For too many years, the success of a PIM integration project relied on key IT personnel, and a heavily-invested product owner. Architects would show up at an early stage, and provide their view on where PIM would fit in the complex puzzle of their legacy system. But times have changed, and it seems that a post-COVID era has given companies the time to rebalance this arm wrestling battle between IT & Business and reshuffle the cards. Before signing on a new platform, we here at Valtech have seen a wave of longer RFPs with more detailed functional requirements, and business & IT teams slowly coming together to revisit their approach to system implementation, listening to the needs of their business users.  The complexity of the implementation project might vary depending on a range of reasons, including

• A greenfield Implementation  
• Replacing a legacy PIM 
• A migration from an on-premises PIM to a SaaS Platform 
• Integrating existing PIM with new systems 
• Expanding the existing PIM in new Business Units and Markets 

But to ensure your PIM, DAM, PLM, or other key product catalog management system implementation success, you’ll need adoption (as seen in previous blog). And to make sure adoption happens, you’ll need to make sure that the team implementing your magical new system will understand the audience, their habits, their fears, their needs, and more!  This is where having a programmatic approach to such a digital transformation is key. It’s not a punctual project, it’s an ongoing one that actually starts at the end of your initial implementation. And driven by the key pillars of change management!   

How to Build a Successful PXM Program

#1 Assemble the right PXM program team players

Success starts with aligning key players, and building the right dynamic around the right objectives with not only your integration team, but also your client team. But with situation like this where IT & business skills are heavily intertwined, who do you really need on your team? Here’s our recommendation if you’re working with a PXM integrator. 

Core PXM implementation team

• Lead consultant or Practice lead. He/She is often the most senior PXM/PIM consultant in the crew. They’ve seen it all and typically have a minimum of 5 years of experience. With a 20-30% of allocation to your program, they can guide the business consultant who has to do most of the configuration heavy lifting.
• Business consultant. Depending on the size of the project we recommend a senior or at least an intermediate consultant who Iwill help you get the most out of your data model, product lifecycle, and workflows strategy. They’ll also be your go-to person for adoption. We talk in general between 50% to 100% allocation to a project.
• Tech consultant. This is a profile we created as most PIM solutions are now leaning towards SaaS only. Their role is between a developer and an architect, and although they don’t need to know customization, they’ll need a deep business understanding and a mastery of API connections to be able to adapt and succeed. The tech consultant’s primary role is to keep the technology easy to maintain. We talk in general between 30% to 80% allocation to a project.
• Architect. Depending on the size of the program and the number of other systems connected to your PIM, a solutions architect could be helpful. Their role is mainly to align integration strategies during the discovery/design phase. His role is between 10-20%.
• Project Manager. You always need to have that person in your team that understands how to keep the group on track, on budget, and on time! In general, we ask this person to understand the biggest milestones associated with the project, along with the roles and responsibilities of each and every one of your team members. We typically allocate between 10-15% to the project.
• Practice Director. A PXM program will get you into politics really fast. If you want to make sure the quality control is there and you have an opinion of someone who has significant expertise in this space, then you might want to establish a Practice Director with at least 7+ years of experience.. The allocation of the budget is not more than 5-10% in case necessary.

Client PXM team 

Once your PIM is successfully implemented, you’ll need a team of PXM enthusiasts to ensure that your project is successfully adopted, and continues to live and get the necessary upgrades to make your product catalog shine!

• PIM Champion. You need one leader who is willing to make the harsh decisions, who has a bird’s eye view on all of your processes, who will act as a moderator for your teams and understands their pain points, and who will be responsible to control the use of your solution. When doing the adoption, he either leads it or designates the person he believes will help get you the stickiness such a platform needs.
• Product Content Specialists. These are either your product managers, product marketing specialists, or in certain cases a vendor who deeply knows your product catalog and the detailed specifications. They will help you select the proper attributes, the proper format, and the values to make a difference.
• Channel specialists. Typically from the marketing or the merchandising team, the channel specialist will master all the requirements necessary for your attributes to be received by different channels (i.e. Amazon, Home Depot, any distributor, etc…). They are also responsible for reviewing the data before sending it to their respective channels and can work along with the PIM champion & product specialists in order to refine the data.
• Architect. This will be the exact same role as you had in the Core PXM Implementation team, except  this one will be on the client side.

Additional optional roles depending on the size of the project include:

• Solution or enterprise architect. Really senior architect for whom additional systems have no secret. This role would be necessary when many systems are connected to your PIM so they can apply the perfect strategy to secure technical scaling over time.
• Additional Middleware engineers. Middleware is the future as more and more platforms become SaaS. There needs to be a middle-man to be able to improve performance when transferring a huge amount of data.
• Other solution developers or technical consultants for API integration. No comments here 🙂
• Change management specialist. When the organization is too large, or if the legacy systems and employees have been here for a long time, there needs to be someone specialized in the analysis and communication of potential resistance to change to help ensure that you have a successful, efficient adoption process. 

Have additional questions? Don’t hesitate to refer to the 5 must have roles in a PX team!  

#2 Adopt an interactive delivery methodology

 In today’s world with dynamic requirements and flexible needs, customers look for three key things:  

• Easy configuration   
• Early improvements  
• Continuous delivery  

PIM projects can also be adopted for agile delivery. Most SaaS platforms have a standard set of out-of-the-box features and connectors to help set up the base PIM with almost zero advanced development required. Some PIM tools allow business users the ability to configure features without the need for additional developers  Quick setup of easy components in the early iteration cycles provides the opportunity to prioritize  more complex custom requirements for development in the upcoming iterations. An agile approach like this allows teams to work in an iterative model where self-organization and cross-collaboration are key. Iterative development allows the opportunity to break down a complex implementation to smaller iterative cycles of Build, Configuration and Test activities.  The above diagram shows a visual representation of a development initiative broken into smaller iterations where each iteration is a condensed version of the SDLC methodology in itself.  Organizations may choose to cover different initiatives during each iteration based on the larger goals, priorities, and complexities.  

Benefits of an Agile-Based Iterative Development  

1. Close Collaboration – Allows for a better understanding of not just requirements and expectations but also mitigation of measures and fixes. 
2. Early Visibility – Early view of initial high priority features in the form of an MVP: e.g. Product Attribute pages available to be shown with sample data at the end of Sprint 1. 
3. Continuous Improvements – Opportunity to fix and improve something that does not meet customer expectations – e.g. Update the Naming Convention of Images coming from DAM to include additional fields.
4. Risk Reduction – Continuous iteration and feedback lowers risks and allows opportunities to correct mistakes at an early stage.

