Product Data Unification and Standardization - A critical process for successful B2B Commerce initiatives.
Why data unification and standardization are foundational for superior digital customer experiences in B2B
Data unification and standardization is the process of ingesting data from various operational systems and sources and combining them into a single source of truth. How? By performing transformations, schema adaptations, deduplications, and general cleansing of records to standardize and achieve data completeness, accuracy, and richness. And by performing quite a bit of manual labor, that's still holding true.
While this is a general process applicable to likely various data types throughout the organization, B2B sellers seeking to offer their customers superior e-commerce experiences, need to embrace this process specifically when it comes to Product Data.
Business leaders understand the importance of leveraging quality data for their company’s success. Often several factors prevent organizations from successfully managing this process and the reasons are manifold. Important to note: it is not the lack of technology options, but rather it is the lack of focus on the necessary organizational changes that are required to establish an internal data aggregation and enrichment discipline.
As a digital services firm executing and delivering content & commerce solutions for leading manufacturers, distributors, and wholesalers, we see challenges with this process specifically in relation to Product data and product content assets (such as images, video, specifications sheets, manuals, etc.).
In this blog we explore data unification and standardization challenges when it comes to product data and offer practical guidance how to tackle this important aspect of successful digital execution. In our experience, good data unification and standardization of product data and content begets good financial outcomes because there is a direct correlation between the quality of your product data, attribute richness, meaningful descriptions, etc. and the performance of onsite and offsite search results.
Many organizations are uncertain about how to carry out this journey. Turning to technology is necessary but not sufficient to succeed with this process. Organizational aspects and readiness to build the necessary supporting infrastructure comprised of people, process, and platforms is required to cope with this vast scope. This article provides guidance on how organizations can begin mastering the critical aspect of building a rich, complete, structured, and well optimized body of product data and content optimized for digital commerce in B2B. We will also discuss how such optimized product information can then be leveraged with powerful merchandizing and “searchandizing” strategies as offered for instance by the search platforms or how it can be brought to live throughout visual purchasing experiences.
Sourcing and Standardizing of Product Data and Content
Depending on whether your organization is a manufacturer or distributor, product data is the ultimate currency to support any sales efforts offline or online. However, specifically for online sales, it is paramount that rich and “web-ready” product content is being created, curated, sourced, and aggregated. While manufacturing organizations often originate product information and syndicate it, distributors source it from a variety of suppliers. We do not differentiate further between the two models and the nuances between manufacturers and distributors in this post, but instead the key challenges around unifying and standardizing this product data and content are similar:
If you are unsure of where to start when looking at PIM platform choices and operational needs, Xngage can assist in finding the right platform and can guide the operational transformation that is required to ensure appropriate resources are in place to handle the product information challenge from a people-and-process perspective.
Many other aspects regarding sourcing, cleansing, and aggregating high-quality product content need to be considered. The B2B experts at Xngage can consult and guide you on this immensely important pre-requisite for a successful e-commerce initiative in B2B.
Benefiting from and Using High-Quality Product Information
Let us turn to how organizations can benefit from investing in the process of aggregating high quality product information described thus far. Again, going through the effort of creating high-quality product information is a pre-requisite for building superior experiences. When it comes to powering their e-commerce presence, sellers can achieve great levels of differentiation in the customer experience. A lot of this experience is directly tied to having done the leg work outlined thus far. The other aspect of superior experiences hinges on the tools and technologies employed in the delivery of content & commerce solutions. Presenting a digital product catalog online has long become table stakes: organizations must push beyond manual product exploration (such as category drill-down or brand-based product browsing) giving customers informed, relevant visibility not only into the catalog but beyond.
What powers relevant digital customer experiences in B2B
There is much talk about the importance of the customer experience. Even software vendors lead with the idea of user-centric design and 'digital experience platforms'. Yet when it comes to B2B commerce, truly understanding what such experiences look like will yield a differentiated digital presence for manufacturers and distributors alike.
In working with many diverse clients, Xngage has found that in B2B commerce, the most relevant digital customer experiences are intelligent, personalized, and predictive. Platforms such as Coveo and Optimizely (formerly EpiServer) can help make every digital experience a relevant one. Let us unpack what that means in the context of B2B commerce.
This can be achieved with search technology that goes beyond basic retrieval of information using scoring models. True differentiation is achieved by search that can yield relevant results, based on context. This may include boosting of products a customer has frequently purchased, search results that are ranked based on real-time user behaviors, and search results which understand intent.
Another very important area of intelligent search is search which can yield product results, content results, and deep-indexed assets (e.g., documents) results. B2B buyers are looking for specific information and at times such information is contained in product spec sheets or product manuals, warranty certificates, or other unstructured content.
Finally, intelligent search is about a learning system which can, over time, understand purchaser intent and product association and relationship. B2B customers often are looking for technical details or specific fit of a product or part into a larger device or piece of equipment, they may be looking for a certain technical product specification to meet construction job requirements, etc. Hence, detailed search, also with phrase matching, allows customers to be specific is mandatory and goes beyond short, exact-match queries.
Search results which take into consideration restriction rules and product assortment constraints are personalized search results in B2B. Search results which can correlate product availability (inventory) to influence ranking are relevant B2B search results. This is equally true for a single global inventory or even multiple local (branch-level) inventories. Customers in B2B need to understand which product they can procure from where and when.
If search is context aware and understands who is querying, relevancy can be increased.
AI-powered product recommendations can promote (and surface) products which are contextually relevant based on the current product detail page, or based on cart contents, or based on the purchase history, or the click-path through the site, or some combination of all of it. Some platforms, such as Coveo, even go so far to allow the seller to chose the appropriate recommendation model by leveraging algorithms based on interest, frequently viewed, purchased together, popular items, and more. Important in this context again, is the ability to create a unified index allowing the technology to derive its “smarts” based on information contained across different sources and systems to serve the right recommendation at the right moment.
Finally, predictive experiences are those that are informed by experiments and by what works best. For instance, it is now possible to use A/B testing within product recommendation engines to understand what types of up-sell or cross-sell strategies yield the biggest commercial impacts or which product mix yields the best margin impacts based on cost structures and availability. With our partner Optimizely, we can deploy experimentation and thus allow sellers to measure and optimize the buying experience.
Unified and standardized product data is the core of great B2B experiences. B2B businesses often have a complex value chain, and this unified and standardized data index across products, customers, orders, and assets can create competitive advantages for organizations. The ones that understand this and leverage it will set the pace for what all superior B2B experiences look like, and gain sales and loyalty as a result.
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