ERP, PIM, DAM, MDM: To what extent do these solutions address retail sector challenges?

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ERP, PIM, DAM, MDM: To what extent do these solutions address retail sector challenges?

par Aurélien Dumont3 October 20190 commentaires

ERP, PIM, DAM, MDM… when it comes to organizing product data, there’s no shortage of tools to choose from. But are these solutions enough to address the challenges faced by brands and retailers? Here are our thoughts.

Let’s be honest: at first glance, information systems can sometimes resemble a game of pick-up sticks, a tangle of occasionally-complex IT tools. This is particularly true for retailers, who might be using multiple systems at once: ERP, PIM, DAM, MDM and more. This can get confusing. What role do each of these solutions play? Are they able to effectively facilitate information sharing among brands and retailers? What can companies use to complement these tools? Let’s take a closer look.

ERP, the all-purpose tool

Of course, not every company has access to all of these solutions. How many they use depends on the company’s size, the size of their offering, and the complexity of their processes. At the least, PMEs will typically use an ERP (Enterprise Resource Planning) solution, a generalized tool that manages subjects from accounting to HR, from order management to logistics.

Even if ERP tools are advertised as being retail-oriented, in practice, companies will often need to turn to additional solutions to manage data for the various different aspects of their products, as well as to organize all related marketing content. In addition, these tools rarely include features for exporting product data in retailers’ Excel templates or sending product sheets via the GDSN network.

The electronic catalogue, a (narrow) bridge

This is why SMEs often use electronic catalogue solutions to complement their ERP tools. These catalogues offer a way to send or receive information via the GDSN network (also referred to as the GDSN data pool), thus allowing products to be listed by supermarkets, food service companies, travel retailers, etc. With this standard, it’s no longer necessary to worry about naming product attributes or choosing a technical protocol – everything is ready to use.
However, as they’re often specialized in facilitating data sharing via GDSN, these solutions don’t (or only minimally) cover new channels such as marketing tools, apps and consumer-facing websites, or even service providers like Google. This means that companies who use these catalogues still need to duplicate their data for each of these channels. Note that these solutions all include a PIM-style tool, with each offering a different assortment of features.

PIM and DAM tools, the cornerstones of comprehensive product data

As they further develop their offering, small and medium-sized businesses often expand their information systems by adding PIM and DAM solutions. PIM (Product Information Management) tools provide a database that is used to centralize and manage all required elements of product information, such as complex product hierarchies and products organized by types of target consumers, distribution channels, etc. This provides a framework for product marketing.

The more a company develops its marketing, the more materials are created: both printed documents as well as visuals and videos for digital channels. Managing the lifecycle of these assets quickly becomes its own challenge, which can be addressed with DAM (Digital Asset Management) tools. These help companies organize this content, set up validation processes (including those with service providers), and categorize content with metadata before exporting it into the various required formats. DAM solutions also offer configurable toolkits to export data using different web protocols, but are rarely compatible with protocols specific to certain sectors, notably retail’s GDSN protocol. Compared to data pool electronic catalogues, PIM and DAM solutions are mainly aimed towards data storage and the company’s internal collaborations, offering generic methods of exporting product information. This means client companies need to configure all parameters to ensure data is sent correctly to each of their different recipients.

With one or both of these solutions, companies have access to the tools they’ll need to manage rich and complex product data. Nonetheless, they’ll still face three key challenges:

  • To ensure quicker time-to-market, manufacturers will need to correctly anticipate their retailers’ data requirements in order to provide them with the exact information they need, right from the start. They’ll also need to adapt to all of the different technical formats used to share product data.
  • For retailers, a key concern is product data quality. How can their data be verified and improved on a regular basis?
  • Finally, all businesses have to ensure that their teams remain agile, notably by enabling them to monitor data completeness and compliance and even to further develop data for a specific channel if necessary.

MDM, the company’s dictionary

For companies with large-scale operations, each subsidiary, business unit or national office will typically manage their own ERP, PIM or DAM. A few questions inevitably arise: Who really owns all of this data, and who therefore can manage it, modify it, etc.? Where is the reference data located? How can this data be centralized? To address these questions, another tool is often added to the mix: a MDM (Master Data Management) solution. This consolidates all reference data and allows for permissions to be configured (who can view the data, modify it, etc.)

