• Written by Sonali Subhadarsini, Product Manager

The category manager for a large retail chain sits at her desk, staring at a spreadsheet that is supposed to help her plan the next quarter's sales, but is instead causing her more headaches than insights, as she tries to make sense of the complex interplay between historical sales data, inventory positions, and promotional calendars, all of which are critical components of AI process automation for sales.

Mapping the current process before automating any of it

Before automating any part of the sales process, it is essential to map out the current workflow, including all the steps involved in planning, forecasting, and executing sales strategies, which can be a complex and time-consuming task, but one that is necessary to identify areas where automation can have the most significant impact, such as streamlining handoffs between different teams and departments, like the shift supervisor and the category manager, who need to work together to ensure that inventory levels are aligned with demand.

This mapping process involves identifying all the stakeholders involved in the sales process, including sales teams, marketing teams, and logistics teams, and understanding how they interact with each other and with the various systems and tools used to support the sales process, such as customer relationship management software and enterprise resource planning systems, which can be linked to AsscherAi to provide real-time answers to questions about sales data, as described in the sales teams section of our knowledge base.

The handoffs that create most of the delay

One of the primary causes of delay in the sales process is the handoff between different teams and departments, such as the handoff from the sales team to the logistics team, or from the marketing team to the sales team, which can be prone to errors and miscommunications, and can result in delays and lost sales opportunities, highlighting the need for AI process automation for sales to streamline these handoffs and improve communication between teams.

By automating the steps around these handoffs, such as automated notifications and updates, sales teams can reduce the time and effort required to complete these handoffs, and can focus on higher-value activities, such as building relationships with customers and identifying new sales opportunities, which is critical to the success of AI process automation for sales.

The sales data this depends on: historical sales at planning grain, order book and pipeline, inventory positions, promotional calendar

The success of AI process automation for sales depends on access to high-quality sales data, including historical sales data at the planning grain level, order book and pipeline data, inventory positions, and promotional calendar data, which provides the insights and visibility needed to make informed decisions about sales strategies and tactics, and to identify areas where automation can have the most significant impact.

This data can be used to identify trends and patterns in sales, and to forecast future sales, which is critical to making informed decisions about inventory levels, pricing, and promotional activities, and to ensuring that sales teams have the insights and tools they need to succeed, which is a key benefit of AI process automation for sales, as it enables sales teams to make data-driven decisions and to respond quickly to changes in the market.

What it changes about: a forecast nobody can interrogate and inventory out of step with demand

One of the key benefits of AI process automation for sales is that it enables sales teams to create forecasts that are based on real-time data and insights, rather than on intuition or guesswork, which can be prone to errors and biases, and can result in inventory levels that are out of step with demand, leading to lost sales opportunities and wasted resources.

By automating the forecasting process, sales teams can create forecasts that are more accurate and reliable, and that can be easily interrogated and updated, which enables them to respond quickly to changes in the market and to make informed decisions about inventory levels, pricing, and promotional activities, which is critical to the success of AI process automation for sales, as it enables sales teams to stay ahead of the competition and to drive revenue growth.

Measured with: forecast accuracy, win rate, stock cover

The success of AI process automation for sales can be measured using a range of key performance indicators, including forecast accuracy, win rate, and stock cover, which provide insights into the effectiveness of sales strategies and tactics, and into the impact of automation on sales performance, highlighting the need for ongoing monitoring and evaluation to ensure that automation is delivering the expected benefits.

By tracking these metrics, sales teams can identify areas where automation is having a positive impact, and where further improvements can be made, which enables them to refine and optimize their sales strategies and tactics, and to drive ongoing revenue growth and improvement, which is a key benefit of AI process automation for sales, as it enables sales teams to stay focused on their goals and to drive continuous improvement.

What this does not do, or when it is the wrong choice

While AI process automation for sales can be a powerful tool for driving revenue growth and improvement, it is not a solution that is suitable for every sales team or organization, and there are certain situations where it may not be the best choice, such as when the sales process is highly complex or customized, or when the sales team is not yet ready to adopt automation, in which case it may be more effective to focus on other areas, such as sales training or process improvement, before implementing automation, and to contact us at AsscherAi to discuss the best approach for your organization.