, there are a few foundational features that any successful software will have. 

Data Collection and Integration

Data collection and integration is a crucial aspect of predictive analytics software. The feature facilitates the collection of data from various sources, ensuring comprehensive coverage for analysis. It allows users to connect to databases, extract data from APIs, import data from spreadsheets, and integrate data from different systems within the organization.

Data Preprocessing and Cleaning

Another fundamental feature of predictive analytics software is the ability to preprocess and clean data. This allows users to address common data quality issues such as missing values, outliers, duplicate records, and inconsistencies. This feature can also provide automated mechanisms to detect and handle missing values, either by imputing them using statistical techniques or by removing them based on predefined rules. This ensures that the data used for predictive modeling is complete and accurate.

In addition, these features should support outlier detection and treatment. Outliers are data points that deviate significantly from expected patterns. Outlier detection features can identify these outliers and remove them, transform them, or treat them as separate categories based on previously implemented rules or requirements.  

Machine Learning Algorithms 

Effective predictive analytics software incorporates a wide range of machine learning algorithms, which provides users with powerful tools to build accurate and reliable predictive models. These algorithms form the backbone of a software’s capabilities and enable users to leverage the predictive power of their data. 

Model Training and Evaluation 

Predictive analytics software should also provide robust functionalities for model training and evaluation, enabling users to build accurate predictive models and assess their performance effectively.

To ensure optimal model performance, software should have options to fine-tune the model’s parameters and settings. Users can experiment with different configurations and optimize the model to achieve the best possible results. This customization capability allows users to adapt the model to their specific use case, maximizing its predictive accuracy and relevance.

Once the model is trained, the software facilitates a thorough evaluation of its performance. Users can assess how well the model generalizes to unseen data by employing various evaluation techniques, such as cross-validation. Cross-validation involves splitting the data into multiple subsets, training the model on a portion of the data, and evaluating its performance on the remaining subset. This process helps estimate the model’s predictive accuracy and identify any potential overfitting or underfitting issues.

Visualization and Reporting Capabilities

Lastly, predictive analytics software should offer robust visualization and reporting capabilities to help users understand and communicate insights effectively, which helps transform complex data into intuitive visual representations and actionable reports.

Users should be able to easily create visual representations of their data, allowing for quick and comprehensive analysis. Visualization options often include bar charts, line charts, scatter plots, heat maps, and geographic maps, among others. These visualizations enable users to identify patterns, trends, and relationships within the data, facilitating deeper insights and understanding.

Furthermore, predictive analytics software should support interactivity in visualizations, allowing users to explore data from different perspectives and drill down into specific subsets of information. Users can interact with the visualizations, apply filters, and dynamically adjust parameters to gain more detailed insights and make data-driven decisions.

Predictive Analytics Implementation and Best Practices

Implementing predictive analytics involves a structured approach to ensure that the data-driven insights generated are accurate, actionable, and aligned with business goals. Here are some key steps and best practices for successful predictive analytics implementation:

1. Define Clear Objectives

Before embarking on a predictive analytics project, it’s essential to clearly define the objectives. Determine what specific outcomes you want to achieve and how predictive analytics will help you reach these goals. Whether it’s improving customer retention, optimizing inventory management, or reducing operational costs, having a clear objective will guide the entire process.

2. Assemble the Right Team

Successful implementation requires a team with diverse skills, including data scientists, data engineers, domain experts, and IT professionals. Data scientists and engineers are crucial for building and maintaining the predictive models, while domain experts ensure that the insights generated are relevant and actionable. IT professionals play a key role in integrating predictive analytics tools with existing systems.

3. Foster a Data-Driven Culture

For predictive analytics to be truly effective, it must be embraced across the organization. Encourage a data-driven culture by promoting the use of data in decision-making processes. Provide training and resources to employees to help them understand and leverage predictive analytics insights.

Jumpstart Your Predictive Analytics Solution With InMoment

The world’s top brands partner with InMoment AI, the leading predictive customer analytics solution, to facilitate the discovery of real-time insights, drive individual customer recovery, and turn unstructured feedback into a predictable source of business growth. To see how what predictive analytics can do for your business, schedule a demo today!

References 

Forrester. “Data Governance Unlocks The Impact Of Analytics: Data Strategy & Insights 2023” (https://www.forrester.com/blogs/data-governance-unlocks-the-impact-of-analytics-data-strategy-insights-2023/). Access 03/16/2024.

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chatgpt for customer experience

It seems like the internet is full of ChatGPT “hacks” these days. We are all inundated by articles and webinars that start with  “How to Use ChatGPT to…” I have also had way too many conversations with my Gen-Z son and millennial colleagues about how they use the tool to make everyday tasks go by more quickly. And I wouldn’t be the true customer experience nerd that I am if I didn’t ask: “Could we customer experience (CX) professionals leverage ChatGPT customer experience survey questions?”

