AI to leverage your Product data quality
Product data quality is related to heterogeneity
One of the most challenging issues you may encounter those days with product data quality is its heterogeneity.
As you may know, product data is very important because the final consumers’ choice fully relies on its desirability, accuracy, and completeness. This is what drives business and it stands at the end of the conversion funnel.
30% of shopping cart abandonments are related to poor product description.
(source : Shotfarm)
This means that ignoring the importance of this data consistency and quality equals losing engaged prospects. It should also be noted that probable errors in the product data sometimes end up with legal issues and have a direct impact on your brand image. 87% of consumers say they would be unlikely to make a repeat purchase with a retailer that provides inaccurate product information. And that is understandable because 52% of consumers return their online purchases because of poor product content. (Source: Shotfarm)
If you are a retailer, marketplace, or a multi-products brand, and you receive a lot of files and data from your suppliers/vendors and other internal teams creating product data, you are definitely exposed to those issues.
Your product data is collected from different suppliers/vendors and each of them has his own product taxonomy.
Impacts of poor product data quality
The following are the five key effects of poor data quality:
– Ineffective decision-making: Data of poor quality leads to poor decisions. A conclusion is just as good as the data it is based on, and vital decisions based on low-quality data can have negative impacts. This is one additional reason to ensure that your data accurately reflects reality.
– Heavy team corporate inefficiencies: Poor-quality data causes chaos on corporate operations that rely on data, from reports to ordering products, and everything in between. Instead of focusing on essential activities, these inefficiencies may result in very costly repetitive efforts validating and correcting product data inaccuracies.
– Skepticism: Data of poor quality promotes distrust. Especially in businesses where standards restrict interactions or transactions with specific customers. Maintaining high-quality data might be the difference between complying with regulations and paying millions in fines. Time, money, and reputations can be wasted if the data is incorrect, negatively impacting your organization and reducing client confidence. This is what happens when uncertain or incorrect product descriptions or attributes are displayed on your website/marketplace.
– Decreased business opportunities: A company could lose out on a potential chance for new product development or client requirements, which a competitor with a better knowledge of data may seize. Granular data makes you able to segment your products. Missing granular data and sticking to just macro-categories, you won’t be able to find out micro trends and will miss a lot of opportunities.
– Lower revenues: In a variety of ways, low-quality data might result in income loss. Consider products that are not sold because the underlying product data is wrong or incomplete. Inaccurate targeting and communications can result from bad data, which is especially problematic in multi-brand retailing.
Good quality data is a valuable commodity that is not only desirable but also required for fraud prevention, performance evaluation, financial management, and effective service delivery. While data quality is vital, it is sometimes hastily overlooked to complete all of your other tasks. Give your company’s data the attention it deserves so you can enjoy the benefits.
On the one hand, there are organizations that keep shared product data the way it is. And it leads to poor data quality and missed business opportunities due to wrong/impossible proper analysis. On the other hand, some organizations put a lot of effort to homogenize this data according to a unique proper standard. However, it is a long manual process that slows products’ go-to-market and most of the time, impossible to do humanly.
Product data quality issues
That is why AI and NLP techniques today have been improved to help automating this normalization process and open closed doors for businesses.
Who comes across with this problem?
Everyone dealing with voluminous product data: category managers, buyers, and product managers followed by data analysts, marketers, and all jobs dealing with product data analysis or creation. Those business profiles often don’t have advanced data technical skills as data scientists. When they receive product data, they can be exposed to these issues:
– Wrong columns matchings
– Wrong values
– Missing information in columns
– Heterogenous naming conventions. For example, “Starbucks” can be written in 4 ways: starbucks, starb, starbx, strbx
– Unstructured data and structuring it is however very time-consuming, repetitive, and with low added value, but MANDATORY for data quality. And all these operations are still only possible for small data amounts.
AI and NLP at the heart of the data quality process.
AI is often correlated with the use of in-depth analysis/prediction use cases. Nowadays, AI-based approaches to data preparation are rare. Apart from the automatic change of date formats and time slots, AI remains very focused on the creation of models for the use cases themselves (clustering, cohort analysis, dynamic pricing, etc.) which basically need consistency and proper.
As a result, NLP and AI are also used today to automate product data normalization. New technologies have made it possible through natural language processing which deals with textual data effortlessly improving product data quality becomes a reality. Updated algorithms enable now to understand semantic links between words, context, and word sequences.
However, today, people who have the skills to use those NLP and AI approaches properly are very technical people contrary to product experts.
If the product managers and business experts continue doing the process manually, as the volume of product data will explode, this already painful process will even become impossible humanly. Therefore, they need to automate and make this process smarter and get rid of putting so much time doing it manually. To reach this goal, they tend to hire technical people to do it internally or outsource it, STILL IT COSTS THEM A LOT.
Thus, at YZR, we made this issue our main mission. So we worked to create a no-code data tool to automate the product data normalization process, combining AI and human collaboration. The platform will divide by 10 the time spent on your product data normalization but also makes product data reliable. That means more accurate, consistent, and efficient product data, while you enjoy doing the task!
Therefore, based on their own testimonials, our clients choose YZR to:
– Correct and standardize data effortlessly thanks to our NLP technology
– Add custom categories and detect significant attributes automatically
– Ensure proper products’ description and categorization
– Fasten products’ go-to-market
– Make product segmentations more reliable
– Enable granular analysis to find new opportunities for business
And this became possible thanks to a hand-in-hand collaboration between human expertise and advanced NLP technologies.
Human & AI at YZR
So for all those use cases and more, don’t hesitate to contact us for further information. We will be very pleased to show you our no-code data tool and hopefully make the painful normalization process a new child play.