Chapter 6: Applying AI for Innovation –Consumer Electronics Deep Dive
As we've explored in previous chapters, AI is no longer just a buzzword. It has become a critical component for the growth strategies of many companies, with the majority of leading executives saying that their company is investing in AI or machine learning. This chapter will explore how consumer electronics brands can leverage AI to improve their product innovation and drive growth.
Consumer electronics brands have long relied on their ability to innovate new products to keep pace with the rapidly changing trends in technology. Innovating new products enables these brands to stay relevant and attract consumers who want to experience new ways of engaging with technology.
This applies as much today as it ever has. For example, when consumers are choosing from an array of smart home devices at their fingertips, they need compelling reasons to choose your brand over someone else's. By taking advantage of emerging AI technologies, consumer electronics brands can create products that are more immersive, interactive, and enjoyable than ever before – enabling them to stand out from the increasingly crowded tech shelf.
In this chapter, we'll cover the following topics:
- Understanding the challenges faced by consumer electronics brands
- Analyzing the product data for consumer electronics brands
- Using Commerce.AI for consumer electronics brands
We'll learn about how consumer electronics brands are facing new challenges when it comes to the connected consumer, the content consumer, and greater competition from all sides. We'll also explore how to collect, analyze, and use consumer electronics data to become more innovative and overcome various challenges.
Understanding the challenges faced by consumer electronics brands
Let's start by exploring the challenges of consumer electronics brands to understand why new, innovative, data-driven, and AI-based solutions are needed to drive product success.
Some of the challenges we'll cover include the needs of the connected consumer, the new reality of short-term attention span, the demands of the content consumer, and growing competition from emerging markets.
The needs of the connected consumer
The connected consumer is a recent phenomenon, but one that has become the new normal for consumer electronics. Practically, every consumer now expects to be able to connect to the internet and engage with their technology in some way.
This isn't just about smartphones and tablets anymore. It's about wearables, home automation, and smart speakers such as Alexa or Google Home. All of these devices enable consumers to interact with technology in meaningful ways.
Brands have been slow to recognize this shift as it's still relatively new. But that will change over time as more people adopt these devices for everyday use, which is why brands need to pay close attention now if they want to optimize their business strategy moving forward.
For brands to succeed in today's connected world, they need a strong identity – and that starts with understanding how people want to interact with them through technology. To do this effectively, brands should look at how people currently use technology (in other words, how are you already winning?).
Then, brands need to think creatively about how they can leverage those strengths into something bigger – something that provides them with more opportunities for engagement and deeper relationships with their customers.
For example, if we think back to when the first smartphone came out – the iPhone – it changed everything because it was incredibly accessible and intuitive. Apple took advantage of this familiarity and made the switch to touchscreens much easier than any other company could have done. It did this by leveraging its strength in product design as well as software development.
The importance of product design cannot be overemphasized here: people don't merely buy things; they use things and experience things. If your customer doesn't naturally gravitate toward using your product because it feels natural or intuitive, then neither will the market at large.
A new reality of short-term attention span
The average attention span used to be far longer. Today, it's commonly said to be just 8 seconds. People have many options for what they want to pay attention to and what they don't want to pay attention to – and with social media platforms from Twitter to TikTok, there is no shortage of opinions about brands and products.
The impact on social media
Brands must think about their digital footprint as much as their physical footprint if they are going to succeed in today's increasingly digital world. In other words, how consumers perceive a brand on social media matters just as much as how they perceive it in person when making purchasing decisions.
Today, more than ever before, consumers are aware of the power they wield over brands through social media – even if some brands might not always act in a way that respects those powers (and can get them into hot water).
For any brand – whether it's a consumer electronics brand or any other kind of brand – to be successful today, it needs an active and engaged community online that feels connected to the brand and its values. This means actively engaging with your community on various channels (for example, Facebook groups, Instagram stories, and TikTok shorts) so that you can build meaningful relationships with them.
The key here is figuring out how best to use your existing resources (your employees) while at the same time identifying ways you can add new resources (such as contractors and freelancers) that will help take your company's marketing efforts to the next level.
