Chapter 9: Delivering Insights with Product AI

Data is a goldmine for product teams, allowing you to develop better, more targeted offerings for your customers. You can use data to build actionable insights into how people interact with your products and make decisions that will help you deliver the best possible product experience.

But simply having data isn't enough. What matters is how you use it. The key, as we'll discuss in this chapter, is to take a holistic approach that integrates data into the entire product innovation life cycle. In this chapter, you'll learn about how to integrate data into the product innovation life cycle, including decisions around product development, product launches, and more. In short, you'll learn how to use data to bring better products to market, faster.

We'll discuss how to use Commerce.AI's Product AI features for every stage of the product life cycle, from market research and product ideation and creation all the way to post-launch ads and sales. In particular, we'll cover the following topics:

• Commerce.AI for product concept and development
• Commerce.AI for product launches
• Commerce.AI for product management

Commerce.AI for product concept and development

Product conception is an old art, but the underlying principles are still valid. The main principle is that people buy for a reason—they want to solve a problem or meet an unmet need.

After all, guesswork will inevitably lead to a product that consumers don't want or need, which will fail in the market. Therefore, product teams need to first conduct extensive market research in order to understand consumer wants and needs before the product ideation process.

Let's explore the following areas within product concept and development in the next subsections:

• Market research
• Understanding demand
• Product ideation

Market research

The product development workflow starts with market research, followed by concept ideation, design, and engineering. This is how most products are launched into the market. But in a world of on-demand AI solutions that can provide data insights from inception, many companies have begun to use AI to shorten the product development life cycle (PDLC). By applying AI at each step of the PDLC, you can make sure your products aren't just informed by data but actually reflect it throughout the entire process.

Market research for product teams is all about understanding the needs of customers as they engage with a product or service. It's about finding out what customers really want, and it's about figuring out whether a new idea will work.

Market research goes beyond simply understanding what people currently think about an idea or product. Marketers also need to understand how people will change their minds in the future, and why. And market research is not just for product teams; all teams at any stage of product development (marketing, design, engineering) should consider market research a critical part of the process.

Further, market research is one of the most cost-effective ways to develop product ideas, especially if you do a lot of user testing to validate your hypotheses. The cost of gathering some key data points can save you months of development time when compared to trying to design things blindly. That said, traditional market research has its limitations.

Limitations of traditional market research

Traditional consumer surveys are tedious, time-consuming, and cost a lot of money, and they often prove disappointing when it comes to product innovation challenges. Moreover, consumer preferences change quite frequently. Traditional research methods are hardly equipped to capture the complexity of users' motivations.

With AI in commerce, you can update your process for market research to provide exciting new avenues for product innovation. You can supplement qualitative market research with automated quantitative market research, which can provide the data insights you need to help validate product concepts.

With Commerce.AI's AI-powered market research reports (https://www.commerce.ai/reports), you can streamline the research process with low-cost, online reports to discover exact insights about customers and the sizes of market opportunities.

Leading product firms are applying AI to market research to improve product development. After all, speed to market is a competitive advantage, and keeping up with customer input and feedback is key in this.

Understanding demand

Understanding demand through market signals is one of the most important steps in product innovation. For decades, market research has relied on a combination of quantitative and qualitative methods for identifying trends in consumer behavior that can be used to inform your product strategy and design.

There are many different types of market signals that marketers can observe, but two major categories include quantitative and qualitative observations:

• Quantitative signals include things such as search volume or sales velocity.
• Qualitative observations involve talking to customers—both directly (via voice surveys) and indirectly (via online forums, social media, or product review replies).

Simply put, the digital space offers new ways to observe how people are using products and services—and in fact, there's no shortage of data available now about what people do when they interact with technology. Businesses can collect this data, and it can be of great value.

The challenge, however, is that the data itself is not market research—it's a starting point for market research. Marketers need to transform this raw information into insights about what people want, how they want it, and why they want it, in order to understand what will result in demand.

