Have you ever walked into an Apple Store? If you’re being honest with yourself, it surpasses all other retail experiences. Everything is organized and immaculate. Every detail of the consumer’s interaction is meticulously designed to engage you and subtly convey the value of Apple and the products they sell. There’s no cluttered mess of inventory crammed into every nook and cranny. 

Apple understands the importance of allowing customers to become fully immersed in what they sell. Products are displayed invitingly with ample space, and potential buyers are given everything they could want. They can touch products, operate them, and understand every feature and technical specification they can think of. 

If a customer shows the slightest look of confusion or doubt, they are promptly pounced upon by one of the many Apple specialists waiting patiently to offer assistance. The result of this investment in experience speaks for itself; Apple has the highest retail sales per square foot of any brand worldwide.

If you talk to corporate marketers about the in-store Apple experience, they’ll roll their eyes at you. It’s been analyzed every which way by MBA programs everywhere and is considered old news. There are no new or profound insights to be found. Give customers a good experience, especially around your products, and they’ll buy more. Isn’t it obvious? 

But for some reason, this acknowledgment of the importance of product experience hasn’t fully translated to digital channels. Scrutinize almost any brand’s product pages, and you’ll quickly find errors. These problems could include missing images, sparse filtering options, indifference to regulatory compliance, or my pet peeve, uninspired short-form product descriptions that have bubbled up from the depths of an ERP system. 

This online shopping experience is the antithesis of the Apple store experience; this is the blind spot. 

Many brands don’t appreciate that good product experiences are critical wherever you sell. This oversight can be costly, with over 63% of consumers saying they would abandon a brand they trusted following a negative product experience. 

Brands selling on digital channels need to realize that the consumers who have been so thoroughly convinced by Apple’s in-store experiences over the last 10 years are the same consumers they’re trying to win over every day online. Before investing in the latest and greatest technology, SEO optimization, new sales channels, or more extensive catalogs, brands need to take a step back and think about the product experiences they provide their customers. These experiences need to be the foundation of their digital commerce strategy.

In fact, these organizations should have their own Product Experience (PX) Strategy, one that considers every interaction a customer, partner, or employee has with a product. 

So, how can brands ensure they’re providing a good product experience? By implementing a PX Strategy that ensures their product experience offering across all channels is:

1. Accurate

Product experiences must properly convey the correct information to customers. This means being free of formatting errors, inaccurate information, missing or incomplete information, and even translation errors. Every brand needs clearly defined data governance for its product data. If you fail to provide accurate information, every other component of your product experience will be compromised. 

2. Comprehensive 

How many times have you scanned a product listing looking for one specific piece of information, like the maximum resolution of a monitor or the washing instructions of a new blouse, only to realize it isn’t there? If a critical piece of information you need to make a purchase doesn’t exist, you’ll probably buy elsewhere. The more information you can provide your customers, the more likely they are to actually buy a product from you. Take things to the next level by also telling buyers how they can use your product, preferably in conjunction with another product or service you sell. 

 3. Discoverable

Products need to be easy to find for customers. The products you sell must be easy to discover in search engines, 3rd party marketplaces or retailers, and your own digital solutions. The two previous steps play a critical role in helping to support search; even the most sophisticated SEO solution is hampered without correct and comprehensive product information. Discoverability is also supported by things like facets on a website, metadata, and compatibility with other products and services you sell. 

4. Consistent 

Consumers today bounce between mobile apps, marketplaces, social media, and even traditional print and in-store buying experiences. Your product experiences need to be consistent and high-quality across every interaction you have with a customer. Just because your customers tend to buy on your eCommerce site doesn’t mean they aren’t engaging with your products elsewhere. With over 70% of consumers saying they research multiple touchpoints to gather more information to make a purchase decision, brands today can’t afford to have a single weak link in their omnichannel product experiences. 

It’s also essential to adapt your content consistently to every region and language you sell to. This doesn’t just mean translation; it also means considering cultural and regulatory expectations such as Natasha’s Law in the UK, which requires allergen information be place prominently on pre-packaged food, or Digital Product Passports that are set to be a requirement for certain businesses that sell in the EU by 2026. 

