PART (1/3) – Why do you need “Sales Automation Strategy”

Companies are pioneering sales automation with inspiring results. Using five or key prominent technologies — machine learning (ML), robotic process automation (RPA), natural language processing/generation (NLP/G), smart workflows, and cognitive agents — companies are reducing costs significantly, improving customer service tremendously and scaling operations immensely.

Some of the sales automation’s overarching benefits, how it can be applied to standard sales processes, and how to successfully adopt new processes to improve your team’s ability to sell are pointed things that organizations new to sales automation are now looking at in detail before moving into implementation phase and believe me this is across all sectors/industries.

Benefits of Sales Automation

In addition to improved service, sales automation can improve accuracy and consistency by eliminating human errors and streamlining processes – which range right from the start of sales enablement to closing sales and then servicing the client. It can boost scalability by allowing flexibility to grow key activities while sustaining execution quality. Customer coverage availability increases via the use of RPA and virtual agents and traceability expands, which improves audits and root-cause analyses. For. Eg – Can we automate lead identification process? Build first level auto cuts of proposals? Running analytics on the sector data before going for a client meeting? Pre-empting customer challenges/issues via data analytics and automated learning capabilities?

Here are a few examples of how automation functions within each stage of the sales process:

Pre-Sales Improvements:

Automated web crawlers collect market and competitor data to analyse and predict, for example, an industry’s total addressable market (TAM) and a customer’s present and future potential. AI allows organizations to better project market trends by region, which can be used to automatically update sales frontline quotas. Organizations can use advanced analytics to automate and systemize sales-call plans. RPA and ML tools can automatically detect bottlenecks and their root causes in the sales process and help sales-funnel management. ML tools can automatically generate call-lists and guide salespeople to call the right customers with the appropriate topic at the right time.

Sales Improvements:

NLP-based tools can decrease request-for-proposal (RFP) response time. Infact, automation tools can be tuned and trained to identify clauses on tender documents which are not in the interest of an organization. This can help plan and internally strategize next steps on such clauses. RPA and ML tools automatically analyse and export pricing schemes. Automated workflows can optimally accelerate discount/rebate setting and management. ML tools can generate insights on actions and behaviour associated with high closing rates. Billing workflows in turn are automated via RPA-based account receivables and payable management (e.g., invoice generation and processing).

Post-Sales and Sales Operations Support:

RPA- and NLP-based tools allow the automation of customer feedback and behaviour analysis, enabling account managers to improve customer satisfaction and first-call resolution (FCR) while freeing up full-time equivalent (FTE) capacity. In consumer sector for eg., RPA and ML tools can identify customers with a high probability of churn and auto-generate promotional offers to retain them.

Part 2 (Planning and Executing Sales Automation Strategy) and Part 3 (Sustaining Sales Automation Strategy) will follow...

#planning #strategy #leadership #strategicplanning #strategicconsulting #automation #SalesStrategy #HBR