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Predictive Analytics for Business Forecasting: 4 Ways to Reduce Expenses and Overhead

Read Time: 5 minutes

Every business owner knows (and dreads) the time of the month when rent and utility bills are due. Unfortunately, neither your landlord nor the electric company cares whether your business is wildly successful or struggling in the red.

Overhead costs are unavoidable and don’t directly contribute to the product you sell. However, these expenses are an important and inevitable aspect of running a business. Fortunately, there are ways to tighten up these fixed costs. By chipping off dollars here and there, businesses can end up with considerable annual savings. This is where companies are finding success using Predictive Analytics for business forecasting. Companies can devise a data-based plan that will make the most of their resources while minimizing overhead expenses.

Analyze the Energy Efficiency of Your Building 

When revenues dip, companies often react by considering employee layoffs. However, they continue to pay pricey overhead costs because they erroneously believe that they’re set and unavoidable. Two common overhead costs are business premises and utilities. By using high-tech sensor power and Predictive Analytics for business forecasting, companies are successfully identifying areas where they can reduce expenses, without the need to reduce staff. 

For instance, energy consumption is one of the biggest outlays in U.S. supermarkets, with refrigeration and lighting accounting for approximately half of the total energy used. These energy bills are a huge expense. Businesses are realizing that decreasing energy costs is a more efficient way to boost their profits—far more effective than attempting to increase sales in order to account for their costly overhead expenses.

Let’s take the example of a large supermarket chain that implemented thermal energy storage (TES) technology to optimize their energy usage. Without taking the time to collect and analyze data about energy consumption, temperature, and peak business hours, this cost-saving solution would have remained unrealized. Not only did Predictive Analytics help optimize this supermarket’s strategy, but the improved energy efficiency also benefits the environment. In some regions, companies may also qualify for energy efficiency-related tax incentives.

Facility managers are using Predictive Analytics to evaluate heating and cooling needs. Energy is often wasted when temperature set points do not take room occupancy and usage into consideration. In large buildings with hundreds of rooms, this metric becomes almost impossible to monitor. Building analytics systems use device-level sensors for lighting, occupancy, HVAC, etc. This data can be coupled with big data analytics engines to create a comprehensive view that can be used to increase operational efficiency.

Evaluating the Cost of Renting vs Buying Electronics and Equipment

Every business needs equipment such as computers, machinery or vehicles. The decision between leasing and purchasing is not always simple. Beyond evaluating overall cost, businesses need to also weigh in maintenance, tax deductions, availability and the frequency of replacement. From a cash flow perspective, renting electronics and equipment may hold a better incentive, especially if your company needs the newest electronics and vehicles (which means you’ll need to replace every year or so). However, it’s important to note that companies tend to pay higher costs over time when they lease instead of purchasing outright. So for companies that can use a single piece of equipment for many years, purchasing outright is often the better choice. 

Companies may also consider maintenance costs, particularly for equipment that requires heavy, specialized maintenance. If you’re looking at a time frame that spans years of equipment ownership, then maintenance costs could quickly add up. In contrast, if your company uses technology that quickly becomes outdated—say, in a matter of months—then leasing may be the better option. It is important to not become stuck as the not-so-proud owner of obsolete equipment. 

Equipment sensors can also be useful for maintenance purposes, as they can be programmed to issue an alert when failure is imminent. This ensures that critical machinery is serviced before it breaks down, which would cause downtime and financial loss. A well-crafted Predictive Analytics platform can even consider important secondary factors related to safety, compliance, risk, efficiency and more. Once all this data is fed into predictive models, patterns begin to emerge. These findings help organizations choose what will best boost their profit margins.

In summary, the best option will vary depending upon the type of business, the type of equipment you’re utilizing and the company’s usage patterns both now and in the future. Running the numbers manually is nearly impossible, so the best choice may be to input key metrics and usage patterns into an analytics engine that can process the numbers. This will give you a clear data-based idea of which solution is best for your needs. You may even find that your needs change over time, which underscores the importance of leveraging Predictive Analytics for business forecasting, especially if your company is rapidly evolving. 

Analyzing the Impact of Outsourced Labor vs. In-house Labor

The cost of employee onboarding can be significant, but early attrition arising from a poor culture fit can drive up costs even further. Employers are expected to spend an estimated $680 billion in turnover costs by 2020, according to the Work Institute National Employee Turnover Report 2018. To make a dent in these projected costs, many organizations are already using Big Data to track trends, in addition to analyzing potential candidates to determine how well they align with a company’s culture. It’s even possible to use this technology to track employee behaviors for patterns that may indicate an impending departure from the company. 

Businesses also reduce their head-count by outsourcing certain roles, particularly for short-term projects. This prevents a high turnover rate or the need to layoff essential and talented team members when the company needs to cut costs. One tradeoff is that outsourced partners might be less invested in the company’s success. Some companies also find that it’s challenging to bridge the gap in time and distance for temporary staff who are located in a remote location. 

Third-party vendors tend to be most popular for simpler, repetitive tasks such as bookkeeping and accounting, virtual assistant-type tasks and one-time projects, such as website development. A well-crafted Predictive Analytics engine can be leveraged to calculate precisely how outsourced staff measure up when compared to in-house teams. What’s more, companies can monitor third-party work moving forward, evaluating how this impacts the company budget and ROI.  

Analyze Business Travel and Forecast ROI in Advance

Travel and entertainment (T&E) is a difficult expense to budget for. It is hard to come up with the travel volume for a year or even a quarter in advance. The cost of any trip is highly variable as it depends on market conditions at any given time. Further, traveling is often decentralized unlike other expenses and makes for even more difficulty in budgeting.

A survey by JP Morgan Chase revealed that traveling is the biggest expense after payroll; These expenses can account for 10-12% of a company’s annual budget. What’s more, business travelers are not the same, and their spending can have dramatic differences. Predictive forecasting can help decrease the uncertainty involved and include more accurate numbers into the annual forecast budget. Big data analytics usually collect historical data from two sources. The first source is data from travel expenses from previous periods. This booking data can be gathered from online booking tools or through a corporate agent. The second source is from employee expense reporting. An analysis of trip counts and travel spending can determine a recommended per-employee monthly average travel expenditure. This will be modified against the company’s growth and sales plans. By using Predictive Analytics and smart incentive structures, companies can reduce travel expenses by 25% or more.

With Predictive Analytics for business forecasting, companies don’t have to feel trapped by their expenses. Data-driven insights empower and allow businesses to optimize their expenditures and overhead costs. If you are interested in learning more about how Predictive Analytics can work for your company, 7T is here to help. Our product, Sertics, is a powerful data lake creation tool that enables the power of Data Visualization and Predictive Analytics for business users. 

7T also specializes in all facets of custom software development and mobile app development, including UI/UX design, mobile security and app testing. So you can proceed with confidence knowing that your new data governance tool will be user-friendly and secure. 

With headquarters in Dallas, 7T maintains regional offices in Houston, Austin, and Chicago. Our clients are located worldwide, so we’re ready to overcome the challenges that distance can create. To discuss your development project or experience the power of Predictive Analytics, reach out to the team at 7T today.  

Reach out to our team today!

Anand Balasubramanian

7T is a custom software and mobile app development company serving clients in Dallas, Austin, Houston and beyond. Our specialties include augmented reality (AR), data governance, UI/UX design, ERP development, CRM development, mobile security, and more.


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