Predict the unpredictable through data
Data is the new oil. Companies that put data to use effectively can predict and analyze trends accurately to make sharper(or data-backed) decisions that accelerate business growth.
The modern CFO today is not only expected to manage near past and present transactions but effectively play the role of a strategist in predicting future trends and aligning financial decisions to long-term business goals. Here, predictive analytics can help CFOs make data-driven decisions for the organization’s long-term success.
What is predictive analytics?
Predictive analytics essentially involves analyzing large volumes of data to find patterns and predict trends for accurate future planning. Predictive analytics helps organizations armed with quality data to use mathematical techniques to derive predictions based on well-founded assumptions.
Data plays a key role in the success of predictive analytics. The type and span of data periods depends largely on the subject or type of predictive analytics to be carried out. For instance, to predict survival rates of machinery or people, data spanning the entire lifetime is required. However, to predict consumer spending behaviour, data spanning a few years is enough. Nevertheless, the larger the amount of data, the more accurate can be the prediction.
The many use cases of predictive analytics
One of the foremost use cases of predictive analytics is demand forecasting. Predictive models simultaneously analyze historical sales data with various current and futuristic variables to derive an accurate picture of demand in the next month.
With sharper clarity on future demand, finance teams can direct procurement to manage purchases accordingly. It helps streamline spending on overheads, manufacturing, and purchases.
Predictive analytics can be used to monitor the financial well-being of supply chain partners based on payment track record and proactively identify potential risk of default for better collections.
Demand forecasting enabled Nestle, a leading food and beverages company, to become a proactive instead of reactive company. By combining data analytics and their abundant domain knowledge, Nestle used predictive analysis to predict demand signals that drive customer purchasing trends.
Netflix uses Predictive Analytics to predict viewing patterns and suggests users what to watch next. By analyzing the data collected, such as used keywords, preferred genre, device used to watch, and time spent on watching, Netflix uses predictive analytics to streamline and personalize customer experience.
Predictive analytics has various other use cases like quality assurance, risk modelling, and content recommendation to name a few. However, all these are made possible by predictive analytics tools. You can select the tool that suits your company the best. Some of the top predictive analytics tools are SAP Analytics Cloud (overall solution), SAS Advanced Analytics (analytics software), and RapidMiner.
Benefits of predictive analytics
Predictive analytics with the correct data and efficient tools can help companies get accurate results that guide them in making the right decisions. Here are some benefits predictive analytics offers organizations to be future-ready:
Putting one’s finger on weak links in the system helps improve its overall health. With predictive analytics, one can now identify slow payers and forecast cashflows near accurately to earn higher returns on capital and make better business decisions.
One of the most efficient tools to help forecast cashflows is Cash Analytics. It allows multinational companies to improve their accuracy of cash and liquidity. With the help of CashAnalytics, a Europe-based consultancy firm called BearingPoint built a data-driven cashflow forecasting process that enabled automation and accuracy.
With the help of historical sales data, predictive analytics can forecast future demand. Organizations can use this information to budget money, supplies, and resources that need to be invested each month into a product and improve profitability.
SAP Advanced Planning and Optimization is one of the best tools to balance supply and demand across a company’s supply chain. It aids companies in effectively forecasting demand, planning multi-level production and a comprehensive supply network.
Predictive risk analytics can help monitor multiple variables and detect threats to secure the organization’s future. Detecting these issues is the first step to improving efficiency, and data can help do it effectively. Analyzing the data can help companies continuously improve operations and processes for long-term success. By identifying and fixing the issues internally, businesses can ensure the smooth production and delivery of their products.
Every business needs reliable supply chain partners to grow and scale. Predictive analytics can help analyze supply chain partners based on their business health to envisage strategic partnerships that are profitable for the business. It is crucial because the business health and financial well-being of individual supply chain players underpin the overall performance of the ecosystem.
One way to check an organization’s health is through Vayana Network’s Good Business Score (GBS). It will help corporates understand their supply chain partner’s business health and understand what’s working well for them and what can be improved. The score helps identify partners and nurture lucrative partnerships that may benefit an organization’s supply chain.
Suppliers can regularly check their GBS score to understand fluctuations in business health, helping them keep everything in shape. Accessing the GBS score is a simple process. All companies have to do is enter their PAN and GST details and the mobile number to obtain it. Since the score is confidential, organizations can rest assured that it will not be shared with anyone without formal consent.
Vayana Network also assists businesses in making data-driven financial decisions by facilitating cash flow-based lending. Based on the GBS score, trade and transaction data history of the supply chain player, Vayana assists financial institutions in better assessing trade relationships and sanctioning credit limits. It considers factors such as Trade and Payment Records, GST Data, Bank Statement, and eKYC as assessment metrics. It allows for last-mile assessments to be done, at scale, without compromising the safety of any of the parties involved.
Predictive analytics uses quality data sets and deeper analytics to identify insights and forecast trends that help businesses assess risks, predict cash flows, and plan ahead to secure the organization’s future. With the help of Vayana Network’s data-driven financing tools, companies can now take the guesswork out of the decision-making process and make informed decisions that lead to growth.