 

#3 Develop a clear roadmap & vision

A systematic approach towards implementing PIM starts with building a scalable PIM roadmap that is  adaptable and scalable for foreseen and unforeseen risks to come in the future.  A PXM-focused roadmap will:  

• Define the strategic vision of the PIM and end-goals of the organization 
• Act as a guiding document for implementation and adoption
• Provide visual clarity of the Scope of Work 
• Enable both internal (i.e. key users) and external (agencies, consultants, etc…) stakeholder alignment 

While ensuring the above, the PIM roadmap should clearly represent the order of various executable phases of the project with clear milestones with the mindset of making it evolutive. An example of a 3-year PIM roadmap is shown below:   Explicitly stating what is in-scope and out-of-scope of the PIM project ensures that both timelines for each phase are met and that the project stays within budget. When the roadmap is built and the respective phases are identified, teams need to define the realistic achievable goals within a specific timeline, and also identify what may not be achievable. A 6-month implementation timeline may involve deploying standard out-of-the-box components with some basic customization while any complex customization can be considered out of scope and can be taken up in a future phase if resources allow. We recommend not starting with an MVP that is beyond 3-4 months – why? Because delaying key business users’ adoption is a threat to the project’s success!  

#4 Establish proper risk management processes

Any major transformation program comes with its own set of risks and hurdles, and PIM implementation is no different. Hurdles can appear in many areas and are not just limited to scope management.  If we divide the hurdles of a PIM project into 3 buckets of Business, Technical and Management, the classification would look something like this:  

Business Hurdles 

• Little to no understanding of the product catalog structure 
• Failure to understand the creation, enrichment, and syndication process workflows 
• No data governance policies established 
• Underestimating product complexity

Technical Hurdles 

• Complex integration landscape 
• Data and information security 
• Lack of understanding of the capabilities existing legacy platforms 

Management Hurdles 

• Lack of subject matter knowledge to execute a PIM project 
• Failure to get commitment from leadership 
• No strategic vision for enterprise adoption 

For example, if we consider the issue of a Complex Integration Landscape – where the PIM is required to integrate with several complex systems – anything without a proper assessment can lead to risks of delays and rising costs. A  strong Risk Management plan can help tackle risks arising out of the above hurdles.  Tools like Risk Matrices can help assess likelihood and impact of risks, thus enabling: 

• Strategies to mitigate the risks 
• Prioritizing and tracking on a severity level basis 
• Deploying resources to neutralize risks 

A risk management matrix can also help customers identify opportunities that were previously unknown. From a PIM standpoint a very simple example can be considered as follows:   While building the integration with ERP, it was found that the ERP data had quality issues with a lot of junk values. While this presents a risk of having bad data flowing into PIM, it also presents an opportunity to review and clean up the data in the ERP.   Overall, risks and rewards go hand in hand; an effective risk management strategy can help firms identify enough opportunities to expand a PIM program into various new and exciting initiatives, markets and regions.   

#5 Establish change management

Whether the transition is from an MS-Excel & email-based process to a PIM, or from an on-premises PIM to a cloud-based SaaS PIM, or any upheaval in habits or processes that impacts people, – change matters, and its management is important for adoption. Change management is especially crucial for organizations that are implementing a new solution among “old habits” and a sticky legacy system. You need to provide a clear and structured approach to transitioning individuals, teams, and the entire organization from the current product data management to the future one.  Here are some reasons why it’s essential to consider the change management during any solution integration: 

• Stakeholder Resistance: If you have any sort of old system already in place, it’s common for a lot of users to be used to these older systems, processes, and execution methods. By identifying the different sources of resistance, it will be easier to set the right strategy to make the change happen. 
• Uncertainty: Not understanding why a new solution is implemented can lead to real misalignment among all stakeholders. It’s crucial to correctly communicate the change, purpose, objectives, and advantages of this new integration and align all the stakeholders involved before, during, and after the integration. Everybody needs to understand the real impact of integration on the global business strategy. 
• Lake of Engagement: It becomes important to have the right team of experts who can help determine the current pain points affecting the current product experience. Whether it’s harmonizing data or launching a new eCommerce portal, setting correct business goals is critical. Involving the right team to address the real business challenges is key. If you involve the right key users, it will bring harmony to the entire organization. 
• Skills Gaps: By integrating the new PIM solution, some users may feel as if they don’t have the skills to adapt to the new processes and technology, but this is not the case as this knowledge will be useful to build the new integration! Train, support, and guide the users to make them feel safe and ensure a better adoption process. Change requires constant monitoring and evaluation to ensure that it is permanently integrated into process, people, and technology!

A changing mindset is not only key to driving adoption through training but it is also critical in driving system enhancements. A PIM which has just been deployed may be able to solve immediate use-case identified; however, as businesses continue to grow, feature-enhancements and additional integrations will be required. As time passes, new users and business units are onboarded who may require additional functionalities to be onboarded.  For example:

• A leading Food & Beverages manufacturer from India decides to enter the Canadian markets and launch its own website along with other channels like Walmart and Loblaws; that would require onboarding new BUs and users from Canada 
• To meet regulatory requirements in Canada, the Marketing Description needs to be displayed in both English and French 
• The existing PIM solution would then require enhancements applicable to Canada which would include addition of new fields, addition of new export templates, minor changes in the User Interface etc. 

In the above scenario, an open communication between entities of both the countries is crucial to drive user adoption. The users should be open to grasping how the changes implemented can lead to higher process efficiency, lower enrichment times, and faster go to market in both countries.   Change management is essential for the successful implementation of organizational changes. It helps align people, processes, and technology with the business goals and objectives of the organization, facilitating a smoother transition and maximizing the chances of ensuring a successful PIM integration. Involving the right people at the right time, and ensuring transparency through clear communication of objectives, advantages, expectations, and guidance ensures a smooth change management process! Looking for more PIM implementation support? Check out some of our other Valtech featured articles diving into the people-first approach to product experiences, how to maximize the ROI of your PXM organization, or the steps to perfecting your PIM selection process. This article is a guest post by Akeneo partner, Valtech.

Benjamin Franklin once famously said, “The only guarantees in life are death and taxes.”

Well, I’d like to petition to add one more guarantee to that list: returns.

In the era of “serial returners”, retailers across the globe have seen a spike in return rates and shoppers are showing no signs of slowing down.

While returns are an inherent part of the retail landscape, high return rates can have far-reaching consequences that extend beyond the bottom line, including:

 

Financial Impacts

High return rates can significantly impact a business’s operating costs in several ways. Firstly, there are logistical costs associated with processing returns, restocking inventory, and managing reverse logistics. This involves labor, transportation, and storage expenses, which can quickly add up, particularly for large retailers handling thousands of returns daily.  As a reference, in 2023, the average retailer incurred $145M in merchandise returns for every $1 billion in sales.

Returns often also result in inventory depreciation. Items that are returned may not be in pristine condition, requiring markdowns or even liquidation to clear out excess stock. They can also put a strain on customer service resources – dealing with return inquiries, processing refunds, and managing customer complaints all require time and manpower, diverting resources away from other areas of the business.

 

Environmental Impacts 

Returns often lead to increased carbon emissions due to additional transportation and handling as each return necessitates transportation back to the warehouse or retailer, contributing to fuel consumption and greenhouse gas emissions. In 2022 alone, emissions from shipping returns equaled 24 million CO2 metric tons.