On the surface, it seems like an obvious application for a ChatGPT customer experience approach. A survey is pretty straightforward, correct? Not so fast.

Keep reading to find out what happened when I tested this approach and why it may not be the best way to go when it comes to your customer listening approach.

Testing ChatGPT for Customer Experience Questions

I started off with a simple question for ChatGPT, hoping for a simple customer satisfaction survey, typing in, “Write me a survey.” You can see the screenshot of the output below.

ChatGPT customer experience survey- Should you use ChatGPT to write customer experience surveys?
A ChatGPT Customer Experience Survey

After reviewing the generated answer, you may be asking, “what’s missing?” Well, to the untrained eye, there could be little to no difference between a traditionally written survey and a ChatGPT customer experience survey. After all, there are demographic questions, the typical “How satisfied were you with your experience,” and other basic survey asks.

But here is what stands out to me as a glaring absence. What is missing is pretty much the most important part of any survey: the link to the business questions you are trying to answer by launching a survey in the first place!

Quick PSA from Jim: Creating surveys is an important topic,  but I would be remiss if I didn’t mention that while surveys are a tried-and-true method of collecting customer feedback, they are not the only way (or the best way, in many cases) to hear from customers. With so many channels available for you to monitor the voice of the customer, to restrict yourself to surveys alone is to limit your insights. This is another topic for another day (but if you’re interested, you can learn more about other listening channels here). End of PSA.

 For now, let’s talk about the risks of using AI like ChatGPT to write surveys!

ChatGPT Customer Experience Risks & Best Practices You Need to Know

ChatGPT Customer Experience Questions Miss the Point

Let me ask you a question: Is the point of your CX program to launch surveys? Now, many of you are likely rolling your eyes at me, but I promise, there’s a point to this. Hopefully, you answered no. Because the point of customer experience is not to ask questions, but to listen to customers and the market to help guide your path to achieving business goals. The questions are simply a vehicle to gain insight into what will help or hinder your business on the way to realizing those  goals.

When you look at the output of ChatGPT customer experience questions in the screenshot above, these questions really miss the point. Yes, they are generic questions that we have all likely seen in surveys before, but what are they getting at? The only results I can see this survey gleaning is a scoreboard metric and some customer demographics that we might already have access to via other data sources. 

When you craft surveys, the first questions you should ask should be for you and your team. Do you have a set of northstar goals (GOALS not scores!) for your customer experience program already? Great! If not, start that conversation with your executive stakeholders and team. Only then can you truly design your program, surveys, and other initiatives with the end goal in mind. 

Once you have agreed upon a desired end goal, then you need to ask:

  • What are we hoping to learn?
  • Who are we hoping to learn from?
  • Do we already have access to this data?

If you want to gut-check your surveys, you can check out this CX survey assessment my colleagues developed to help you optimize your surveys!

ChatGPT Doesn’t Know Your Customers Like You Do

Context is everything. And when it comes to ChatGPT customer experience questions, they won’t have any of the contextual data that you do. If your CX program has been around for a while, you likely have a mountain of customer data around. And that existing data will shape what you already know, and what questions you still need to ask. 

(Additionally, you might be tempted to feed ChatGPT some of your customer data, but that can unearth a whole boatload of security complications. Do you really want every ChatGPT user having access to your customer data? Didn’t think so.) 

An effective customer listening strategy is personal and targeted. Speaking to the customer in their language is critical. Many brands have worked hard to develop a brand persona. Asking customers for feedback in a sterile, canned voice will not yield the best results or further endear your brand to your customers. I don’t believe your brand personalization  can be accomplished by a ChatGPT survey—at least not today.

ChatGPT Is a Starting Block, Not the Finish Line

Now you may be thinking, “Jim, you’ve made a good case for the risks of using ChatGPT for customer experience surveys. But there has to be some way I can use it.” I’m glad you asked and yes, there is! 

I know we have all heard the fear-mongering conversations about AI taking jobs. And if we’re being realistic, AI will eliminate some jobs, but it will also create new ones. Those who will be safe from that chopping block are those professionals who learn how to leverage AI to increase efficiency and  perfect skills that AI alone just can’t manage without human input.

In the customer experience space, this could be leveraging ChatGPT as a starting point, then leveraging the additional context you have about your customers and your brand’s identity to perfect its suggestions. 