The impact on product teams
It's not just the social media landscape that's shifting due to today's digital first world. A short attention span means that consumers are less likely to be willing (or able) to invest the time and energy into fully experiencing a product that they have little or no emotional connection with.
To maximize engagement with their content – whether it's a movie trailer, song lyric video, TV show preview, or book excerpt – the consumer electronics brands need to think about how they can create more engaging experiences that go beyond just the content itself.
This requires understanding how to use technology in ways that enhance the consumer experience of your content instead of replacing it. This includes allowing users to interact with your content in new ways (such as AR glasses for interactive storybooks); using movement-based media (such as dancing GIFs) instead of static images; creating cinematic experiences through 360° videos; using live streaming platforms leveraging virtual reality platforms; and so much more!
The bottom line is that the consumers are spending less time engaging with traditional media and more time engaging with digital media – and brands should utilize this opportunity to build meaningful relationships with their communities.
Meeting the demands of the content consumer
The content consumer has changed since the days of the compact disc. Today, consumers are bombarded with a constant stream of information and entertainment. The rise of online video services such as Netflix and YouTube have made it easier than ever for consumers to access an immense amount of content at their fingertips. So, how does a consumer electronics brand differentiate itself amid this sea of video content?
The answer is by creating compelling experiences around the content itself, rather than trying to compete on the price alone. People watch billions of hours of videos each month. As such, brands must think beyond just the product itself when delivering compelling experiences around their offerings. They must create compelling narratives around their products and services to stand out in a crowded marketplace where there is plenty of competition for customers' attention and wallets.
Content creation is no longer the domain solely for large media companies or Hollywood studios; smaller creators can now reach massive audiences with relatively little investment in production value or infrastructure.
What this means for brands is that they can't simply rely on their product offering as a way to differentiate themselves from competitors; they need to go above and beyond with engaging content if they want to capture customers' attention and loyalty in an increasingly competitive marketplace.
Brands should also pay close attention to customer expectations regarding social media engagement throughout the entire customer journey – from product development through distribution and beyond – so that the customers feel included throughout each stage of the experience process.
The need to become data-driven
Consumer electronics brands need to become data-driven. Data is the new oil. It's what fuels a company's growth and innovation and it's what makes or breaks consumer brands.
In many cases, consumer insights come from data analysis, not just human intuition. The companies that can leverage data effectively will be able to improve product and service offerings while increasing engagement with the customers.
On the other hand, those that don't have robust analytics capabilities will be at a disadvantage compared to their competitors, who do. So, how do companies go about becoming more data-driven? We'll answer this in the Analyzing product data for consumer electronics brands section.
Emerging consumer electronics markets
The rise in the popularity of crowdfunding and the explosion of interest in new and innovative technologies have created a boom for the consumer electronics industry. The number of people who use technology daily is in the several billion and is continuing to grow rapidly in emerging markets. Some see this as an opportunity for disruption, while others view it as a threat.
Some experts believe that with so many new users entering the market, incumbents will be challenged to maintain their dominance due to increased competition.
Brands must also contend with rapidly changing trends, such as virtual reality (VR). Today, we are already seeing major brands such as Apple, Samsung, and Facebook enter the VR space. As consumers become more familiar with VR technology, it's likely that traditional brands will adjust their strategies accordingly – and could even get bought out by the tech companies.
To stay competitive in this environment, you'll need to think about how you can attract more customers while maintaining your brand identity across multiple channels – digital and physical retail locations, online stores, and e-commerce platforms are just some examples of where you can engage your audience today.
Now that we understand some of the major challenges faced by consumer electronics brands, let's look at how to analyze the product data to overcome these obstacles and move ahead.
Analyzing product data for consumer electronics brands
Consumer electronics firms rely on product data to understand their customers and market trends. Product data is critical for understanding how your products are performing, what consumers want, and how they interact with your brand.
The data you collect can help you identify issues, measure success, and make better strategic decisions. But it can be difficult to find the right kind of product information that's actionable and helpful – especially if you're not a consumer electronics expert.
The data-driven product strategy is about using data and analytics to develop new product ideas, evaluate existing products, and improve the overall experience of customers. The goal is to create more customer engagement by changing how people interact with your brand.
