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Emerging Technologies in Gartner Sales Technologies Bullseye

September 03, 2019

Contributor: Jordan Bryan

Algorithmic-guided selling, next best action (NBA) and predictive analytics are featured among current and emerging technologies that drive sales execution.

With an influx of vendors and advancements, leaders responsible for sales technology implementation feel they are chasing a moving target. As the complexity increases, sales leaders look to quickly understand the advantages and disadvantages of various solutions. 

In the 2018 Gartner for Sales Leaders Market survey, more than 250 sales leaders across 11 industries globally evaluated 47 established and emerging technologies, assessing how they improve sales effectiveness. Specifically, the survey asked, for each technology:

  • To what extent it is currently being used
  • The return it provides organizations
  • What bets sales leaders would place on its future importance

We used the findings to map technologies on a Gartner Technologies Sales Bullseye, which shows to what degree different technologies are embedded in today’s sales landscape, their current return on investment and how important they will be to sales leaders and sales organizations in the future.

The largest slice of the Bullseye relates to sales execution technologies. (Others relate to sales training, sales content management and sales operations.)

Sales execution technologies

The execution slice covers 23 technologies. Among the most widely deployed are technologies related to customer relationship management (CRM), sales performance management (SPM) and strategic account management. They also have the highest ROI and greatest future importance. These three platforms help sales leaders and their teams to manage the complexity of the current sales environment, complete a variety of critical sales activities and embed in sales organizations across industries.

Gartner Sales Technology Bullseye related to sales execution.

Algorithmic-guided selling

Algorithmic-guided selling uses predictive and prescriptive machine learning algorithms to manage the sequential sales actions that managers expect sales users to consistently execute. These algorithms intend to augment the seller’s ability to engage with prospects, manage the buying process and generate quotes. Application leaders supporting sales select these tools to improve sales effectiveness, using them to enforce process discipline and to remove the uncertainty about "what to do next" in complex sales processes.

Intended to augment more traditional sales tools, such as sales playbooks, it leverages emerging artificial intelligence (AI) technology and existing sales data to guide sellers through deals, automating manual sales actions while reducing the need for individual seller judgment in the sales process. It is a newer sales technology and currently has relatively low levels of adoption and ROI, but the potential is huge. 

Algorithmic-guided selling is relevant to all parts of a company's sales processes. It can be used for inside sales, helping sellers with the right cadence to contact prospects. It can also be used for complex sales cycles, showing sellers what type of content or messages have a higher likeliness to move deals forward.

“ Only 14.5% report they have no plans to pilot or deploy, and 34.5% expect algorithmic-guided selling to be more important to their organization in two year’s time”

Most forms of algorithmic-guided selling are supported by machine learning technology. It is a form of AI that builds statistical correlation models between the actions that sellers take and the subsequent impact those actions have on measurable outcomes. When used in sales technologies, machine learning models drive the prescriptive next best actions that tell sellers what to do next with an account or with an opportunity.

These technologies have the potential to be powerful sales productivity tools, but their effectiveness relies heavily on the underlying data. Incremental changes in the quality of the data source can therefore lead to disproportionate changes in the accuracy of its predictions — and ultimately ROI.

“Despite the complexity of this technology and its reliance on pre-existing data collection and management, it is a technology most sales organizations are actively exploring,” says Tad Travis, VP Analyst, Gartner. “In fact, only 14.5% report they have no plans to pilot or deploy, and 34.5% expect algorithmic-guided selling to be more important to their organization in two year’s time.”  Sales leaders who consider adopting algorithmic-guided selling should:

  • Identify points in the sales process where automation would have the greatest impact to augment seller judgment or automate manual operations. 
  • Implement strict data hygiene principles to ensure accurate recommendations. 
  • Prepare underlying content for integration so that it is available for algorithmic-guided recommendations.

Predictive lead analytics

Elsewhere in the sales execution technology slice, according to the Gartner Hype Cycle for CRM Sales, sales predictive analytics, which apply heuristic and machine learning algorithms to a CRM account’s historical opportunity and data, is still in its adolescence. This market is expected to grow quickly in the immediate future. 

Predictive analytics is the practice of extracting information from existing data assets to determine patterns and predict future commercial outcomes and trends. With this insight, commercial organizations are able to get in front of the customer’s purchase process and guide them toward a solution before they realize they have a problem. The predictive scoring models are based on fit and intent scores. 

Considerations for implementation

Sales leaders should consider these use cases:

  • Filtering and prioritizing inbound lead scoring: Evaluate whether predictive lead scoring capabilities within SFA and CRM lead management systems are sufficient to meet your scoring needs. If they aren't, consider stand-alone predictive lead scoring solutions, but limit contracts to one or two years.
  • Prospecting and demand generation: Evaluate vendors that offer data intelligence solutions for sales applications powered by AI/machine learning.
  • Sophisticated account selection for ABM programs (500 or more accounts): Evaluate account selection capabilities (based on AI/machine learning, intent, engagement or a combination of all of those) that exist in more comprehensive ABM solutions.

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