Unlocking the Potential of Analytics to Ignite Growth

The emergence of advanced analytics, AI, and Machine Learning (ML) – and the massive new sales engagement data sets to support them – represents the most significant opportunity to accelerate sales growth since the scale adoption of call centers (40 years ago), CRM (30 years ago), and digital channels (20 years ago). This revolution in advanced sales analytics offers growth leaders unprecedented potential to improve the productivity of revenue teams, multiply the return on selling assets, and create firm value. This makes the ability to capture and unify customer data and convert it into customer and seller insights that optimize and automate cross-functional sales, marketing, and service workflows a big priority.

Businesses and investors agree that better insights can fuel new revenue and profit growth. Growth leaders are investing heavily to realize this potential. On average, investment in advanced analytics will exceed 11% of overall marketing budgets by 2022.⁴ Spending on AI software will top $125 billion by 2025 as organizations weave AI and ML tools into their business processes.³ Ninety percent of organizations are using AI to improve their customer journeys, revolutionize how they interact with customers, and deliver them more compelling experiences.³⁶

The leaders we spoke with were turning these investments into value by using advanced analytics to reinvent customer journeys, automate sales activities, and extract better prices. Others were leveraging insights to optimally allocating sales resources, better managing sales teams, and improving the performance of sales channels.

In parallel, investors have poured more than $5 billion into over 1,400 AI-fueled sales and technology companies to meet this demand.⁶⁶ So it's no surprise that over 90% of the top 100 solutions we identified in our analysis of the 100 technologies transforming the commercial model are using advanced analytics, AI, and machine learning to better enable sales, marketing, and service teams.²⁶

Individually these innovators are connecting the dots across the sales and marketing technology portfolio to optimize resource allocation, direct revenue teams, enable individual sellers, and measure and motivate performance, while also working to personalize communications, pricing, and offerings. Collectively, this group of platforms are fast becoming the linchpin of the Revenue Operating System. They turn legacy investments in sales and marketing technology, selling channel infrastructure and customer data into selling outcomes that grow revenues, enterprise value, and profits.

At its core, this new Revenue Operating System creates value by unifying and monetizing customer data and insights at the center, while enabling and automating cross-functional sales, marketing, and service workflows at the periphery.

In effect, it's forcing executives to reimagine their technology stacks and go-to-market models around platforms that aggregate and orchestrate customer engagement data rather than CRM. This has led to a Copernican Revolution in how companies generate revenue by harnessing the full potential technology to accelerate profit growth. This revolution is blurring the lines between traditional software categories and making the ability to turn customer data into insights the primary driver of value creation in sales, and the key to increasing the return on selling assets.

The combination of these forces compels growth leaders to change in several ways:

• Curating platforms that orchestrate commercial insights will create more value. This places a premium on platforms that can coordinate and deploy customer engagement and seller activity data faster and better than CRM. Businesses are creating value by finding ways to convert big data into insights that prescriptively inform seller decisions, actions, and conversations in real time at the “moments that matter” in the human selling process. “Growth leaders need core central system of decision-making,” says Viral Bajaria, the Founder of 6sense.⁷⁰ “Every company now has 20, 30, or even 50 plus tools to help them grow. And even more media and marketing partners to reach customers. They need to start to bring the engagement data from all those tools together in one place. To create the center decision-making system. And then once the decision is made, push those insights to the system where you can use that information to create value. There is a place for best-of-breed solutions and a place for platforms in this ecosystem. But at the end of the day you need a system to pull it all together.”
• Relying more on algorithms to define territories, account priorities, and the allocation of selling effort. The megatrends toward remote selling and the alignment of sales, success, and marketing into one revenue team are releasing the legacy constraints of geography, function, and role on the allocation of selling resources. This is allowing B2B sellers more freedom to leverage AI to define data-optimized territory boundaries, seller assignments, account priorities and the allocation of resources and effort. “In the short term, sales organizations are deploying algorithms that help with the basics of account prioritization, lead qualification, recommending the content or sales action that will lead to success, and reallocating sales resources to the places they can have the most impact,” reports Leonard Lodish, Professor of Marketing at the Wharton School of Business.³² “More sophisticated organizations using AI tools can also create algorithmically derived customer response models to help take the guesswork and gut feel out of aligning sales resources across geographies, accounts, and business lines.”
• Connecting sales and marketing solutions into closed-loop systems that better support planning, measurement, and execution of the day-to-day selling motion. AI is rapidly converging the traditional sales and marketing software categories in ways that will make frontline selling faster, simpler, and more consistent. This confluence of capabilities represents an immediate and significant opportunity for B2B organizations to generate the next level of growth from their revenue teams. Growth leaders that configure these platforms in ways that make them simpler to use by sales, marketing, and customer success reps will realize the immense promise of this latest generation of selling technology. The most advanced organizations are demanding that their enablement teams connect their sales enablement, sales engagement, sales readiness, and conversational intelligence capabilities into closed-loop processes that deliver real-time guidance and coaching at scale to every member of the frontline revenue team.
• Providing managers greater visibility into seller activity, customer engagement, forecast commitments, and pipeline health and enabling more data-driven decision making. Greater visibility is now essential in a post-pandemic economy where work at home, hybrid work, and work from anywhere practices have become the norm. It gives sales managers the information they need to better manage, measure, coach, and empower remote revenue teams at the edge of the organizations to make the right decisions faster. Sales leaders are building KPIs based on seller activity and customer engagement that provide more transparency of seller performance, customer engagement, and forecast commitments that pipeline health and enable more data-driven decision-making. Building KPIs based on activity and engagement makes practical sense in the face of remote selling and the fact that linear waterfall metrics don't accurately reflect the ways customers buy or revenue teams sell. Analytics and AI also allow them to create better incentives for all customer-facing resources based on account profitability and contribution to firm financial performance.

This means getting better control over the customer engagement and buyer activity data that they already have.

Like the heads of sports teams, marketing, sales, and service leaders need to orient their teams toward a common goal. In other words, they need to use advanced analytics and AI to turn sales engagement data into a common set of measurements and financial incentives that get sales, marketing, and services working toward the wins of firm value, customer lifetime value, and profits.

Professor Adi Wyner, who leads the Sports Analytics and Business Initiative at Wharton Business School, sees close parallels between the cultural and business transformation that sports teams have experienced in the past few decades and the challenges sales and marketing leaders face today. He often points to the challenges faced by Billy Bean, General Manager of the Oakland A's, in the book Moneyball as a lesson about leading transformation from the top. Bean was forced by competitive circumstances to challenge baseball orthodoxy by using analytics-based approaches in order to find better ways to build and run his team. His efforts to transform his organization ran into the same cultural, organizational, and capability obstacles as sales and marketing transformation programs do. To ultimately succeed, he had to push hard to invest big resources on building analytics capabilities, acquire the right talent at the right price, and give general managers more power and control over coaching tactics, game strategies, and play calling. “Billy Bean was remarkable not because he discovered the value of walks as a predictor for player performance – but because he was a leader who allowed himself to listen to what the analysis was telling him when it contradicted conventional wisdom and entrenched beliefs of his organization to find ways to transform his business” according to Professor Wyner.¹⁰⁵

To make this a reality, sales leaders are increasingly looking to a new set of analytic tools – including customer data platforms, sales engagement platforms, and sales analytics and automation solutions – that can convert data into information, and information into value. These tools are taking on the hard work of coordinating data across channels and making it available to sales, marketing, and service reps in real time. In practice, businesses are putting these solutions in place in ways that integrate with, augment, and in some cases, bypass legacy CRM systems to combine and monetize customer engagement and seller activity data.

This represents a material shift in the center of gravity of the systems that support growth to platforms that are able to aggregate, transform, orchestrate, and disseminate customer insights in ways that are faster, more proscriptive, predictive, and actionable. This trend is evidenced by the rapid growth of what are commonly called Customer Data Platform, Sales Engagement, or Revenue Acceleration Platforms. The names may vary, but these types of platforms and solutions are among the fastest-growing companies in our analysis. For purposes of this book, we call that “center of gravity” of the Revenue Operating System the Engagement Data Hub. This chapter will detail how the best organizations are aggregating data from marketing and sales systems and turning it into insights that inform selling actions and resource allocation decisions.