Moreover, returned items may not always be resold immediately, leading to excess inventory or even disposal. This not only wastes valuable resources but also adds to the growing problem of textile waste in landfills; the volume of returns ending up in landfills in the U.S. nearly doubled from 2019 to 2022, amounting to 4.3 billion tons.

 

Customer Impacts 

One of the most significant yet often overlooked impacts of high return rates is on customer loyalty. 96% of consumers review retailers’ return policies before making an online purchase, and 63% of consumers express a negative feeling about a retailer charging them for returning a product.

While offering a generous return policy can initially attract customers, an excessively high return rate may signal underlying issues with product quality, sizing accuracy, or fulfillment processes. This can erode trust and confidence in the brand, ultimately leading to decreased customer loyalty and repeat purchases.

Moreover, the hassle associated with returns can negatively impact the overall customer experience. Lengthy return processes, restocking fees, or difficulty in obtaining refunds can frustrate customers, driving them away to competitors with more seamless return processes.

In recent years, “omnichannel” has become more of a buzzword than a true strategy. “Omni” is a Latin prefix that means “all” or “every.” Yet nowadays, organizations tend to use the word “omnichannel” to refer to their D2C site and an in-person presence. Maybe a listing on Amazon if they’re more technologically advanced. However, true omnichannel experiences extend far beyond mere presence across online and offline channels. It’s about creating seamless, integrated journeys for customers across every touchpoint, ensuring that they have the information they need to browse and buy at any point along their shopping experiences.  Omnichannel experiences are a critical ingredient for attracting and retaining customers. With 71% of consumers claiming that they find additional product information when they look in multiple places, this is becoming less of a recommendation and more of a requirement for success. Outside of traditional eCommerce, a true omnichannel strategy needs to account for:

1. Social Commerce: With the rise of social media platforms, consumers increasingly turn to these channels not just for socializing but also for discovering and purchasing products. Brands that effectively leverage social commerce can tap into vast audiences and drive sales through targeted advertising, influencer collaborations, and shoppable posts.
2. Customer Support: Exceptional customer support is integral to fostering loyalty and trust. Beyond resolving issues, it presents an opportunity to enhance the overall brand experience and promote cross-selling or upselling related products. Whether through live chat, email, or phone support, customer support is an often undervalued avenue to connecting with consumers and increasing revenue.
3. Print: While digital channels dominate discussions, traditional print media still holds relevance in the omnichannel landscape. From catalogs and brochures to magazine ads, print materials offer a tangible connection with consumers and can complement online efforts by driving traffic to digital platforms.
4. Brick-and-Mortar: Physical retail stores remain a cornerstone of the shopping experience, providing opportunities for hands-on interaction with products and personalized assistance from knowledgeable staff. Integrating brick-and-mortar locations into the omnichannel strategy enables seamless transitions between online browsing and in-store purchases, creating a cohesive customer journey.
5. Innovative Channels: As technology continues to evolve, new channels emerge, offering unique opportunities for engagement. Voice search, augmented reality, and the metaverse represent cutting-edge avenues through which brands can connect with consumers in innovative ways. Staying abreast of these developments and adapting strategies accordingly is key to staying ahead in the omnichannel game.

The Secret to Omnichannel Success

So the question becomes, if we know what a true omnichannel presence looks like, why aren’t more organizations executing true omnichannel strategies? The hard truth is that most of these organizations don’t have the foundation of reliable product information to support all of these diverse channels and touchpoints. Replicating data for each channel manually is a cumbersome and error-prone process that leads to inconsistencies and inefficiencies, and makes activating products on an array of existing channels, plus those to come, nearly impossible. By centralizing product data management, businesses can ensure that information is accurate, reliable, and up-to-date across all channels. This foundational approach streamlines operations, minimizes errors, and facilitates seamless integration with existing and future channels.  Whether it’s updating inventory levels, pricing, or product descriptions, changes made in the centralized repository are reflected instantaneously across all touchpoints. To truly harness the power of omnichannel, businesses must expand their perspective beyond eCommerce and embrace a holistic approach to customer engagement.  Investing in a robust Product Information Management (PIM) solution lays the groundwork for success, empowering businesses to deliver consistent, personalized, and seamless experiences across all touchpoints. In doing so, they not only attract and retain customers but also drive sustainable growth in an increasingly competitive marketplace in true omnichannel spirit. Are you interested in learning more about creating omnichannel bliss for your organization? Register for the premiere PX conference Unlock 2024 to hear real-life PX and eCommerce experts dive into their success strategies and omnichannel tips and tricks.

Love is in the air, and regardless of how you feel about the ooey-gooey Hallmark holiday, this is always a good time of year to reflect on the compatibility of your relationships, both emotional and technical. 

Yes, you should dump him. No, you shouldn’t cut off your mother. Yes, you should upgrade your product experience strategy.

Because here’s the thing; just like love, modern product information management (PIM) is complicated. In the early days of eCommerce, a monolithic software system was the one-stop-shop solution for supporting a single D2C website, or maybe a small presence on a retailer’s marketplace. It was simple and straightforward, like writing a love letter or serenading your true love in the old days.

Nowadays, your customer’s experience with your product is an intricate, ever-evolving web of interactions and touchpoints, some owned by you and some not. There’s your own eCommerce site, third-party retail sites, your Amazon shop, social media outlets, influencer marketing programs, maybe even a brick-and-mortar operation or print catalog.

And that’s not even accounting for all the cutting-edge technology that’s leaking into the retail landscape, like voice searching, virtual reality shopping experiences, the metaverse, and artificial recommendation services such as chatbots and virtual shopping assistants.

And as for love? Somehow, even worse. My partner’s sending me a TikTok while I’m Googling an anniversary gift, we usually text on WhatsApp but call on Facebook Messenger and video chat on FaceTime, and we initially followed each other on Instagram for a while because we have mutual followers but we officially met on Bumble.

The point is, life is now omnichannel. So are your shoppers. Every interaction, every touchpoint along the entire expanse of the customer journey is an opportunity to connect with your customer and provide a compelling experience that will make them want to come back for more. 

With these new touchpoints emerging and old touchpoints evolving, it’s all too easy to make the wrong impression. Two-thirds of shoppers say that they would abandon a purchase due to bad product information on any channel, and three-quarters say that they look in more than one place for product information before purchasing. So not only do you have to be available on every channel, but you need to ensure that the experience you’re providing is reliable, cohesive, and optimized for each specific channel.

Unfortunately, outdated product information practices and monolithic architecture simply cannot support the type of Product Experience (PX) Strategy needed to execute a cohesive omnichannel customer experience. It’s like using a pigeon to send a love letter instead of making a phone call.

This is where modern PIM solutions become crucial; the foundation of any and all product experiences is product information and requires best-of-breed technology that is highly scalable and flexible in order to support these omnichannel experiences.

 

Finding Your Perfect Pair

Just like how everyone has different preferences and needs in relationships, there is no one-size-fits-all modern PIM solution that will meet the needs of every organization; different businesses need different things out of their PIM.