By understanding demand, companies can go beyond simply creating a product or service that someone wants and start thinking about how to create a product or service that people don't even know they need yet (but will end up loving nonetheless).

In Figure 9.1, we can see a mockup of Commerce.AI's Market signals dashboard, which it fills with relevant product data from a variety of sources for the brand using it:

Figure 9.1 – A mockup of the Commerce.AI Market signals dashboard

As seen in Figure 9.1, consumer goods firms discovered during the COVID-19 pandemic that bidets and toilet paper surged in demand, which signaled an opportunity to create or enter into a new category of hygiene products. With Commerce.AI market signals, brands can also set alerts to notify them about important events, such as a spike in demand or interest in a particular product or service.

The key to making this process work is using data and insights to make informed decisions about what will result in demand. The more data and insights you have, the better you can create a roadmap for your product or service, which will help guide you as you move from concept to launch. And the better your product roadmap, the better your product or service will ultimately perform.

Product ideation

After exploring demand opportunities and market signals, it's time to start thinking about your next steps. One of the next stages in the PDLC is concept ideation. In the past, you might have drawn on your own experience for ideas. But with data, that's a thing of the past. With AI, you now have access to user data at scale to provide context for new products.

Let's look at two main ways in which to use AI for product ideation. First off, AI fueled by product data can uncover what features and attributes your competitors are already providing to consumers. These insights can help you ensure that your next product release doesn't overlap with an existing one. This enables you to enter new product markets with more competitive differentiation. It can also extract patterns and trends from the data to help you understand what consumers want and need.

For example, let's suppose you're trying to expand your product line of electronics accessories and sell a cell phone charger. As seen in Figure 9.2, we can view an AI-generated feed of leading brands and products in this area. In particular, Anker is the brand, and its three main strengths are that it's fast, it holds a long charge, and it's durable. Therefore, we know that to compete with Anker, those are the top attributes that we'd have to provide to consumers as well.

Figure 9.2 – Commerce.AI Dashboard Summary for wall chargers

The second way AI can help you is by generating new product ideas. AI can generate any number of product ideas in a matter of seconds, and this can be a huge time saver.

Let's look at exactly how this works. The data used to train the algorithm can come from a variety of sources, including your own product data, market data, and user research data.

For example, in Figure 9.3, we use the Commerce.AI Product Idea Generator to create a new idea for a cell phone charger. The idea is a high-speed charger that is small enough to fit in my pocket. This generated idea makes sense, as typical chargers are often very bulky and inconvenient. Perhaps a product team will go along with this idea, or perhaps it will spur another idea for a different kind of product.

Figure 9.3 – The Commerce.AI Product Idea Generator

The machine learning algorithm will analyze the data and come up with a list of potential product ideas. This list can include new product features, new product categories, and new product pricing models.

The key takeaway here is that AI can generate creative product ideas in a matter of seconds. This can help speed up the creative process, as it's easy to run into creative blocks when you're faced with constantly coming up with new product and feature ideas.

After product conception and development, it's time to strategize and execute a successful product launch.

Product launch

In the past, product managers had to rely on gut feelings and their own experiences to make the right product decisions. Today, AI is changing this dynamic by providing a more objective way of making product decisions based on data and past interactions.

Let's take a step back and look at how AI is changing the world of products, with a focus on four areas:

• How AI is changing product launches
• Predicting demand from early signals
• AI for the two types of product launches
• Using AI for product launches—advantages and disadvantages

Let's start off with an overview of how AI is changing product launches.

How AI is changing product launches

Traditional product innovation was built around the interview-centric approach. In other words, product managers assumed that the best way to understand their customers was to spend time with them. This approach has evolved quite a bit over the past few decades. The trend now is toward more data-driven approaches to product management.

In the last decade, the hype around AI was growing rapidly. The concept of using software to automate repetitive tasks seemed like a no-brainer and something every company needed to be doing if they wanted to stay competitive in an increasingly fast-paced business environment.