Building Block #4: Revenue Intelligence – Manage and Measure Financial Value

Visibility into opportunity potential, account health, seller performance, and pipeline activity are regarded as the four most important insights managers need to better manage their selling system.9 Companies that make progress creating more accurate metrics, dashboards, and incentives have a significant advantage over the competition, reports Brent Adamson, distinguished Vice President in Gartner's Sales practice.129 “Companies that align their metrics and incentives with customer buying behavior will give them a much more accurate picture of the cost of sales, the opportunity cost of selling time, and how different resources contribute to their commercial organizations in terms of commercial outcomes,” advises Adamson. “This will allow them to make much better decisions about how to allocate people, technology, data and content resources based on what they are contributing to the top-line, bottom-line and value of the company.” (See Figure 8.1.)

Advanced analytics can give you much better visibility into account health, pipeline accuracy, opportunity potential, and the like. These insights can be derived from the data your organization currently collects from customer engagement, product usage, financial transactions, and seller activities.

Building KPIs based on pipeline activity and seller engagement makes practical sense in the face of remote selling and the fact that traditional marketing funnels and linear waterfall metrics don't accurately reflect the ways customers buy or revenue teams sell anymore. Analytics and AI also allow you to create better incentives for customer-facing resources based on account profitability and contribution to firm financial performance.

There are several practical ways that sales leaders take advantage of advanced sales analytics, engagement, and performance management platforms to get greater visibility into seller activity, customer engagement, forecast commitments, and pipeline health and enable more data-driven decision making. Specifically, they include:

1. Creating more precise measures of account health and lifetime value. Sales and marketing leaders like AT&T, Ciena Networks, Marketo (Adobe), and DHL are taking steps to align their metrics and incentives with the activities and behaviors that lead to better selling outcomes, greater customer lifetime value, and improved account health. They are using advanced customer engagement analytics and sales AI to create customer engagement metrics to serve as the foundation for performance measurements based on real-time information about sales engagement, deal attractiveness, content usage, and personal-level interactions to provide management a more accurate proxy of the current buying reality.
2. Automating and improving sales forecast accuracy. Sales analytics leaders, such as Aviso and Clari, are leveraging data from across enterprise systems to create more accurate revenue forecasts. Currently only about a third (34%) of sales leaders have intelligent forecasting. Ninety percent of those that use this capability say it helps them do their job more effectively.¹⁷
3. Quantifying seller performance, capacity, and consistency. Frontline sales managers can use AI to significantly measure the performance of the “B and C players” on their revenue teams. They can now use advanced analytics to automate the evaluation and coaching of sales talent, create measures of seller performance based on activity and behavior, and improve the coverage and penetration of key accounts using ABM data and insights.

   The best way to get sales, marketing, product, and service reps working together to improve account health and penetration is to create data-driven incentives and KPIs to foster a common purpose in working as a team to grow customer lifetime value. Sales leaders are starting to use advanced analytics to derive new measures that more accurately quantify the collective engagement and customer experience our teams are creating within accounts. Teamwork-oriented leaders are evolving beyond outdated and dysfunctional waterfall metrics that lead to handoffs, leakage, and waste by putting sales, marketing, and service in conflict with each other. They are creating data-driven metrics that quantify account profitability, pipeline health, and seller performance on a scale of 1–10. They are also drawing on sales analytics solutions like Xactly and Captive IQ to track the behaviors and activities that define team success while laddering up to innovative data-driven customer engagement measures and incentives tied to account health and value creation.

4. Using simulations to test and evaluate growth strategy scenarios. Best-in-class companies are using simulation tools to compress time, evaluate multiple scenarios, navigate trade-offs, and accelerate consensus building. AI-driven simulation-based tools offer faster and more collaborative approaches to generating territory, product launch, account-based marketing, and business unit growth plans.

