There's vast potential in sales analytics tools and tech backed by artificial intelligence. Solutions based on AI for sales organizations can help companies stay one step ahead in the new era of data-driven B2B sales. Strategic investments in AI-driven analytics tools and tech can automate, guide and improve your overall sales process and bolster overall sales performance.
Agile B2B and SaaS Sales organizations with a digital-first mindset looking to “future-proof” their own sales operations have long had these innovations on their radar. Recent reports reveal many others are catching on. Industry expert surveys state that 88% of CSOs have considered or have already taken the leap towards investing in AI analytics tools and technologies. Is your organization among them?
Artificial intelligence and machine learning have come a long way in the last decade. So, how are companies currently leveraging AI for sales? In this article, we’ll outline some of the most common capabilities and practical uses particular to AI for sales. In addition, we touch on some of the key ways sales organizations are actively utilizing artificial intelligence to boost revenue.
According to Gartner, B2B buyers spend a mere 17% of the entire buying process meeting with sales reps. For sales organizations, this extremely limited window urgently underscores the need for effective and efficient digital selling initiatives and measures across the board.
One result of the process of leveraging digital channels to find, engage and connect with prospective buyers is simply more data. And especially in complex day-to-day sales environment, sales reps need reliable tools and technologies to streamline the process of collecting, analyzing and deriving actionable insights from this wealth of information. This is where AI and ML come in.
According to Gartner, the following AI-based solutions are among the most frequently used in sales:
Natural Language Processing (NLP) is geared towards bridging the gap between natural language and systems. It represents the ability of a program to understand human language as it is spoken and written. NLP is designed to recognize, read, interpret, tag and generate language. In sales, Natural Language Processing finds frequent use with emails and sales-call transcripts, in automated data entry and note-taking, sales-call analysis and virtual agents.
Diagnostic Analyticsis the process of using data to analyze correlations among variables in a dataset and identify causes of trends. It is the logical next step following using descriptive analytics to identify trends. The determined relationships between variables among this data allow for sales-specific problem and cluster data analysis. Common uses include personalization, segmentation and tiering. In short, diagnostic analysis tries to explain why a particular event happens.
Predictive Analytics (also predictive analysis) describes a method of analysis that uses new and historical machine learning data to predict future events (such as specific human or machine behavior and trends) and their probability of occurrence. This process involves identifying factors which influence outcomes and understanding how they do so. Predictive analytics finds use in AI-driven sales forecasting, helping automate projections based on data-driven, intelligent analysis of key metrics.
Optimization and Prescriptive Analyticsis often referred to as the recommender/next-best-action system. This form of analytics utilizes optimization techniques to recommend users the best course of action during complex decision-making involving tradeoffs between business objectives and constraints. One common use is in guided selling. This is an optimized sales process designed to assist sales reps in navigating an increasingly complex sales environment, typically employing AI and machine learning.
Smart Processing Automation (SPA)is a category of innovative technologies that combines Robotic Process Automation (RPA) with Machine Learning. It is essentially the next version or step in innovation of RPA (Robotic Process Automation), sometimes also referred to as RPA 2.0. Current SPA capabilities enable the automation of tasks with greater complexity and reasoning required to process. Common uses in B2B marketing and sales include social listening, customer relationship management and lead nurturing, automated CRM data input, categorization and display, as well as social selling.
AI can be pivotal in achieving better business results and revamping your sales process. However, it’s equally important to take note of certain prerequisites for success implementation. Some of these include proper use-case selection, good data quality and particular attention to employee upskilling.
Artificial Intelligence (AI) for Sales: A Current Outlook
The Gartner 2021 CSO Priorities Pulse Survey states that investments in AI analytics and technologies are on the rise. Close to nine out of ten of CSO survey participants report having considered or have already taken first steps towards implementation.
So, where does artificial intelligence comes into play? Here are some prime examples of common uses and benefits of AI for sales.
Sales Training and Coaching
Industry experts at Gartner also predict that AI will deliver foundational changes to the field of sales training and coaching. The incorporation of artificial intelligence into company training programs can help provide a more individualized learning experience, tailored to individuals with unique learning styles.
Complex ML and AI algorithms can supply tailored recommendations on training and coaching solutions while analyzing which types of content are best suited to the learner. The result? Greater efficiency with less time and money spent on training sales rookies and more opportunities to engage in activities that drive revenue.
Sales reps can also make use of AI for sales-specific insights into their own seller types. Developing an understanding of their own sales personality types can help sales reps better assess performance in and suitability to a particular role and further diagnose potential strengths and weaknesses.
2015 saw the first examples of AI for sales guidance, and guided selling technologies overall have witnessed further advances throughout the past half-decade. However, these have since proven to be quite limited.
For one, the software’s ability to effectively evaluate results has fallen short of providing statically relevant recommendations for action, Gartner states. Existing guided selling technologies also usually focus either on data, automation and tasks, and rarely combine these in a meaningful way. Current trends and developments in sales tech suggest this is soon to change.
According to Gartner, 75% of B2B organizations are projected to implement AI Guided Selling solutions into their existing sales operations by 2025. The active interest in investing in AI and machine learning (ML) technology is in response to mounting pressure to leverage massive amounts of data, most of which goes underutilized.
AI Guided Selling bundles sales activity tracking, evaluation and guided selling into one. Guided selling software powered by artificial intelligence works to boost sales rep productivity with tailor-made recommendations on what to focus on and when. This way teams don’t just get more done but focus their time and effort on the steps that count.
However, AI and ML solutions still need your valuable experience, knowledge and input to reach these new heights. It takes top-notch CRM data quality and the right sales activities to make ML models work for you.
Dealcode’s Free eBook provides an in-depth summary of how your team can remain a decisive step ahead of the competition on into the new era of high-tech, “future-proof” data-driven B2B sales. Click here to Download the eBook as a PDF.
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