For example, ChatGPT can give you phrasing ideas for your survey questions as long as you are very specific in your prompts. It can also help you to think of other ways to ask questions you’ve been posing to customers for a long time, giving your same old relationship and post-transaction surveys a refresh. 

It’s not about AI or humans. It’s about humans using AI to improve and become more imaginative and efficient.

I will end with this. I do not want to come off as a “debbie downer” or, even worse, as naive. AI is going to have an increased role in customer experience and in creating the listening posts that practitioners create to capture customer insight. But, I believe true value will be well beyond simply crafting a survey. 

The real power of ChatGPT and other AI tools will be to help understand the data that comes from a survey or the multiple direct and indirect data sources that make up the voice of the customer. And, just to validate this statement, I asked ChatGPT why the voice of the customer is important? In this case, ChatGPT was spot on:

I think we can all agree that ChatGPT is right on target with that answer.

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customer experience speakers xi forum sydney

After 14 customer experience speakers, 250 delegates, two hands-on workshops, and hours of networking on the Sydney Harbour cruise, the 2022 Sydney XI Forum is done and dusted. That means it’s time to take what you’ve learned and start doing the work to elevate your experience program! 

We heard from award-winning customer experience speakers from some of Australia’s biggest brands—Craveable Brands, The NRMA, Rest Super, Foxtel, and JAX Tyres & Auto—not to mention two of InMoment’s global leaders. The day was filled with practical tips that you can apply to your program from day one.

If you missed out on the event, don’t worry—here are five key takeaways you can use to apply to your experience program today! 

5 Pieces of Advice from Our Customer Experience Speakers

#1: Managing Experiences Is Not Enough—The Future Is Experience Improvement

InMoment’s Global CMO, Kristi Knight, took us through the evolution of customer experience (CX). CX started out in the golden age of advertising, market research, and understanding consumers. Then, the internet was born, and online surveys were created to collect customer feedback in a timely manner. Next, we started managing experiences, and we recognised that the total experience a customer has is a collection of moments and interactions along their journey. The idea of simply “managing” metrics tells your business where you are and where you’ve been, not necessarily where you’re going. The future of CX is moving past managing experiences, to actually improving them through experience improvement

#2: Instead of Collecting More and More Data, Take Action On the Data Your Already Have

The CX industry has made big promises to brands; Essentially, if you listen to customers and act on that feedback, you’ll see results like loyalty, retention and other positive business outcomes. The XI Forum challenged our perspective on the traditional model of listening to feedback and collecting endless data. The ultimate goal for brands is to move beyond collecting operational and customer feedback, toward building differentiation from competitors, and ultimately designing and innovating new revenue streams and customer segments for the future. Make sure your CX platform is equipped to layer all types of feedback, whether that be direct (surveys) data, indirect data, or inferred (CRM) data.

#3: Make a Plan to Leverage AI in Your Experience Program

Like most industries, customer experience leaders are currently challenged to integrate AI into their programs to free up some of the manual tasks of improving experiences. If done correctly, AI can power your natural language understanding capabilities to show your business which actions to take to move the needle.  Ideally, every CX  platform should tell brands WHAT to do next.

To do that—survey data is not enough for AI to work properly, and there isn’t a robot sitting behind the platform making sense of your customer data and creating business insights for you. What you can do today is create bespoke AI models that will help make your platform smarter—for one, you can train your platform on what a churning customer looks like, and set up triggers to reach out to valuable clients when they are signaling dissatisfaction. 

#4: When It Comes to CX and the C-Suite, Optimise Your Dashboards

The C-Suite of any business is typically one of the most important stakeholders in your CX program. They need to be informed about what kind of CX initiatives are happening, where the CX problems are, and the plan to tackle them—AND they are extremely time poor. 

To solve this, optimise your C-Suite CX dashboards using these principles: 

  • Purpose: make the purpose of the dashboard crystal clear
  • Relevance: make sure each element of the dashboard will resonate with your audience, which may require bespoke configuration 
  • Engaging: these data visualisation on a C-Suite dashboard, should be simple and straightforward—take the guesswork out of convoluted charts and diagrams. Add branding, colours, and themes to make it visually appealing. 
  • Story: create and place the widgets in isolation and then decide the consumable order—the ultimate goal should be an overarching CX story that your C-Suite can easily understand 

#5: Level Up Your Experience Program by Marrying Together Multiple Voices

In the last keynote of the day, the CEO of JAX Tyres & Auto, Steve Grossrieder, described how their business is layering together voice of customer, voice of employees, and even voice of franchisees for a complete view of the customer journey. In doing so, the entire culture of the business is focused on the customer. For true customer centricity, it might be time for your brand to consider adding in another data set to further understand your employees, franchisees, partners, or other stakeholders.

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