However, assembling the right team and building an AI product from scratch was quite an undertaking back then. In fact, just getting access to good datasets took significant work. Companies either had to build their own, or they had to spend huge sums acquiring them from competitors.

With Commerce.AI, this treasure trove of product data is readily available to any company that wants it. The idea behind AI-powered product launches is simple—automate as much work as possible, and create an environment where humans can focus on innovation and creating something truly unique for customers.

The end result is a faster time to market for new products with fewer risks along the way. This also means freeing up valuable resources for other initiatives within your company. So, how exactly does AI help with product launches? One way is by predicting demand from early signals. Let's explore that concept in detail.

Predicting demand from early signals

The first thing to realize is that AI can't be relied on 100% of the time. It doesn't always work, and it has its limitations. However, when used properly, AI can provide invaluable insights into customer behavior that can help companies make informed decisions about product launches while reducing risk along the way.

This type of approach is called pro-active demand analysis because it uses machine learning techniques to predict demand before it arises (rather than reactively, during times when sales are slow or zero). By doing so, companies can also ensure that they have enough inventory on hand to meet peak demand after a product launch—which could result in lost sales if an item isn't restocked quickly enough.

This concept goes beyond product launches—anything where inventory management plays a role should be improved with AI capabilities. After all, most commerce businesses need to constantly replenish the stock of any given SKU.

The key takeaway is that these types of machines learn patterns and thus enable humans to do high-level tasks, such as anticipating changes in consumer behavior and taking action before something becomes an issue, rather than dealing with issues once they arise.

AI for the two types of product launches

To better understand the challenges and opportunities companies face with launching new products, it's helpful to see how these issues play out in different environments. In particular, we need to pay attention to two main types of product launches:

• Hard launch
• Soft launch

Let's first explore the concept of a hard launch and how AI can help optimize this event.

Hard launch

A hard launch is a full-on, company-wide campaign that is designed to introduce an entirely new product or service to the marketplace. Typically, this involves extensive media buys, advertising campaigns, and events—all designed with one goal in mind: getting as many people as possible to try your product for the first time. The hope is that once they do, they will become brand advocates who will tell their friends and family about your product.

With Commerce.AI, you can use AI to optimize hard launches by automating much of the communication and content creation process. You can also use the toolset to plan ahead and prepare in advance of a big launch to ensure your content is optimized for success.

Additionally, you can segment your audience to ensure that each person is getting the most relevant content so they have the best chance of engaging with your company. You'll also be able to analyze the performance of your marketing campaigns right after the launch, seeing not only how many people viewed your content but also which pieces performed the best.

Soft launch

A soft launch is a controlled, limited release of your new product or service to a select group of people who have been handpicked by you, the company. The goal here is to gauge customer reactions and measure customer acquisition costs (CACs). In other words, you are testing out your product or service to determine how much it will cost to acquire customers, and then adjusting your launch strategy accordingly.

Typically, this involves sending an email newsletter announcing the soft launch to a specific list of people, before opening it up for anyone who wants access. You can also do a soft launch on social media—just announce that your new product is available for early access and only invite select people via invitation emails. You can test different price points as well as different types of content in order to determine what works best on each platform.

Even if you don't get enough interest during the soft launch period, if you use analytics tools such as Commerce.AI, you can see which messages resonated with customers and adjust future campaigns accordingly.

For example, if a particular message brought in more sign-ups at a higher average revenue per user than others did, that would be an indication that this message would work well as part of a larger campaign later down the road (once you had collected more data about customer behavior from real users).

Voice surveys are another way to gain feedback from any kind of launch. Unlike traditional text-based surveys, voice surveys have particularly high engagement and completion rates, and they can be used to gain valuable insights after a launch. In Chapter 12, Voice Surveys, we'll discuss their use in detail.

Using AI for product launches—advantages and disadvantages

In addition to helping companies avoid potential supply chain disruptions, pro-active demand analysis has numerous advantages as well. It helps eliminate guesswork regarding what products may sell better based on previous experience with similar products. It also enables companies to avoid under/overstocking based on potential demand, which can lead to greater efficiency and profitability, as well as less inventory risk.