One benefit of MDM tools is that they offer a way to centralize data and ensure its quality, while keeping all of the company’s systems up to date. However, like PIM and DAM tools, they don’t offer users a way to request or share their data needs with their interlocutor, track data quality based on these needs, or share product data according to retail’s business and technical standards.

Here’s where the limits of these technological solutions in a retail context can be seen:
for small and medium-sized companies, these solutions’ complexity (or cost) can render them infeasible. For both SMEs and larger companies, though these tools help them centralize and organize their business data, they don’t address the challenges that come with publishing this data in a highly-regulated and ever-changing omnichannel environment – an environment that requires adjustments to be made in an agile manner, and all published information to be monitored constantly.

Are you a small or medium-sized company?

Read about how Mémé Georgette automated their process of sharing data with retailers

Are you a retailer?

See how Franprix uses Alkemics to complement their PIM

Study by OpinionWay for Alkemics: Perspectives on Food Transparency

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Study by OpinionWay for Alkemics: Perspectives on Food Transparency

par Aurélien Dumont23 May 20190 commentaires

People in France are seeking changes to food labels, finding them incomplete, inadequate, unclear and lacking in transparency.

In France, 1 in 2 shoppers have decided against purchasing a food product because of a lack of product information! As access to information about food products and food safety have long been matters of concern among consumers in France, Alkemics was looking to highlight shoppers’ views on food labelling.

With this study, Alkemics and OpinionWay set out to understand how and why people in France search for product information, as well as find out their opinions on this information, what they expect from food labels, and how food labels influence their purchasing behaviours.

Key figures from the study

Main findings

  • People in France seek out information about food products, mainly to select healthy or higher quality foods and choose, whenever possible, products manufactured in France.
  • The presence of product information on packaging at the moment of purchase is invaluable for shoppers in France.
  • The level of trust held by consumers in France varies considerably depending on the type of information in question.

How to easily connect to the GDSN

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How to easily connect to the GDSN

par Aurélien Dumont12 March 20190 commentaires

While GS1 and the GDSN network represent a series of standards, sharing product data remains a complex subject in the current omnichannel landscape. Here are a few tips to simplify the process of adopting these standards, while using your product catalogue to accelerate your business.

E-commerce pure players (such as Amazon and CDiscount), food information apps (such as Yuka), platforms like Google Manufacturer Center and third-party services (like Swaven and Clic2Buy), and of course, buying groups and retail stores: all of these are part of a new omnichannel context in which new channels are emerging, but are rarely replacing existing channels — just complexifying them. This can prove to be a struggle for brands, who find themselves faced with more and more challenges in the process of sharing their product data with retailers.

This suggests a need for a standardized approach to sharing product data. In fact, this standard already exists (the GDSN) but the various different aspects of this can sometimes be poorly understood. Let’s be honest, the term itself can sometimes result in confusion: “GDSN” (Global Data Synchronization Network) only refers to the network that is used to transfer product data between a manufacturer and a retailer.

In practice, despite the technical terms often used to describe it, the network is very similar to tools that we use every day, such as email. Within this network, each actor has their own address: in this case, it’s the GLN (Global Location Number), which can be compared to an email address. Each item also has a code, the GTIN, which corresponds to the bar code on the product packaging. This would be the subject line of our email. The body of the email would correspond to the product page, which also has a specific format, established by the GS1 organization.

As GS1 has defined a set of standards for sharing product data, what role does a platform such as Alkemics play in this context? Firstly, it represents an access point for the GDSN network. This network uses Internet protocols, but that doesn’t mean that it is public. To share data, you need to use an accredited gateway such as Alkemics, which is a GS1 certified platform.

Secondly, Alkemics makes it easier for brands to adopt GS1 standards. Using the platform, product pages can be exported in GS1 format with just a few clicks. Finally, though developing standards is not Alkemics’ primary mission — that’s the objective of GS1 — like all contributors, they can test new product attributes that are not yet part of the standard and suggest them to GS1, thus playing a role in the large-scale process of standardization.

Have all retailers decided to use the GDSN network to share product data?

