How to reduce customer churn in B2B distributor sales: 4 AI-powered use cases

A foodservice distributor sales rep’s smartphone pings. It’s an alert from her personal sales analyst, offering fresh intel about one of her larger operator accounts.

The rep opens her mobile app and views the notification. The message tells her that not only has the customer’s spend declined in recent months, but that the key contact on the account hasn’t been opening emails with promotional deals as frequently as before.

In this case, the personal sales analyst doesn’t just function as an early warning system by raising those red flags, however. It also recommends specific product and pricing deals and other actions it believes, based on a deep dive into the data, are most likely to coax this customer back into full buying mode.

As on-target as the analyst’s suggestions typically are, the sales rep knows to trust these new recommendations and follows them as prescribed (even augmenting them with a timely in-person visit with her account contact). Soon thereafter, the customer, who may well have been close to defecting to a competing distributor, is not only content to stay put, but their spending returns to and even exceeds prior levels.

Battling customer churn in distributor sales

Scenarios like this don’t always end on a positive note. In fact, customer churn is a huge problem for B2B distributors, as well as other types of wholesale distribution companies.

Globally, B2B distributors experience an annual revenue loss ranging from 4.33% to 15.07% due to customer churn, according to a 2022 benchmarking report from Zilliant. For a $1 billion company, this equates to an annual revenue loss of $43.3 million to $150.7 million.

In the aforementioned foodservice distributor scenario, however, the outcome is a win for the distributor, the sales rep, and the personal sales analyst — which, if it wasn’t evident already, isn’t a human being at all, but rather a generative AI-driven tool within a customer relationship management platform.

That tool provides timely, actionable insights in language that sales reps can easily understand. What’s more, scenarios like this aren’t just hypothetical, they’re playing out in real life. AI assumes a key role for distributors and their sales teams by generating customer-specific insights, and by using its predictive powers to prescribe targeted actions around products, pricing, rebates, and promotions to aid in revenue recovery and growth.

Targeted business use cases like this reflect a growing interest in looking beyond what I call the casual application of AI, where people and businesses use it as a general-purpose tool, to use cases that apply causal AI to employ models that can reason and make choices to solve a specific business problem or otherwise create value for the organization. As companies in the distribution sector and across the business landscape are realizing, that’s how to generate solid returns on AI investments.

Today, sales management tools that incorporate causal AI are proving their value to distributor sales teams, providing them with the data-derived insight they otherwise likely would never have gleaned, along with reliable next-best actions and other forms of support that enable sales reps to spend their time on high-value work that only humans can do, like relationship building, so they can do their jobs better.

4 use cases for AI in distribution

Ultimately, as illustrated in the four use cases detailed below, AI helps sales reps better understand what’s causing churn, takes much of the guesswork out of how to counter it, and identifies the best levers to pull with specific customers.

This in turn grows the distributor’s top line by increasing customers’ loyalty and overall lifetime value.

1. Price optimization: AI harnesses the power of data science to enable sales reps to customize pricing for individual customers. Algorithms digest a huge amount of data about buying patterns, relative margin changes, cart behavior and more to make highly segmented pricing recommendations that are likeliest to move the needle with specific customers. Here, it’s important to note that price optimization capabilities like these depend on the distributor also having robust customer segmentation tools.
2. Personalize promotions: analyze and optimize rebates, promotions and other offers. As illustrated in the foodservice scenario described earlier, AI can identify potentially troubling customer behavioral trends to anticipate and reduce risk of customer churn. It can  then suggest which levers for sales reps to pull — new rebate models, promotional offers and other custom-tailored offers (such as increased credit ceilings and longer payment windows) — to give them the highest probability of reversing those trends. Here’s another area where segmentation capabilities are a must, as is the ability to analyze individual customer journeys.
3. Customized product recommendations: configure and refine value-added service to maximize customer appeal and profitability. AI can support distributors’ efforts to create appealing, sustainably profitable services (such as kitting and predictive maintenance) around the products they provide. According to IDC, by the end of 2024, 33% of G2000 companies (essentially, the world’s largest companies) will exploit innovative business models to double the monetization potential of Gen AI. Used with advanced analytics and modeling capabilities, Gen AI can provide guidance on the optimal configuration and cost of a service so it strikes the right balance between revenue and profit margin for the distributor and value for the customer.
4. Predictive customer insights: hyper-customize product and service recommendations to boost retention. Why is a specific customer no longer buying what once was one of their highest-volume products? How could the depth or width of your product catalog be adjusted to reverse a revenue slide within a specific strategically vital customer segment? AI can provide on-target answers to these kinds of questions.

Using AI in distributor sales

To get started exploring AI in a sales context, as always the best advice is to start small. Use a methodical proof-of-concept approach to evaluate how it performs in specific distributor business use cases, such as price optimization or service analytics.

Then, based on that evaluation, decide whether and how to iterate and scale into other areas of your sales operation and the broader business.

Finally, in evaluating where to start, focus less on casual AI and more on specific uses for causal AI to support human decision-making. In a sales setting, it can help your sales reps feel empowered in their work, which provides an edge in attracting and retaining top sales talent. It helps them understand exactly which levers to pull — pricing, promotions, services, products — so your customers stay put instead of straying.

Editor’s Note: This article first appeared on MDM and is republished here with permission.