Chapter 10: Delivering Insights with Service AI

Just as artificial intelligence (AI) has enabled product teams to enhance the product life cycle, service teams can now leverage AI to enhance the service life cycle.

As we know, it's not enough to provide a great solution to a customer challenge or problem. The customer insight process and the ensuing service design must be central to any organization that wishes to differentiate itself and deliver a higher-value experience to customers. Now, you can use AI to leverage data and gain actionable insights on how to better serve your customers in the future.

It's important to note that AI cannot read minds. However, it can process vast amounts of data and deliver actionable insights. With AI today, service teams can gain an unparalleled understanding of specific elements of the customer journey, and this can lead them to provide a better service.

In this chapter, we'll cover how to analyze data to better understand your customers, stores, and even employees. We'll then explore how to deliver these findings to your teams to gain competitive advantages.

We'll explore the key areas of focus in Commerce.AI's Service AI:

• Empowering your front line
• Managing your locations
• Enhancing service offerings

By the end of this chapter, you'll have learned how to better understand customer affinities, purchase reasons, and challenges, and turn your next interactions into great brand experiences. We'll also explore how to compare and monitor customer reviews across locations at scale to optimize your branches, employees, and services quickly. Finally, we'll identify growth areas and opportunities to boost customer loyalty, find new uses for your store, and get a picture of bottlenecks before they escalate.

Empowering your front line

When it comes to empowering your front line, it's all about providing your service teams with meaningful insights about your customers and customer interactions. Let's look at four ways Service AI makes this possible:

• Better understanding customer affinities
• Better understanding purchase reasons
• Better understanding customer challenges
• Turning your next interactions into great brand experiences

Let's start with using AI to better understand customer affinities.

Better understanding customer affinities

By using an AI-powered solution such as Commerce.AI's Service AI, you can identify the product affinities and service preferences of your existing customers. This data can then be leveraged to create a personalized experience that caters to individual customers' needs and interests.

For example, a restaurant could leverage its customer database, including its customer information, such as demographics, past purchases history, preferred dining times (for example, weekend brunch), and so on, to offer late-night delivery services for orders placed on Fridays and early morning delivery services for orders placed on Mondays.

Or another restaurant could leverage their past purchases to determine what items might be popular with people who typically dine later at night or prefer to have their food delivered (such as desserts). Using this information along with other related data points could help the restaurant better segment its customer base and find new market opportunities based on tailor-made offerings that meet people's specific needs – be they food - or non-food-related in nature.

As consumers, we all have different needs when it comes to buying products and services. We each have different preferences about timing (early morning versus night), days of the week (weekend versus weekday), how much notice we need for an order, what delivery method is most convenient (regular mail versus courier), and so on.

But being able to know what customers' preferences are before they go into a store, website, app, or another purchasing medium greatly increases their chances of finding something that fits their needs. It also gives them a chance to see if there might be other options out there that they didn't know existed but would fit their needs better. This could be for anything from food and clothing items to vacations and investments – the list goes on!

The key takeaway for you is that if you can identify your customers' affinities, there are endless possibilities as to what value-added services you can offer them – and from where those offerings could come.

Better understanding purchase reasons

Customers buy services for many different reasons, which go far beyond their financial value. One of the top reasons for people to buy things is to define their social identity. It's not just a matter of self-image either – it's about being part of a group, which creates a sense of belonging.

In some cases, this can be tied to your community – for example, buying local or consuming content that reflects where you live or interests you have. The key here is that most purchases are driven by emotions whose roots are tied to the customer's community. People want to feel connected in order for them to be happy in life – they need those connections and emotional ties.

People often don't do something until someone else has done it first. This means they'll copy others around them, whether they realize it or not. This applies as much to the service side as it does with all other purchases, especially those purchases that consumers perceive as status symbols (such as a premium airline membership) rather than purely functional (such as insurance). These purchases are often about identity more than value – they symbolize who you are.

Our actions demonstrate our values. By dedicating resources to help your team engage with customers, you're demonstrating that as a business, you care about your customers and value them enough to go above and beyond. This can lead to repeat customers and referrals because people trust a brand when they feel valued.

The lesson here is that AI can be a powerful tool for understanding your customers better, and it's a great way to understand what they think about the offerings you provide and why. Using these findings, you can then create strategies that more closely align with your customers' needs and desires. In essence, AI is about removing constraints so that you can grow in ways that weren't possible before, inspiring innovation along the way.

