In our previous two posts in this series, we talked about how machine learning and data can help technology providers present the right product with the right price to channel partners looking to meet end customers’ needs. The ability to identify the combination of products, features, and services that matches what end customers want, at a price that’s attractive to both partners and customers while maximizing margin, is key to generating robust profitable growth.
In this post, we discuss the third important lever that can influence sales: the time it takes to get partners what they need to consummate a deal.
In today’s hyper-competitive technology market, providers don’t have the luxury of taking their time to respond to partners, which are under pressure from their customers to quickly find the right solution for their needs. Partners and end customers expect providers to be far more agile now than even a decade ago, and they won’t hesitate to move on to other providers if they’re not satisfied with a provider’s response time. In short, the faster a provider can get a quote or information a partner seeks, the better its chances of winning the deal.
The importance of clearly defined decision rights
So how does a provider cut the time it takes to respond? A big part of the answer is what we explored in the previous two posts: using machine learning and data to quickly configure and price an offering instead of forcing partners to sort through extensive price lists and product catalogs to spec out a solution on their own. But there’s more to it than that. A provider also has to have in place clearly defined decision rights that spell out how potential deals are handled, who within the provider’s enterprise has the ability to get involved with a deal, and what kind of latitude that person has to influence what’s provided to a partner.
For example, in virtually every deal, partners will look for some kind of discount. They’ll likely first ask a sales rep what kind of break they can get and, if they’re not satisfied, the request gets bumped up to the sales manager. If that doesn’t work, the request gets further escalated, and if the deal is big enough, may ultimately end up with the CFO. By nature, this is a time-consuming process—the antithesis of the speed and agility partners and customers value. A company can have all the latest and greatest technology tools, but they won’t help get things done quickly if the underlying approval process is slow.
This is where decision rights come in. They provide clarity on which types of deals are eligible for discounts or special pricing, how deals get escalated and to whom, and who has the right to do what for which kinds of deals. Decision rights go hand-in-hand with the three types of interactions or sales motions that are common in most technology providers: no-touch, in which the partner uses self-service tools to complete the purchase alone with no interaction with anyone at the provider; low-touch, in which someone from the provider, usually a sales rep, supports the sale in some way such as determining pricing or sharing relevant product information; and high-touch, which typically involves significant support from a representative at the provider.
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A company can have the latest and greatest technology tools, but they won’t help get things done quickly if the underlying approval process is slow.
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Analyzing key deal characteristics to speed decision making
A wide range of factors, independently or in combination, can play a role in determining how each deal gets treated. These include deal volume or revenue, type of partner and its performance, end customer or market, geography, and products involved, as well as the provider’s own revenue or market share goals, inventory position, and go-to-market strategy.
Take, for instance, a new partner with no performance track record that wants five laptops for a small business serving a market the provider considers a low priority. That’s a no-touch deal: The partner simply gets the standard price and can’t ask for any kind of discount. When a partner is looking to buy a more sizable number of a product that can be bundled with a service the provider is trying to get out to the market, that might trigger a low-touch motion: A sales rep steps in to offer help with pricing, accessing the right sales collateral, or understanding how similar deals in the past have worked. Large, high-dollar, complex deals involving sophisticated products and big enterprise customers usually get the high-touch treatment: a dedicated sales rep who supports the partner in pricing, solution configuration, and other needs throughout the sales process.
Machine learning can play a major role in helping a provider apply decision rights to deliver the right support to partners as quickly as possible. With access to all relevant data, a machine learning engine can rapidly analyze the characteristics of every deal and determine the optimal treatment. How big is the order? How much business has the partner done with the provider? How has that partner been performing? What kind of discount would unlock further opportunities with the partner? How much inventory of the product being ordered is on hand? Does the provider view the end customer’s industry strategically important? What is the competition doing in this space? Does the provider need to boost its sales volume to meet its quarterly targets?
By answering these and many other key questions, machine learning can help determine the optimal approach to each deal. It may determine a deal is a no-touch transaction and simply on its own go straight to the partner with the best offer. Or it could decide that a sales rep, sales manager, or the CFO should get involved and recommend to that person the offer and extent and type of engagement with the partner that would most likely seal the deal.
Importantly, the more the machine learning engine learns over time, the less intervention from humans it will need. So, more deals—potentially upwards of 70 percent—can end up in the no-touch bucket because the system is continuously gaining intelligence, and thus, the provider’s trust. And that means the overall sales process continues to get faster. Even high-touch deals can be completed more quickly, with the machine learning engine sorting through massive caches of data to provide information and recommendations at every step of the way.
A good, quick offer beats a perfect, late offer
Partners and their customers today face constant pressure to act quickly to remain competitive, and they have plenty of options for where they get high-tech products. To be relevant, providers need to operate at the speed of the market.
Machine learning and data enable high-tech providers to move in concert with what partners and customers demand. It brings agility and speed to the sales process, allowing providers to quickly determine how each prospective deal should be treated, who if anyone should be involved, and what the optimal offer should be. And in today’s environment, we know that the fleet-footed are best positioned to win the race.