Expense reduction is a constant goal for most companies. Fortunately, data analytics can assist with keeping costs down in several ways. Here are six of them.
1. To Cut Fleet Management Expenses
There’s a rising trend in equipping vehicles from company fleets with Internet of Things (IoT) sensors that give management personnel details about things ranging from truck routes to driver fatigue.
One company that participated in a research study to pinpoint the effects of big data analytics on logistics operations found it was possible to reduce fuel consumption and CO2 emissions by relying on data analytics software.
Other applications include depending on sensor data to inform maintenance needs, which could cut costs associated with breakdowns, or using the data to assess which drivers frequently engage in risky practices that make those employees liabilities for their companies.
2. To Lessen Instances of Employee Turnover
Human relations professionals are familiar with the extensive costs associated with employee onboarding. But, the total expenses could climb even higher if employees are poor fits for the company and leave quickly after getting hired. According to 2018 data from Work Institute, employers will pay $680 billion in turnover costs by 2020, and companies could prevent 77 percent of turnovers.
Many companies use analytics before hiring candidates because it allows them to analyze information, such as the likelihood of someone aligning with a company’s culture. Big data can also track trends that could indicate a person currently working at a company is getting frustrated in the role and might leave for another opportunity.
As such, businesses that use data analytics in these ways could avoid the costs associated with training new employees that don’t stick around, or not recognizing when an employee is so unhappy they want to leave.
3. To Manage and Minimize Indirect Costs
Indirect costs are those associated with the operations of a company, but not related to products sold. Statistics indicate reducing indirect costs could save companies more than 25 percent in overall expenses. The categories of indirect expenses vary by each enterprise that incurs them, but some of the common ones include rent, utilities and office supplies.
Companies can’t start to reduce their indirect costs without knowing the average amount they spend on things each month. Big data analysis helps in this area by providing baselines that inform enterprises of their most substantial indirect expenses. Then, people can start figuring out where to make improvements.
One accessible way for companies to get started is to invest in IoT utility products like smart light bulbs and thermostats. Those items typically let users know statistics such as the average amount of energy used per month. Some even give tips for cutting utility bills.
Plus, printers and copiers can predict future supply needs based on usage patterns, then alert users to order things like ink and toner before those things run out. People can also log in to specialized dashboards to study trends.
4. To Shorten Testing Processes
Companies frequently go through tests associated with segments of their target markets before launching new products or updating their websites. Such testing helps avoid failures that could occur when businesses don’t connect with their audiences. Analytics platforms make tests less time-consuming, and thereby not as expensive.
Chime Bank wanted to increase the number of people signing up for new accounts and believed personalized content would help reach that goal. When choosing new content for its website, the company deployed a predictive analytics platform that used artificial intelligence to make the process more efficient. Doing that enabled the company to test 216 homepage versions and 21 ideas in only three months.
5. To Avoid Making Customers Upset
Businesses must not overlook how unsolved grievances may cause customers to get frustrated, leading to a rise in preventable costs. According to a report from NewVoiceMedia, there’s a rise in “serial switchers,” or people who willingly go to other providers after getting displeased with the former ones due to bad experiences.
Coverage from Forbes about the report says poor customer service costs brands more than $75 billion annually. But, high-tech analytics software, such as what many call centers use, can evaluate characteristics like tone of voice and word choice to determine when customers start to get frustrated.
Also, Salesforce has a platform called Customer 360 that aims to soothe customers differently. It allows customer service representatives to see the full picture of a customer’s interactions during communications. Then, a caller does not have to keep explaining their situation over and over again to workers in different departments.
6. To Monitor for Cyberattacks
Cyberattacks can disrupt website functionality, erode consumer trust and lead to decreased employee morale, among other adverse effects. Moreover, companies often do not anticipate the total expenses of those issues. A 2019 report from Radware found the average cost of a cyberattack was $1.1 million.
Data analytics platforms for cybersecurity purposes can check network traffic continually and give notifications of suspicious behavior that could indicate breach attempts. Many offerings have AI components, too.
Data Analysis Makes Expense Reduction More Straightforward
It’s not easy to assess where and how to cut expenses. But, these examples show how data analysis can help people make those judgments with confidence.