While it may take an upfront investment, data analytics can transform business operations
Key takeaways
Analyzing your data can identify seasonal fluctuations and forecast future demand
Identify bottlenecks, allocate resources effectively and ensure projects stay on track
Data analytics is instrumental in optimizing business operations for service companies. Every company deals with challenges, including customer experience, inefficiencies, workforce utilization, and inventory management. Leveraging the proper data analytics tools can address these issues. While it may require an investment upfront, if done properly, companies will reap the operational and financial benefits down the lines. Data analytics can help in the following areas:
Lead/quote-to-cash
Service companies can examine customer data, lead sources, and conversion rates through data analytics. This enables them to identify the most effective lead generation channels and optimize marketing strategies, thus improving sales conversions. Data analytics can also help streamline the quote-to-cash process by identifying bottlenecks in the sales cycle, improving quote accuracy, and decreasing the time between making initial contact and providing a quote.
Inventory management
Service companies can analyze historical demand patterns, identify seasonal fluctuations, and forecast future demand. This enables them to optimize inventory levels, reduce stockouts, and minimize carrying costs. Additionally, data analytics can identify slow-moving or obsolete items, allowing for timely corrective actions.
Use case No. 1
Company:
[Sanitation company]
Sector:
Janitorial services
Size:
$1.2 billion
16,000-plus employees
400-plus service locations across North America
Problem:
The client was managing numerous segregated, on-premise systems across four businesses with limited functionality for integrated enterprise analytics. Extensive manual manipulation of data and reports resulted in time-consuming and stale metrics. Also, their enterprise resource planning (ERP) system was outdated and no longer supported by the vendor.
How RSM helped:
RSM consolidated and integrated nine data systems into an enterprise-grade data warehouse, reducing the critical report count from 120 to 28. RSM also streamlined and automated the client’s data intake process along with cross-business reporting capabilities not previously available.
Impact:
Reduced data and report preparation time by an average of 90%; enabled self-service reporting for over 200 users; nightly data refresh averages less than one hour to complete.
Project management
Data analytics can provide insights into project performance, resource allocation and task dependencies. Service companies can identify bottlenecks, allocate resources effectively, and ensure projects stay on track by analyzing project data. Data analytics can also help monitor total project costs and projected profitability based on resources allocated to the project. Companies can gain insights into the leverage model needed to complete the project, allowing them to manage their job profitability and overall profit and loss (P&L).
Customer experience (CX)
To understand customer behavior, satisfaction levels, and sentiment analysis, few aspects of the customer experience are as crucial as data analytics. Service companies can identify pain points, improve service quality, and enhance the customer experience by analyzing customer feedback, social media, and transactional data. Data analytics can assist in monitoring customer sentiment in real-time, allowing for proactive intervention and issue resolution. Companies can also identify opportunities for upselling, cross-selling, and customer-retention strategies, which helps companies be proactive in serving customer needs.
Use case No. 2
Company:
[Tax, audit, and consulting services company]
Sector:
Tax, audit, and consulting services
Size:
$68 million / 300-plus employees
Problem:
The client was in dire need of a digital transformation from their antiquated operational processes. They had limited data analysis capabilities with their existing data and systems architecture, inhibiting their ability to stay competitive.
How RSM helped:
RSM implemented a cloud-based, centralized, enterprise data warehouse and reporting system with corresponding training sessions to end-users, providing the client the ability to perform self-service reporting and enabling key reports distribution across all business areas.
Impact:
29 days reduction in lead time for executive reporting; over 60,000 clients served utilizing the automated metric generation method.
Workforce utilization
By analyzing performance data, service companies can identify high-performing employees and deploy them to the most critical and high-risk tasks, optimizing workforce scheduling. Data analytics can provide insights into workforce productivity, individual margin analysis, and the strengths and weaknesses of employees. Companies can assess rates based on the market and task, thus providing a more refined scope of services. Data analytics can also identify training needs, skill gaps, and opportunities for process automation.
Contract writing
Data analytics can provide historical contract data, clauses, and templates. Service companies can identify common risks and pricing sensitivity within a subsector by leveraging data analytics. This helps streamline the contract writing process, reduce errors, and improve efficiency and effectiveness.
Data analytics empowers service companies to make data-driven decisions, optimize operations, improve customer satisfaction, and drive business growth. By leveraging insights from data, companies gain a competitive edge in the market and deliver superior services to their customers.
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