Updated: Aug 1
Sales process and marketing campaigns impact sales results. Yet, business leaders find it challenging to identify bottlenecks and optimize sales management processes and marketing strategies since these processes are dynamic, complex and impacted by external factors. Process AI can tackle this issue by visualizing the sales processes and comparing them with the defined targets.
Here are 6 process AI use cases for marketing and sales:
1. Monitor sales processes
Challenge: Commercial leadership provide ideal sales guidelines to follow during customer-sales teams interactions. However, businesses often do not know to which extent sales reps follow these rules and procedures.
Solution: Process AI helps analysts visualize the entire sales process from lead generation to the closed deal. This provides insights about interaction with customers, steps the sales reps follow and throughput time between each activity. Analysts can monitor the actual sales processes and compare them with the ideal models by using process discovery and conformance checks.
For instance, a Portugese retailer used process mining to continuously monitor their sales processes, specifically customer return sales and analyzed their customer support quality and its impact on customer satisfaction.
2. Design ideal sales processes
Challenge: Sales teams often complain about designing a sales process that can be flexible depending on the different customer sizes, sectors, product types and contract sizes.
Solution: Process AI can be helpful to define key sales activities for different attributes and generate segmentation criteria. Based on these criteria, sales teams can design processes targeting each category of customers, products and sectors.
For example, process AI allows the companies to determine the activities that move deals forward and design their sales processes based on these insights in order to speed up the sales process.
3. Improve B2B sales process performance
Challenge: Sales processes are dynamic and complicated because they include multiple parties, sequential activities and process changes depending on deals, market variations and time. Therefore, it is difficult to track variations or issues at any step of the entire sales process.
Solution: Process AI can monitor processes, assess process performance through KPI analysis and identify the variations, bottlenecks, duplications and wasted steps in the processes. It measures the resource costs for each variation or bottleneck. Thus, it can detect the root causes such as resource deficiencies, under-performance, manual tasks, or bureaucracy by employing root cause analysis. Process AI allows sales teams and business analysts to discover opportunities for standardization, automation, or modification to improve lead time and resource management. Also, some process mining tools allow sales teams to set up warnings in cases throughput times exceed the ideal response time.
For example, A manufacturer in Finland applied process AI to sales processes to increase visibility of operations. Process AI enabled the manufacturer to discover steps and activities in the sales processes. Based on the insights, the firm minimized rework and improved execution time, which ensured the on-time delivery.
4. Increase B2C conversion rates
Challenge: As in B2B, B2C sales processes need to be constantly improved. Conversion rates are a critical metric to improve since most companies are able to convert only a small percentage of prospects. Sales deals can be lost due to external factors such as competitors or internal factors such as
activities leading to delays and dissatisfaction
marketing strategies that do not convert into sales
Solution: Analysts can leverage process AI to assess the impact levels of the factors over the outcomes. Then, they can make actionable advice to the teams to eliminate these factors or modify marketing strategies so that they can increase conversion rates.
Also, process AI performance analysis serves to measure sales teams’ performance within market segments and regions. Business analysts can detect the best practices increasing conversion rates to share their experience with the rest of the team through workshops, coaching and short training.
For example, Amway Korea, a global network sales company, employed process AI to understand why they lose customers. The analysts found out that website processes were one of the major factors leading to high customer churn. Therefore, the advice was to improve website processes by streamlining home-page user interface.
5. Develop and optimize marketing strategies
Challenge: Though businesses implement sales and marketing insights while planning their marketing campaigns and strategies, they find it difficult to turn the qualitative tactics into measurable and objective marketing ROI.
Solution: Process AI overcomes this challenge by extracting the data from every system storing lead interaction data. Sales and marketing teams can use process mining as an analytics tool to visualize the entire process where marketing leads convert into customers. They can categorize the leads based on the channels the customers find the product or service, personas the customers fit into and marketing tactics the customers respond to the most. They can compare these categories to invest in channels and tactics accordingly.
For example, Anyway Korea that deployed process mining to analyze mobile and website usage behavior in online shopping. Process AI enabled the company to design statistical models to detect business builders and customer segmentation, measure KPIs, and analyze customer behavior based on these customer segments. The firm identified the key patterns in these customer segments to make data-driven marketing campaigns.
6. Enhance customer satisfaction
Challenge: Often, sales teams and customers find themselves in a conflict about product specifications, price and delivery details which eventually lowers customer satisfaction.
Solution: Process AI can tackle this problem by predicting the downstream activities. Predictive process mining leverages historical process data to identify similar cases and generate predictions based on these cases. These predictions can support sales reps when they interact with the customers.
For example, order cancellations or changes can imply lower customer satisfaction. Sales teams can leverage process mining to discover and optimize their lead-to-order and order-to-cash processes. With process AI, teams analyze the historical data to diagnose the root cause of these issues and predict for the future, which stabilizes the processes, reduces sales cycle times and and minimize the cost.
Please visit www.perceptif.ai for a demo or trial to see how Process AI can help your organisation.