No, not really. In practice, each company chooses based on their existing IT resources, their organization and their processes. Many companies still use Excel. More precisely, each of these retailers have defined their own format for sharing product data using Excel. Or rather, their own formats, as each retailer will have various different listing processes, each with its own specific format. They may have one Excel format for nationally listed products, another for promotions, another for regional buying groups, etc. Unsurprisingly, while large companies may have the necessary resources to manage this complex technical challenge, it can quickly become a nightmare for small brands. And there’s a good reason for this: as the number of channels increases — along with the formats used to share data — it may become necessary to maintain multiple versions of a product catalogue and use various different software solutions. Fortunately, the aim of solutions such as Alkemics is to prevent the need to have many different versions of these catalogues.

How can you prevent your product catalogue from becoming a work-intensive technical challenge, and instead turn it into a resource that can boost your business?

Here are 5 key steps:

  1. Whether you’ll be entering information manually or importing data in bulk, the key is to use a flexible solution that is well adapted to the company’s needs, tools and processes.
  2. Manage various different types of data
    Have you entered the necessary logistics data? Have you added additional content (such as visuals and videos) to highlight your products? Your catalogue must offer a complete and scalable template for these different types of data.
  3. Verify data quality
    Check if your product data is compliant with your retailers’ requirements, as well as with regulatory requirements (such as INCO, CLP and CELEX). To do this, it is extremely helpful to use a system that automatically detects if these fields have been filled in, while also checking for errors.
  4. Publish your product data
    Whether you’re using a tool to generate the Excel files required by your retailers, or connecting directly to the GDSN network, improve efficiency by publishing product data from a single catalogue.
  5. Steer your operations
    Reply to retailer requests, verify that the necessary data has been entered, etc. Centralizing all of this data in a single location simplifies this process and ensures accuracy.

How can you determine if a solution is truly able to address these needs? Here are a few questions to consider:

  • Does the solution require software to be deployed on the site, or does it have a quick online set-up?
  • Is the solution user-friendly, and does it offer a simple way to enter information and verify this information quickly?
  • Can users search for products by GTIN, EAN code or brand?
  • Do product page templates take into account regulatory and retailer requirements?
  • Is the data quality verified as soon as the information is entered, and before it is sent?
  • Can errors be easily identified? Are there tools to detect errors and automatically suggest corrections?
  • Does the solution address all of the different ways of sharing data (GDSN network, Excel, pure players’ APIs and connections to professional and consumer apps)?

In summary, connecting your product catalogue to the GDSN network is an important task, but it is not the only element to take into account while looking for the solution that will best meet your needs. In our current omnichannel landscape, brands need to connect to new channels in addition to the GDSN, ideally without needing to deploy a solution or a specific version of their catalogue for each of these channels.

How artificial intelligence helps speed up collaborations between retailers and brands

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How artificial intelligence helps speed up collaborations between retailers and brands

par Aurélien Dumont8 March 20190 commentaires

When supported by high-quality data, artificial intelligence is able to offer pertinent suggestions, leading to valuable productivity gains for both retailers and brands.

Many different types of information contribute to the overall quality of product data, including product category and packaging information, labels, warning symbols, allergy information, storage information, etc. It can seem to retailers that it is becoming “humanly impossible” to verify the accuracy and completeness of product data. This is because there is such a large volume of data to check, and the complexity of the rules that need to be followed presents even further challenges. In addition, in an omnichannel context, this verification process (and any necessary corrections) need to be completed as quickly as possible – almost in real time.

If the task is becoming “humanly impossible”, how can companies accomplish it anyways? Initial approaches to the challenge are based on a heuristic approach. Concretely, this means configuring a series of rules for these verifications. Using tools such as regular expressions, it is possible to accomplish tasks such as pairing the word “wheat” in a product’s composition with a note about presence of gluten, in adherence with regulations. The problem is that these rules can quickly become complex, and adding another language means they all need to be reconfigured.