The data-driven product strategy has many benefits, including the following:
- Increasing customer loyalty and retention through targeted offers
- Lowering costs while maintaining quality by streamlining operations
- Increasing profitability by improving revenue or increasing margins through innovation
- Reducing risk in the marketplace through enhanced understanding of consumers' preferences
Key considerations in the data-driven product strategy
Each company's data landscape will be different, depending on its size, industry structure, and business model. At its most basic level, a data strategy involves creating a dataset that can be analyzed for insights into consumer behavior.
For example, you might collect behavioral data from customers who have recently purchased one of your products or services. You could also look at historical sales to identify patterns that indicate which combinations of features are the most popular among customers. This information can then be used to inform future product development and marketing campaigns.
Companies can also use AI and machine learning algorithms to determine what types of products their customers are likely to buy based on their behavior online or offline, for example, by looking at past purchases or search history. Finally, companies can analyze this information with teams across various departments (marketing, engineering, and finance) to identify opportunities for new products or services that meet the needs of the consumer within the broader community of users they serve.
In our experience, we can advise the clients to build a data strategy for consumer brands across all industries e-commerce, FMCG, and B2B). Here are some key considerations:
- Focus on customer needs: Identify what you know about your customers' behavior based on the existing datasets or market research studies (online/offline). Then, identify what you don't know about them yet – what do you need more data points for before you can start validating hypotheses?
- Define roles and responsibilities: Who will own each stage of the process? How long will it take? What tools/methods will they use? Defining these roles is crucial to ensure that the project gets done and has ownership.
- Build capacity: Who will ensure that there is an ongoing commitment from senior management? This will ensure that oversight and support are provided when necessary.
With Commerce.AI's data engine, most (if not all) of the data you need will already be available. Our product's data engine features over a trillion data points on hundreds of thousands of products and services, retrieved from over a hundred sources in a variety of languages.
Now that we understand the key considerations in the data-driven product strategy, let's look at how to collect that data.
How to collect consumer data
Consumer data is the lifeblood of any brand. A strong consumer data program can help a company understand what motivates the customers to buy and, ultimately, influence their purchasing decisions.
In the consumer tech world, product reviews are an accepted and effective way for brands to collect customer feedback on products. A review from a satisfied customer can be just as influential as a glowing press release in marketing or PR campaigns.
Another great source of customer feedback is social media: Instagram posts can provide valuable insight into how the consumers feel about your product's aesthetic appeal and ease of use, while Twitter feeds can show how people interact with your brand through jokes, memes, and other forms of social commentary.
You should also pay close attention to what people are saying about your competitors' products – a competitor's post about new features in an upcoming update might catch fire among existing users who want to stay up to date on the latest features before making their next purchase decision.
With so much consumer data available at our fingertips these days (through online forums such as Reddit, app analytics platforms such as Mixpanel, and email marketing services such as Mailchimp), it can be easy to overlook some less obvious sources of product feedback that are often more actionable than written reviews or tweets.
Speaking candidly about experiences with problems with your products is one such source of valuable input that many companies fail to capitalize on. This is due to a lack of experience collecting this type of feedback directly from customers. This is where Commerce.AI voice surveys come into play: they enable businesses across all industries to listen in real time as people voice their concerns about their products through audio.
By taking advantage of tools like these – combined with robust online communities such as Reddit – brands can learn more about what's driving customer satisfaction or dissatisfaction than they ever could, by simply reading user reviews online.
How to integrate data into the product design
A lot of research has been done on how to design products that are more appealing to users, but very little research has been done on how to design products with the end user in mind. There are four stages in the end-to-end persuasive design process:
- Understanding your users
- Using personas
- Creating personas based on data
- Identifying the pain points within each persona
Understanding your users
Understanding who you're designing for is important because it helps you understand what they want from a product and why they might want it. The more you know about your users, the better designed your product will be (and the less likely it will have usability issues).
In the past, product designers had to rely on intuition and anecdotal evidence to understand how their users used the technology. Nowadays, with the availability of large amounts of data, it is possible to learn a lot about the users by observing how they interact with products.
