In previous years, multiple funding rounds meant companies could easily replenish cash reserves, which imbued spend management practices with an occasionally lenient attitude. The current economic climate demands more operational rigor and robust spend management aided by data analytics and AI recommendation engines. “We’ve transitioned out of the environment where money was cheap,” says Saum Mathur, COO of Paro. “Today, the top line is pressured by macroeconomic conditions, which means a focus on responsible spending and cash management.”
Cash flow management is crucial for businesses to elongate their runway. Private companies in particular need to elongate their runway to survive. The key to better cash flow is in efficient spend focus. These steps can help your company build a rigorous analytical framework to improve spend and cash flow.
Step One: Locate Areas of High Spend & Low ROI
Any money spent should generate revenue, margins or cash in the short term. In short, any spend should pay for itself. This means all cash-requiring initiatives must be micromanaged. This is where prioritization of data analytics comes into play.
A company’s analytical function—whether an FP&A professional, CFO or someone else— locates a company’s least efficient spend by thoroughly examining large spend buckets. These are the cash-consuming operations like marketing, sales, customer service, logistics and supply chain or product development. “The question to ask is: ‘Are we getting enough return on investment for what we’re spending in these areas?’” says Mathur.
Step Two: Identify Causes Using Advanced Data Analytics
After locating the macro bucket, the next step is to identify the root cause of inefficient spending. “This is where you move from simple data analytics to more causal analysis, which may require advanced algorithms.” If, for instance, marketing spend is revealed to be the most inefficient, there could be many contributing factors, such as:
- Cost per click as paid to Google
- Overall cost per campaign
- Expense incurred for digital demand generation
- Poor lead quality or training
But trying to process data from such diverse streams can take too long. However, advanced algorithms can analyze multiple dimensions at the same time. “AI can identify the largest spending inefficiency not just at the departmental level, but at the process level,” states Mathur.
Step Three: Eliminate Spend Inefficiencies
Once a company has pinpointed the root cause, advanced data analytics offers the operational rigor required to eliminate inefficient spending. “Computational techniques allow you to optimize marketing campaigns, for example, in real time through an AI-based recommendation engine,” says Mathur. These data analytics tell you how much money to spend on which channel to get the best ROI for that spend.
For example, advanced AI techniques are able to quantify:
- The most effective sales motions for successful conversion
- The most profitable coverage for customer support centers by hour
- The most economical logistics for a demand-based supply model
- And more
You Don’t Need a Sophisticated Data Infrastructure to Find Cost Savings
These are not new solutions, but previously AI-driven data analytics were restricted to only the largest corporations. Today, smaller private companies are equally bearing the burden of cash flow issues without access to the same foundational corporate technology packages.
But not having baked-in data infrastructure is not the same as not having the data. Most companies have the required data. It may be in spreadsheets, or trapped in a small ERP system, but it exists. “The first step in this process is to go get your arms around that data,” says Mathur. “You don’t want perfection, you want results, fast.”
Have the data examined and harmonized by engineers, or a CFO who works with data teams. The first two steps, locate and identify, can be performed with this and similar rough-and-ready techniques. But automated recommendation engines require a more sophisticated data infrastructure.
The Importance of Data Visualization for Actionable Insights
The business environment changes month by month, week by week and day by day. A real time recommendation engine is the best way to keep up with that constant fluctuation. The recommendations steer companies away from unnecessary spends and boost cash flow. The results will quickly show on healthy P&L statements.
It’s important to establish operational KPIs to measure improvement before you start. “Agree on those operational KPIs and dashboard them for measurement,” says Mathur. Dashboarding metrics makes complex financial data more accessible and reveals any improvements in spend management. For example, you might measure the dollar value of sales against the dollars spent on marketing. When the former rises as the latter falls, your cash flow position is stronger.
Prioritize Your Spend Management With the Right Investments
In the future of finance, technology and advanced data analytics enable more proactive cash flow management and spend management. AI recommendation engines can orchestrate fixes across departments and operations, right-sizing your spend and improving cash flow. “Acquiring this technology is one of the biggest priorities if a company wants to stay in business,” says Mathur. “This is how to elongate the runway. There’s no other trick.”
With technical expertise, your business can take the next step in better cash flow and spend efficiency. Paro can help your business or firm increase efficiency through tech stack evaluation and implementation of data analytics and AI-enabled tools. Our services help you enhance your forecasts, dashboards and more to drive better decision making across your organization.