But there is one trend in product experience management that has emerged over the past few years and shows no indication of slowing down; and that would be composable commerce.

Instead of a traditional approach, composable commerce software communicates almost exclusively via API, meaning that each individual solution in your tech stack can be updated or replaced at any time without disrupting the rest of the system.

Composable commerce is a more flexible approach to your eCommerce stack that ensures you’re only utilizing best-of-breed technologies, and as soon as a piece of software is no longer meeting your needs as you scale and grow, you can replace it with speed and ease.

Imagine if you could do that with your relationships as well – no more petty exes!

Composable commerce has been growing in popularity over the past few years, with 95% of respondents in Elastic Path’s report saying that composable commerce is the approach enterprises should take. In that same report, it was found that nearly three-quarters of respondents who had only heard of composable commerce but not implemented it believed that all companies will be working with a composable commerce framework within five years.

This modular, plug-and-play approach is one of the driving forces behind MACH technology (Microservices-based, API-first, Cloud-native SaaS, and Headless). Essentially, it’s a framework to analyze software with to ensure that they’re future-proof and scalable.

• Microservices-based: enables modern PIM software to reduce time-to-market, accelerate new product launches, and react quickly to real-time updates
• API-first: Seamlessly connects your modern PIM to the other technology in your ecosystem today, and ensures that you have the flexibility to add or remove any piece without disrupting the entire system
• Cloud-native SaaS: Offers organizations a modern PIM that supplies the scalability flexibility needed to accommodate fluctuating customer expectations and demands
• Headless: Decouples the front-end from the back-end, allowing for a better user interface and promoting wider user adoption

Modern PIM & MACH: A Compatibility Test

So, why do modern PIMs and MACH ideology go hand in hand like flowers and chocolate? The answer is quite simple, really; MACH-Certified PIMs provide the flexibility and scalability in your tech stack to grow as your business and customer journey grow. Instead of reacting sluggishly to new channels emerging or new marketplaces opening up, you’ll have a proactive strategy in place that allows you to move quickly and efficiently. 

The MACH framework is the theoretical soulmate to the modern PIM technology, setting the healthy foundation of communication and collaboration early on in the relationship to ensure that the product record is optimized, up-to-date, and activated everywhere.

Plus, like the perfect partner, it doesn’t stifle your need or desire to grow; the headless technology enables a modular approach that means that pieces of your technology stack can be added, removed, and replaced depending on your needs. As new channels and touchpoints arise, you’ll be able to provide the compelling omnichannel experience that customers are looking for by showing them the right information, in the right place, at the right time.

So this Valentine’s Day, as you take a deeper look at your personal relationships, check on the relationship with your PIM. If it feels like something is missing, maybe your MACH-certified PIM soulmate is out there.

Did you know Akeneo is a MACH-certified PIM? Feel free to reach out to one of our experts to see a demo, or just to chat on all things composable and PIM.

Once confined to the speculative world of science fiction, Artificial Intelligence (AI) has officially permeated our everyday lives and entrenched itself into our reality with the rise in popularity of tools like OpenAI’s ChatGPT, Bing Copilot, and Google’s Bard. 

Yet if you’ve interacted with these tools at all over the past year, you’ll have noticed a few things: one, while these solutions are an exciting step forward, they are not quite as autonomous or advanced as many people try to claim. Few people, even self-reported AI experts, seem to actually understand the capabilities of this technology today and how it will evolve to affect the customer experience in the coming years.

Two, the quality of the outputs that you receive from these technologies is heavily dependent on the quality of prompt and information you provide from the outset, particularly when it comes to content generation.

 

A Foundation of Product Information

The recent strides in AI, particularly with content generation, have been remarkable, yet the crux of the matter is identifying the right attributes, data, and values essential for crafting meaningful inputs. Or, more colloquially speaking, it’s a “shit-in, shit-out” situation. 

This paradox underscores the complex nature of AI and its ongoing journey from concept to seamless integration into our everyday lives, and highlights one very important caveat: as exciting as AI technology is, it will always fail if you don’t have the right attributes, the right data and the right values available.

AI algorithms, particularly those powering recommendation engines or content generation, analyze product data to understand customer preferences. Accurate information ensures that recommendations align with individual tastes and needs, leading to more personalized and relevant suggestions.

Take, for instance, the fashion industry. AI-driven tools can revolutionize the customer experience by providing personalized recommendations based on individual style preferences, such as suggesting a certain skirt that is frequently purchased with a top. However, the effectiveness of these recommendations hinges on the quality and specificity of the product information fed into the system.

Better, More Complex Inputs

Enhanced input diversity is a crucial aspect of advancing artificial intelligence capabilities, and it encompasses various dimensions for a comprehensive understanding of user preferences. One facet is textual data enrichment, where high-quality product information includes detailed descriptions, customer reviews, and specifications. An analysis of diverse textual data, such as customer reviews praising safety features or product compatibility, enables nuanced recommendations that reflect user sentiments.

Visual and auditory input integrations are equally essential, though is often considered as the more underdeveloped area of AI. Diverse data, including images, videos, and sounds, enhances AI models’ capabilities to understand and cater to customer preferences.

Moving towards comprehensive results, the amalgamation of traditional machine learning (ML) with deep learning models is a powerful strategy. Researchers are already combining these models to create hybrid systems that leverage the strengths of both. Traditional ML models excel in handling structured data, while deep learning models are adept at processing unstructured data like images and text. A hybrid model of the two can analyze both structured data like technical specifications and unstructured data like customer reviews and product images, resulting in a more nuanced understanding of a product’s appeal.

 

Seamless Integration in Everyday Workflow

The seamless integration of AI technologies into everyday workflows is pivotal for optimizing operational efficiency and user experience. To achieve this, a user-centric design approach is crucial, ensuring that the integration minimizes disruptions for teams working on a daily basis. This involves creating intuitive user interfaces that allow team members to interact with AI tools effortlessly, promoting accessibility without the need for extensive training.

Invisible automation also plays a key role in enhancing productivity by automating repetitive tasks without constant manual intervention; these are applications of AI that your team doesn’t always even need to be aware of. Automated workflows, such as inventory management systems automatically restocking low inventory levels, contribute to operational efficiency in sectors like retail. Additionally, predictive maintenance powered by AI anticipates potential issues and addresses them proactively, reducing downtime and ensuring smooth manufacturing processes.

Transparency in AI decision-making is equally essential for fostering trust among team members. AI systems should be designed to be explainable, enabling users to understand the reasoning behind the decisions made. Clear communication about the role of AI in daily workflows is paramount, ensuring that team members are informed about how AI contributes to their tasks and the overall organizational goals. This transparency is particularly crucial in scenarios like customer service, where the use of AI to prioritize and route queries requires clear communication to build confidence in the decision-making processes.

The Akeneo Angle

At Akeneo, we’re not merely following the typical AI playbook; we’re rewriting it. Collaborating with our recently acquired partner, Unifai, we’re driven by a willingness to leverage AI and apply it to diverse needs. Our commitment to innovation goes beyond the ordinary – we strive to think outside the box and explore new applications.