For example, you can observe how long each user spends on a screen or a page before clicking away, or which parts of your site are least used. You can also see where people click or scroll before making a purchase decision. All this information will help you improve the usability of your product and ultimately increase its conversion rate (the percentage of visitors who become customers).
Tracking data also allows us to iterate quickly on the design until it matches our ideal user experience, by continuously running A/B tests.
Using personas
Personas are a tool to help you understand your users. They can help you identify needs, goals, and motivations that will provide information about the design of your product. A persona is a fictional representation of an individual that represents some aspect of a target user group. The idea is to create a living document that describes the target user's characteristics, motivations, and expectations.
This allows you to test the assumptions about how your product or service might be used by real people before building it. Personas are useful because they allow you to focus on what's important, instead of assuming how users will behave. It also helps prevent making the same mistakes over and over again with new users by focusing on who those new users are, rather than trying to guess how they might behave based on past trends or the behaviors of other groups in your target market.
You can use personas in many different ways as part-of-the-puzzle pieces, including identifying pain points in the design process, understanding customer needs and wants early on during development, creating an internal vision board for your team, or even testing new ideas with the potential customers before building something just because it looks cool.
Creating personas based on data
You now have a list of people who fit certain characteristics and motivations, but this isn't enough information on its own – you need data too! If possible, try to find quantitative ways of understanding who these people are and what motivates them. By doing this, this information can be incorporated into your personas, as well as into the rest of your analysis process.
For example, instead of creating fictional characters based on qualitative observations, create an analytics report showing those observations numerically. This information could then be used by someone at an e-commerce company to estimate the sales volume they should expect if they ran an online store during the Black Friday weekend – something qualitative insights alone wouldn't necessarily tell them.
Identifying the pain points within each persona
Now that we have our personas figured out, the first step is finding where they don't get everything that they want, need right now, or easily enough – these are their pain points.
A pain point is a problem that a customer experiences with a product or service. It starts with identifying where your customers are struggling with your product and then finding ways to provide them with the solution they need.
At its most basic, this process should start with asking yourself, what don't my customers have? This is important because it forces you to think about what features you want to build into your product and how you can make those features more accessible.
Once you have identified these pain points, the next step is determining which ones are critical enough for you to invest in solving for your customers. Remember, failing to solve an important pain point will mean that users will simply look elsewhere for what they need, so you must pick the right ones.
It's important to remember that not every pain point needs to be solved by building new products or services. Sometimes, there are existing solutions on the market already, and sometimes, there are things that can be done within the existing constraints of technology or design.
To figure out which problems need solving, we often look at existing trends and patterns in our industry, as well as data from similar companies within our space that have been successful at addressing these problems before us. We also ask ourselves questions such as the following:
- Do our current customers feel like they're missing something?
- Is there another way of doing this already in existence?
- What other companies within our space do well?
- Do we have anything unique, but can we borrow from their playbook?
- Are there any patterns we see across industries when looking at similar products/services?
The answers to these questions help inform you of what pain points to address, which is a crucial component of product innovation.
Now that we know how to collect, analyze, and use consumer electronics data, let's explore the next stage of turning this data into insight: using Commerce.AI.
Using Commerce.AI for consumer electronics brands
As Commerce.AI runs the world's largest product data engine, there's a whole host of opportunities for consumer electronics brands to become more innovative. Let's explore how to use Commerce.AI to better understand product positioning, analyze the consumer electronics market, improve research initiatives, generate product ideas, and more.
Understanding product positioning
Understanding product positioning is critical for understanding the market potential for any given product. The following diagram shows how the Commerce.AI positioning chart can be used to understand where the different products stand in their market space by comparing their sentiment scores (vertical axis) and Number of reviews (horizontal axis):
Figure 6.1 – A product positioning chart mockup comparing sentiment and number of reviews
In the case of a camera, for instance, an entry-level point-and-shoot model may have a lower position on the chart than a higher-end digital SLR that has better features such as a faster lens, a larger sensor, and more advanced image processing capabilities.
