March 10, 2021
March 10, 2021
Contributor: Jackie Wiles
The future of sales analytics leverages intelligent applications and new data sources to drive more commercial impact. Learn what lies ahead.
The future of sales analytics brings with it a more accurate yet dynamic picture of buyer behavior and needs, which will drive significantly more commercial impact for frontline sales teams and commercial leadership than is typically offered by sales analytics today.
“Sales analytics functions that don’t fully understand the information needs of the larger organization are missing the opportunity to share insights among commercial functions to drive more cohesive decision making,” says Steve Rietberg, Senior Director Analyst, Gartner.
Knowing what will characterize valuable sales analytics going forward gives sales operations leaders a set of objectives to strive for so they can promise more value to their stakeholders.
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To grow revenue, sales teams need accurate insights about buyers and their behaviors and intent, but today’s sales analytics are increasingly unfit for the task due to changing conditions such as:
In response, Gartner expects the state of sales analytics to shift in these ways.
A group of intelligent application capabilities, collectively known as augmented analytics, can automate data preparation; intelligently structure and tag that information for further analysis; and use machine learning to discover and deliver insights directly to sellers and managers — on demand, just in time or even before those users ask a question.
Augmented analytics solutions greatly improve data accuracy because sales force automation (SFA) software can perform many of the error-prone, inefficient tasks that historically stood between raw data and universal access to sales insights. These augmented analytics provide frontline and commercial leadership with seamless access to the insights they need to succeed.
An emerging class of intelligent technology — what Gartner calls “X analytics” — can capture much of the unstructured business process information that has resisted measurement in the past. X analytics can detect, evaluate, extract and organize data from written text, spoken words and video recordings.
Inputs that lend themselves to this technology include new sources such as Internet of Things (IoT) data. For example, sensor data from manufacturing equipment may show that a component is operating inefficiently, indicating an opportunity for the supplier to upsell the customer a more modern and cost-efficient unit.
Few sales organizations have an integrated-enough view of a buyer’s digital and nondigital interactions. Even fewer have the technology to interpret those signals, assess the progress of a purchase decision and recommend next steps.
Continuous intelligence technology solves this problem by integrating analytical decision support directly into audiences’ day-to-day business activities — in real time. Continuous intelligence draws on multiple technologies, including X analytics. Live tracking signals extracted from sales activity data are combined with current and historical data to derive decision-support insights. These are pushed to users just when they need them.
Building the ecosystem needed for a functional continuous intelligence model is a multiyear endeavor, but the payoff is a clearer view of the purchase decision process across channels.
Sales analytics organizations currently need data science specialists to envision, develop and harness the potential of augmented analytics, continuous intelligence and similar innovations linked to AI.
But the native intelligence within these technologies will soon extend access to sales analytics more broadly across the organization on a day-to-day basis. With this democratization, end-user consumers of sales analytics (within sales and beyond) will depend less on sales analytics experts to understand, model and answer many ad hoc questions.
The importance of close coordination between sales analytics and enterprise technologists (corporate IT and business intelligence leaders) will grow even more important as these trends take hold. Data governance will be critical to ensure well-designed guardrails and control points as access extends beyond the silos of business functions.
The expansion of AI technology will bring the differences in the analytic needs of multiple audiences into sharper focus. For strategic, centrally focused users of sales analytics (CSOs, C-suite peers and EVPs of major divisions, for example), AI will enhance decision making by flagging patterns and forecasting broad outcomes better than humans can.
For sellers themselves, the functionality of augmented analytics will improve short- and long-term decision making. Except for the most standardized, transactional settings (e.g., a high-volume call center), the value sellers receive will come in the form of data-led decision support at the portfolio, account and opportunity level.
Ultimately, AI technology will learn to deliver sales analytics tailored to specific sellers, customers and products. As these just-in-time sales analytics become common, the traditional one-size-fits-all approach — customizing dashboards to roles — will become less ubiquitous. In its place, a chance to create new analytics insights, enhanced by a narrower focus on specific audiences and use cases, will arise.
Understanding these sales analytics trends will help sales operations leaders determine how to prioritize the path of improvement for their own organization. The Gartner sales analytics roadmap envisages sets of initiatives that build on each other over time, based on a candid assessment of the current state, clear future-state objectives, and timelines for achieving key milestones and objectives.
“Sales operations leaders must align stakeholders on a vision, prioritize use cases for sales analytics, and then establish governance, elevate data literacy and prioritize analytics technologies,” says Rietberg. “These objectives will take time, but can be scaled down into tangible and attainable milestones.”
Sales operations leaders can customize and prioritize the plan according to the current and future states of their own sales analytics operation, but the evolution for most will be as follows:
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