Detecting and improving the formatting of allergens in the ingredients text field

Machine learning is becoming increasingly powerful 

This is why Alkemics, while still taking a heuristic approach for certain categories of data, is investing a great deal in in Artificial Intelligence (AI), and more specifically, in machine learning. Machine learning algorithms can be considered to be self-learning: they are able to identify “patterns” based on a large quantity of data. This means that in contexts where a heuristic method becomes difficult to maintain over time, machine learning becomes more efficient as more and more verified data becomes available.This means that the pertinence of the recommendations given by machine learning algorithms directly depend on the quality and the representativity of the data provided. Unsurprisingly, the Alkemics platform, with its hundreds of thousands of products, is an ideal environment for this.

Nonetheless, it is still important to note that artificial intelligence should not be thought of as a tool that “knows the answer”, but instead as a solution to increase productivity — to a significant degree. It is not the AI that decides to put a label on a product, the user still manages and remains responsible for the data. This being said, the suggestions provided by these machine learning algorithms make it possible to accomplish a task that is seemingly becoming “humanly impossible.”

With this tool, retailers are able to browse their product catalogue by manufacturer, brand, category, lifecycle, etc. to analyze data quality. An overall data quality score is calculated based on the descriptions, regulatory data or even media required by the retailer. For product lists, as well as on each product page, AI indicates whether the data is satisfactory, incomplete, or if a correction is required. This is where productivity gains begin, thanks to a clear view of data that needs to be entered or corrected, and these gains only increase throughout the correction process.

Recommended symbols in the "Regulatory Information" section

A valuable tool for bulk data management

When they encounter incorrect or incomplete data, retailers can send a notification to the relevant manufacturer with just a few clicks, with the help of AI. This ready-to-send message includes suggestions to facilitate the process of bringing the product data into compliance. AI also assists the manufacturer who receives the message: algorithms make suggestions for the fields that need correcting, based on the product or the information that has already been filled in.

The productivity gains offered by this AI-assisted process are even more evident when it comes to large-scale data management. One example could be if a retailer received a formal notice from a consumer protection agency, requiring them to bring information about a specific allergen into compliance as quickly as possible — with the help of AI, the retailer can send a standardized note, including suggestions for corrections, for all of the affected products. All of the brands concerned by this will receive this message and be able to act quickly, and retailers can track the progress of these corrections.

AI and retail innovations

AI is already assisting users, both retailers and brands, throughout every step of the process, beginning with the creation of the product page. Once a product’s commercial name has been entered, algorithms suggest a product category, net contents values, suggested labels, regulatory formats for allergens, information about the types of diets that the product is suitable for, etc. This tool, which is able to take advantage of the thousands of available product data points, assists the user in the process of validating all of this information.

These suggestions offer significant time savings, greatly reducing the time required to create product pages that are compliant with both regulations and retailer requirements, while also improving data quality. And this is just the beginning. Multiple AI suggestions can already be accepted at the same time. Very soon, simply entering a product name will enable the tool to predict content suggestions for the various different fields that need to be filled in, such as product category, composition, allergens, labels, etc. This will also optimize analyses regarding consumer habits, for increasingly customized offerings.

NEW ON ALKEMICS – All of these suggestions will now appear at the top of each product page, allowing you to quickly view and validate them. Ensure high-quality data with just a few clicks!

Working together: Creating a consistent brand experience through collaborative data management

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Working together: Creating a consistent brand experience through collaborative data management.

par Aurélien Dumont9 February 20180 commentaires

With the advent of online, and especially multi-channel retail, it is essential for companies to digitize their marketing data and adopt an effective approach to managing digital content, in order to ensure a consistent brand experience across all channels.

This can notably be done through streamlined and effective management of the brand’s content and other data (Digital Asset Management). Centralizing, managing and maintaining this data can prove to be an operational challenge for companies, both internally, within the companies’ various different departments, as well as externally, with its collaborators and communication channels.

This also presents companies with another significant challenge: delivering a consistent brand experience in a complex environment.

For this article, we spoke with Chen-Do Lu, Marketing Manager at Alkemics, and discussed how the collaborative aspects of content management tools are key to addressing these operational and marketing challenges:

Nowadays, why is a collaborative approach essential to managing data more effectively in companies?

Digitization was first implemented by companies to address department-specific needs (logistics, quality control, analyses, marketing, etc.) using specialized solutions, each of which stored and managed data and content in their own ways. However, having their data spread across different types of software and formatted in different manners prevented companies from being able to take full advantage of the opportunities offered by new technologies. This is because it was challenging for company’s teams to access this data, keep it up to date, and ensure quality control.

