Data is today’s currency, but what good is a currency if it is foreign to the country in which you are living? Or differently put, what good is data without its availability, interpretation, aggregation, and unification to support decision-making processes?
Organizations with incomplete inconsistent or missing data will provide unacceptable Ecommerce experiences, and this is high risk when customers are asking for outstanding Ecommerce experiences instead. Nonetheless, not all organizations have complete, consistent product data to support it today and still need to overcome those primary data aggregation hurdles first. But when they do, they are ready to benefit from that data by using the right technology to create a unified index to power better experiences online.
In this blog, we discuss how and why a unified index is crucial for enabling relevant and personalized search results, why it is required to facilitate purchasing decisions, and ultimately why it is the foundation for delivering modern experiences in B2B commerce that enable organizations to compete digitally.
Why Digital Product Catalogs and Search Functionality Aren’t Cutting It
Organizations ready to leapfrog their competitors are those who have overcome the data availability hurdle by sourcing, creating, and aggregating the best possible body of product data. They also understand the importance of enriching and enhancing this organizational asset further because they know that complete, consistent, accurate, and standardized product data & content (including assets) are a matter of differentiation and competitive advantage. Likely, they have implemented some sort of PIM technology and more importantly created a central function of the “data champion” internally.
At this stage, changes are high, they are ready to deliver acceptable commerce experiences online by publishing digital catalogs and by implementing a strong onsite search function. Increasingly though, these accomplishments are no longer cutting it and rather become table stakes. B2B buyers demand more! Like in consumer buying, B2B buyers rely on data to make effective purchasing decisions. Yet how those decisions are made and what it takes to enable such decision making in organizational procurement process is vastly different from consumer buying scenarios.
What It Means to Support Effective Purchasing Journeys for B2B Buyers
Appreciating there are many types of organizational purchasing journeys, we focus on the most distinct two types to illustrate how the unified index is crucial in enabling both use cases (journeys) and anything in between.
On the one hand, there is the “product findability use case” representative of all the various ways in which buyers intend to explore product information and ultimately find exactly the products they are looking for with a high level of confidence. On the other hand, organizational buyers know exactly which products they are looking to purchase. We label this scenario the “replenishment use case”.
Product Findability with A Unified Index
Effective decision making requires more than presenting a list of results based on an inquiry. It requires that the presented results be instantly provided with high relevancy. But what makes search experiences relevant in the context of organizational buying? There are many aspects to consider, but just to name a few, relevant results in organizational buying are contextualized. Not all products are available to all customers.
Conversely, items may be restricted from being shipped to certain destinations. Relevant results are those items which likely need to be purchased again because they have been purchased in the past. That means they can be correlated with the purchase history and frequency. Relevant results are those that yield items that meet exact specifications based on a vast array of attribute data, facets, and data points often technical in nature (i.e., diameter, thickness, material, color, …). Relevant results are those items which have since replaced discontinued items, items which fit a certain piece of equipment, or items that are related to other items, items that are required to complete a job such (i.e., construction of a residential patio). In short, relevant items are those items which are related in complex ways.
A unified index is at the heart of such relevant experiences because it improves discoverability of different content from different sources, it allows the creation of ontologies and semantic understanding of the relationships between content, it can ingest different formats of content and extract information from documents (unstructured content) such as product specification sheets, warranty documents, manuals, CAD drawings, etc. And it can ultimately determine relevancy by ranking content based on a multi-dimensional model.
Since customers already know what they are looking for, findability is not of importance here. Yet, when it comes to replenishment as a use case, the unified index is even more critical to enabling those types of experiences that will radically set organizations apart from their competitors. Those experiences are powered by artificial intelligence and a system-driven rather than human-initiated interaction model.
By that we mean that future purchasing experiences will be system triggered rather than purchaser initiated. For some of our clients, Xngage has implemented auto-reordering agents which can trigger orders based on replenishment schedules. By adding “smarts” into the process and leveraging the unified index which can store ordering information along with product information, auto-reordering can become predictive based on demand forecasting and past purchasing patterns. Machine learning algorithms can interpret customer behaviors onsite (and even offsite) and ultimately enable suggestive selling.
Examples of such applications are system-driven product suggestions such as “frequently bought together” and “customers bought this also bought that”, etc. Even when in replenishment mode, organizational buyers can benefit from system-driven product suggestions which broaden awareness and help sellers offer more visibility into the breath and depth of their assortments ultimately driving Average Order Value (AOV) and conversion.
And best of all, a unified index paired with machine learning represents a self-learning platform that can deliver the right content (and products) at the right time while reducing manual efforts for merchandizers and marketers who historically had to maintain product relationships and associations by hand.
Data Integration for The Unified Index
Data can come from a myriad of sources: transactional data from an ERP or CRM system, open market data, PIM systems, syndicators, and so on. Some companies combine data in homegrown systems that are unstructured and unreadable, hampering their value.
Integrating these sources into a single platform for structured use and retrieval, creates one source of data from which the company can draw much more utility. It means that further down the road, it will be easier to integrate with other systems and create less friction for data flow.
Since B2B businesses often have a complex value chain, unified data can also create competitive advantage. At Xngage we believe that many B2B websites do not take full advantage of a unified view across product, inventory, customer, and ordering data. In a strong commerce solution, B2B buyers can slice product and ordering data and access order history for more than just a listing of past orders. The intelligence that could come out of organizational purchasing history (i.e., demand modelling or understanding buying cycles, inventory needs over time, etc.) is tremendous.
In many cases this can lead to better, more timely decisions on the buy and sell side, improving both procurement and automation. And this ultimately leads to better business outcomes, it leads to digital commerce success.
To discover more about what’s possible with a cloud-based unified index, read What is an index and what purpose does it serve?