Twice a year, B2B Online brings together industry leaders to discuss the trends affecting the B2B ecommerce industry. At the spring event in Chicago, customer centricity underpinned a majority of discussions on B2B growth. This post shares three key customer-centric strategies that leading distributors and manufacturers are investing in now to future-proof their business. 

Frictionless ecommerce is essential for a best-in-class customer journey

Digital transformation has evolved to mean more than simply improving ecommerce; it’s about elevating the holistic customer experience. For manufacturers and distributors, getting ahead of the competition hinges on the ability to deliver a frictionless buying experience across every channel from inside sales to remote digital self serve, marketplaces and more. That means understanding what your customers need at every stage of their buying journey and meeting them with the most salient solutions along the way. For example, according to a recent McKinsey study, one third of buyers are opting for digital self-serve buying portals, and based on the last few years’ growth, this trend is here to stay. 

In an industry built on relational selling, relying on tech’s ability to service buyers with the same personal touch naturally raises doubt. But when done right, customers are able to move through their buying process with ease, efficiency, and autonomy, which is essential for customer satisfaction given the shift in buying behavior. 

A robust self-service buying portal also promotes operational excellence by automating non-revenue generating tasks. For example, rather than spending time looking up a purchase order or building an invoice from scratch, sales teams can focus on higher-value activities such as upselling or cross-selling opportunities that generate more revenue for the business. Once this paradigm is in place, the next step is scaling this personalized level of service.  

A centralized customer data profile paves the way for personalization

In a perfect world, all individual B2B buyers would be able to seamlessly find the right products at the right price, quickly and easily. That’s a tall order for B2B businesses that often sell the same products to businesses with varying objectives, not to mention accounting for each buyer’s contractual and geographic limitations.  

For example, some buyers may want to see technical data first, while others may prefer to see new products. These two experiences would be very different. Leading B2B companies are able to not only understand this divergence in buyer preferences, but accurately predict it and tailor the experience to the needs of each vertical, persona, and other user segment. This degree of personalization can span user experience, content and design to create different experiences all on the same site. 

According to McKinsey, companies that embed technology and personalization through the buyer’s journey are growing market share. At B2B Online, a poll of a few hundred audience members revealed that only a select few have a personalization strategy today. 

The challenge lies in pulling together the right data to build an accurate customer profile. With complicated tech stacks that span dozens of tools, many B2B companies experience issues with data silos, making it tough to develop a 360 view of their customer. Centralizing critical data sources in a clean organizational structure such as a data lake can create an end-to-end view of the customer journey that spans both online and offline channels. While the investment on such a project is non-trivial the results can be game-changing, revolutionizing B2B business operations and securing their future success.

Build for the future with artificial intelligence (AI)

It’s one thing to gather all your volumes of data and another to know what to do with it – that’s where AI comes in. AI has the potential to help businesses better manage data complexity by observing and expertly analyzing large amounts of data to predict future outcomes. The best AI is able to connect to more of your data pockets to augment your strategies for areas such as personalization, semantic search, and product grouping. 

In the context of product search and discovery, AI can surface products that share attributes with the products that customers have previously purchased such as size, color, category, etc. For example, Chief Process and Innovation Officer from Kravet Furniture, described how AI expanded their list of 15 product colors to 300+ with AI-generated synonyms, providing more choice for their customers. These types of small smart wins help foster a culture of innovation and build momentum that compounds over time to seriously bolster your business. 

While AI is wicked smart and rapidly developing, humans must still test and validate it to perform desired functions. AI doesn’t work in isolation but within the confines of existing tools, platforms and processes. That’s why it’s important to define clear use cases and success metrics for AI. What is it you want to make smarter, faster, or cheaper? 

Even so, for many manufacturers and distributors, migrating to new tools isn’t easy. Many B2B companies have been in business for 50-100 years, with longstanding customer relationships and brand reputation on the line. Moving away from legacy systems has the potential to introduce technical instability or otherwise jeopardize what’s working today, which is why many B2B companies are cautious to make big changes quickly. The good news is that new approaches have emerged to mitigate business risk.  

The best path to implementing new IT solutions

Distributors and manufacturers looking to implement a new solution generally have two options – they can either buy a hosted solution or build one in-house using open-source code. The chosen path will impact cost, capabilities, maintenance, and the search experience delivered to customers. 

For those interested in buying a hosted solution, a MACH approach — microservices, API-first-, cloud-native, and headless — has been game-changing for the velocity of innovation. With a headless architecture, businesses can slice out the systems they want to migrate into a composable stack and keep what’s heavily customized in their legacy stacks. This allows them to manage risk by upgrading a portion of the tooling without overhauling the entire stack all at once. From there, they get access to best-of-breed solutions that can reduce costs, plug into their existing tooling, and add flexibility, scalability, speed, and personalization to the customer experience.

On the other hand, open source solutions allow for much more customization but can be extremely expensive to build and maintain since the business itself would be responsible for building new features, troubleshooting any arising issues, and fielding unexpected costs. This tends to result in lengthy implementation cycles and higher average cost over time, so it’s important to weigh the tradeoffs. 

In today’s rapidly evolving environment, businesses that put the customer at the heart of their strategy are on the right track. By investing in the necessary technology and analytics, companies can audit and improve the end-to-end customer journey. This is foundational to translating relational sales to online personalization at scale and growing market share. One way to achieve this is by adopting a headless architecture to leverage AI-powered tools compatible with their business, which is a cost-effective way to meet their unique business goals. Companies that aren’t yet investing in learning how to use AI today could be missing out on untapped growth opportunities and falling behind industry peers.