Nowadays, this content is extremely important, especially for marketing departments, as it is the foundation of a company’s online visibility and product listings. This means that companies need to centralize their data, as well as use collaborative digital content management solutions that both complement the department-specific tools, used daily by different teams to keep the data up to date, and are specifically designed for omnichannel content sharing.

Previously oriented towards training needs (such as digital learning and MOOCs) and internal communications in companies, more and more collaborative platforms are becoming available that are designed to address more operational needs. These models no longer follow a top-down approach when it comes to data, but rather a horizontal, team-focused model, in order to make the data management process more collaborative.

By offering all contributors a single, connected communication channel, business processes and organization are optimized, providing a significant increase in efficiency and operational excellence. Moreover, more efficient data maintenance, sharing and verification processes, as well as the use of collaborative platforms for the digitization of these processes, allows them to be steered more effectively — companies can view indicators on the maturity of these processes, including completeness, quality, compliance, how the data is used, etc.


Can improvements to how operational data is managed also benefit the rest of the company’s ecosystem?

This approach can certainly be used by the company’s entire network. Collaborative tools and platforms link the network together, providing the entire ecosystem with a consistent way to to share data.

The increasing number of collaborative platforms available, as well as the ability to link these together through the use of APIs or web service bindings, bridges the gap between companies and their collaborators, facilitating the entire collaboration process. Some software companies, such as Alkemics, are truly creating a collaboration network that connects a wide variety of different actors.

This approach fits in perfectly with new developments in omnichannel business, in which different collaborative tools, connected to external platforms, enable companies to enter data in a single location and share it with their entire ecosystem.

This collaborative model takes a more qualitative approach, creating a bridge between data and those that use it. For example, these platforms facilitate collaborations between brands and retailers in the mass-market retail sector, notably by making it easier for them to collaborate on e-commerce projects. With these types of projects, managing and ensuring the consistency of online content ensures better visibility, and most of all, a seamless brand experience.

At the same time, though collaborative tools are breaking down the barriers between companies and their collaborators, the implementation of these solutions also enables each actor to focus on their core business. Once again taking the example of the grocery retail market, Alkemics enables retailers to collect brand content and data in real-time. This allows companies to enhance their customer experience, providing information and assistance to customers and improving customer engagement. Suppliers once again have the ability to steer their brand experience, while ensuring that retailers also benefit from this.

What impacts do these initiatives have on customers’ brand experience?

As we mentioned before, collaborative content management platforms are primarily designed to share, verify and maintain company or brand data, comprehensively and across all channels. Using a single platform for both data entry and management ensures the consistency of the brand’s digital content.

Over time, this approach to digital content management, which we could refer to as brand consistency, will strengthen the brand experience. A concrete example of this is a consumer’s ability to view identical product pages on all of the e-commerce sites that they visit, regardless of which device that they are using.

This strategy will also facilitate communications throughout the company, breaking down the arbitrary separation between online and offline activities. Having a more complete understanding of products and consumer behaviour will make it easier for companies to determine which channels to sell their products on, manage stock in a consistent and connected manner, and better understand consumer habits and trends.

Consistent data management is therefore one of the key drivers of a seamless brand experience. Through Digital Asset Management, new practices are also emerging. Effective data management also offers companies the ability to improve their products’ rankings on e-commerce sites, via a sort of specific SEO for online stores. This is truly becoming a form of digital merchandising, in which creating effective product listings will improve their visibility on websites, whether via a search by product uses or characteristics, or through filters and navigation elements.

In summary, using a collaborative data management platform enables companies to increase their productivity, both in internal processes and communications with collaborators. These tools ensure more effective data management, as well as consistency throughout the process. Over time, this consistent and multi-channel approach to sharing data strengthens and supports the brand experience, ensuring it is effectively managed across all channels. All of the practices mentioned here are the first steps towards adopting a Smart Data approach, in which effectively using and managing data strengthens the brand experience. What’s the next step? Go beyond optimizing the multi-channel data sharing process and the brand experience, and work on drawing insight from consumer data. In short, develop a Big Data strategy.