Data Analytics Can Reduce Business Expenses

In today’s fast-paced world, companies face stiff competition to stay in business. They must position their products and services to keep customers coming their way. A significant part of that is related to money – companies keep their prices competitive to gain an advantage. While companies cannot increase prices at will to maintain their profitability, they can still minimize their costs. This is where data analytics, specifically expenditure analysis come into the picture. Data analytics can help stakeholders reduce business expenses to drive profitability.

What are Expense Analytics?

Expense analytics, simply put, is using data obtained from various transactions and cash outflow processes to good use. With robust algorithms and the power of AI, businesses can gain powerful insights into their expenditure sheets.
Data is the primary currency of the 21st century. As such, having large datasets at hand and generating insights from those is a boost for any company’s operations. Efficient processes and better decisions result in outcomes and growth prospects that are desirable.

How Does Expense Analysis Help?

With greater control over expenses, managers and stakeholders can decide where costs can be optimized. Trimming inefficient processes, keeping track of monthly billings, handling recurring and non-recurring expenses, or any other cost can be optimized, thereby reducing the load on accountants who toil day and night to keep the books updated.
A major pain point in corporations is the lack of traceability for small and medium expenses. So is keeping track of POs and their payment schedules, which result in over expenditure at times, and at times delayed payments to vendors.
With one platform pulling data from every relevant process, businesses can then understand where the company’s monies go. When vendors are onboarded, they can receive their payments on time, ensuring smooth delivery of their deliverables on time.

Impact of Expenditure Analysis?

Research from 2019 suggested that about 21 cents out of every dollar spent after marketing efforts is wasted. Marketeers spend up to 2 hours every day in finding useful data and insights, a lack of which causes approximately 20% of marketing budget losses. This is something managed analytics for expenditure tracking can achieve. Optimized expenditure for marketing efforts could generate up to 10% more leads for similar costs.
Similar impacts can be seen around recurring non-fixed costs such as power bills, petty cash expenses, and other minor and major expenditures. Floor managers can look at how much expenditure is happening when the office space is not being utilized and can reduce those costs by up to 10%. For medium and large office spaces, this is a considerable number.

How to Implement a Process?

The solution seems simple – get a team, set up a server, and start collecting and analyzing data. It is when we venture into those waters, we realize its depth. Finding a good team to work with it is quite a task itself. Plus, starting something from scratch is bound to take much time i.e., setting up your own process may not give you any short-term returns.
A workaround is hiring a company that operates in the data analytics space and then letting them handle analytics for you. This is a cost-effective method that has minimal latency. Businesses can start seeing results much faster than they would if they were to set up everything by themselves. Data companies have expert programmers and experienced data scientists who can quickly identify data sources to pull data from and begin implementing expense analytics insights.

In a Nutshell

Reducing costs is a major part of optimizing businesses. With better technology at our disposal, which will only get better with time, we can help reduce companies’ many costs they incur, which decision-makers could otherwise avoid. Setting up its own process would save costs for the company in the long, but for a business with low bandwidth, going for a managed analytics service seems to be the optimal choice.

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