How Solving the Big Data Problem Can Fix B2B Ecommerce

 How Solving the Big Data Problem Can Fix B2B Ecommerce

Gartner predicts 80% of B2B sales interactions between suppliers and buyers are expected to occur in digital channels by 2025. As more global commerce moves online, B2B organizations are struggling to deliver on the same customer expectations established by the more broadly used D2C and e-tail platforms. In fact, an alarming 65% of B2B executives agreed that ecommerce is broken in their organizations, according to a recent survey by Forrester Consulting. Lack of consistent, high-quality data is the root cause of the challenges in B2B ecommerce.

Most companies surface fewer than 60% of their available products in an ecommerce environment because of the sheer volume, lack of standardization in product data, complexity in product bundling, and the siloing of product data, according to the Forrester Consulting survey. B2B ecommerce leaders need to focus on three strategies to solve these challenges, optimize their digital transformation, and capitalize on their ecommerce growth potential.

1. Prioritize Data Hygiene

Manufacturers are racing to expand their product portfolios to gain market share while also increasing customization to cater to buyer demands. These two trends are increasing the volume of SKUs and the complexity of configurable product options, making it difficult for buyers to discover the products they need from B2B companies in an online environment. Equally daunting is that the data needed for product discovery is often not designed to be readable by ecommerce systems—they’re scattered across disparate siloed databases in various formats, including spreadsheets, presentations, PDFs, and videos. Worse still, these data sets often lack standardization and are missing critical information about product attributes, such as weights and measures, or compatibility with other products. This lack of data quality makes it extremely challenging to automatically ingest complex products into an ecommerce environment, forcing companies to choose between a long and painful manual process to create a viable ecommerce offering or having no offering at all.

Recognizing this challenge, many companies have begun using AI-based platforms for extracting, enriching, categorizing, structuring, and normalizing product data from all sources. With clean and normalized product data, the relationships between the various product attributes can easily and automatically be established so that numerous configurations and product-set bundles can become discoverable and personalized. As the adage goes ‘garbage in, garbage out,’ which is why perfectly clean data is so critical for B2B ecommerce.

2. Leverage Zero-Party Data

B2B businesses are looking to zero-party data—information that customers willingly and explicitly provide about their buying needs and preferences—to create hyper-personalized buying experiences as third-party cookies are phased out. While the volume of third-party data needed to power a traditional product recommendation engine comes with the risk of poor data quality and accuracy, zero-party data comes with none of this risk and has the advantage of being easier and less time-intensive to collect. Zero-party data also gives companies a much more accurate snapshot of a buyer’s intent. When shoppers directly answer thoughtful, prompting questions about their product interests within a guided selling environment or save an ad-hoc product configuration on a website, this data becomes invaluable in presenting personalized product recommendation pages. This enhanced match rate of product recommendation to customer needs increases consumer confidence, conversion rates, and brand loyalty.

3. Apply AI Cautiously

As ecommerce platforms integrate generative AI to deliver engaging and personalized consumer shopping experiences, they’re faced with the limitations of AI capabilities. One of the biggest concerns with integrating generative AI is the risk of providing buyers with inaccurate answers to product queries. Nothing could be more damaging to a brand’s reputation than an ecommerce system that hallucinates, offering wrong, biased, or improper product recommendations. While AI is helpful to many companies in writing product and marketing copy, there’s risk in providing online buyers with an open-ended product search feature that’s sourcing its answers from an untrained large language model (LLM).

Data is the key to deploying AI to deliver perfect product recommendations along with open-ended ecommerce queries. When generative AI querying technology is only applied to perfectly clean, enriched, and contextualized product data sets, the buying experience is significantly enhanced. Make sure your AI technology adequately protects, rather than detracts, from the integrity of your ecommerce environment.

By applying effort and resources toward these three data-driven strategies, ecommerce can move from its perceived broken state to one where the buying experience reaches a new level of satisfaction.

About the Author

Jonathan Taylor is the CTO of Zoovu. He is a technology veteran with expertise in Product, Architecture, Engineering and Technical Operations and has played a key role in developing Zoovu’s technology to meet emerging industry challenges. Zoovu is the #1 AI-powered product discovery platform, helping B2C, B2B, and retail companies unlock their product and customer data to build exceptional ecommerce experiences and drive breakthrough results.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: https://twitter.com/InsideBigData1

Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/

Join us on Facebook: https://www.facebook.com/insideBIGDATANOW



Read the Full Article from Source

admin

admin

Related post