The Future of B2B Sales: Blending Inside & Outside Teams

By Zilliant on MarTech Zone

Aug 10, 2020

This article first appeared on MarTech Zone.

The COVID-19 pandemic set off rippling repercussions throughout the B2B landscape, perhaps most significantly around how transactions are taking place. Certainly, the impact to consumer purchasing has been immense, but what about business to business?

According to The B2B Future Shopper Report 2020, a mere 20% of customers buy directly from sales reps, down from 56% in the year prior. Certainly, the influence of Amazon Business is significant, yet 45% of survey respondents reported that buying online is more complicated than offline.

This indicates that the traditional sales channel mix nirvana of inside and outside sales teams has been heavily disrupted. Ecommerce is now an essential channel with companies racing to make it easier for customers to buy from them online, inside sales teams quickly adjusted to performing their jobs from home, and branches and storefronts stayed open if deemed essential. The field sales reps did their best to quickly adjust their normal jobs on the fly to be available to their customers without being able to call on them in person.

Almost 90% of sales have moved to a videoconferencing/phone/web sales model, and while some skepticism remains, more than half believe this is equally or more effective than sales models used before COVID-19.

McKinsey, The B2B digital inflection point: How sales have changed during COVID-19

The future of the sales landscape has shifted quickly under the burden of disruption, but savvy business leaders are adjusting in-step, utilizing predictive sales analytics to blend inside and outside sales and better serve each customer.

Untapped Opportunity in the Long Tail of Customer Accounts

Within a B2B company, 20% of the customer base is typically in the strategic account category — and for good reason.

It’s not uncommon for 80% of revenue to be derived from this top tier of accounts. Rightfully, the most knowledgeable sales reps are appointed with maintaining and growing those relationships.

Over time, through product line proliferation or mergers and acquisitions, companies have grown to a complex scale that simultaneously asks sales reps to cover more accounts while accepting that, by doing so, a significant amount of customers aren’t receiving the dedicated attention needed to maintain and grow wallet share. However, in the face of COVID-19 disruption, it begs the question:  How much revenue are you missing in the long tail?

Findings from our global benchmark report indicate that the total available opportunity of empowering sales reps to retain and grow the accounts within your existing customer base is significant. In terms of both customer churn and cross-sell, B2B companies fail to capture anywhere from 7% to 30% of available revenue.

Download the Global Benchmark Report

The Future of B2B Sales: A Blending of Inside and Outside Sales

As noted by McKinsey’s report, outside or field sales reps are operating more like their inside sales counterparts. The time saved travelling and visiting their top accounts presents a new, reimagined opportunity for this highly-skilled sales team: Turn their white-glove sales style toward the long tail of customer accounts and empower them to treat every customer like a strategic account.

This long tail of customer accounts, sometimes referred to as house accounts in distribution, are typically served when visiting a branch or calling in when they need something. Utilize the newly available bandwidth of outside sales teams by giving them growth and recovery actions to take with these customers. Predictive sales analytics can quickly deploy these insights at scale, accounting for all customers and product categories.

Predictive sales analytics generates growth actions with advanced data science to create ideal purchase pattern profiles based on a company’s best customers, considering spending patterns, total spend, and breadth of products purchased. Utilizing clustering and affinity-based algorithms, it matches each customer to the closest purchase pattern profile to guide reps directly to the items customers are not currently purchasing… but should be.

It also uncovers recovery actions by identifying “at-risk” customers that are showing early signs of defection on one or more product categories using advanced, patented algorithms to serve up specific areas where revenue is declining or has been completely lost. Contrasted with traditional business intelligence reporting, this approach eliminates noise by accounting for buy-cycle patterns, seasonality, one-time purchases, or volatile buying behavior, to exclude false positives from the recovery insights.

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