BlinkSync

What is predictive analytics?

The value of survey research lies not just in reflecting past and current action, but in providing foresight and context. Decisions are undeniably better when shaped by knowledge of what people think, why they think it and how these thoughts influence their actions. Predictive analytics pairs data with artificial intelligence (AI) to identify the likelihood of future outcomes based on historical data. Forward-looking metrics fuel the decisions you make today.

However, data that does not capture the accelerated pace at which the world is changing nullifies the applied AI. You need data that can benchmark results to real-world outcomes. 

How predictive analytics helps finance teams

Predictive analytics provides a real-time, data mosaic of how consumers think and feel about your focus list of companies, as well as an understanding of key inflection points and trends across the peer group.

Here are four core areas that predictive analytics can strengthen your team and the company:

Consumer analytics

Consumer analytics is the system of data and applications that businesses use to make decisions based on customer behavior. What makes consumer analytics especially powerful is when they’re powered by predictive metrics with demographic depth. 

Predictive metrics track key indicators of what people are going to do, not what they’ve already done. This way, you can know the trends of both today and tomorrow, and how both will impact your investment thesis. 

Today’s leaders need the ability to see real-time category and brand change at scale, in connection with a deep understanding of consumers and market trends. This is where demographic depth comes into play. Ideally, your consumer analytics program will allow for visualizing customer views broken out by income levels and key labor markets in the U.S. These markets could include agriculture, manufacturing, financial services, retail, health care, hospitality and more.

The challenges of consumer analytics — and overcoming them

Here’s the problem with most customer analytics: It’s difficult to know which datasets you have that are actually predictive and what they can predict. Teams usually have to do modeling on their own time to figure out what the data is signaling. But a strong intelligence provider can bring these datasets to life. When identifying the right partner, here are the three components we advise looking for: interactive brand metrics, economic trend tracking and competitor comparison.

1. Interactive brand metrics: Data visualization tools can help you quickly derive critical insights into a company’s brand market performance, with real-time results on key performance indicators, including:

• Usage
• Purchasing consideration
• Brand favorability 
• Brand awareness
• Trust
• Net promoter score
• Employer admiration 
• Buzz
• Value 
• Community impact

2. Economic trend tracking: What if you could segment your data findings by 100+ audiences? We would recommend finding a platform that allows you to slice and dice your core consumer audience data by more than 100 demographics, profiles and custom segments.

3. Competitor comparison: Ideally, your intelligence platform will allow you to compare your brand performance with competitors’ across brand metrics, economic, social media and news media metrics. Enhance your due diligence by tracking financial companies and sector-level performance across various brand metrics and economic indicators of sentiment, usage and purchase consideration.

Revenue and cash flow forecasting

A cash flow forecast can be a vital tool for your business, helping you to identify when it’s time to invest or grow. You can make informed decisions and assess financial risks with an accurate vision of what your cash position looks like now and in the future. 

What considerations should go into this forecasting? Here are our key suggestions: 

• Where can financial processes be automated with thoughtful upfront investments? For instance, would technology that increases processing speed of cash applications be worth the labor and startup costs?
• Where should your company’s capital be invested for highest growth, based on nontraditional information sources (e.g., social media listening) as well as traditional economic databases?
• What is the best mix of debt, equity and internal financing to help your company prepare for an uncertain future? 

When thinking about the questions above, predictive models are important tools to give you a 360-degree view into key considerations for thinking about cash flow and revenue. Macroeconomic trends, supply chain versus consumer demands and projections of significant events should all play a role.

Macroeconomic trend reporting

Macroeconomic trends influence consumers’ thinking, and ultimately your growth. Most of the macroeconomic trend reports are only released monthly, quarterly or annually — but you need them more frequently. Having daily consumer tracking for U.S. and global populations will help you spot trends as they’re happening — especially when those trends can be segmented by income, employment status and job role. 

Another critical macroeconomic factor to watch is the global supply chain. For those in consumer goods, knowing what’s happening right now can help you make financial decisions around your company’s own supply chain more efficiently, ensuring vendors have enough production time and reducing wasted time, labor and packaging.   

Lastly, projections of significant events, such as how a recession will impact customer demand, can give your team more time to recommend changes to investments and other assets before the bottom line is significantly affected. 

Thinking about the above factors, as well as adding in other considerations specific to your organization, can help shape the economic well-being, finances and purchasing power of your business.

Risk management in the financial industry

Another use case for predictive analytics tools is identifying potential risks in advance, analyzing them and then taking measures to mitigate or minimize the risk. When your organization makes any financial decision, there is always a measure of risk involved, even with the most careful planning For instance, it’s not always intuitive which datasets are predictive, which means a finance team has to do the modeling on their own time to determine what the numbers are signaling, if anything. Also, an organization’s tools and systems are not always well integrated, and it can take time and effort to track down the many variables. Even then, you can’t be sure you’ve captured them all.

That’s why having the right economic data, at the right time can mitigate financial risk. For example, is your organization well-positioned to respond to an unexpected global crisis? Tracking macroeconomic trends can help pinpoint what movements pose the greatest greatest risk to supply chain, getting items on shelves that customers want and where possible points of expansion or retraction may be needed. 

Predictive analytics in finance can also help pinpoint which growth direction will prove most profitable, and therefore where to invest your capital. Instead of wasting funds on transitory trends and fruitless endeavors, your team can recommend investments that position your organization as forward-thinking and modern, while also creating a stable, profitable future.

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