 5. Personalized

Personalization is a hot topic in commerce. Consumers increasingly expect a personalized shopping experience. This includes product recommendations based on website interactions, purchase history, loyalty clubs, gamification, or product compatibility. This could also include analyzing the purchase or browsing behavior of similar customers and recommending products or services that were also purchased by those customers.

6. Compelling 

A product experience is about more than just product information. It’s an opportunity to help sell a product, convey brand values, and build customer loyalty. (Hint: If your ERP system is the source of truth for your product descriptions, you’re failing at this step.) Compelling product experiences are created by providing rich visual assets and marketing copy, telling brand stories, and meeting customers’ expectations around regulations and sustainability standards. User-generated content is also compelling for buyers. 

It may be impossible to replicate the Apple in-store experience online. Even the most innovative AR technologies or creative UX designers can’t replace the sensory experience of a physical retail location. But this doesn’t mean product experiences are less important in the digital world. 

Consumers browsing your websites still visualize themselves holding a product, consider how a product makes them look, or strategize about how a product can solve a problem they have. 

People always want to engage with the products they’re about to buy, even if it’s behind a keyboard. Don’t be blind to this crucial component of customer experience, and build a good product experience that has its foundation in accurate, up-to-date product information.

Harnessing the power of Artificial Intelligence (AI) has become the ultimate key to unlocking global success. Whether you’re a multinational corporation or a budding startup, the ability to seamlessly translate and localize your content for diverse audiences can make all the difference.

But harnessing this ground-breaking technology is not as easy as some AI enthusiasts would have you believe. So let’s dive into five practical use cases of utilizing AI technology as a trusty sidekick to reaching new markets and making the world a smaller place, one language at a time.

5 Use Cases for AI-Driven Translation & Localization

1. Translating Product Descriptions

Imagine you’re a fashion retailer based in New York, and you want to expand your reach to Paris, Tokyo, and São Paulo. The advent of the Internet means that it’s much easier to access these international markets than ever before, but access doesn’t always equate to genuine connection. The language barrier is a significant hurdle that many brands and retailers face, and historically, translation agencies or services have not been cheap, with many charging fees per word count or charging extra for complex translations or edits. 

AI-powered language translation tools can open the floodgates of communication by providing accurate, SEO-friendly translations of your product descriptions, titles, search filters, and back-end keywords into any number of languages almost instantaneously. While these tools still require a decent amount of human oversight to ensure that grammar is correct and certain phrases are translated correctly (a witty product description in English won’t directly translate to something as witty in French or Japanese), these tools can provide a foundation of translated and localized content to work from that ensures a consistency and accuracy that enhances the shopping experience for global customers.

2. Converting Sizing or Units of Measurement

Global retailers face the challenge of different sizing standards and units of measurement in various regions. This inconsistency can create confusion and inconvenience for customers when they are trying to understand the specifications of a product; if you can’t understand the scale of the measurement that’s being provided to you, you aren’t going to be inclined to purchase. 

Whether it’s inches to centimeters or pounds to kilograms, AI steps in to simplify this complexity by seamlessly converting measurements. Shoppers in different parts of the world don’t need to perform manual conversions or struggle to understand unfamiliar units of measurement. They can easily comprehend the product’s size and weight in their preferred measurement system, making the shopping experience more straightforward and user-friendly. 

3. Connecting with Global Audiences on Social Media

Social media platforms have become central to modern marketing strategies. They provide a direct and interactive channel for brands to engage with their audience, gather feedback, and build brand loyalty. However, effective engagement on social media requires understanding how your brand and products are perceived by users in different markets.

AI-driven sentiment analysis is a sophisticated tool that enables brands to decipher the emotions, opinions, and attitudes expressed by social media users regarding their products or services. This analysis goes beyond simple metrics like likes and shares; it delves into the sentiments behind comments, reviews, and mentions. Armed with sentiment analysis data, retailers can craft messaging and campaigns that align with the cultural nuances, preferences, and values of specific regions. By tailoring content in this manner, brands can demonstrate a genuine understanding and respect for local audiences, which fosters a stronger connection.