   Using simulation tools to have your leadership team “war game” more scenarios has a variety of benefits when compared to the traditional top-down strategy development approach used by most companies. First, it compresses time to test go-to-market strategies and scenarios seven years into the future. Given that most strategies will not bear fruit or fail until several sales periods after inception, this can be a huge advantage.

   Second, AI-driven simulations can manage millions of scenarios and possible resource allocations to find the best combinations to maximize growth. They allow managers to test and balance different combinations of sales force emphasis, calling priorities, customer targets, and treatment types to generate the greatest profit and growth contribution, ROI, and quota attainability. Because they can incorporate dozens and even hundreds of field leaders into the process, simulations allow you to combine bottom-up local market knowledge and performance insights with top-down focus on realizing the greatest profit, revenue, and opportunity share. They also let planners “pressure test” and adapt plans to deal with rapidly changing and different customer and market scenarios. Finally, they accelerate the time between strategy development, tactical planning, buy-in, communication, and implementation by revenue teams.

5. Using advanced analytics to improve the accuracy, predictability, and quality of growth plans, forecasts, and predictions. Due to rapidly changing customer behavior, shorter product life cycles, the complexity of omnichannel and virtual selling systems, sales modeling is increasingly critical to sales resource allocation, forecasting, and strategy developments. Advanced modeling technologies have been made practically and financially viable thanks to the broader availability of better data to inform growth strategies and the democratization of analytics.

   Modeling allows managers to balance and tune six interrelated inputs – the size, segmentation, and emphasis of the sales force, the design of territories, the segmentation of markets, and the treatment of customers – against corporate growth goals and resource constraints. Modeling also forces management to blend quantitative data inputs and objective empirical analysis with estimates based on management judgment and local market knowledge in ways that must achieve growth priorities, targets, and strategies defined by firm leaderships. “It's important to understand what Machine Learning and AI tools are good for, and what they are not” reports Ron Cline, Head of US Marketing Data and Analytics at the TD Group.³¹ “These tool and modeling techniques allow us to analyze more data sources, faster, with more statistically valid results, and less work.”

   Advanced modeling and analytics techniques can significantly improve the process and the outcomes they achieve in five specific ways. Improving critical assumptions about the sales response function predicts the incremental revenue associated with an incremental increase in selling effort. Better understanding of this relationship is critical because it underlies all sales resources, budgets, quotas, and territory definitions. Modeling also leads to making better assumptions, allowing organizations to evolve beyond simple heuristics or rules of thumb that assume linear sales responses or equal allocation of efforts against all customers and territories, and to more nuanced and accurate assumptions that reflect the true nature of demand and sales response based on decision science.

6. Creating measures of the financial contribution of long-term growth investment. Accountability is fundamental to scalable growth, according to Tony Pace, CEO of the Marketing Accountability Standards Board (MASB). “Greater financial scrutiny and improved marketing accountability for the financial return on all marketing investments is fundamental to protecting, unlocking and growing the financial value they create,” says Pace, who is also the former CMO of Subway. “Unfortunately, with current budget setting processes, financial reporting standards, and measurement practices – over two-thirds of companies cannot effectively measure their financial return on investments that create value through improving brand preference, the customer experience, sales activation, customer loyalty and cultural relevancy according to the MASB research.”

“The central problem is marketing lacks the kind of accountability and metrics that are common along the value chain of the rest of the corporation,” according to David Stewart, the editor of the book Accountable Marketing.¹³⁴ “Marketing remains a ‘dark science’ where its practitioners can generate desirable results but cannot tell you how they achieved them.”

Executives leading Fortive and Lionbridge agree with this reality. “Holding marketing assets, investments, and front-of-the-funnel marketing activity accountable for financial returns is fundamental to profitable growth,” according to Jaime Punishill, CMO of Lionbridge.¹¹³ “Financial returns are the only vocabulary you can use to communicate the return on critical growth investments like digital selling infrastructure, selling content, brand building and the customer experience to the CEO, CFO and the Chief Revenue Officer. So we shifted our focus to understanding, measuring, and improving the contribution of the commercial assets we manage – digital technology, data, content, leads, and brainpower – to growth, profits, and firm financial performance.”