When it comes to AI in the context of product launches, this leads to lower costs and more streamlined processes overall. Additionally, AI helps companies focus their resources on creating something truly unique for customers, rather than spending time and resources building out infrastructure that they might not need.

Using AI will free up team members who are better suited for other tasks, such as managing growth or developing new products altogether. While a successful product launch is key, that's far from the end of it, so let's explore how to best use data and AI to improve post-launch product management.

Product management

It's fair to say that many product managers have over 100 tabs open in their browsers at any given time. But what if there was a single tool or service that could monitor all of your products, across all channels?

And what if you could use it to get actionable insights into how your products were performing in the market? And what if this information was available on an ongoing basis (real-time), so you always knew where your product stood and what the trends were? With Commerce.AI, you can monitor your products in the market and across channels.

The core principle behind AI in product management is to leverage machine learning and AI techniques to give you actionable insights into how your products are performing. By using AI, you'll be able to make better decisions about what changes should be made to improve your product's performance, or even pivot and change the direction of your product entirely.

In this section, we'll look at AI for product management across six areas:

• Tracking product wishes
• Brand management
• Using AI for consumer insights
• Using AI for product tracking
• Marketing and merchandising
• Customer support

Let's get started by looking at how and why product managers can use AI to track product wishes.

Tracking product wishes

One significant use of AI in product management is sentiment analysis. Sentiment analysis uses natural language processing (NLP) techniques to analyze written text for signs of positive or negative sentiment. Sentiment analysis can reveal whether users think a particular feature of a product is useful—that is, it can tell you what people like or dislike about your product. In other words, sentiment analysis provides information on how people talk about your products—good and bad!

As a result, product managers can use AI to track product wishes, both in terms of features to improve or remove, and features that consumers want to see added to a product.

Tracking a product wish list is an important exercise for any product manager, but it can be
challenging to maintain the data and have a system that you can refer back to throughout the life cycle of a product. This can result in a dilution of focus as well as losing track of key considerations, such as the following:

• What are the top features or use cases desired by your customers?
• How does your product stack up against these use cases or wishes?
• Are you meeting them, exceeding them, under-delivering, or falling short?
• What else do you need to know about this topic in order to build a great product?

Commerce.AI offers real-time tracking on all your products across channels, and it also offers historical insights for you to have context over time. With Commerce.AI, you can set parameters on what changes matter most when building out new features, test new concepts by enabling experiments on a subset of users and measure their engagement, and understand how these new ideas impact revenue or other key performance indicators (KPIs). Then, you can make informed decisions about which ideas to prioritize and invest resources into moving forward.

All this is possible from within one tool—no third-party software is required. We believe this kind of holistic insight into product performance will become even more critical for many organizations in the years ahead.

Brand management

Product management and brand management go together like hand and glove—they're the core of a product company. Without branding, we wouldn't know what to make of a company's offerings, how trustworthy they are, or what their target market is. With the rise of AI and the integration of machine learning into product management tasks, it's now easier than ever to successfully use big data to manage brands.

Companies are now able to leverage AI to gain a better understanding of who their customers are, how they behave, what they like, and where they are. Commerce.AI can be used for a blend of brand and product sentiment analysis, consumer insights, and product tracking in order to identify opportunities to create and maintain better brand experiences.

Using AI for brand sentiment

Commerce.AI's brand sentiment tools have been built on the NLP stack, which means that we leverage techniques such as machine learning and deep learning to analyze spoken language in natural contexts (for example, from customer service interactions). This allows us to automatically extract information from unstructured data, such as social media posts or comments on product review videos.

This technology gives us the ability to identify either positive or negative sentiment about brands in social media posts and comments. Sentiment analysis is a well-known field of study within NLP. Software designed for this purpose is now widely available, with many tools being developed over the past decade.

