By David S. Bauders, CEO SPARXiQ
“If they knew better, they’d do better.”
With novel applications of AI coursing throughout the economy, we are also seeing impressive advancements in the world of Industrial B2B sales. As a multiplicity of new data sources and tools unlock new insights, sellers can improve their time allocation, productivity, organic revenue growth and net profitability.
Historically, a Wild West approach to sales — “Leave ‘em alone and let ‘em sell” — meant that sellers were highly reactive or pattern-based in their call planning and time allocation. With little insight into buyer changes in supplier preference or supplier position, call planning (if planned at all) was based on customers’ geography (travel logistics), sales revenue, or gross margin levels. This scattershot approach to sales resource allocation produced a random distribution of profit-winning and -losing sales calls. With up 2,000 hours per year per seller available for sales calls, the scattershot approach leaves a lot of wasted time, money, and underserved, high-potential accounts. SPARXiQ analysis of sales data shows up to 30 percent of sales calls have a negative or zero impact on expected net profitability.
The advent of buyer purchasing platforms provides anonymized source data to help machine learning models predict supplier share of wallet and trends, customer engagement and loyalty, price sensitivity, and upsell and cross-sell opportunities. These insights can support tools that optimize the use of sales effort. On purchasing platforms, industrial B2B buyers set up and connect all their regular suppliers to streamline and automate requests for quotes and purchasing.
The platform thus captures the wallet share of each supplier and the relative pricing of each supplier in each quote and transactional line item. With the platform providing the source of truth as to customer engagement, wallet share, cross-sell/upsell opportunities, and pricing, AI models can infer and predict the same metrics from a single distributor’s own quote and transactional dataset. As point-of-sale datasets are aggregated, the learning models further improve in scope and accuracy.
It’s extremely important for sellers to understand their supplier position with their customers. The typical Industrial B2B buyer purchases from three to five suppliers. Interviewing buyers and analyzing anonymized buyer data from purchasing platforms, we see five major supplier positions emerge:
- Primary Supplier: A dominant share of wallet with high customer engagement, market-basket diversity, across a diverse range of transaction types.
- Secondary Supplier: A strong but not dominant share of wallet with strong customer engagement, across multiple transaction types; the “backup supplier.”
- Tertiary Supplier: A fill-in supplier of common products with low engagement, intermittent purchases, and lower market-basket diversity.
- Specialty Supplier – Niche: A dominant share and engagement of a narrow range of products across a diversity of purchasing situational types.
- Specialty Supplier – Project: A strong share of wallet and market-basket diversity in a narrow range of purchasing situational types, such as large projects, buyouts, etc.
These ML-based assessments of supplier position, trained from actual purchasing data, can be applied to a distributor’s own POS data to divine the likely position, health scores, and engagement scores of all their customers.
There are, of course, major implications for growth and profitability from the supplier position a seller obtains. In addition, there are prescriptive actions, such as sales calls or pricing moves, that can predictably improve customer engagement, supplier position, and profitability. Hence, AI tools enable prescriptive call plans that optimize sales productivity and profitability.
Furthermore, AI trained on purchasing platforms helps sharpen price optimization tools. Historically, price optimization tools relied on difficult calculations of price elasticity to predict price sensitivity. While there were additional algorithms to replace or improve upon elasticity-based calculations, price optimization remained an extraordinarily valuable but imperfect solution for supporting seller revenue growth and profitability. With real buyer insights into the choices made and their relationships to relative competitive price positions, price optimization tools dramatically improve effectiveness at driving revenue growth and profitability.
Thus, AI trained from purchasing platforms is leading to breakthrough insights from quoting and transactional data in account health, customer engagement, price sensitivity and, ultimately — with the addition of cost-to-serve data — net profitability. With stronger predictive models around how sales calls drive metrics of success (account health and engagement, supplier position, price sensitivity and profitability), AI can prescribe call plans that maximize the expected value of profit contribution from each call. These linkages allow for prescriptive, data-driven call plans that optimize the profit outcomes of sales calls. Tracking acceptances and sales outcomes, the recommendations quickly and continuously improve.
At a macro level, sales AI can inform territory design and rationalization, as well as support hybrid sales approaches that further unlock productivity and profitability gains. With the typical distributor paying up to 20 percent of revenue for the sale organization, Sales AI’s benefits are too big to ignore. The old ways of Wild West selling cannot compete with sellers who leverage prescriptive AI to guide their daily call plans. Market-leading distributors are augmenting their sellers’ training and experience with AI-infused sales tools to improve customer experience, account health and engagement, wallet-share, pricing, and profitability. The future is very bright indeed for those companies who embrace new technologies to help their sellers navigate changing markets.