Fortive Business Systems, an industrial conglomerate, is using analytics to create feedback loops to evaluate outcomes, attribution, and performance across sales, marketing, and customer success. Kirsten Paust, VP of Fortive Business Systems has worked hard to help business units put in place analytics that create feedback loops that measure the sales impact of actions and investments. “The whole idea of accountability for growth outcomes and attribution of revenue is really important in our culture,” shares Paust.¹³⁰ “That's a critical role for analytics because if we want to excel at sales and marketing as a company then we have to measure it. Our leaders work to really understand the connection between investment in sales and marketing and growth outcomes. This understanding is so critical because it turns sales and marketing into an enabler of growth, not a discretionary expense.”

Building Block #5: Engagement Data Hub – Leverage Advanced Analytics to Connect Growth Assets to Value

Customer data is one of the most valuable assets a company has. For example, creditors valued the customer data assets of United Airlines at higher than firm value, according to Doug Laney, author of the book Infonomics.¹³¹ Finding ways to harvest the first-party customer engagement data your company already owns is an obvious way to grow sales with no incremental investment. Unfortunately, only 38% of CMOs believe their investments in analysts, data, and analysis fully support their decision-making process.¹⁵

Digital marketing programs, digital selling platforms, and third-party data providers generate information that can signal buying intent, propensity to buy, or the risk of attrition. Frontline sellers can use this information to make decisions about next best actions and prioritize urgent opportunities and leads and identify new buyers within target accounts. But they need that information in time to act on it. This makes the ability to speed that information from the source (e.g. a website, chatbot, or mobile app), to a relationship manager or customer service rep who can act on it, a priority. Often in real time. This makes improving the speed of information flow a management priority.

Finding ways to use advanced analytics to collect and monetize the data generated by legacy investments in CRM, sales enablement, and digital selling systems is at the heart of modern selling. On a fundamental level this places a premium on solutions that unify, deploy, and monetize customer data and insights to create value. As we mentioned earlier, businesses and investors universally believe AI and ML can fuel new revenue and profit growth by reinventing customer journeys, transforming the customer experience, and optimizing investments in marketing channels.

Sales systems are transforming into Customer Data Platforms that are faster, more proscriptive, predictive, and actionable. This trend is evidenced by the rapid growth of customer data platforms (like Snowflake, Lytics, Blueshift, and Tealium) and sales engagement platforms (like Outreach.io, Salesloft, and Xant.ai). These solutions are among the fastest growing companies in our analysis of the 100 technologies that are defining and enabling the twenty-first century commercial model.²⁵

There are a range of ways to aggregate, consolidate, analyze, and deploy customer engagement data to augment and enhance this foundation of known customer data that help revenue teams save time, make better decisions, allocate resources more effectively, and deliver better customer experiences. Specifically:

1. Aggregating third-party data from many sources to uncover selling triggers and insights. Sales analytics solutions like Oracle Sales CX, Insight Squared, Cognism, XiQ, and People.ai are aggregating data from many external sources to uncover event triggers, buying signals, and churn risks in the client and prospect base. Customer data platforms like Hull, Blueshift, Zylotech, and Tealium make this happen by automating the integration and delivery of behavioral and trigger data from digital marketing platforms with sales and marketing teams. The best blend it with first-party account and prospect data from CRM to develop and deliver actionable insights to frontline sales, marketing, and service reps in real time. These solutions automatically log data into CRM systems and append data records to improve data quality and gain a 360-degree view of the customer. Third-party data solutions like Bombora, Discover.org, and Everstring are appending and enriching internal customer and prospect data sets in real time with buyer intent, prospect, and event triggers so sales reps can prioritize opportunities and act quickly while the prospect is still “in market.”
2. Managing and organizing data around account structures. Account data management and orchestration solutions like Lattice, LeanData, Jabmo, and 6sense are helping account teams to manage and orchestrate prospect and customer engagement data from inside and outside the company around accounts to make it easier for account teams to develop, cross-sell, upsell, and penetrate key accounts.
3. Automating the consolidation, harmonization, and cleaning of data from customer-facing systems. A primary use case of customer data platforms is to automate the process of onboarding, orchestrating, and synchronizing data from first, second, and third-party data sources your organization already has in real time. Automating this process helps ensure that customer profiles relay the most up-to-the-minute data. Sales analytics solutions like Insight Squared aggregate data from email, calendar, content, and call recordings and integrate it with records in CRM. Sales engagement platforms like Salesloft, Outreach.io, and Xant.ai automate the process of aggregating buyer data, targeting anonymous interactions, and cleaning records. Sales automation solutions like Seamless.ai and Node clean customer data and enrich customer profiles to make them more predictive and usable by frontline revenue teams and the specific applications they use to engage customers.
4. Aggregating customer activity, seller activity, product usage, and financial transaction data around a common customer profile. Most high-performing marketers have developed a single view of the customer to direct targeting.¹⁵ They do so by unifying data from many touch points, channels, and media interactions into a common customer profile. Customer data platforms like Treasure Data, Snowflake, and Openprise have automated the orchestration and management of this data across individual customer profiles as well as key accounts. Sales automation solutions like People.ai and Collective[i] automatically log customer interaction data to enrich CRM files and get a 360-degree view of the customer. Other solutions can help enrich customer profiles with third-party prospect, trigger, and behavioral data. For example, sales automation solutions like Seamless.ai and Node clean customer data and enrich customer profiles to make them more predictive and usable by frontline revenue teams and the applications they use to engage customers.
5. Integrating data from many customer engagement systems. The modern selling engine relies on data sourced from many channels, systems, and touchpoints to support selling decisions, priorities, and presentations. Most organizations are sitting on large amounts of customer engagement data in a variety of revenue enablement systems – including CRM, exchange (email and calendar), content management, marketing automation, websites, social media, and customer engagement management systems. And that's not counting one of the biggest sources of customer intelligence – data from recorded phone calls and Zoom meetings. Organizations that are able to capture and unify customer data and convert it into insights that enable, optimize, and automate cross-functional sales, marketing, and service workflows will have a competitive advantage over those that don't. Conversational AI solutions like Revenue.io capture, transcribe, and integrate engagement data from live sales calls and AI assisted service agents so it can be used to inform sales coaching, prioritization, and action recommendations. Customer data platforms like Blueshift and Zylotech help to streamline the onboarding and integration of first-party data from across in-house digital marketing, website, and e-commerce platforms.

Building Block #6: Customer Intelligence – Use Customer Data to Inform Decisions, Actions, and Conversations

Digitally enabled customers want answers that are faster, better, and relevant. That puts more pressure on sellers to deliver a superior customer experience in the shrinking window of time they are directly engaged with customers. This has accelerated the pace at which customer information must be commercialized and shared across the organization. The speed of selling has gotten so fast that revenue teams often need selling insights in real time to compete.

These pressures have growth leaders looking for better ways to use advanced analytics to turn the customer data in their CRM, sales enablement, and digital marketing systems into actionable insights. The executives we spoke with defined actionable insights as intelligence that can directly inform the decisions, actions, and conversations that frontline sellers must make every day at the moments that matter in the human selling process.

“The revenue team doesn't want big data – they want guidance, recommendations and prioritization on what to do next,” explains Howard Brown, CEO of Revenue.io, a business that has helped hundreds of organizations leverage insights to grow. “Sellers need to know what actions will drive value and generate the highest return on their time and attention. We need to use data to focus revenue teams, not overwhelm them. To transform noise into sales guidance.”

The need for actionable insights places a premium on systems, processes, and operations that unify, transform, and interpret data from many customer engagement systems to answer critical day-to-day selling questions like:

• What content or offers to present?
• Which stakeholders and decision makers to engage in accounts?
• How to respond to customer questions, RFPs, and RFIs quickly and compliantly?

The pressure to turn big customer data into actionable insights is also a big reason why over 90% of the executives we spoke to are consolidating the operations that support sales, marketing, and success. They've realized they must take a more coordinated approach to managing the data from their sales conversations, marketing systems, and customer service interactions and make it available to their revenue teams faster. No longer can they afford to manage customer data in six or more operational and technology silos.