In general, there are several categories of sentiment analysis algorithms:

• Word count algorithms (which count the number of times certain words occur)
• Co-occurrence algorithms (which look at how often specific words occur together)
• Polarity/agreement algorithms (which determine whether a post expresses more positive or negative sentiment)

By using these well-established technologies to mine public conversations online combined with deep neural networks, we can gain insights into what people think of various brands—both good and bad—helping you to understand your customers better and devise strategies for promoting your brand in a way that will play to its strengths while avoiding potential weaknesses.

By using machine learning techniques to mine public conversations, you have an opportunity to read between the lines without having to talk directly with customers face-to-face or go through lengthy market research studies.

Next, let's look at consumer insights through the lens of product management.

Using AI for consumer insights

One area where AI is especially well suited to product managers is consumer insights. As a brand manager, you can use machine learning and NLP to identify the types of data that will be most relevant to your particular business objectives, and then you can automate the collection and processing of this type of data as part of an ongoing effort to stay abreast of your target market.

For example, if you're developing a new mobile app, you may want first to look into some basic demographic information about your users (that is, age, gender, location, and so on)in order to understand more about them as customers before building out additional features that will appeal to them specifically as individuals.

Likewise, if you're a retailer trying to develop a new e-commerce site or chatbot experience for your customers, knowing what kind of products they are looking for right now can help inform what you build in the future.

This is also true if you're building a brand new website or mobile app. You want the site or app development process itself to be customer-centric from the ground up so that it's always focused on understanding who your users will be. With AI, you can more deeply understand your customers and therefore design more tailored experiences for them.

Now let's look at another important aspect of managing brands using AI—product tracking.

Using AI for product tracking

Managing product life cycles isn't something that should be done manually anymore—there's too much detail involved with validating all the different touchpoints within each stage of the product life cycle, all the way from idea generation and development through to testing and launch.

Fortunately, AI can help streamline this process considerably in order to get products out faster while maintaining high standards. If properly implemented across multiple teams within an organization (for example, product management, engineering, operations), automation efforts can also lead to increased accountability among team members.

Product managers are tasked with ensuring that a company's offerings meet customer needs while staying on time, on budget, and within scope. And like many roles within companies today, this often involves juggling multiple tasks—managing stakeholders across multiple departments, keeping abreast of competitive developments in the industry, understanding customer behavior patterns—it's no wonder that coordinating these efforts is a full-time job!

However, integrating AI into your workflow can free up valuable time so you can focus on more strategic activities that will benefit your organization.

Marketing and merchandising

When it comes to merchandising, an obvious step to take with AI is selecting the right products to display at the right time. In fact, this may well be the quickest win you can get from AI.

The key here is to apply AI in a way that's actionable for your business. For instance, if you're a retailer, applying AI to product selection could mean identifying bestsellers among similar items (for example, which sneakers/boots are selling well?) and using A/B testing or other tools to determine what specific attributes of those products make them sticky, and pushing more of those kinds of items onto shoppers while curating less popular items off the site altogether.

A/B testing is an effective way to measure the impact of specific product attributes on customer behavior, and it's often used by e-commerce companies to optimize their site for increased conversions.

The letters A and B in A/B testing refer to control and treatment, respectively. The control group is the original, while the treatment group receives a specific change. Suppose you were testing a new pair of sneakers on Amazon. You might change the default color from black to red or raise the price by 10%. You'd then measure how many people click on that specific item, compared to how many clicked on the original sneakers.

For example, if you're an online fashion retailer, using AI for product catalog optimization could mean identifying top sellers based on attributes such as popularity and price-to-value ratio and then using data science to optimize what goes into your product catalog so that it matches shopper needs as closely as possible.

With the explosion of online data sources at our fingertips, we can now analyze previously uncalculated data points, such as traffic trends over time or how different types of shoppers interact with your website compared to others. We can also mine that mountain of data for patterns and use them to inform better decisions across multiple channels (such as which personality types might respond best to different campaign content).

