6 Ways Process Mining Supports Sales & Marketing Teams

Sales process and marketing campaigns impact sales results.

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Yet, business leaders find it challenging to identify bottlenecks to optimize sales & marketing management processes. After all, these processes are dynamic, complex and impacted by various external factors. Process mining, analytics and automation can tackle this issue by visualizing the sales processes and comparing them with the defined targets.

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Here are six process-driven AI use cases on how it can benefit sales and marketing:

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1. Monitor sales processes

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Challenge:

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Commercial leadership provide ideal sales guidelines to follow during customer-sales teams interactions.

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However, businesses often do not know to which extent sales reps follow these rules and procedures.

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Solution:

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Process mining helps analysts visualize the entire sales process from lead generation to the closed deal.

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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.

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For instance, a Portuguese 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.

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2. Design ideal sales processes

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Challenge:‍

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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.

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Solution:‍

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Process mining and analytics 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.

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For example, process-driven AI softwares like Perceptif allow 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.

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3. Improve B2B sales process performance

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Challenge:‍

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Sales processes are dynamic and complicated because they include multiple parties, sequential activities and process changes depending on deals, market variations and time.

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Therefore, it is difficult for sales managers to track variations or issues at any step of the entire sales process.

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Solution:

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Process mining and analytics can monitor processes, assess process performance through KPI analysis and identify the variations, bottlenecks, duplications and wasted steps in the processes.

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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 mining allows sales teams and business analysts to discover opportunities for standardization, automation, or modification to improve lead time and resource management.

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Also, some process mining tools allow sales teams to set up warnings in cases throughput times exceed the ideal response time.

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For example, a manufacturer in Finland applied process AI to sales processes to increase visibility of operations. Process mining 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.

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4. Increase B2C conversion rates

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Challenge:

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As in B2B, B2C sales processes need to be constantly improved.

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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

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• activities leading to delays and dissatisfaction
• marketing strategies that do not convert into sales

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Solution:

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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.

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Also, process mining 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.

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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.

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5. Develop and optimize marketing strategies

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Challenge:

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Though businesses implement sales and marketing insights while planning marketing campaigns, many find it difficult to turn the qualitative tactics into measurable and objective marketing ROI.

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Solution:

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Process mining and analytics overcome this challenge by extracting the data from every system storing lead interaction data.

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Sales and marketing teams can use process mining as an analytics tool to visualize the entire process where marketing leads convert into customers. Marketing leaders 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.

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They can compare these categories to invest in channels and tactics accordingly.

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For example, Amway Korea that deployed process mining to analyze mobile and website usage behavior in online shopping.

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Process-driven 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.

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6. Enhance customer satisfaction

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Challenge:

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Often, sales teams and customers find themselves in a conflict about product specifications, price and delivery details which eventually lowers customer satisfaction.

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Solution:

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Process mining can tackle this problem by predicting the downstream activities.

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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.

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For example, order cancellations or changes can imply lower customer satisfaction.

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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.

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Reach out to Perceptif team for more information or redeem a free business process diagnostic assessment.

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Get started today. Enjoy an all-access 30-day free trial on us!

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