For example, teams could use Commerce.AI to analyze store reviews, social media posts, and other customer feedback to understand what customers are saying about their service. This can help teams better understand the pain points that the customers are facing, which will then inspire service innovation.

Another example is using AI to analyze purchase data to understand why people buy certain products over others. For instance, if you sell insurance, you could use AI to determine which types of insurance policies have the highest retention rates, and then you could use this information to inform your future service development efforts.

Better understanding customer challenges

From the service perspective, there are a few main challenges that your customers may face:

• Questions about your product or service: The first challenge is that they may have questions about your product or service. This could be a simple question about the eligibility criteria of a particular offer, or it could be a more complex inquiry, such as, Can you explain this product feature in detail?

  When you think about how to solve this problem, you will want to consider the data that your customer has already provided to you. Where possible, you should look at past interactions with customers and leverage that information to help guide future interactions. If there are any patterns that emerge from these interactions, it can save time and effort for both parties involved. It also helps if those patterns align with business goals (such as increasing sales).

• Providing personal customer interactions: The second challenge is related to understanding the context of an interaction. In many cases, today's consumers are accustomed to receiving information through digital channels without having to actually talk with another person. As such, providing customers with information in an impersonal way can feel unhelpful for both sides involved. In today's increasingly connected world, it is important for companies offering services, as well as their customers using those services, to become more familiar with each other so that relationships can develop naturally.

With AI, you can gain unprecedented insights into customer challenges, enabling your service teams to empathize with your customers, and therefore providing a more personal and heartfelt experience.

For example, by analyzing the conversations that people have with your service representatives, you can gain insight into the types of questions that customers ask. This information can then be used to train the next generation of customer service agents.

Another example is using AI to search and understand the products that are most in-demand for a particular customer, and then suggesting similar products when they place an order. By understanding the context of an interaction, you can create a more personalized experience for your customers, which will ultimately lead to higher retention rates and greater lifetime value.

Turning your next interactions into great brand experiences

Customer interactions over the past 100 years were driven by utilitarian logic (get the job done) without much concern for the human element. This has changed in recent years with attempts to increase human engagement and improve emotional ties between companies and their customers.

With Service AI, brands can better understand their customers and the real needs behind individual customer interactions, while overcoming the huge hurdle of comprehension. Service AI makes it easier to communicate across digital experiences and deliver on outcomes that matter to your customers, whether you're a small business or an enterprise company.

The adoption of Service AI will enable companies to create more human experiences for their customers, and the technology can be used across digital touchpoints, physical stores, or any other brand experience. It's about creating meaningful interactions that drive loyalty.

The concept of emotional connections is a growing focus in the study of customer interactions. These moments can help build trust and loyalty with your customers. And when you have a strong emotional connection with potential customers, you increase brand affinity – that is, the customers are much more likely to buy from you again than a company they have no relationship with.

To give a specific example, suppose a customer loses their wallet and needs to replace it. They could go to a store to buy a new one, but they may not remember the brand or model number of their old wallet. In this case, the customer would be better off just buying a new wallet online – no need to go into the store at all.

But what if that customer is forgetful and prefers in-person shopping? What if they're looking for something specific, such as a certain color or design? Or maybe they want to compare prices with other retailers?

If you can use AI as an assistant in these situations, you can help your customers get exactly what they need and save them time and hassle.

On a larger scale, you can use AI to recommend products and services to your customers based on their individual needs. For example, you can use AI to recommend a new car model to a customer based on their driving habits. Or, you might use AI to recommend an insurance policy for a customer based on their age, location, and risk factors.

Using AI in this way requires data that is not only actionable but also accurate. You need data about your customers' behaviors and preferences so that you can make the most informed decisions possible when recommending products or services.

Commerce.AI provides the world's biggest product and service data engine, which helps you gain this critical intelligence.

In this section, we learned about four ways to provide insights about your customers that prove to be invaluable for service teams. Once you understand your customers, it's time to zoom out and look at your locations more broadly.

Managing your locations

Now that we understand how to empower your service's front line, let's take a look at how to manage your store locations. Your store locations are a vital factor in the success of your brand, so it's vital to optimize everything you can for them. In particular, we'll look at the following:

• Optimizing your branch
• Optimizing your employees
• Optimizing your service

These three areas are interrelated, and it's important to optimize them all. Let's look at each in detail.

Optimizing your branch

People post reviews about everything, and retail locations are no exception. With the right approach, you can leverage reviews as a source of competitive intelligence to help you understand what's working well at your stores and how you can make them work even better.