 As the world’s first intelligent Product Cloud, we can provide your team with access not only to a centralized source of truth for all your product information and an extensive network of expert partners, but also to product data that is cleansed, enhanced, and enriched with native AI capabilities.

Looking ahead, we envision expanding the applications of AI into areas such as retail activation and content generation. However, we are a customer-centric company! Our roadmap is shaped by the feedback and needs of our customers; our goal is to bring significant improvements to our customers through our technology, prioritizing solutions that truly matter to their businesses.

I lied; it’s actually six questions. The first one is simple though:   

What is Master Data Governance?

Master Data Governance: The framework of policies, processes, and standards that ensures high data quality, security, and compliance within an organization. Talking with brands about master data governance when it comes to their product catalog tends to elicit one of two feelings: fear, or boredom.  The fearful response comes from the database managers, or those involved day-to-day with product enrichment; people who understand the unpleasant truth of “how the sausage is made.” These individuals often aim to go all in, wanting to cleanse and standardize every piece of product data. They’re scared because they know just how much work this requires.  In contrast, the bored response arises from business users or leadership who are more detached from the realities of the data and primarily concerned with an impending Go-Live date. These folks often prefer to ignore data governance altogether, or pretend that AI can do it all for them (hint: it can’t). Neither of these differing approaches is realistic for most organizations. A middle ground must be found.  We could write a book about what a proper master data governance policy entails (people have). You need to account for things like data ownership, lifecycle management, auditing and compliance, collaboration policies, integration standards, training procedures and more.  But many brands don’t have the time or resources to build out a full-fledged data governance policy for product data. So how should you incorporate data governance into your PX Strategy?  Below is the cheat sheet version of data governance for brands trying to move quickly and efficiently. By addressing these questions in your product catalog and implementing ongoing controls, you’ll be able to realize the most important benefits of data governance without incurring the time or expense of implementing a full-fledged program for your Product Data.   So, ask yourself….  

1. How will you ensure your product data is complete?

  One of the quickest ways to harm your reputation with a consumer is to present them with an empty field instead of the product information they expect. A missing image, product name, or critical technical spec can crush your conversion rate. It’s critical that you have a mechanism in place that will prevent you from sending incomplete product data to customers.  Many brands make the unfortunate mistake of creating large “Description” text fields that are ultimately nothing more than a jumble of ungoverned data. They have no way of knowing what is or isn’t there. For proper governance, these key product data points should be separated into discrete fields, allowing easy validation of their completeness. A robust Product Taxonomy with appropriate attributes and differentiation between product types is essential to help ensure this completeness across an entire catalog.  Once individual fields are validated, they can be concatenated programmatically to build descriptions, or even serve as prompts for a generative AI solution.  

 2. How will you keep your product data consistent?

  Your product data must be consistent AND displayed consistently. Product data goes to many different endpoints within an ecosystem, and without a PIM system providing a single source of truth, it’s not uncommon for siloed teams to create different variations of product data for the same product. This not only creates inefficiencies, but also inconsistent experiences that can deter customers. Just as critically, you must ensure that data itself is consistent. In the governance process, each data field format must be identified and assigned a standard that is always enforced. For instance, a data field like ‘Seat Height’ for a furniture distributor should convey precisely the same meaning for every product in the catalog and be described in a uniform number and measurement format, such as 45.5 CM. Failure to enforce this standard may lead to various interpretations of how to record the data (e.g., 45.5 Cent., 45 ½ Cent, Forty-Five and a half centimeters, etc.).  This inconsistency is terrible for website filtering and SEO, and also looks sloppy to customers. Maintaining this level of consistency becomes even more challenging when dealing with a larger number of suppliers, each with their own naming conventions and data standards; AI can prove incredibly helpful in bringing order to this potential chaos.   

3. How will you know your product data is accurate? 

  Your product data may be complete and consistent, but this doesn’t mean it’s correctly describing your product! Accuracy refers to the precision and correctness of the underlying data. Getting it wrong can lead to an irritated consumer, an expensive return, or a lawsuit in extreme cases. Ensuring accuracy can be difficult, but here are a few options that can help: 

1. Establish rules and validations: If you sell shoes between sizes 4 and 12, any number greater than 12 or less than 4, including negative numbers or fractions, can be ruled out programmatically. 
2. Verify with 3rd parties: If other sources maintain the same data field, cross-reference it to identify any discrepancies (suppliers or even competitors can be useful).
3. Human checks: Ensure one or even multiple employees check product listings as part of an approval process before they go out to customers.
4. Create a feedback loop: Mistakes will be made, but the faster you can find and fix them, the less damage they’ll do. Leverage your customer support teams, user generated content, or even sales data to quickly find product information errors. 
5. Audit: Routinely carry out audits on small sections of your product catalog to understand its health. Many accuracy issues are systematic meaning they can be pretty easily identified and quickly fixed.

 

4. Do you have enough product data? 

  A data science purist may roll their eyes at this one, but I’m including it based on past experiences. When working with brands, one of the first inquiries I make is around the condition of their product data and the types of errors and issues they encounter.  Every brand has data quality issues, and most readily admit to it when prodded enough. However, I consistently come across brands that insist they have perfect data. In this instance I almost inevitably go on to find  that the reason their data errors are so scarce is because their product information itself is so scarce!  Think of it like performing a pencil dive during an Olympic diving competition. Sure, you may not technically have committed any errors, but you’re not getting a 10.0! These brands are leaving their customers in a product experience desert, a situation particularly common with B2B distributors and fast fashion retailers. It’s easy to govern your data when you’re only providing a Product Name, Main Image, Short Description, and a handful of product attributes. But this doesn’t contribute positively to the customer experience. That’s why one of the most critical questions to consider when revamping your product data is whether there’s enough of it. A properly built product listing should include well over a dozen product attributes, with more complex products having 100 or more.  At the minimum, you should ensure that you’re providing customers with:

1. Technical Specs (width, height, weight, tolerance, materials, voltage, etc.)
2. Marketing Copy (product/brand/designer story) 
3. Assets (Images, video, audio, manuals)
4. Usage (instructions on how to use your product)
5. Regulatory compliance (environmental impact of product, regulatory compliance etc.)
6. Compatibility & Cross/Upsell (what other products are similar or work well with this one) 

 

5. How will you handle future changes to your product data? 

  Your product catalog is never complete. There will be new SKUs, new product types, new ways of describing products, new suppliers, new places to sell, and an endless stream of directives from the powers that be to change the way you operate. It’s critical that each one of the controls you implement as part of your data governance policy have a mechanism to incorporate change.  Document all governance policies from the beginning and determine who’s responsible for incorporating future changes, the process for making these changes, how frequently you’ll make these changes, and what your succession plan is should employees leave.   