4. Providing Multilingual, 24/7 Customer Support or Service

As businesses expand their operations globally, providing efficient and effective customer support becomes increasingly complex. Customers may be spread across different time zones and regions, each with its own language and cultural nuances. One of the most popular applications of AI today are customer support chatbots, and for a good reason; AI-powered chatbots have a general satisfaction rate of 90%, and can reduce customer operation costs by as much as 30%.

Equipped with natural language processing (NLP) capabilities, these bots are able to provide round-the-clock, multilingual assistance for common technical issues or simple troubleshooting requests, freeing up human customer support reps to focus on complex customer questions.

5. Complying with Local Regulations

Navigating local regulations is a crucial aspect of global retail operations; AI technology can be equipped to ensure that brands stay compliant with regional laws and standards. Whether it’s adhering to regulations like Natasha’s Law in the UK, which mandates detailed allergen information on food products, or data privacy laws like the California Consumer Privacy Act (CCPA), AI helps brands navigate complex regulatory landscapes efficiently.

AI technology can ingest vast amounts of data at speed, meaning that it can continuously monitor changes and updates in local regulations across different regions, reducing the risk of non-compliance due to outdated information. This technology can also perform automated audits of product labels, data handling procedures, and other compliance-related aspects to identify discrepancies or areas where adjustments are needed, reducing the need for manual, time-consuming audits.

AI In Retail: Opening Global Doors

As the retail industry continues to evolve, embracing AI-driven content translation and localization is not just an option; it’s a necessity for global brands and retailers looking to thrive in today’s diverse and interconnected world. Though it’s no magic wand, AI technology can help enhance internal customer support and product data processes to ensure all your content is translated, localized, and optimized for any market.

Interested in harnessing AI technology to improve your global presence? Reach out to an Akeneo expert today to learn more, or check out our comprehensive AI Center of Excellence for more resources.

The Challenge: Manual Product Classification

INTERSPORT, like many other retailers, faced a formidable challenge in the mundane, repetitive, and time-consuming task of manually classifying new products. With over 700 hundred stores in France and over 1.5 million products processed every year, the need for a faster, more accurate solution for managing, optimizing, and sharing product information across scattered teams and technologies quickly became imperative.  

The Solution: Akeneo’s AI-powered Supplier Data Manager Classification

INTERSPORT’s quest for data efficiency led them to Akeneo’s Supplier Data Manager for an AI-powered product classification solution, and Akeneo for a centralized source of truth powered by a comprehensive Product Experience (PX) Strategy.  Unifai seamlessly automated the classification of new products, slashing the time required by this arduous task by an impressive 50% and without compromising any accuracy. In fact, it improved product classification accuracy by 95%, ensuring that Intersport’s data remained reliable. This newfound efficiency wasn’t just about saving time and improving the quality of data; it was also about freeing up employees to focus on more complex and value-add work.

The Tangible Benefits of Akeneo’s Supplier Data Manager

1. 50% Reduction in Classification Time: INTERSPORT cut the time it took to classify new products in half, allowing their teams to focus on more strategic tasks.
2. 95% Improved Classification Accuracy: Supplier Data Manager plus AI technology provided unmatched precision, ensuring that INTERSPORT’s product data was as accurate and up-to-date as ever.
3. 18 Hours Saved Per Week: The freed-up time equated to 18 extra hours per week for the data integration team and buyers to dedicate to higher-value activities.
4. 30% Efficiency Boost: The product classification process became 30% more efficient, leading to faster decision-making and product placement.
5. Data Accuracy for Business Operations: Accurate data is the backbone of any retail operation, particularly for budget tracking and sell-out/stock tracking. The accuracy of Supplier Data Manager’s AI model ensured Intersport’s business ran smoothly.