Solving this problem is one of the most impactful ways data-driven algorithms can create value. AI and advanced analytics are very good at prioritizing and qualifying leads and recommending the next best sales action. These tasks are easier for organizations to execute with limited analytics acumen and data scientists in short supply, according to Professor Leonard Lodish. “There's a broad continuum of applications of AI in the selling model ranging from relatively simple to very complex,” reports Lodish.³² “There are many high-impact and simple to implement sales AI applications most organizations can be taking advantage of today. Organizations are dramatically improving sales performance by using algorithms to help with the basics of account and lead prioritization and qualification, recommending the content or sales action that will lead to success, and reallocating sales resources to the places they can have the most impact. In customer service, AI is opening entire new frontiers in customer experience and success by applying NLP, sentiment analysis, automation, and personalization to customer relationship management.”

A wide range of AI tools are now available to help sales teams prioritize opportunities based on buyer intent, recommend next best sales actions, and automate or augment the day-to-day planning, content gathering, and data entry that eats up two-thirds of selling time. While fewer than half (46%) of sales reps currently have data insights on customers' propensity to buy, the majority (62%) of high-performing salespeople see a big role for guided selling that ranks potential opportunity value and suggests next steps.¹⁴

There are several practical ways leading organizations are converting the customer data they already have into customer intelligence that can inform decisions, actions, and conversations. They include:

1. Enriching customer profiles with third-party prospect, trigger, and behavioral data. Sales analytics (Revenue Intelligence) solutions like 6sense, Oracle Sales CX, Insight Squared, Cognism, XiQ, and People.ai are aggregating data from many external sources to uncover event triggers, buying signals, and churn risks in the client and prospect base. Then they blend it with first-party account and prospect data from CRM to develop and deliver actionable insights to frontline sales, marketing, and service reps in real time. These solutions automatically log data into CRM systems and append data records to improve data quality and gain a 360-degree view of the customer. Sales automation solutions like Seamless.ai and Node clean customer data and enrich customer profiles to make them more predictive and usable by frontline revenue teams and the applications they use to engage customers.
2. Opportunity prioritization based on propensity to buy, intent, and opportunity potential. Sales reps spend 7% of their time prioritizing leads and opportunities.²⁰ But a range of solution providers have emerged that support predictive lead scoring and lead prioritization models based on customer engagement data from inside and outside the organization. For example, sales engagement platforms like Xant.ai prioritize daily tasks and plays for sales teams using real-time buyer intelligence from billions of sales interactions. Third-party data providers like Bombora and TechTarget make those models even better by enriching them with customer intent data that lets them know a prospect is in the market for a solution.
3. Analytic engines that recommend contextual content and next best-selling action. Sellers are using AI-enabled recommender engines to make intelligent suggestions about the content, conversations, and actions that will advance the sale in a given situation. At the basic level, they help sales reps find the right content for a particular selling situation. This is useful because sales reps spend almost 10% of their time on call planning and content preparation.¹⁸ Sales enablement solutions like Highspot are now using AI to push specific content based on customer preferences, past success, and client need.

   Platforms like RFPIO use AI and machine learning to analyze question-and-answer interactions from across the organization to provide highly personalized and contextual responses to complex inquiries like RFPs, RFI, and specific questions. This is a discipline called Intelligent Response Management, an advanced concept that uses AI and machine learning to create a knowledge base of content based on actual question-and-answer exchanges between customers and sales reps. The software makes this aggregate knowledge available to entire revenue teams, on demand, with no wait times. A simple query provides the answer your salesperson needs to deliver their best answer, with immediate results whether a rep is writing a text, email, proposal, presentation, or an RFP. They leverage question-and-answer interactions from across the organization to deliver answers fast, in the context and format of the inquiry, and in compliance with regulations and internal standards. Sales engagement platforms like Salesloft and Outreach.io create next-best offer algorithms to recommend content, playbooks, and even in-call guidance with real-time flash cards. This is a big opportunity to improve because only 37% of sales reps report they get algorithmic suggested next steps on an opportunity.²⁰