Master Data Governance for Your Products 

  Data governance doesn’t have to be a complex, expensive proposition when it comes to your product data, but it also cannot be ignored. It is what ensures the experience you provide your customers is error-free and up to standards.  If you’re implementing a PIM solution or revamping your product catalog, be honest with yourself and the state of your catalog by reviewing the questions we just covered. You may not be able to fix everything at once, but acknowledging and documenting the problem is the first and most difficult step. Changes can then be implemented incrementally over time as you juggle competing priorities.  If you’re ever unsure of where to go in your data governance journey, take a step back and consider your customer and what changes will have the most positive impact on their product experience.  And if you’re looking for some support on improving or implementing your data governance policies, feel free to reach out to a PX expert today.

Nobody knows your product better than those who use it day in and day out; that’s right, your customers.

While some of us have been turning towards new technologies like AI to improve product information accuracy, others have turned to their in-house community of product experts to source product information corrections, enhancements, and additions.

In an effort to ensure that 42 million data points for over 6 million products is up-to-date and accurate, Swiss retailer Galaxus recently introduced the ability for customers to correct and supplement product information to enhance the quality of details published on product pages.

Outdoor clothing & gear retailer Backcountry has been sourcing product information from consumers for years now – On every product page they have a link where you can report incorrect product information, and updates promise to be reflected within 72 hours.

Both of these examples underscore a fundamental truth: amidst the allure of cutting-edge technologies, the authentic voice of the customer remains an invaluable asset. The shift towards community-sourced product information recognizes that the lived experiences and nuanced understanding of customers bring a level of authenticity and depth that algorithms alone cannot match.

As this momentum of customer-centric information sourcing gains momentum, let’s take a look at the basics of this strategy and how more reliable and up-to-date product data can positively impact the customer experience.

 

What is Community-Sourced PX?

Community-Sourced Product Experience (PX) is a collaborative approach in the retail and eCommerce landscape where brands and retailers leverage the expertise and insights of their customer community to enhance and refine product information. In this model, customers actively contribute to the accuracy, completeness, and overall quality of product details, transforming them from passive consumers into engaged participants.

This process involves allowing customers to identify and rectify missing or inaccurate product information, ranging from specifications and usage details to reviews and recommendations. Brands embrace the collective intelligence of their community to ensure that product pages reflect a comprehensive and authentic portrayal of their offerings.

Community-Sourced PX goes beyond traditional methods of data curation, recognizing that customers, as end-users intimately acquainted with the products, bring a level of authenticity and nuanced understanding that automated systems or internal teams might not possess. It fosters a sense of collaboration, turning customers into valuable contributors who actively shape the narrative of product information, ultimately influencing the overall customer experience and building stronger connections between brands and their audience.

 

What are the Benefits of Community-Sourced PX?

Create a Community of Contributors

In the conventional retail model, customers often play a passive role, limited to the transactional aspect of purchasing. However, the advent of Community-Sourced PX elevates customers to the status of active participants. They are no longer mere observers but contributors, actively shaping the landscape of product information.

This shift is akin to inviting customers into the inner circle, where their insights and expertise are valued. As customers see their suggestions and corrections influencing product details, a sense of ownership takes root. They become stakeholders in the brand’s success, fostering a deeper connection that extends beyond individual transactions. This sense of community becomes a powerful driver of brand loyalty as customers no longer perceive themselves as detached consumers but as integral members of a community invested in the brand’s growth and improvement.

Proof of Customer-Centricity

Brands that actively listen, acknowledge, and implement customer feedback demonstrate a genuine commitment to evolving in tandem with their audience. This responsiveness is not merely a reactive measure but a proactive stance, illustrating that the brand is attuned to the ever-changing needs and expectations of its consumer base.

As customers witness their feedback translating into tangible improvements or corrections in product information, a powerful narrative of customer-centricity unfolds. The brand becomes more than just a provider of products; it transforms into a responsive partner, actively seeking to enhance the customer experience. This proactive engagement fosters a sense of trust, as customers recognize that their opinions matter and are instrumental in shaping the brand’s direction.

Promote UGC (User-Generated Content)

The concept of Community-Sourced PX is a goldmine for User-Generated Content (UGC). As customers actively contribute key information or share their genuine experiences, brands and retailers are able to enrich product pages and encourage other customers to share their own experiences or feedback, creating a better overall customer experience.

The authenticity embedded in UGC is unparalleled. It goes beyond the glossy marketing language and staged visuals, offering a real and unfiltered glimpse into the product’s impact on the lives of actual consumers. Community-Sourced PX provides a platform for customers to share their triumphs, challenges, and unique use cases, creating a mosaic of diverse perspectives that resonate with a broader audience.

Flag Missing/Incomplete Data & Patterns

Customers, intimately acquainted with products, are quick to identify gaps or inaccuracies.Their keen observations stem from a genuine connection with the products they use, making them adept at recognizing nuances that automated systems or internal teams might overlook. 

This collaborative approach helps flag missing or incomplete data and uncover patterns that might have been overlooked. The community acts as an extended set of eyes, collectively ensuring that product details are not only accurate but also reflective of the nuanced qualities that make each product unique. Plus, if your team notices that certain products or product lines are continually getting flagged for incomplete or inaccurate information, it may help your team identify gaps or inefficiencies in your publishing processes.

Improve Overall Quality and Reliability of Product Information

With the collective contribution of the community, brands can significantly enhance the overall quality and reliability of their product information, ensuring accuracy and completeness. This process is akin to a collaborative audit, where the community serves as a vigilant team, ensuring that every product detail aligns with the reality perceived by those who actually use the products. Whether it’s correcting specifications, validating usage scenarios, or adding nuanced information, the collective intelligence of the community becomes an invaluable resource for brands striving to deliver accurate and comprehensive product information.

Plus, this collaborative approach fosters a culture of continuous improvement. As community members actively engage with product details, brands gain real-time insights into evolving customer needs and expectations. This iterative feedback loop allows brands to adapt swiftly, ensuring that product information remains relevant and aligned with the dynamic nature of consumer preferences.

 

What are the Benefits of Providing More Reliable Product Information?

Reduced Return Rates

When product details are accurate and comprehensive, customers are empowered to make informed purchasing decisions, reducing the likelihood of dissatisfaction and subsequent returns. This, in turn, results in a significant decrease in return rates and the associated costs for both customers and brands.

Plus, the decrease in return rates contributes to improved customer satisfaction. Satisfied customers are more likely to become repeat buyers and brand advocates. They appreciate the brand’s commitment to providing reliable information, fostering loyalty and positive word-of-mouth. In the long run, this positive cycle not only minimizes the financial impact of returns but also enhances the brand’s reputation and customer relationships.

Better Searchability & Site Functionality

Accurate product data lays the foundation for precise and relevant search results. When customers search for specific products or features, the accuracy of the data ensures that the search algorithm can effectively match their queries with the most relevant products, eliminating the frustration of irrelevant results and allowing customers to quickly and effortlessly find exactly what they are looking for.

An up-to-date and accurate product catalog also enhances the overall navigation experience. Customers can intuitively explore product categories, filter options, and refine their searches based on accurate specifications. This organized structure contributes to a user-friendly environment and streamlines the decision-making process, enabling customers to browse and purchase with ease and confidence.