You'll be able to answer questions like these: What do customers like most about your stores? What do they criticize? Are there any common threads among their complaints? It sounds obvious, but quite often, it's not until you look closely at an issue that you can identify how to solve it.

One powerful feature of Service AI is the ability to explore aggregated reviews. You can look at all the locations and then drill down on a subset based on attributes.

Next, we can take this approach even further by breaking down our various subsets based on external factors or categories (for example, store type: retail and restaurant). Each subset could then be scored relative to other subsets in terms of their performance so far over time: Are some stores outperforming others? Is it clear why some are outperforming or not performing well yet?

Once we begin to understand what differentiates the good performers from the not-so-good ones, it will help us create actionable insights such as identifying which stores could benefit most from additional training or doing something different in their marketing strategy. This will inform our decisions on where we invest our limited resources in terms of operations, personnel, and capital facilities, which are all critical things needed for any retail business.

You can also use Service AI to analyze trends across locations over time using multiple metrics, such as the number of reviews over time, the average of review scores, and more. This can also give you a line on where the sweet spot is for your locations: Where are they performing well compared to other locations that have similar performance? Are there any outliers that don't fit this pattern? How large is their deviation from the trend, and is it growing or decreasing in magnitude over time?

Sentiment plays a big role in understanding any store in a given branch. By analyzing sentiment across store locations, you can pinpoint areas for improvement. This isn't only relevant to physical stores but also services that vary based on location, such as a telecom service. In Figure 10.1, we can see a mockup of a telecom service provider using Commerce.AI to analyze sentiment by location and uncover customer intents, shown in the Wishlist, Praise, and Warranty sections:

Figure 10.1 – A mockup of Commerce.AI analysis for a telecom service

Wishlists are simply features or offerings that consumers express interest in and can help inform your product or service roadmap. By aggregating a consumer's wishlist, Commerce.AI helps service teams understand what consumers want and will help guide future product development.

Praise is a great indicator of customer satisfaction, and when viewed through the lens of sentiment analysis, it's clear that these consumers are very satisfied. When employees interact with customers in each store location, they should be likable and helpful, and when they do something right, it's always good to celebrate!

Warranty is another key customer intent to keep track of. Segmenting customers who express interest in warranty information is a great way to understand your customers' needs and provide the right level of service.

Ultimately, these analyses come down to making wise decisions that are informed by data and analytics. By using Service AI as one of many tools for analysis, we can pull insights from several sources and help make more informed business decisions than through traditional efforts alone.

Optimizing your employees

Service innovation is a top priority for nearly every company, but one challenge is that service employees are often overlooked as an asset. The difficulty for service providers is finding the right talent and developing them into team members with the skills necessary to deliver a great customer experience.

In the following list, we'll explain how some of the world's fastest-growing companies are leveraging AI and data science to give their service teams the advantage they need to drive growth and profitability. We'll cover the following steps:

1. Understanding where service employees can bring value: The first step in optimizing service employees is understanding where they can bring value to your business. Often, these opportunities arise from machine learning or AI algorithms that provide smart recommendations for customers based on their previous interactions with your brand.

   For example, a hotel might recommend specific amenities based on a guest's travel preferences or past stay history. By using AI to create personalized interactions between customers and brands, companies can deliver exceptional experiences at lower costs than traditional marketing approaches.

   This insight holds true for all types of businesses and not just for those involved in customer services (such as hotels). Any organization can apply data science and analytics across multiple touchpoints to identify opportunities for personalized engagement that could lead to higher lifetime value (LTV) for each customer. However, when applied correctly, AI presents tremendous opportunities for product managers, designers, writers, engineers, or anyone involved in creating products or services.

2. Sentiment analysis and customer experience analytics: The next step in optimizing service employees with AI involves sentiment analysis and customer experience analytics. These tools can help service employees improve how they interact with customers by providing actionable insights into customer complaints, positive interactions, and other useful information for improving the customer experience.

   For example, an airline could use sentiment analysis to identify the top three reasons why a customer would choose another airline over their own. By using these insights, they can improve the in-flight experience by addressing any issues that are driving customers away from their brand. This type of data science can also be used to improve retention rates and reduce churn (customer turnover) by proactively reaching out to customers who have started to leave your company (or stopped engaging with you altogether).

