Tensions between sales teams and finance departments are as old as corporations themselves. The source of that tension stems primarily from the lack of visibility that finance teams have into the sales pipeline. To achieve that visibility, many organizations have invested heavily in customer relationship management (CRM) applications. But finance teams today are still generally unable to determine when the data being entered in a CRM application by a sales team represents on overly optimistic forecast or may be an attempt to lower overall sales revenue expectations as part of a “sandbagging” effort to artificially reduce pressure on the sales staff.
To eliminate any of that potential uncertainty, providers of finance and CRM application software are now starting to embed machine learning algorithms and other forms of artificial intelligence (AI) that make it much simpler for finance teams to highlight aberrations in sales forecasts by using historical sales data to more easily identify deviations.
Henner Schliebs, global vice president for audience marketing for SAP, says even before meeting with sales leaders, the finance department will have a better idea what the sales forecast for any given period should be.
“The finance person is going to have more information than the sales executive,” says Schliebs.
Armed with that data, organizations will be able to not only implement sales quotas that more accurately reflect the true market opportunity, but also include stretch goals tied to the higher margin products and services. Finance teams will also be able to determine what types of sales leads have a higher likelihood of resulting in a sale being generated.
Recent surveys suggest infusion of AI into financial forecasts is occurring faster than most sales professionals might appreciate. For example, a recent SAP survey finds 32 percent of businesses have either already implemented or plan to implement AI in the next year, with another 25 percent planning to investing in next year.
A recent AI survey conducted by Constellation Research finds 70 percent of respondents indicate their organization currently employs some form of AI technology. Those investments, however, are somewhat modest. A total of 92 percent of respondents say they will spend less than $5 million on AI in 2018. However, respondents indicate significant year-over-year increases in AI budgets, with 60 percent of respondents registering a 50 percent increase in AI budgets compared to last year.
In a similar vein, a recent survey of 500 finance executives conducted by Capgemini, a global IT services provider, suggests that over half the respondents (54 percent) said finance should be driving automation across the entire organization and half (50 percent) say they have already automated several financial processes. A full 43 percent say they expect those investments to enable finance to play a much more strategic role inside their organizations.
But Byron Matthews, president and CEO of Miller Heiman Group, a provider of sales tools and training, says organizations need to proceed with caution when it comes to applying AI and other forms of automation to sales management. It is clear that organizations are looking to derive more business value from their sales data, says Matthews.
“Sales data is becoming weaponized,” notes Matthews.
But applying machine learning algorithms to analyze more data may result in sales people holding back more data even more than they do today. Just because a CRM application exists doesn’t mean that sales people are pouring everything they know about a customer relationship into those applications, notes Matthews. In fact, many sales people manage their own personal CRM application or equivalent that contains information and data never shared with their employer.
Most enterprise-class CRM applications are acquired by finance teams trying to create a system of record that provides some visibility into projected revenues. But when finance teams opt to use that data aggressively to ride herd on a sales team, they run the risk of increasing sales staff turnover, notes Matthews.
In an ideal world, AI should be used to provide additional structure to the sales process, adds Travis Bickham, vice president of marketing for Concord, a provider of a contract management application delivered as a cloud service.
“AI creates a guard rail for the sales team,” says Bickham.
At the same time, Bickham says, organizations should not expect too much from AI applied to CRM data. While CRM applications provide access to relevant historical data, the only reliable source of projected revenue is being able to track which contracts are pending, says Bickham. The data entered in a CRM application is a best guess pertaining to how the sales team might feel about a customer prospect, but none of those feelings represent a real revenue opportunity until there’s a contract in front of the customer, says Bickham.
In general, sales people prefer to keep their customer cards as close to their chest as possible. In fact, many sales people often don’t distinguish between who is their customer versus the customer of the organization they are employed by. When a sales person leaves a company to join a rival organization, it’s not uncommon for them to try to take a few customers along with them. Everyone agrees sales is about relationships. Where the confusion sometimes arises is determining who owns those relationships.
To minimize that risk, many organization are reorganizing sales teams. Sales representatives are being tasked with primarily finding new customers, while a separate inside sales team or customer service representative is tasked with maximizing customer satisfaction and, by extension, revenue from existing customers. That bifurcated approach minimizes the risk of losing a customer whenever one single salesperson leaves the company. When a sales person does inevitably leave a company, it becomes a lot easier to train their replacement, especially when there is a digital assistant infused with AI capabilities that can make helpful recommendations concerning what actions should be taken next.
At this point, it’s now only a matter of when and to what degree machine learning and other forms of AI will be applied to sales management. Most of the initial exposure to AI will come in the form of next-generation packaged sales management applications. But as AI technologies become more accessible, they will be pervasively deployed across the enterprise, with one of the primary areas of focus undoubtedly being the further automation of sales management.