More Consistent Omnichannel Customer Experience

Whether a customer engages with a brand through the website, a mobile app, social media, or in-store, having consistent and accurate product information creates a unified brand presence. Accurate and uniform product data facilitates a smooth transition for customers moving between channels. Whether they research online and buy in-store or vice versa, having consistent data eliminates friction points, making the transition effortless and enhancing overall customer satisfaction. This cohesion reinforces brand identity, conveying a sense of reliability and professionalism to customers, regardless of their chosen touchpoint.

For brands aiming to deliver personalized experiences, consistency in product data is paramount. A unified understanding of product details allows for more effective cross-channel personalization strategies. Whether recommending related products online or offering tailored promotions in-store, a foundation of consistent product data enables brands to understand and anticipate customer preferences across the entire omnichannel landscape.

 

The Future of Community-Sourced PX

Community-Sourced PX invites customers into an exclusive inner circle, where their insights and expertise are valued. The shift from passive observers to engaged participants fosters a sense of ownership and community. Customers become integral stakeholders in the brand’s success, nurturing a deeper connection that transcends individual transactions. This newfound sense of community emerges as a powerful catalyst for brand loyalty, as customers recognize themselves as vital contributors to the brand’s growth and improvement.

While the concept of Community-Sourced PX might still be in its infancy, it holds the power to not only redefine how brands curate information but also reshape the entire customer journey. This collaborative evolution paves the way for a retail experience that is not only accurate and reliable but also deeply rooted in community, authenticity, and a customer-centric ethos. 

Interested in learning more about how you can harness the power of your community to improve your product experience offering? Reach out to a PX expert today!

The adoption of Artificial Intelligence (AI) has become unavoidable for companies wishing to remain competitive and innovative. Every decision, every step of the integration process, can have a considerable impact on ultimate success.  According to a recent study by Gartner, over 40% of companies that invested in AI technologies without a clear strategy ended up spending more than expected. So how do you take full advantage of the benefits of AI while avoiding potential pitfalls? How do you ensure a smooth transition and seamless integration? Let’s dive into the keys to success for implementing an AI solution at home, while explaining how to find the perfect AI tool to manage your product data!   

Demystifying AI in Retail

Around the water cooler, you may have heard “AI is a danger, I’m about to be replaced by a robot”, or at lunchtime you’ve heard “Wait a minute, working with AI is expensive!” Whether you’re overseeing an AI project or navigating the challenges of gaining buy-in from colleagues, breaking down misconceptions is crucial. Despite some prevalent negative opinions, AI is now a strategic pillar in various industries, from customer relations to logistics and research. It’s essential to dispel myths and showcase the diverse applications and benefits of AI in today’s business landscape. AI offers unprecedented optimization opportunities for companies thanks to three main elements: 

• Task automation: Using AI to perform repetitive, rule-based tasks without human intervention, ranging from simple data entry to complex business processes.This frees up valuable human resources to focus on more strategic, creative, and high-value activities. 
• Predictive analysis: Using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes or trends.By analyzing past data, AI can predict future trends, customer behaviors, and market changes, allowing companies to make informed decisions, anticipate demand, optimize resource allocation, and proactively address challenges before they arise.
• Pattern recognition: Using AI to identify patterns, trends, or anomalies within vast datasets that may not be apparent to human observers. AI excels at quickly and accurately recognizing patterns in data, enabling businesses to extract valuable insights. This is particularly beneficial in areas like fraud detection, customer behavior analysis, and quality control.

According to a recent Usine Digitale survey, 71% of the executives see the optimization of internal operations and processes as the main incentive for adopting AI. This trend is far from trivial, as it enables companies to increase efficiency, reduce costs, and improve the quality of their products or services.  To take this technological revolution one step further, it’s essential to discover how to improve product data quality problems with AI, ensuring that this innovative technology is seamlessly integrated into everyday business operations.  

Avoid Common Pitfalls When Implementing AI

Integrating AI into your business processes promises many benefits, but it also presents challenges. Let’s take a look at a few of the most common pitfalls to ensure that your organization is able to steer clear of them. 

Ignoring data security 

According to a Gartner study, 41% of organizations have already experienced an AI-related privacy breach or security incident. Selecting an AI tool with robust security protocols, data encryption, and compliance with privacy regulations is crucial for safeguarding sensitive information.

Not involving stakeholders and end users 

The engagement of stakeholders and end users from the project’s inception is vital. Understanding their needs ensures the selected AI tool aligns with expectations, delivering real added value. Effective communication and collaboration throughout the implementation process are key to successful AI adoption.

Poor communication and support

AI adoption extends beyond technology; it’s about empowering the people who use it. Gathering feedback from the team and providing necessary resources for effective tool utilization are essential. Continuous communication, feedback loops, and ongoing support contribute to a successful AI integration.  

Steps for Optimal AI Adoption in Retail

Are you a PIM manager, ERP manager, or data manager ? Is your role to manage and optimize the quality, consistency and distribution of product information data? Then implementing an AI solution within your company is a major project requiring a methodical approach. To ensure optimal adoption, follow clearly defined steps and focus on communication.

Step 1: Assess your needs

Every company is unique. Clearly identifying your needs will help you identify the AI solutions best suited to your context. Before adopting an AI solution, ask yourself what your real needs are:

• How much data do you need to manage? 
• Do you have any specific cleaning or categorization problems? 
• What processes would you like to automate or optimize?
• Do you need to enrich your descriptions, keywords, or titles?
• To date, how much time do you set aside for this type of task and how much do you want to reduce it?
• What kind of results/ROI do you expect?
• How can AI fit into your current workflow?

By understanding your requirements, you can better understand how AI can meet your challenges.

Step 2: Select the right technology

Choosing the right AI solution involves aligning it with your objectives and infrastructure. Integration with existing systems, precision in meeting needs, and adaptability are crucial factors. Opt for a platform designed to adapt and evolve with a company’s changing needs; particularly those platforms with flexible architecture and integration capabilities are ideal for companies looking to centralize the management of their product data. To find out more : Is your product data AI ready? 

Step 3: Train and communicate with your employees 

Implementing a technology is a process, but its full adoption by teams is a transformation. Support your employees in this transition with relevant training. After all, no matter how advanced the technology, it’s only effective if it’s used properly.  In addition, establish transparent communication on the benefits, changes, and support available to reassure and support your employees throughout this transition. Anticipate reluctance, answer questions, and highlight successes to show your teams the concrete benefits of AI in their processes.  

Harnessing AI for Growth

Artificial Intelligence is not just a technology. It is a vector of transformation for businesses. It offers incredible opportunities. Thoughtful, structured adoption of AI is a tremendous lever for growth. It’s time to embark on this adventure, armed with best practices and a clear vision. By keeping these points in mind and choosing reliable AI solutions, you can guarantee the successful and secure integration of AI into your operations. This article is a guest post from Akeneo partner, Unifai.

