Using Advanced Analytics to Connect The Dots Between Sales Data and Business Value

A revolution in advanced sales analytics –accelerated by a confluence of demographic, buying, data, and management trends – offers growth leaders unprecedented potential to improve the productivity of revenue teams, multiply the return on selling assets, and create firm value.

The emergence of Advanced Analytics, Artificial Intelligence (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) in sales.

Businesses and investors universally agree that AI and ML tools and algorithms can fuel new revenue and profit growth by reinventing customer journeys, automating sales activities, extracting better prices, optimally allocating sales resources, better managing sales teams, and improving the performance of sales channels. Growth leaders are investing heavily to realize this potential. On average, investment in advanced analytics will exceed 11% of overall marketing budgets by 2022.

A significant amount of these resources and investments are focused on sales AI. Spending on AI software will top $125B by 2025 as organizations weave AI and Machine Learning tools into their business processes. 90% of organizations are using AI to improve their customer journeys, revolutionize how they interact with customers and deliver them more compelling experiences.  In parallel, investors have poured more than $5 Billion into over 1,400 AI fueled sales and technology companies to meet this demand. Most of the sales technology providers we track – including sales data, sales development, sales enablement, and conversational intelligence providers – have pivoted in the past several years to incorporate AI capabilities into their solutions.

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. 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. 

The real engine behind the growth  and potential of AI to drive sales is 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 web sites, blogs, mobile apps, marketing automation, ecommerce, social media, and email platforms. These platforms have now incorporated automated data entry, data capture AI, lead scoring, 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.

The keys to realizing significant revenue, profit margins and firm value from these massive new data sets 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.

CONNECT THE DOTS BETWEEN SALES DATA AND BUSINESS VALUE

connecting the dots between sales engagement data and business value

There are five specific ways growth leaders can deploy Sales AI technologies to create value and EBITDA expansion:

  1. Revenue resource optimization. In the short term, some of the most practical and impactful ways data-driven algorithms can create value is to help with the basics of lead prioritization and qualification, recommending the next best sales action that will lead to success, and reallocating sales resources to the right accounts, territories, and markets. These tasks are easier for organizations to execute with limited analytics acumen and data scientists in short supply according to Leonard Lodish, Professor of Marketing at the Wharton School of Business. “There’s a broad continuum of applications of AI in the selling model ranging from relatively simple to very complex. There are many high-impact and simple to implement sales AI applications most organizations can be taking advantage of today,” reports Professor Lodish. “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.” 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. 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.
  2. Revenue Enhancement. Sales teams can use analytics to improve the “4Ps” of selling by optimizing pricing  dynamically based on willingness to pay and personalizing products and proposals to deliver and capture more value from sales transactions. For example, more disciplined and algorithmic pricing offers up to five times the profit potential of cost and growth initiatives because it can expand margins by 3-10% with existing resources and improve earnings multiples with limited investment. And rising customer demands for more relevant and personalized content and offerings are regarded as the top way remote buying behavior will change the go to market model in the Remote Sales Productivity Study.
  3. Revenue Team Management. Front line sales managers can use AI to significantly improve cross sell, account penetration and 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 Account Based Marketing (ABM) data and insights. This will yield significant value because most (57%) of sales leaders regard visibility into seller activity and performance as their top sales productivity challenge and even more (62%) lack confidence in their organizations ability to cross-sell, upsell or expand key account relationships, according to Miller Heiman.
  4. Revenue Intelligence.  Advanced analytics can give business leaders much better visibility into account health, pipeline accuracy,  opportunity potential, and create better incentives for all customer facing resources based on account profitability and contribution to firm financial performance. Visibility into these factors were identified as the top four drivers of sales performance by sales leaders and effectiveness professionals.  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. Steve Lucas, the CEO of iCIMs and author of Engage to Win recommends growth leaders “define as an organization what a 10 out of 10 looks like in terms of customer advocacy, quality of interaction, content sharing, and other relationship health metrics. And then use advanced analytics to build composite metrics that quantify and track customer engagement quality on a customer and account level.
  5. Revenue Channel Optimization.  AI enabled service automation, virtual assistants, contactless selling, and conversational intelligence tools can significantly improve the cost, effectiveness, and experience in virtual, direct, and physical sales channels.  In the short term these tools automate sales tasks, guide sales conversations, leverage agent time and capture sales conversations for analysis, prioritization, and personalization. Over time, AI tools will create even more value helping virtual sales reps assess customer sentiment, intent and building trust in the absence of non-verbal cues and body language according to Raghu Iyengar, Professor of Marketing at the Wharton School of Business.

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.

Like most technology trends it will take time for managers to learn how to deploy, use, and harvest the potential of these tools. So, it’s not surprising that the immediate impact of advanced analytics technologies on revenue growth and firm value remain mixed. 70 % of current AI initiatives have shown little or no return. The majority of CFOs struggle to prove the financial return on their investment in analytics. And the impact of analytics on growth and firm performance has remained essentially flat for the past 8 years, according to research by Duke Fuqua School of Business.  

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 (62%) 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 according to an analysis by Forbes.
  • 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.
  • Rethink the entire sales technology ecosystem from the top down with value creation and capture in mind. This includes a strategy for realizing returns on the entire portfolio of selling tools in the enterprise. Sales leaders already suffer from too much technology and not enough impact.  On average half of organizations already have Pricing, Online Training and Coaching, Content Sharing, Activity Management and Tracking, Contract Management tools in place according to recent research completed by Miller Heiman. Too often these tools are duplicative, difficult to use, or disconnected from the other parts of the system. This fragmented and tactical management of these expensive growth assets makes it very difficult to stitch together technology and data in the five ways that create value.

You can learn more about ways you can leverage advanced analytics to transform the cost, coverage, control, and customer experience of your commercial model at the Revenue Enablement Institute.

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