CONNECTING THE DOTS BETWEEN SALES ENGAGEMENT DATA AND BUSINESS VALUE

The emergence of advanced analytics, AI and ML – and the massive new sales engagement data sets to support them – represents the most significant opportunity to accelerate sales growth. Sales organizations are taking practical steps to reconfigure their technology portfolios to realize the unprecedented potential of advanced sales analytics and AI to improve the productivity of revenue teams, multiply the return on selling assets, and create firm value. Specifically, they are assembling various pieces of their technology portfolios into data-driven algorithmic selling ecosystems that connect the dots between their sales engagement data assets to dramatically improve the visibility, speed, engagement and productivity of sales and marketing resources

These strategies take advantage of the real engine behind the growth  and potential of AI to drive sales – the rapid emergence of very large “sales” data sets (vs. marketing data sets) to support AI learning and data-driven sales. Historically, most AI innovation was fueled by large digital marketing and media data sets. Now sales leaders have access to large and rich sets of customer engagement data to support the development of algorithms that can improve sales resource allocation, model customer response functions, determine an optimal price point, and ultimately influence customer sentiment and intention to buy.

For the past three decades, most sales data sat in CRM systems and was manually entered and not very complete or useful. However, in the past few years, there has been an explosion in sales engagement data that has become available to analytics teams from four core data sources:

  1. Exchange data – Sales solutions providers have become adept at capturing and analyzing the email and calendar data of sales and service agents from exchange servers, Microsoft Teams, and collaboration systems like Slack.  This data augments CRM account and opportunity data to create a much richer picture of buyer engagement, seller activity and pipeline health.
  2. Content data – Most organizations are now systematically tracking content distribution, engagement and consumption with buyers and prospects from digital asset management, sales enablement, or marketing automation systems. The data this content distribution creates is like tracer bullets that map buyer engagement, flesh out buyer teams and signal buyer intent.
  3. First party data – Most organizations have robust owned digital sales infrastructure to engage customers online. These include websites, blogs, mobile apps, marketing automation, ecommerce, social media, and email platforms. These platforms have now incorporated automated data entry, data capture AI, lead scoring, Natural Language Processing (NLP), and revenue acceleration technologies which allow them to capture a much higher percentage of this data in CRM systems or Customer Data Platforms.
  4. Recording calls – The percentage of inbound and outbound sales and service calls that are being captured and digitally recorded has grown 10-100-fold in the past several years. The scale adoption of collaboration platforms like Zoom, AI guided agents, contactless selling and conversational intelligence tools has grown the amount of customer engagement data available for sales analysis and resource optimization dramatically. As an illustration, consider the number of Zoom calls recorded has gone up thirty fold since the start of the Covid-19 pandemic began, over 200 million participants interact using the Microsoft Teams collaboration tool in a single day, Customer handling times have been reduced by 40 percent in call centers by AI automation and chatbots, and voice shopping is predicted to become a $40 billion business in the next two years.

Growth innovators have realize the keys to achieving significant revenue, profit margins and firm value will be to use AI to “connect the dots” between these massive new sales engagement data sets to the five ways they create value: delivering better channel performance, resource allocation, people management, measurements, and product channel readiness.

Using advanced analytics to create and capture value in these five ways will be the primary driver of growth in the next decade. It is also the key to improving the historically low returns on sales assets – including people, data, technology, and content. To realize the full potential of AI in sales, business leaders will need to:

  • Find practical ways to get control of the four core sources of customer engagement and seller activity data that already exists in their content, email, calendar, and conversational commerce systems.  Right now, most organizations are not fully leveraging the first party data they have resident within CRM, Marketing Automation, and Digital Marketing platforms to support business decision-making or drive measurable growth.
  • Avoid letting perfection and ubiquitous data integration get in the way of “good enough” facts to inform selling. The reality for most sales teams is that in the absence of perfect data, far too many decisions are still made on gutfeel and historic precedent in a data-driven world. 80% “good enough” data is a vast improvement over the current situation – which is largely flying blind.

You can learn more about the data-driven algorithmic selling, and ways your organization can accelerate growth and transform selling by reading the entire the 21st Century Commercial Model study.

https://www.revenueenablement.com/product/the-21st-century-commercial-model/