It's also important to take advantage of customer habits. Customers now expect a seamless shopping experience across various channels, and they're willing to engage with brands in new ways. With that in mind, it's worth considering which shopping behaviors have the highest value for your brand and why. Some key questions to ask yourself include the following:

• How does product interest vary by day of the week?
• What types of purchases are shoppers most likely to make when they're planning a vacation?
• Are there any other shopper personas you should be paying attention to?

Besides taking advantage of customer habits, it's important to empower consumers through discovery. When customers are seeing products and services they're interested in, they'll stick around longer. So, using AI, you can help them discover relevant content based on related searchers and past purchase history.

And if that content doesn't exist yet, you can create it. It's never been easier to develop new content and even new product ideas with AI. The technology is getting better at understanding what consumers want and need, so you can create new types of content and product experiences that didn't exist only a few years ago.

Product copy and packaging

There are many ingredients to successful marketing and merchandising beyond the product itself, including ad copy, product descriptions, and product packaging.

In Figure 9.4, we can see how a mockup of Commerce.AI can be used to automatically generate product copy for product catalogs, listings, ads, Instagram posts, and more:

Figure 9.4 – A mockup showing Commerce.AI's AI Content Generation features

The first ingredient, ad copy, is a means to convey your brand message while establishing credibility and trust. It's how you tell people what your product or service can do for them. When it comes to ad copy, it's all about the benefits you communicate in an effort to unlock the value of your offering through different channels (AdWords being one example).

Another ingredient in successful products is the product description. The product description is how you tell people about the product and why they need it. It's a means to help people understand what your product can do for them and how it will benefit their lives. The key here is to communicate the benefits of your offering in terms that are relevant to your target audience so that they can understand its value proposition.

Lastly, we have product packaging, which is a means to help people see and experience your product or service before they buy it. It's all about making sure that the package itself conveys the right message so that when someone opens up the box or unwraps the item, they get a sense of what you want them to feel or experience with your product. This helps create an emotional connection between you and your customer so that when they make a purchase decision, they choose to buy from you instead of someone else.

So, these are some of the ways in which you can use AI for marketing and merchandising. Now, let's dive into how AI can be used for generating ad copy and product descriptions in greater detail.

How AI can help you write better ad copy

Ad copy is critical because it helps convey who you are as a business, what makes you different from everyone else out there, and why someone should choose to do business with you over another company or option available. And if done right, this will result in more conversions (people buying).

The first step in writing effective ad copy is understanding who exactly will be reading it (the person who sees it on social media or elsewhere) and what their motivations might be for doing so. Once we know this information, we can start crafting our ad copy around these insights in order to ensure that our message resonates with our intended audience.

The second step in writing effective ad copy is defining the core benefit or promise of your product or service so that you can communicate this directly to your intended audience. This is where we start crafting our message around the benefits that your offering provides for people's lives so that they understand why they should choose you over someone else.

With AI, you can scan a mountain of data around your consumers and other competing products to understand exactly what kind of messaging you should create. Similarly, you can use large language models in Commerce.AI to actually automatically generate that copy.

How AI can help you write better product descriptions

The next ingredient in successful products is having an effective set of descriptions on each item that goes along with it.

Just as AI can be used to generate ad copy, the same models can be used to generate product descriptions, which are similar pieces of text designed to convert viewers into buyers. Unlike ad copy, you have a lot more room for text and explanations in product descriptions, which also play an important role when it comes to search engine optimization (SEO).

By using AI to generate product descriptions for all your products, you can ensure that consumers find your products, effectively boosting sales.

Customer support

Product teams are increasingly using AI to help them build better products. But there's a disconnect between the insights that product teams can gain from AI and how they use them to support their customers. In this section, we'll explore how product teams can use AI for customer support.

The first step is understanding what customer support means in the context of product development. Customer support is about helping your users solve problems with your product or service. It's about anticipating issues before they happen and providing actionable advice when they do. It's about understanding what makes people tick so you can anticipate their needs and provide them with the right information at the right time. And it's about proactively addressing non-issues so that people don't have to call you for help in the first place!