We’re kicking off the new year with a Built In bang – Akeneo was recently honored in Built In’s 2024 Best Places To Work Awards! Best Places to Work is an awards program that gives recognition to companies who offer the best compensation packages, total rewards and culture  programs among their peers.The annual awards program includes companies of all sizes, from startups to those in the enterprise, and honors both remote-first employers as well as companies in large tech markets across the U.S.  Built In determines the winners of Best Places to Work based on an algorithm, using company data about compensation and benefits. To reflect the benefits candidates are searching for more frequently on Built In, the program also weighs criteria like remote and flexible work opportunities, programs for DEI, and other people-first cultural offerings.  

This recognition is a testament to our unwavering commitment to fostering a workplace that values innovation, inclusivity, and the well-being of our incredible team. At the heart of our success lies a deep dedication to our people, and this acknowledgment by Built In serves as a validation of the culture we have cultivated here at Akeneo. Our commitment to providing a workplace where employees thrive has been a central focus as we strive to create an environment that fosters both personal and career growth.

ABOUT BUILT IN

Built In is creating the largest candidate generation platform for technology professionals globally. Monthly, millions of the industry’s most in-demand professionals visit the site from across the world. They rely on our platform to stay ahead of tech trends and news, learn skills to accelerate their careers and find opportunities at companies whose values they share. Built In also serves thousands of innovative companies from startups to the Fortune 500. By putting their stories in front of our uniquely engaged audience, we help them reach otherwise hard-to-hire tech professionals. www.builtin.com.  

ABOUT BUILT IN’S BEST PLACES TO WORK 

Built In’s annual Best Places to Work program honors companies with the best total rewards packages across the U.S. and in the following tech hubs: Atlanta, Austin, Boston, Chicago, Colorado, Dallas, Houston, Los Angeles, Miami, New York, San Diego, San Francisco, Seattle and Washington DC. Best Places to Work is distinct because its algorithm selects tech companies that build their offerings specifically around what tech professionals value in a workplace. Winners are announced in early January 2024. https://employers.builtin.com/best-places-to-work/    

ABOUT AKENEO

Akeneo is the product experience (PX) company and global leader in Product Information Management (PIM), enabling organizations to build and deliver world-class product experiences across every customer touchpoint through a comprehensive PX Strategy. By providing best-of-breed technology and expertise, Akeneo’s intelligent Product Cloud accelerates growth, reduces time-to-market, and gives organizations the competitive edge needed to convert browsers into buyers.  Leading global brands, manufacturers, distributors, and retailers, including Chico’s, The Very Group, TaylorMade Golf, Rail Europe, Kering, and more trust Akeneo to scale and customize their omnichannel commerce initiatives. Using Akeneo’s AI-Centric Product Cloud, companies can activate product experiences in any and all channels that drive an improved customer journey from discovery to purchase, resulting in increased sales, reduced returns, faster time-to-market, and increased team productivity. For more information: https://www.akeneo.com.  

Failing to add a high-quality image to a product listing is like lighting money on fire. 

People are visual, and this oversight can crush your conversion rate. When shopping online, we want to see engaging and informative images and videos of the products we’re going to buy. 

Digital Asset Management solutions (DAM) are the Rolls Royces of media management and what many brands turn to when it comes to handling their media assets. But how exactly do DAM systems fit into the Product Experience ecosystem? 

Let’s take a look and see how PIM and DAM systems are, in fact, complementary technologies that allow organizations to deliver top-tier experiences for customers by combining rich media assets with compelling product information.

 

What is DAM?

Digital Asset Management (DAM) software solutions are tools designed to efficiently organize, store, retrieve, and manage digital assets within an organization. These assets can include images, videos, documents, and other multimedia files. 

DAM systems provide a centralized and searchable repository, enabling teams to easily locate, share, and collaborate on digital content. By streamlining workflows and ensuring version control, DAM software can enhance productivity, brand consistency, and the overall management of an organization’s digital resources. 

With features like metadata tagging, permissions management, lifecycle management and support for API based integrations, DAM solutions contribute to a more organized and accessible digital ecosystem, supporting businesses in maximizing the value of their digital assets.

 

DAM Systems and Product Experience

DAM systems have an important role to play in the product experience ecosystem. Similar to Product Information Management (PIM) solutions, DAM systems serve as a centralized hub for content management. 

However, DAM goes a step further by focusing specifically on multimedia content. This specialization makes DAM a powerful complement to PIM, offering a dedicated repository for managing and distributing high-quality visual and audio assets. This ensures that marketing teams, designers, and other stakeholders can swiftly access and utilize compelling images and videos for product listings, eCommerce platforms, marketing campaigns, and other customer-facing materials. 

It’s important to note that DAM systems are intended to manage the entirety of a business’ media content, not just product related assets. This means DAM systems manage media needs for many departments within the organization and store assets for things like corporate communications, legal, human resources, training & development, brand assets and more. 

For organizations just looking to store product related assets, a dedicated DAM solution may be overkill in terms of cost and complexity. Many PIM solutions also include the ability to handle product-related media assets, which can be a great alternative if there’s no need for the full power of a dedicated DAM solution. 

While PIM and DAM share the overarching goal of centralizing content, they serve distinct purposes within the broader product experience ecosystem. PIM excels in managing complex taxonomies of product data, including technical specifications, marketing copy, and sustainability and compatibility information. It’s designed to adapt this information and ensure consistency and accuracy across the entire customer journey, 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. 

On the other hand, DAM specializes in the visual aspects of product representation and their efficient storage, editing, organization, and retrieval. 

 

DAM to PIM Integration Best Practices 

It’s important to note that there are many different ways to integrate PIM and DAM systems within an ecosystem. Generally, when integrating a DAM system to a PIM system, establish a single source of truth for each data field and determine who can access and manipulate what within each system. 

In most cases, the DAM system will host the media itself as well as metadata, and serve the images via a CDN. The PIM will allow teams to then combine the product record with media originating from a DAM system. 

The PIM can be considered a consumer of information from the DAM. Many modern PIMs manage media links directly from a DAM system without actually storing any physical media files.These links can still be used to generate a preview within the PIM though, allowing merchandising teams to work with a complete product record in a single place. 

It’s critical to have a strategy for linking images to products. When a product catalog can have hundreds of thousands of products with millions of images, it’s important that a consistent mapping process is in place. Establishing a naming convention is often a good approach. 

The actual mapping of images to products can happen within an integration layer, but many PIM solutions also include a rules engine that can automate this effort with no additional development work needed. 

Finally, media transformations and editing (cropping, resizing, recoloring, watermarking etc.) typically occur within the DAM system, which has more robust capabilities in this space. 

 

PIM & DAM in the Product Experience Ecosystem

In conclusion, the combination of Digital Asset Management (DAM) and Product Information Management (PIM) solutions represents a strategic synergy within the product experience ecosystem. While PIM excels in managing comprehensive product data, DAM specializes in the efficient storage, organization, and retrieval of visual assets. 

Together, these complementary technologies empower organizations to deliver a unified and seamless combination of rich media assets and comprehensive product information.