   Service teams often operate in isolation from other company divisions. This is partly because traditional marketing methods don't work as well with service teams; segmentation and demographics are less relevant for them because they're focused on providing one-on-one customer interactions. However, it's also due to an unfortunate legacy of service companies being seen as subpar performers relative to their counterparts in product-focused industries.

3. Delivering insights: In reality, however, service organizations are just as capable of delivering profitable growth as any other business unit within any organization – and they shouldn't be overlooked. Using machine learning and AI tools like those available in Commerce.AI allows service organizations to leverage data science across multiple touchpoints so that employees can identify new opportunities for engagement and innovation at every turn.

   By applying data science across all contact points with customers, companies can better understand what motivates their users (as opposed to relying on gut instinct) and deliver exceptional experiences that create long-term value for both parties.

Optimizing your service

The services you provide to your users are at the core of your business. You don't need to be an expert in data science or machine learning to see that. The quality of your service is directly correlated to customer loyalty, and if they are satisfied, many customers will do business with you again and recommend your service to others.

That's why it is so important for service providers to leverage the growing capabilities of data science and machine learning in their businesses. In particular, sentiment analysis and customer profiling are two powerful tools that many service businesses have not fully tapped into.

Through sentiment analysis, you can understand how your users feel about the quality of your service very early in a product's life cycle and find opportunities to improve. For example, if your product is customer service, you might want to monitor social media sentiment about your team and make changes to improve the experience. Or, if your users are critical of the quality of food at a particular restaurant or hotel, you can use sentiment analysis to see if there are ways to improve that experience.

Similarly, if you are a TV show producer, you can use sentiment analysis to understand what your audience thinks of particular shows and plot a course for future programming. If your audience is tired of the same old crime shows and wants more comedy, that's an opportunity to increase viewership.

Customer profiling is another powerful data science tool that service providers can use to improve the quality of their service. With customer profiling, you can gather a wealth of information about your users – such as where they live, what they do for work, or their hobbies and interests – and use this information to provide them with customized experiences based on these preferences.

For example, if many people in one part of town watch sports highlights in real time during game day, then a sports bar might want to install high-definition TVs in that area so customers there can watch sports highlights while waiting for the game to start. This type of insight helps businesses avoid wasted investments and provide better services at a lower cost for their customers.

There's no doubt that these two forms of data science – sentiment analysis and customer profiling – will play an increasingly important role in improving the efficiency and quality of services going forward. They will become even more critical as companies look to differentiate themselves by offering enhanced customer experiences or personalized content (think Netflix versus Blockbuster).

This is why it is so important for service providers to consider how they might leverage these tools today (to improve customer experience now) to build loyal customers who will come back in the future.

In this section, we learned how to manage and optimize your store locations in terms of your branch, your employees, and your service. Each of these plays a vital role in your service. Next, let's dig deeper into using AI to improve your service offerings.

Enhancing service offerings

Enhancing your service offerings requires a holistic approach, where you look at your offerings from end to end. Your competitors are constantly improving their service offerings based on customer feedback and analysis, so it's crucial for you to do the same. In particular, you can use Service AI to enhance your service offerings in five ways:

• Identifying growth areas
• Leveraging AI for creating stronger service offerings
• Identifying opportunities to boost customer loyalty
• Finding new uses for your store
• Getting a picture of bottlenecks before they escalate

Let's explore each of these areas in detail.

Identifying growth areas

Growing revenues is hard. Businesses that have succeeded at scale have all had one thing in common: the ability to continuously generate new value and enhance existing offerings by giving customers more of what they want. In the process, these businesses have created tremendous customer loyalty, which has been key to their success.

Today, many companies are trying to figure out how to grow revenues in a world where there is less money around and more competition, especially as consumers look for digital alternatives to traditional brick-and-mortar shopping experiences. With e-commerce on the rise, it's no wonder why so many companies are focused on building stronger e-commerce capabilities within their organization.

But what does this mean for service teams? Now, we'll explore how we can leverage AI in order to create even stronger service offerings for our customers and potentially enhance revenue growth along the way.

Leveraging AI for creating stronger service offerings

One of the most important things that service teams can do to help their companies grow is to ensure that customers are happy. There's a lot of research showing that customer retention rates (that is, how long a customer remains loyal) are directly correlated with revenue growth. Just think about it: If you could keep 100% of your customers, wouldn't that be great?

But think about the implications here: Even if you can only retain 50% of your customers, if you were able to do so by continuously providing them with more value and enhancing existing offerings, wouldn't that make sense?