In other words, customer support is all about empathy—understanding your users and anticipating their needs so you can provide them with relevant information at just the right time, whether that's during their use of your product or service or after a purchase has been made. This requires an intimate knowledge of your users, which you can gain by asking the following questions:

• Who are they?
• Where do they come from?
• What are their goals?
• What are their pain points?
• How do these intersect with your own business goals?

And most importantly—how can you best serve them by leveraging insights around product issues, comparative advantage, and use cases to empower your interactions wherever you have them?

In this way, customer support and product innovation are closely intertwined. Therefore, let's look more specifically at how product teams can use AI for customer support.

How product teams can use AI for customer support

Product teams can use AI to stay ahead of their customers and leverage insights around product issues.

Product teams rely on data to inform decisions around design, engineering, marketing campaigns, partnerships, and more, but data alone isn't enough—it must be actionable data that helps drive results for the business. Data without insight is noise; insight without action is blind speculation; action without accountability leads to missed targets, or worse yet—no targets at all!

So, how does a team stay accountable when using analytics as part of its decision-making process? The answer lies in applying critical thinking skills to the data. When you apply critical thinking skills to your data, you're able to see patterns and draw inferences that can lead to insights.

With critical thinking, you start to understand what is happening in your product or service, why it's happening, and what you can do about it. This requires a shift from purely analytical thinking (which focuses on the numbers and metrics) toward a more holistic approach that considers context and user experience (UX).By applying critical thinking skills to your data, you're able to see patterns and draw inferences that can lead to insights.

Insightful questions product teams should be asking themselves include the following:

• What are the most common customer issues?
• What are the top reasons for customers churning?
• What are the most commonly reported bugs?
• How does our product compare with similar products in terms of usability, accessibility, and more?

These types of questions require an understanding of how people interact with your product so you can make informed decisions about design, development, marketing campaigns, and partnerships, based on actual user behavior rather than just assumptions or best-guess estimates.

It also allows them to take action earlier in the process, so they aren't left scrambling at the last minute and trying to fix things after a release has gone live.

For example, a team may have identified from online data sources that many of its users are having trouble signing up for an account. The team could use AI tools such as chatbots or virtual assistants (VAs) as part of its support strategy so that users don't have to call customer support for help setting up their accounts—instead, they can simply message their VAs, who will guide them through the process over video calls or instant messages (IMs).

In this scenario, using AI would allow the company not only to save money on customer service costs but also provide a better experience for its users by eliminating unnecessary steps from their onboarding process.

Ultimately, product management is a multi-faceted and complex process that spans from tracking the market and managing your brand through to merchandising and customer support. Product managers can use data and AI to improve efficiency and effectiveness in all these areas.

Summary

As we've seen in this chapter, Commerce.AI's Product AI features make it easy for product teams to explore new ideas, gain insights into user behavior, and pivot rapidly. It can also help teams launch products earlier and with a stronger, data-driven approach.

One of the first places teams should use Product AI is during product discovery. Whether you're working at a small, local commerce firm or an international conglomerate, it may be wise to take some time to understand what customers actually need before diving into developing new features.

Using Commerce.AI's Product AI features during this discovery phase will give you valuable insights into customer needs and behavior, and this can inform both your product strategy as well as your feature prioritization once you have advanced your idea.

Once a product is ready for launch, Product AI can be used to optimize its performance. This means you'll be able to test ideas quickly and with minimal effort, which can result in a more informed decision on whether or not to move forward with a particular feature set or design.

Finally, once your product is live, Product AI can provide insights into how it's performing and identify areas that need improvement. These insights are an essential component of any product team's ongoing success, and using Product AI's tools and data will help you avoid common pitfalls in the product life cycle.

While Commerce.AI is a powerful tool for product teams, it also has many uses for service providers. In the next chapter, we'll explore Commerce.AI's Service AI features and how they can be used to empower your front line, manage your locations, and enhance your service offerings.