Using Technology in B2B Relationships

By Alan Henson

We often rely on our intuition to help us understand the nature and health of a relationship. Human observation is an effective way to spot behavioral changes in individuals, but it’s easy to miss incremental changes over time — changes that disrupt the polarity map between maximum customer benefit and maximum supplier/partner benefit.

Typically, an organization works with sales representatives when conducting business. These representatives generally rely not only on the strength of the product or service they’re selling, but also on the relationship with the organization’s purchasers and decision makers. Such partnerships are pivotal in conducting business. They should remain a core component of B2B relationship management — even if technological improvements threaten to make those relationships less valuable.

That said, as relationships strengthen and history and loyalty are built, it’s important to remain objective when assessing such relationships. It’s easy to fall into the trap of looking at the health or benefits of a B2B relationship through the lens of personal relationships. These dynamics can make it harder to see imbalances in the polarity map between maximum customer benefit and maximum supplier benefit, but technology can provide focus.

Managing the Polarity Map

Artificial intelligence (AI) and machine learning can help better manage the polarity map by recognizing and predicting when imbalances occur. This recognition works for both sides — it becomes a tool to help the customer monitor the supplier, but it’s also a self-assessment device that helps support value creation across the entire supply chain.

Because AI can process large amounts of data, it can spot patterns that aren’t readily obvious to even the keenest human relationship manager (and do so more quickly). In fact, according to Glen Rabie, CEO of Yellowfin, a Melbourne, Australia-based analytics and business software provider, data analysts spend only about one-fifth of their time on data analysis; they spend the remaining hours on activities of little benefit to the business, like finding and cleaning data.

Additionally, by leveraging available data within a platform along with AI or machine-learning models, companies can look across multiple facets of relationships to monitor health while identifying relationship components performing above or below optimal levels. This approach goes beyond transactional errors and looks at behavioral trends.

Are prices and volumes changing in a way that reflects expectations based on company history and market conditions? Has there been turnover, or have the relationships been long-running? The latter case could suggest a successful personal relationship worth replicating. Or it could flag a relationship to monitor because of its risk to subjectivity, where decisions made for the benefit of the individual relationship might be at odds with the business’s needs — typically an unintentional result of human interaction.

B2B Relationship Management Strategies

Technology is meant to gather insights, and a company’s ability to produce more effective relationships is contingent on how well it leverages those findings. To start putting AI-powered insights into practice, follow these steps:

1) Get insights into the behaviors of B2B relationships. How is your organization conducting and transacting business? How does it extend business, renegotiate rates and execute projects? Look at the landscape of the relationship from different data-driven perspectives.

Consider using AI to manage data and mine it for insights. According to research from Framingham, Massachusetts-based market-intelligence firm International Data Corporation, 30 percent of businesses worldwide will rely on AI to direct B2B relationship management strategies. As such, companies should start building up their AI infrastructures today so they can maintain and improve future relationships.

2) Understand the data you’re collecting. What data is captured, and how is it leveraged? What additional data is valuable — and how might that data provide an objective view of the polarity that balances the benefits between the businesses involved? Systems and models should be set up to help capture, validate and analyze this data.

Start by examining internal systems and processes to determine whether the desired behavior is incentivized by systems and processes already in place. If they are not, determine what should change. The examination is as much a technological approach as it is an audit and self-assessment. Capture lessons learned and update models to support continuous learning.

3) Share the insights. Data sharing is still a challenge, and many companies struggle to get data from customers and suppliers. Even getting data from one area to another within a company can be difficult. Nevertheless, sharing insights gained from relationships with suppliers and partners can maximize customer and supplier benefit.

By leveraging these insights, companies can put B2B relationship-management strategies in place to help monitor and manage the relationship’s health.

Technology is often blamed for the erosion of personal connections. A phone app will never replicate a face-to-face meeting, and data will never be the sole metric that determines a relationship’s value. However, such technologies as AI can contribute to a deeper understanding of business relationships and the strategies needed to nurture them.

Alan Henson is a principal at Dallas-based consulting firm Pariveda Solutions.