AI is taking on a key role for distributors and their sales teams, generating customer insights and using its predictive powers to define targeted actions on products, pricing, discounts and promotions to help capture and grow revenue.
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A food service distributor’s smartphone pings. It’s an alert from her personal sales analyst, offering new insights into one of her biggest accounts.
The representative opens her mobile app and displays the notification. Her message says that not only has the customer’s spend decreased in recent months, but the key contact on the account isn’t opening promotional emails as often as before.
In this case, the personal sales analyst does more than just act as an early warning system, raising those red flags. It also recommends specific product and pricing offers and other actions that it believes, based on a deep dive into the data, are most likely to get that customer back into full buying mode.
As targeted as the analyst’s recommendations usually are, the sales rep knows she trusts these new recommendations and follows them as prescribed (even augmenting them with a timely personal visit with her account contact). Soon after, the customer, who may well have almost defected to a competing distributor, is not only content to stay put, but their spending returns to, and even exceeds, previous levels.
Combating customer churn in distributor sales
Such scenarios do not 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 annual revenue loss ranging from 4.33% to 15.07% due to customer churn, according to Benchmark Report 2022 by Zilliant. For a $1 billion company, that equates to an annual loss of $43.3 to $150.7 million in revenue.
In the aforementioned foodservice distributor scenario, however, the result is a win for the distributor, the sales representative, and the personal sales analyst — who, if it wasn’t already obvious, is not human at all, but rather genetic artificial intelligence-driven tool in a customer relationship management platform.
This tool provides timely, actionable information in language that sales reps can easily understand. Plus, scenarios like this aren’t just hypothetical, they happen in real life. AI is taking on a key role for distributors and their sales teams, generating customer insights and using its predictive powers to define targeted actions on products, pricing, discounts and promotions to help drive revenue recovery and growth.
Targeted business use cases like this reflect a growing interest in looking beyond what I call carefree application of artificial intelligence, where people and businesses use it as a general-purpose tool, to use cases that apply causation AI to use models that can reason and make choices to solve a specific business problem or otherwise create value for the organization. As companies in distribution and across the business landscape are realizing, this is how you can generate consistent returns on AI investments.
Today, sales management tools that incorporate causal AI are proving their value to distributor sales teams by providing them with insights derived from data they would otherwise likely never have collected, along with reliable next best actions and other forms of support that enable to sales reps to spend their time on high-value tasks that only people can do, like building relationships, so they can do their jobs better.
4 use cases for AI in distribution
Ultimately, as seen in the four use cases outlined below, AI helps sales reps better understand what’s causing frustration, takes much of the guesswork out of how to address it, and identifies the best levers to pull with specific customers.
This in turn increases the distributor’s top line by increasing customer loyalty and overall lifetime value.
- Price Optimization: AI harnesses the power of data science to enable sales reps to tailor pricing to individual customers. Algorithms digest vast amounts of data about shopping patterns, relative margin changes, cart behavior and more to make highly segmented pricing suggestions that are more likely to move the needle with specific customers. Here, it is important to note that price optimization such capabilities depend on the distributor also having strong customer segmentation tools.
- Personalization of promotions: analyze and optimize sales, promotions and other offers. As seen in the food service scenario described earlier, AI can identify potentially worrying trends in customer behavior to predict and reduce the risk of customer churn. It can then suggest what levers sales reps should pull — new discount models, promotional offers and other customized offers (such as increased credit limits and longer payment windows) — to give them the best chance of reversing those trends. Here is another area where segmentation capabilities are essential, as is the ability to analyze individual customer journeys.
- Customized product recommendations: shape and improve value-added service to maximize customer engagement and profitability. AI can support distributors’ efforts to create attractive, sustainably profitable services (such as kits 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 leverage innovative business models to double Gen AI’s monetization potential. Used with advanced analytics and modeling capabilities, Gen AI can provide guidance on the optimal configuration and cost of a service to achieve the right balance between revenue and profit margin for the distributor and value for the customer.
- Predictive customer information: hyper-customize product and service recommendations to boost retention. Why does a particular customer no longer buy what was once one of the highest volume products? How could the depth or breadth of your product catalog be adjusted to reverse a revenue slide in a particular strategically vital customer segment? Artificial intelligence can provide on-the-spot answers to such questions.
Use of artificial intelligence in distributor sales
To begin exploring AI in a sales context, as always the best advice is to start small. Use a methodical proof-of-concept approach to evaluate its performance in specific distributor business use cases, such as price optimization or service analysis.
Then, based on that assessment, decide whether and how to iterate and scale to other areas of your sales activity and the broader business.
Finally, when evaluating where to start, focus less on casual AI and more on specific uses of causal AI to support human decision-making. In a sales environment, it can help your sales reps feel empowered in their work, which provides an advantage in attracting and retaining top sales talent. It helps them understand exactly which levers to pull — pricing, offers, services, products — so your customers stay put instead of drifting away.
Breaking the boundaries of wholesale distribution begins HERE.
This story also featured in The Future of Customer Engagement and Commerce.