In other words, only focusing on growth, without looking at ways to boost retention rates, would be like pouring water in a leaky bucket.

What matters is whether or not your company has the ability to continue creating new value for customers over time. This means that service teams need to rethink how they go about servicing their customers in order to create new value and grow revenues at the same time.

When it comes to creating more value for your customers, you don't always have to reinvent the wheel. In fact, many companies already use AI today as a means of enhancing their existing offerings, especially on websites such as Airbnb and Uber, where people are using AI features such as score sheets and driver ratings to figure out who might be a reliable host or driver, respectively.

People are already engaging with these kinds of features on sites like these all the time; all you need is an understanding of behavioral economics (the science behind human decision-making). With this understanding, you can judge how best to deploy these tools in order for them to work optimally for your business goals at scale.

For example, let's say that you own an e-commerce site that sells home decor items such as throw pillows. Every day, thousands upon thousands of people visit your e-commerce site from around the world searching for things such as pink throw pillows, or even more specific terms such as red plaid throw pillows. Data might show that customers who buy red plaid throw pillows tend to also buy pink throw pillows, but if a customer doesn't view either, they won't buy either.

What if you added a new product detail page parameter called color that would show commonly bought colors in pairs? People who searched by color would then be more likely to buy multiple items, thereby increasing the average cart value. This is behavioral economics at work. You've used AI to make better use of your existing inventory by understanding how people actually browse through it.

The key takeaway from this example is that if you know how people browse through your site, you can use that data to inform future decisions about what products or features to include or exclude based on customer behavior. Again, using behavioral economics as a guide, you can create more value for customers by giving them what they want, when they need it most (or at least what they perceive to be the most useful). We call this adaptive customization.

This concept of adaptive customization has huge implications for service teams looking to create even stronger offerings and grow revenues. In fact, applying this same principle to social media platforms has been shown to increase user engagement rates significantly, which means higher retention rates and greater revenue growth over time.

Identifying opportunities to boost customer loyalty

Customer loyalty is a hot topic. Companies spend billions each year to acquire and retain customers, and the stakes are higher than ever, as there's massive competition in the service marketplace.

But with customer loyalty initiatives, which are often limited by resource constraints, it can be hard for service companies to figure out how they can give their customers more value while also boosting retention rates.

In short: How do you create a win-win situation for both your customers and your business?

One promising way is through AI-enabled commerce platforms such as Commerce.AI. These platforms use data science to transform the way that businesses interact with their customers through their service offerings.

For example, let's say you own a small business that offers on-demand home services to help people maintain and repair their homes. You have a great team that provides a high-quality service in a timely manner, but your revenue growth has stalled due to high customer churn.

Your sales team has been trying new ways to engage with your customers, such as creating custom events for them or offering them one-on-one coaching sessions. To add value to your existing customers, you use Commerce.AI to analyze the sentiment of your reviews on Yelp and Angie's List, identify what questions you could address to improve customer satisfaction, and then use that feedback to create new services for your customers.

This approach has the potential to increase customer engagement while also boosting retention rates. For example, imagine one of your customers recently complained about a broken faucet in her home. You quickly created a service offering to take care of it along with other issues she had listed on Yelp. She was thrilled with the level of personalized service and appreciative that you were able to take care of things right away.

The same principles can be applied at scale for service businesses of any size. For example, you can use Service AI to identify your top customers and then use that information to create new services for them. Or, if you have a large number of customers in certain areas, you can use Service AI to identify which customers are using certain features more than others. Then, you can create new offerings for those users based on their needs.

This kind of integration between Commerce.AI and businesses can help them strengthen existing relationships with customers by providing more value than just transactional interactions, which is why we see Commerce.AI being used by leading brands such as Unilever, Netgear, Coca Cola, Suzuki, and many others.

Finding new uses for your store

Your store may have several uses: a place to provide your service; a place to grow your community; a place to gain feedback; a place to do market research; a place to run sales promotions; a place for customers to sign up for your email list; a place to earn referral commissions; a place to sell physical products – the list goes on.

But how do you find new and better ways to serve your customers in each of these use cases and how do you track the results of your efforts?

That's where AI comes in. And because it might feel overwhelming to think about how to use AI to meet all of these diverse needs, we've broken it down into three stages that can help you start improving how you work today:

1. Finding new ways to use your store today
2. Finding new ways to use your store in the future
3. Finding out what works and identifying how to scale it

Finding new ways to use your store today

The first step is to think about how you can use your store today and then explore whether there are any new ways you can use it.