AI for Retailers

For many retailers facing the challenge of manual product classification, artificial intelligence emerges as a game-changing solution that enables enhanced efficiency, data accuracy, and stronger customer experiences. But these benefits all require a foundation of product information to support the AI technology; if your team can’t provide the data needed to train the model, then the model will have nothing to produce.  In an industry where time, accuracy, and efficiency are paramount, combining a centralized record of product information with AI-powered product classification is your gateway to a brighter, more profitable future. To learn more about INTERSPORT’s journey to product excellence, you can download the full case study. Check out the AI Center of Excellence to learn more about harnessing these powerful technologies for a better product experience.

1. Information about your product matters, and it matters a lot. 

• Keywords:  Choose keywords that are highly relevant to your product. Think about what potential customers might type into the Amazon search bar when looking for your item.
• Product images: Use high-resolution images that showcase your product from various angles, with various models, or in various sizes.
• Product titles: Craft clear and concise titles that convey what your product is, but also incorporates important keywords.
• Product pricing: Consider shipping costs when setting your price, and research competitor pricing or offer promotional codes to encourage brand loyalty.
• Product descriptions: Explain the benefits of your product and provide detailed, informative descriptions that are easily readable.
• Key features: Emphasize what sets your product apart from competitors, and include specifications or technical information
• Backend search keywords: Use the backend search keywords field to include additional relevant keywords that may not fit naturally into the product listing .Don’t repeat keywords you’ve already used in other fields, and include synonyms or alternative phrasings to capture a broader audience.

2. Keep everything up to date. Refresh and manage your product listings regularly.

3. Create an EXPERIENCE. 

Strong Product Experiences on Amazon

Starting Point and Title Change

The Role of a Digital Product Experience Manager

Professional Growth and Impact

Advice for Success and Looking Ahead

Conclusion

However, AI hasn’t always enjoyed such widespread acclaim. Frequently, misconceptions surrounding AI have bred apprehension and uncertainty.

The old Myths About Artificial Intelligence

Back in the 50s: The Skeptical Era

The question of whether we can trust artificial intelligence has sparked lengthy debates. Back in the 1950s, during its inception, AI’s credibility was already under scrutiny due to its groundbreaking nature. Early pioneers like John Von Neumann and Alan Turing introduced AI concepts, but skepticism surrounded their endeavors.

The arrival of novel concepts often triggers misunderstandings. As time passed, reactions evolved; worries and hesitations proliferated. This transformation emerged from the mainstream adoption of AI, which brought about both progress and potential conflicts.

The Evolution of AI in the 2010s: A Controversial Phase 

Around the 2010s, a pivotal era for AI commenced. During this period, artificial intelligence witnessed significant advancements attributed to powerful new computers capable of accelerating algorithmic computations, alongside the advent of the internet and widespread data sharing.

It was during this phase that AI permeated daily life. While intended to simplify our routines, it began to overshadow some works of science fiction, raising questions: Would AI endanger job stability? Could it replace human workers? Some even speculated about the potential for AI to achieve “higher intelligence.”

This sentiment is closely tied to Machine Learning, a scientific approach that revolutionized AI during the 2010s. Termed “automatic learning,” this methodology enables machines to self-educate, improving their capabilities over time. This marked a shift from rigidly programmed machines to data-driven AI.

The education of AI enables self-questioning, learning from mistakes through Machine Learning. Enhanced precision, reduced error risk, and cost-effectiveness positioned AI as an asset.

The Progress of AI: A Fostering Trust 

While only a few years out from the rise of Machine Learning and Deep Learning, and the accompanying apprehensions, public perception of AI’s threat has significantly evolved.

In the digital era, while skepticism persists, confidence in AI has grown considerably. This stems from AI’s maturity, no longer perceived as entirely novel but rather an ever-evolving, living entity. Various sectors, including manufacturing, marketing, research, healthcare, and gaming harness AI to advance their domains. Companies are progressively intrigued, exploring AI’s potential in handling product data.

AI at the Service of the Human

AI offers tangible benefits, enhancing comfort, automating tasks, boosting productivity, and propelling progress across industries. For instance, internet searches frequently involve AI. As a sign of confidence, humans are becoming increasingly discerning, intolerant of AI’s errors.

