Sales automation is poised to dramatically accelerate sales growth in 2022, ultimately creating $1.4 to $2.6T of value according to the Harvard Business Review.
Powered by artificial intelligence, sales automation can effortlessly scale resource-intensive steps in the sales value chain, including SDR recruitment, prospect research, deal closing, and everything in between. With these tools, sales teams can delegate administrative work and focus on doing what they love: building relationships and delivering value to customers.
Sales is the backbone of every successful product-led organization. Even teams with industry-defining products still need robust sales processes to drive revenue growth. Unfortunately, these sales processes can be expensive and inefficient, consisting mainly of resource-intensive, low-value tasks. With sales automation, however, teams can execute these tasks with incredible efficiency and ease.
‘Sales automation’ is the process of automating specific steps in the sales value chain, allowing teams to focus less on administrative tasks and more on closing deals. Much of this automation is powered by artificial intelligence and machine learning. While artificial intelligence is an umbrella term for several different technologies, all of which can impact sales in profound ways, machine learning is the most widely applicable to sales teams. Machine learning can effortlessly execute resource-intensive steps in the sales value chain, including predicting which prospects are most likely to close, forecasting quarterly results, optimizing pricing, and much more.
Sales automation is poised to dramatically accelerate sales growth in 2022. In fact, McKinsey analysts writing in Harvard Business Review estimate that sales automation will create $1.4 to $2.6T of value in sales and marketing. While these are ambitious estimates, they are bolstered by sales automation’s historical success: “Companies using AI for sales were able to increase their leads by more than 50%, reduce call time by 60-70%, and realize cost reductions of 40-60%” (AIMultiple, 2021).
While sales automation is receiving well-deserved praise in business journals, the frequent media coverage around this new technology may have teams overwhelmed — and many of them may not know where to start. To remedy this, the Product10x Accelerator white paper on ‘Artificial Intelligence for Sales Automation’ will host a practical, actionable view of AI in sales by minimizing technical jargon and maximizing real-world use cases. After this white paper, readers will be armed with the knowledge needed to accelerate their organization’s revenue through sales automation.
Sales automation is creating tremendous value across the sales value chain. This section will review several real-world use cases in SDR-focused activity and prospect-focused activity.
Before sales leaders can engage in the prospect-facing steps of the value chain, they must first recruit, onboard, and train a robust team of SDRs:
Optimize sales recruiting efforts to build a strong team
Platform integrations allow HR leaders to save time by combining multiple candidate platforms into one centralized hiring repository.
HR leaders can analyze recruiting efficacy across applications, interviewing, and hiring. For more info, please visit this explanatory article about hiring techniques.
Build scalable and repeatable team onboarding processes
Onboarding checklists allow leaders to identify 30-60-90 day tasks for new hires.
Offer video-based, self-paced training to memorialize product knowledge and hold the team accountable for learning and comprehension
Sales leaders can provide training content for a trackable, interactive, and gamified process. For more info, please visit this explanatory article about training techniques.
Sales leaders can improve training processes through new hire feedback tools.
Once sales leaders have built out a robust team of SDRs, they can then move onto the prospect-facing steps of the value chain:
Find potential customers and easily identify the best prospects
Lead generation prevents SDRs from spending valuable time searching for leads. For more info, please visit this explanatory article about lead generation.
Lead scoring allows SDRs to determine the priority of leads by scoring their likelihood of converting. Using various data sources, including industry information, social media postings, and previous interaction history (e.g. emails sent, voicemails left, text messages sent), SDRs can accurately prioritize leads. For more info, please visit this explanatory article about predictive sales.
Prepare for initial contact with a potential customer by identifying the appropriate buyers, developing collateral, and tailoring it to their particular needs
SDRs can use tools such as LinkedIn and ZoomInfo to identify a prospect’s appropriate stakeholders and their roles in the buying process.
SDRs can use content personalization to meet the prospect's unique needs and preferences. For more info, please visit this personalized content article.
Demonstrate how the product or service meets the needs of the potential customer
Setup automation tools can schedule recurring and ad-hoc meetings with ease. For example, Calendly links e-mails and conversations to calendars, while Clara responds to e-mails and organizes meetings.
Maintain relationships with customers — retaining current customers is six to seven times less costly than acquiring new ones
Chatbots can contact customers with personalized chat messages and customized emails, allowing teams to increase sales by 67% on average.
Finally, as sales teams accelerate towards their quotas, leaders can utilize SDR tracking to capture individual performance:
Capture critical KPI data from the sales team and provide insights to management regarding the overall health of sales operations, as well as the viability of each salesperson
Goal tracking allows sales leaders to monitor KPI performance for the overall team and each SDR on a daily, weekly, or monthly basis. Set time-based targets and receive notifications if/when those minimums are not met.
Predictive forecasting allows sales leaders to forecast the team's performance so they can take proactive steps based on the projections.
Actionable recommendations provide prescriptive analytics based on a team’s goals and historical performance. This targeted guidance allows sales teams to focus on the most promising sales activities instead of deliberating about what to do next.
Sales automation is a win-win for customers and management alike. For customers, sales teams can focus on building relationships and delivering value. For management, sales teams can automate their administrative workflows and concentrate on boosting revenue.
Despite all of the benefits of sales automation, there is still concern among the sales community that AI threatens to completely automate sales and remove the need for human interaction altogether. According to McKinsey Global Institute (2020), however, the sales function will never be fully automated. Instead, research suggests that sales teams are augmented, not replaced, by AI systems. Thus, sales teams should regard AI not as a threat to their job duties, but as a powerful tool to rid them of low-value tasks and boost their efficiency.