For example, if you're a service team, you might want to consider using your store as a place for customers to interact with each other. You could host a meetup or an event in your space where people can come together and learn from each other. Or maybe you have some interesting content that people would love to share, such as tips on how to be more productive at work, or how to start a business. Now is the time to start thinking about what kind of content you could create for people who want access to it outside of your newsletter or website.

You might also want to think about ways that your team could use your store as a way for them to engage with customers. For example, you could ask yourself some questions: If you have an e-commerce site, why not host live question and answer sessions with customers? If you have a community site, why not post customer success stories? These are just two ideas, and there are many more possibilities depending on the type of product or service that you offer. The important thing is that this exercise helps teams identify new ways they can use their platform today, so they don't get stuck thinking only about what they plan on doing tomorrow.

AI can help you find new ways to use your store by helping you identify what content is most valuable for your audience and then find the best way to deliver it.

Finding new ways to use your store in the future

Once you've started using your store in new ways, the next step is to start thinking about how you can use it in the future. This might mean starting small and building on what you're doing today, or it might mean diving into something completely new.

For example, while your stores may not currently have the budget to hire a full-time social media manager, you might be able to use your stores as a way for your team members to engage with customers on social media. You could also consider using your stores as a way for your team members to share content that's relevant to your community, such as a great new article that you think people would love, and then ask people in your community to share it with their own communities.

Ultimately, this stage is all about using AI to extract customer desires from reviews and social media posts, and then segmenting out those desires that aren't currently possible, but that can be put on a roadmap for the future. Until then, you can look for creative alternatives to meet customer needs.

Finding out what works and identifying how to scale it

The final step is to use AI to learn from your experiments so that you can scale what works. You might find it helpful to have an external team or consultant to review and validate the findings of each stage as you move forward, as this can help you stay focused on the right things.

Start small, start simple, and don't focus on the things that are most expensive for your budget (such as buying a massive piece of real estate or hiring a huge team of people) until you've figured out how to get started with something much more manageable.

This is also a good time to think about how your team will be able to track and analyze the results of these experiments over time, so they can build up an understanding of which approaches work best in which use cases, while also being able to track the success metrics behind those approaches. This helps the teams understand which experiments worked well and why they worked well, so they can repeat them in other places where there's potential for growth.

Getting a picture of bottlenecks before they escalate

Bottlenecks are the hidden factors that prevent customers from using a service to its fullest potential. These factors may stem from limited human resources, capacity, inadequate processes, or other inefficiencies.

Every business knows that improving the customer experience is key to their success. However, getting a holistic picture of all aspects of their service offerings can be difficult for many organizations. Let's explore how AI can help teams get a clearer view of their service capabilities – by isolating bottlenecks – so they can make smarter and more informed decisions about where to invest their time and resources.

Tracking your store locations, team members, and customer interactions in a centralized system can help you spot trends and identify areas for improvement. It's like having a digital back-of-the-napkin analysis to figure out where your business is strong and where it could use some work.

When applied to service operations, AI tools can provide real-time insights into customer satisfaction, staff performance, and location efficiency. Through machine learning, data-driven systems analyze vast amounts of data at lightning speed, crunching through reams of documents while automatically alerting you about the issues before they become problems.

In the case of service operations intelligence, AI can highlight key bottlenecks that may go unnoticed by human eyes, thereby allowing teams to proactively address these issues long before they become an impediment to the customer experience.

For example, a famous French pizza chain used Commerce.AI to identify bottlenecks at their delivery locations. Using Commerce.AI, chains can easily find bottlenecks in their business processes, such as new hires not having enough training on specific equipment.

Summary

In this chapter, you've learned how to use AI to empower your front line, manage your locations, and enhance your service offerings.

By analyzing customer data, you can extract customer affinities, purchase reasons, and challenges. This information will empower your front line to better empathize with and serve your customers. When it comes to your physical locations, you can analyze data around your branch, employees, and service to optimize processes at every level. Finally, you can enhance and even transform your service offerings by identifying growth areas, finding new uses, boosting customer loyalty, and minimizing bottlenecks.

By putting AI to work on your service data, you'll unlock new insights and opportunities for growth, and you'll further differentiate your company in the marketplace. You'll become an AI-first company.

In our next chapter, you'll learn how to apply AI to market intelligence to generate actionable insights that help you improve your business.