Consequently, AI’s support is now deemed legitimate, no longer arousing past concerns. The perceived myths about AI jeopardizing jobs and security have began to dissipate.

Artificial Intelligence and Product Data: A Corporate Choice 

AI and product data are intrinsically linked. Recognizing this, companies have readily entrusted machines to drive growth. Data management plays a pivotal role in digital strategies, particularly in eCmmerce, differentiating businesses in the competitive landscape.

This strategy hinges on utilizing a PIM (Product Information Management) solution, like Akeneo, and an advanced AI solution, like Supplier Data Manager. By intelligently analyzing product data within a PIM, these technologies categorize, standardize, enrich, and correct product data at scale.  

3 Key Insights About Artificial Intelligence

AI, while complex, often sparks uncertainty about its workings and implications. Amid its advantages and limitations, three key aspects deserve attention in the context of product data processing:

• Artificial intelligence isn’t magic: It necessitates training and ongoing maintenance. Valid information is crucial.
• What humans can’t process, AI also cannot. 
• AI is in constant flux: Performance fluctuations are normal, requiring periodic audits through sampling.

AI: The Past, The Present, The Future

The journey of artificial intelligence (AI) has been nothing short of remarkable, mirroring the evolution of our understanding and acceptance of this transformative technology. From its early days in the 1950s, when skepticism loomed large, to the transformative era of the 2010s, marked by both awe and apprehension, AI has come a long way.

As AI matured, our perceptions evolved, and today, it stands not as a mysterious entity but as a tangible force for good in various sectors. Its role in enhancing our lives, from automating tasks to propelling progress across industries, is no myth, and those organizations who embrace the true power of this technology stand to dominate the market.

Are you ready to leverage AI technology to create consistent, compelling product experiences through your product data? You can learn more about Supplier Data Manager here, or speak with a product experience expert today

What is a Product Experience (PX)? 

A product experience consists of every interaction an individual has with your product information throughout the entire customer journey. PX is a critical investment area alongside customer (CX) and employee experience (EX) to engage stakeholders. PX is focused on the relationship a customer has with a company’s products and the company itself. 

From how products or sets of products are represented in an ad space, displayed in marketing channels, turned into compelling product and brand stories in owned or third-party sales destinations, or even in customer service interactions, every touchpoint between a customer and product provides an opportunity to strengthen or damage this relationship. Product experience is the overall customer perception of these product interactions.

Customer experience is a well understood priority for every brand selling in today’s market. But what’s not often talked about is just how difficult it is to manage the customer experience across every customer touchpoint. Brands today must provide a consistent and quality experience that conveys their values and meets customer expectations across the buying journey, from discovery to purchase and even service and support. 

Trying to control the quality of every interaction is not easy, especially with the proliferation of online marketplaces where control of the customer experience is almost entirely ceded to a 3rd party. But one thing that brands always have control over is their product experience, and PX is a huge component of any good customer experience.

My multichannel quest for a sippy cup is replicated millions of times a day across the world. Consumers are rarely going to one place to buy your products. They’re bouncing between search engines and social media, doing research and price comparison on marketplaces and eCommerce stores, and even going into physical stores to touch and feel your products before purchasing. This buying process is even finding its way into the B2B space where younger generations are expecting the same smooth purchasing experience they enjoy in their personal life.

So, as you’re thinking about your own brand and the experience you’re offering to your customers, consider these two questions:

Are you providing compelling product experiences across all of your customer touchpoints? 

and

What is your strategy for managing these product experiences?

If you’re interested in seeing how far along your organization is in developing your PX Strategy, you can take our self-assessment here, or you can reach out to a product experience expert today.

7 Steps to Getting Started With AI

1. Product Taxonomy: Structured Organization

2. Data Collection: Centralization is Key

3. Data Cleansing: Accuracy Matters

4. Data Enrichment: Transforming Data into Content

5. Information Localization: Tailored for Different Markets

6. Information Distribution: Breaking Down Silos

7. Information Optimization: Tailored for Each Channel

The Foundation for AI-Powered Success