By comparing these products across multiple dimensions of customer value, you can quickly gain insights into the relative value of each product in its market space. By looking at only one dimension (for example, sentiment), you would miss out on important insights. For example, if a certain camera has a very high sentiment, but only 10 reviews, then that sentiment score may not stand up to scrutiny and may fall apart in the real world.
Analyzing the market with consumer electronics AI reports
The consumer electronics market is huge. It's forecasted to surpass $1.2 trillion in sales by 2022, according to Statista research (https://www.statista.com/markets/418/topic/485/consumer-electronics/#overview). Besides smartphones and laptops, countless other devices fit into this space, from tablets to VR headsets to home automation devices. Depending on your definition of the term, there may even be room for wearables such as fitness trackers or smartwatches, if you're willing to stretch the definition a bit.
With so much money being spent on these gadgets, it's no surprise that brands want to know what people are buying and why they're choosing these products over others. Market research firms conduct extensive research into consumer behavior and preferences to understand how trends will play out in the future, as well as what needs people have that can be fulfilled by brands through their products and services.
The problem is that this enormous market is a double-edged sword for product teams: There's a tremendous financial opportunity at hand, but with so much data, it can be difficult to make sense of things. It'd be impossible to manually analyze the product sentiment and reviews of the millions of products out there.
With Commerce.AI's AI-generated market reports, this data is autonomously analyzed to provide insights at unparalleled speeds. What would have previously taken teams of researchers months can now be done in a few clicks.
The following screenshot shows an AI-generated market report on DSLR Cameras, including the number of products in the relevant Amazon category, the fastest-growing brands, the best-seller products, the number of Brands, and an OPPORTUNITY METER that summarizes the size of the market opportunity:
Figure 6.2 – A snippet of an AI-generated market report on DSLR cameras
Brands need to understand how the consumers are using their products; it provides valuable insights into potential product improvements or new features that can be added before the competitors get there first. Market research also allows companies to maintain a competitive advantage by staying ahead of trends before they become mainstream consumer behaviors.
How does Commerce.AI help with consumer electronics brand research?
One way for brands to gain insight into consumer behavior is through analytics software designed specifically for market research purposes.
Since Commerce.AI operates the world's largest product data engine, with over a trillion data points analyzed, the data that's presented to a brand is carefully curated to their needs. Otherwise, there would be information overload, which is the status quo that product AI aims to break through with focused insights. The following diagram shows how a Consumer Electronics dashboard in Commerce.AI is like a blank slate, which fills with the relevant brand data:
Figure 6.3 – A mockup of the blank slate Commerce.AI dashboard
Consumer electronics brand research requires analyzing an enormous amount of data. That's where Commerce AI comes in – we provide powerful analytics software designed specifically for use within the commerce businesses.
Our platform was built from the ground up; we understand the types of data you need at your fingertips when managing a product line. And now, we're taking our expertise one step further by adding deep learning capabilities so that you can gain actionable insights into the consumer behavior inside your product and service line.
Generating consumer electronics product ideas
In addition to analyzing the market and researching trends, we can even use AI to generate consumer electronics product ideas from scratch. Product ideation is a key component of innovation, as failing to come up with novel and exciting ideas can ultimately lead to business failure. After all, the most successful companies today, from Apple to Tesla, are the ones that thought outside the box and broke the status quo.
Let's explore an example in Commerce.AI that uses large language models to generate product ideas. These models are used to predict the most likely words in a text, given its content, similar to the way that the traditional natural language processing (NLP) technologies can be used to analyze text, but at a much larger scale.
Large language models have recently become more widely available thanks to improvements with architectures such as the Transformer, which are now fed on massive amounts of textual data, which includes product descriptions and reviews from all over the internet.
Large language models are particularly useful for generating product ideas because they learn from natural human language rather than from individual words or phrases. As such, they consider broad concepts such as computer or tablet as well as more specific concepts such as iPad or iPhone.
