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The Power of Data: How AI Creates a Shopping Experience from Product Data

16 August 2023
Alisha Janßen, Markus Schmalzl
In today's digital world, relevant, up-to-date, and complete product information is key to creating customer value. Customers need clear and accurate information to make purchasing decisions. High-quality product data helps build trust between customers and brands, increasing customer satisfaction and ultimately driving sales. Let's face it: that's what matters most in the world of commerce.

Why Is Product Information Critical to Customer Satisfaction?

It's no secret that good product information is invaluable. Successful companies understand that it's not enough to have a great product. They need to differentiate themselves from the competition and the crowd. Good product information helps to communicate the benefits and features of the product, giving potential customers a complete understanding of what they are buying. As a result, customers are generally more satisfied with their purchases, leading to higher customer ratings and positive referrals, which directly impact sales.

An essential aspect of marketing is the visibility of a product in search results, where good product information plays a central role. Search engines scan websites for relevant content and rank them accordingly. So, if your company is careful and accurate with its product information, it is more likely to be found by potential customers during their searches. This will drive traffic to your website and ultimately increase sales.

Similarly, companies must ensure that their product content complies with relevant regulations, including consumer protection laws, advertising guidelines, and general legal requirements. Failure to comply with these legal requirements puts companies at risk of legal action and penalties. Unclear or misleading information your company provides creates false expectations and disadvantages customers. Product information must be carefully reviewed to ensure compliance and accuracy. By following these requirements, companies can protect themselves from potential legal consequences and gain the trust of their customers.

Challenges in Creating Product Information

Retailers with a wide range of products face a significant amount of manual maintenance when producing high-quality product data. Cost is a major factor. The manual creation of large volumes of product information is an expensive process that does not scale well. Finding and training the right people in this area also adds to the cost. Another area for improvement is the amount of time it takes. Companies must take the time to ensure that their product information is accurate, precise, and complete. This ties up resources and increases time to market.

Inconsistency is another problem. When cross-functional teams collaborate to create product information, inconsistencies in quality often result. This dilutes brand perception, makes content difficult to understand, and confuses customers. Another challenge is that requirements and regulations can vary from country to country. Companies need to ensure that their product information is compliant internationally. Overall, creating product information is a challenge for any company. However, these labour-intensive processes can be supported by artificial intelligence.

How AI Can Improve the Creation of Product Information

Creating product information has traditionally been a time-consuming, manual, and expensive process. AI applications can help automate the creation process, scale it rapidly, accelerate product launches, and eliminate inconsistencies. Algorithms can recognize patterns in large amounts of data or objects in images and process this information. For example, items can be automatically assigned to the correct product categories in the store, or specific labels can be set to an image. In cases of ambiguity, the results can be passed on to the responsible employee for a final decision.

In the area of product data, three main factors contribute to the creation of product information:

The Journey of Product Data - From Supplier to End Customer

A lot of product information comes directly from suppliers. In theory, details such as color, weight, or materials can be obtained directly from them. However, there are few common standards, and a non-standardized file format is the least of the challenges.

The real difficulty begins when you map supplier data to the appropriate fields and attributes in your Product Information Management system (PIM). Often, the column names in the supplier data are only more or less descriptive.

Even for AI systems, finding a perfect match automatically is virtually impossible. However, they can help by filtering out implausible combinations of PIM attributes and columns or by making suggestions to customers through targeted analysis of column values. This allows many new suppliers to be onboarded quickly.

Even if it is ensured that the product information from the supplier data is correctly placed in the PIM system, the question of data quality still arises. Are all weights in grams? Is the color "sunflower gold" already mapped to the basic color "yellow"? Rarely can supplier data be used directly as it is; it often needs to be transformed to match the target values specified by the customer. Again, AI algorithms can help.

But the journey of product data doesn't end here. When populating PIM systems, it is important to remember that many SKUs (Stock Keeping Units) need to be consolidated into a single product or that existing products may already exist in the PIM system. The customer's shopping experience is quickly diminished if a search query returns a product overview page with only one item in different colors. Detecting duplicates and ensuring that new and existing data is reliably merged is also critical. The ultimate challenge is to strategically enrich existing product data with further information.

However, all the processes described above are only necessary to achieve good product data. The real achievement lies in transforming this standard information into high-quality product data that stands out and adds value to the end customer. Generating compelling product descriptions is just one of many options. Equally important is the compelling presentation and selection of product images to create the overall picture that convinces customers to buy in the webshop.

AI as Support for Data Loading

Many of the processes described here – from onboarding supplier data to presenting product data in the web shop – often limit retailers' ability to expand their assortment or act as a platform. AI algorithms can help overcome these limitations in several ways.

On the one hand, expert systems can map human decisions directly into algorithms. For example, text can be analyzed for specific patterns to help map supplier data to the retailer's PIM structure and attributes. This system does not necessarily have to make fully autonomous decisions from the start; instead, it can be a decision-support tool for humans. Relevant suggestions can be presented through dashboards, allowing humans to define a variety of mappings in a short amount of time.

In addition, AI systems can autonomously make connections and conclude existing data. For example, if SKUs are already grouped into products in a system, machine learning algorithms can identify and derive common criteria for this grouping. A wide variety of supplier catalogs can be processed without further human intervention.

If data is not yet available, there are also ways to train algorithms with manageable effort. For example, suppose a retailer wants to differentiate between clippings of mood images or technical drawings. In that case, an algorithm can be trained by labeling a few hundred images and then easily trained to label entire image databases containing hundreds of thousands of images with sufficient accuracy. Systematic errors in automatic classification can be corrected by targeted re-labeling.

Significant progress has been made in recent months, especially around large AI language models. For example, providers like OpenAI with ChatGPT offer the possibility to generate appealing product texts from the specification of product attributes – in the desired style and length. However, ChatGPT is only the most visible product at the tip of the iceberg of many different language models.

Recommended Actions for Using AI in Business

Supporting the data loading process with tools from the AI toolbox can save cost and time while improving content quality. But how do you identify the correct use case for AI in this area? Here are some recommendations:

pain points and business impact

First and foremost, it is necessary to examine each process step based on pain points and business impact. Performing an initial data analysis is essential to derive quick wins. This allows for a preliminary assessment of the overall feasibility and potential effort required for implementation. Is the data sufficient? What about data quality? These questions can often be answered quickly.

Establish key performance indicators (KPIs):

Clear KPIs should be defined before pursuing a use case further. Possible metrics or key objectives to measure success include cost reduction, time savings, improved content quality, or increased efficiency in the authoring process.

Collaboration with subject matter experts

Collaboration with subject matter experts from different departments is critical. A content writer can provide valuable insight into the content creation process, the marketing team can enrich the target definition, and the sales team can provide analysis from direct customer interactions. Different departments can help identify an appropriate use case for AI.

use of external consultants and service providers

The use of external consultants and service providers helps to accelerate the identification of use cases. Our experts have extensive experience and have completed numerous projects. They are well-versed in the latest developments in the field and can analyze complex data sets to derive valuable insights. Our data scientists and data engineers will work with you to develop customized solutions to meet your specific needs. Our experts are available to answer your questions and help you unlock the full potential of your data.

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