RSMUS.comAdvanced data analytics is changing the game for many middle market companies, improving both top- and bottom-line performance. And as the cycle of commerce produces more data, the value of advanced analytics will grow, widening the gap between leaders and laggards.
Advanced data analytics applies intelligent statistical methods, such as machine learning and predictive modeling, to data. This enables people to uncover new insights and market trends that improve overall business performance. Consider these use cases:
Product development and personalization: Leading retail businesses provide a good example. They’re using behavioral analytics and automation to improve customer experience by offering up personalized offers in real time across channels.
Product tendency: Analytics applications can consolidate and analyze customer data from multiple sources and provide insights for refining customer segmentation and channel distribution.
Automated detection and prevention: Advanced data analytics can be embedded in function-specific applications. For example, cybersecurity applications use advanced analytics to help identify and fend off threats, and supply chain applications use the capability to quickly identify and mitigate bottlenecks.
Sales optimization: Many organizations use advanced analytics to analyze their past opportunities, successes, misses, win rates and other criteria to help their sales teams take actions that improve sales strategies and increase wins.
Siting and capacity planning: Understanding the right balance of physical assets for current demand—and making decisions that enable agility for changing demand—simply can’t be effectively achieved without advanced analytics.
Emerging risks: Risk management, compliance and audit functions are using advanced analytics to identify emerging risks or changes that affect existing risks.
Previously, only large enterprises could afford the people and technology resources needed to harvest the benefits of advanced data analytics. Today, midsize companies can use advanced data analytics as part of their core business applications, such as ERP and CRM.
Take a look at how these three companies use advanced data analytics.
CellSite Solutions purchases used and surplus shelters that house telecommunications equipment for towers, refurbishes them, and then resells them at a fraction of the cost of new hardware.
RSM worked with CellSite’s team to create a new application that drastically improved the efficiency of about 400 on-site audits of used shelters the company performs each year. The application directs auditors to take photos of specific features of the shelter, then immediately uploads it into a pre-defined slot in SharePoint. The platform helps create a more effective vision of the equipment throughout its lifecycle, making comparisons quicker and easier to find what customers need.
Then advanced analytics can be applied to data that flows from customer systems, such as a CRM, to give CellSite visibility into what types of features customers are looking for from shelters. The two systems work together to help CellSite match inventory to what shelters are selling better and what really drives value for the business.
Food and beverage case study: Bare Snacks
Bare Snacks makes healthy snacks such as dried apple, banana and coconut chips. Founded more than 10 years ago by a farm family in Washington state, Bare Snacks has experienced significant growth over the decade, with distribution across the country.
Company leaders knew they needed more mature finance and accounting practices and so implemented NetSuite to improve efficiency and visibility. But the new accounting staff didn’t know how to use NetSuite to achieve those goals.
RSM began working with Bare Snacks to familiarize its accounting employees with NetSuite and provide basic training to help them understand the system from a company-wide perspective and also comprehend the potential of the solution.
Ultimately, with RSM’s help, the Bare Snacks finance team has leveraged NetSuite to provide management and investors with accurate financial information to better compare their costs and consumer price levels and properly plan their budget process.
Custom dashboards and KPIs provide greater visibility into operations, and the increased automation is increasing productivity and efficiency.
Financial services case study: Argo Group
Argo Group is an international underwriter of specialty insurance, with more than 1,300 employees and $9 billion in assets.
Argo previously implemented an internal audit software solution that was misaligned with its needs, including limited project management capabilities, manual issue tracking functionality and various management reporting challenges.
Argo selected RSM’s Auditor Assistant internal audit automation platform based on its proven productivity, efficiency and standardization capabilities and the solution’s alignment with the company’s specific needs.
The solution offers automated analysis to reduce risk through better data analytics. For example, forensics tools within the platform better identify connections or unusual characteristics between disparate datasets that might indicate fraud or other prohibited behaviors, enabling Argo to efficiently target potential high-risk areas within its operations.
For a deeper look at how these companies used advanced data analytics, download RSM’s Guide to Advanced Analytics.