The following screenshot shows how this is done in practice. First, you'll enter a Product Category, such as DSLR Cameras. Then, you can optionally select a Customer Wishlist, which is extracted from product reviews. This Customer Wishlist will be used to inspire the generated ideas. If no wishlist is selected, then the review data will be used to holistically generate ideas. Another area to note is Creativity, which is a type of randomness setting for the large language model. High creativity or high randomness will result in more out-of-the-box, but perhaps less grounded and realistic, ideas:
Figure 6.4 – A product positioning chart mockup comparing sentiment and number of reviews
Using this approach, we've been able to generate thousands of unique products with little effort. In the following screenshot, we generated ideas for new DSLR product ideas, such as a DSLR that you can control from your phone, a DSLR that responds to voice commands, and a DSLR that processes complex colors accurately by using AI:
Figure 6.5 – A snippet of AI-generated DSLR product ideas
While AI won't replace human ingenuity, it can help augment our creativity. Practically, all product teams have experienced the feeling of creative block, in which it's difficult to come up with new ideas. By using AI, consumer electronics product teams can get those creative gears turning and perhaps help find their next big idea.
Extracting insights from Shopify
Shopify is a platform largely aimed at small and medium-sized businesses (SMBs) to power their online stores, including inventory management, shipping, payments processing, marketing tools such as content creation and analytics, and more. Since its launch in 2004, Shopify has grown into the world's largest independent multi-channel retailer.
Commerce.AI can be used to extract insights from your Shopify store by sending voice surveys directly to your Shopify customers. Voice surveys have higher engagement and completion rates than their text-based counterparts, as many people find it easier to speak freely than having to sit down and write their thoughts out.
The data you receive from these surveys can be used to inform product development decisions. For example, your data might show that certain aspects of a product or service are particularly important to your customers. This knowledge can help you make informed decisions about the types of products and services you should offer to satisfy your end users.
As consumers, we all have our individual preferences when it comes to shopping online. Some people prefer to read product descriptions before making a purchase, while others prefer to look at the images and video content instead. As an e-commerce brand, you should consider your target customers and what they find most valuable while browsing your website; this will inform the e-commerce brands about how you design your store and how you format your product descriptions and imagery.
Additionally, some people prefer to interact with consumer electronics through voice rather than text; this is because it's easier for them to speak naturally without having to worry about typos or spelling mistakes.
With over a million merchants using Shopify, there are plenty of opportunities to better understand the market through voice surveys within your customer base – all without any additional development or staff time investment required.
Sharing insights on Slack
Innovation success is largely dependent on successful communication. The ability to effectively communicate new product concepts, and the benefits of those products to internal teams and stakeholders, is critical to innovation success.
Product innovation is not a one-person job, nor does it happen in isolation within a single team or organization. It requires collaboration across multiple departments and stakeholders within an enterprise. The good news is that with Commerce.AI's Slack integration, product teams can easily interact with team members remotely, making innovation much more streamlined.
The goal is to understand whether the customer needs aren't being met, identify opportunities for disruption, test new ideas with potential users early in the process, define and execute on a go-to-market strategy, and then scale once you've validated your assumptions about market needs and desire for your product or service.
To do any of this effectively – especially when it comes to product innovation – you need a way to easily communicate across departments while still maintaining control over who has access to certain information at any given time. This is where tools such as Commerce.AI come in handy; they enable cross-departmental communication that allows teams to collaborate more efficiently.
You should prioritize what information gets shared when the key is to ensure that everyone involved has access to the right information at the right time. This enables every team member engaged in innovation activities within an organization – from engineers building products, through designers creating user experiences, all the way through to leadership deciding which ideas are worth pursuing further – to fully participate in driving innovation forward.
Summary
In this chapter, we learned about the key challenges of consumer electronics brands and how to overcome them by analyzing product data and using Commerce.AI's data engine. These challenges include greater complexity and competition in the consumer electronics space. By analyzing a treasure trove of consumer and product data, consumer electronics brands can uncover insights to overcome these challenges.
AI-based innovation has been disrupting many industries over the last few years. Ultimately, the leading consumer electronics brands are adopting AI to stay at the forefront of innovation.
In the next chapter, we'll explore how to apply AI for innovation in the restaurant industry, which, unlike consumer electronics, has often lagged behind technology trends, but similarly stands to benefit tremendously by using data and AI to their advantage.