Generating reliable business forecasts is among top priorities for any company. Up until recently, organizations were all using very reactive techniques, when it came to planning, management and decision making. All due to the assessment of how the market will behave in the future being associated with great risks.
But recently, that has changed, with the technological advancements in the field of data analytics. Through cutting-edge data mining, statistical modeling, and machine learning services and techniques, companies can now have a reliable way of forecasting and be far more proactive in their operations. This is called predictive analytics.
What Is Predictive Analytics?
In a core essence, it is a branch of the advanced analytics, which takes relevant data and makes a forecast of future events. It is a complex process that involves many subtasks such as data mining, predictive modeling, data transformation, noise filtering and so on.
As a business technique, predictive analytics has existed for quite a while already. However, it had no practical application, due to technological immaturity. But with computational power becoming cheaper and far more accessible as well as a dire need for a competitive advantage in a tough economics of today’s world, predictive analytics finally found its way into the market, and it is here to stay.
Predictive analytics is used in all major industries today by top corporations. From public sector to insurance and manufacturing. Although the actual use may vary quite significantly.
Retail businesses, for example, require quite precise forecasts to meet customer demand and not face overstocks or out-of-stock situations. Same goes for healthcare industry where hospitals require ordering large stocks of pharmaceutical goods. While the financial and insurance sectors utilize predictive analytics to prevent frauds and identify investment opportunities.
But it doesn’t stop there. Companies like Netflix use predictive analytics to find movies that subscribers will most likely enjoy. And Amazon is one of the pioneers of using this technique in the e-commerce sectors. The anticipatory shipping, which is a procedure of delivering packages to a geographic region before the client even orders a product, is even patented by Amazon.
4 Key Benefits of Predictive Analytics Solutions
As you can see, predictive analytics tools are successfully used everywhere nowadays, but what actually does it bring to your business?
1. Optimized marketing productivity
Marketing productivity is very often defined by its effectiveness and efficiency. And marketers are constantly pressing to improve each of these aspects with minimal risks. Predictive analytics allows you to see obscure trends and key insights which are hidden in the fields of Big Data and impossible to identify without in-depth analysis. Such information can enable a better decision-making process and optimize marketing campaigns, by cutting costs on numerous iterations and generating higher ROI business operations.
2. Pricing and supply management
Predictive analytics allows companies to estimate market demand for a product with a great level of accuracy. By knowing the level of customer demand, an organization can set the most competitive prices on their products and have a supply of goods that corresponds to the market’s needs.
Alterations to the supply chain lead to great additional costs that can be avoided altogether with predictive analytics. And situations, where an organization has to over/undersell also lead to repricing of a product, which negatively affects competitiveness of a business.
3. Fraud detection
Fraud management is a nightmare for many industries, such as retail, banking and others, but it is at the same time of paramount importance to such companies. But the use of predictive analytics can be a great tool for reducing the number of resources spent on these tasks while at the same time improving their efficiency. For retailers, analyzing previous customer behavior can significantly reduce credit card chargeback rates, not to mention the reduction of the amount of fraud. While banks can easily identify patterns of fraudulent activities and block such transactions immediately.
4. Business Intelligence (BI)
By applying predictive analytics, businesses can better understand customer needs and adapt its business strategy accordingly. Very often, consumers cannot formulate what is it they want. But such in-depth information can be deduced from capturing customer data and finding hidden patterns. As a result, your business can have a better lead prioritization and generation processes, since you’ll have the knowledge of what makes up a high-value lead.
Differences Between Descriptive, Predictive, and Prescriptive Analytics
The amount of recorded data that is generated in just a past few years adds up to approximately 90% of the entire data pool of human civilization. Businesses now have access to a golden mine of information. But, data, unlike gold or other precious metals, is worth virtually nothing on its own. Data interpretation, however, produces information, knowledge, and insights which can, in the long run, hold value much greater than any resource.
Extrapolating knowledge from data is the primary goal of analytics techniques, and there are 2 other major models in use for this purpose in addition to predictive analytics.
Before the rise of predictive analytics, descriptive was the most popular method of market analytics. It is still used today for certain tasks and processes, where it is still the most effective tool. Descriptive analytics is less about the examination of data and more about its organization. This type of analytics deals with raw facts and numbers. And while you can say that a predictive analytics tells you how you should act, the descriptive simply states what is true.
Probably the most elusive of all, prescriptive analytics deals with a “what if?” scenarios. If a predictive analytics is a natural progression of descriptive, then this model is yet another higher level above all. It takes forecasts and, based on different variables, extrapolates possible outcomes. You are no longer looking at simply emerging trends and future customer behavior, but rather at consequences of a potential business decision. It sounds like looking into a crystal ball and seeing the future, but it’s a data-driven process that deals with probabilities.
Obviously, such “magical” software is much more expensive and far more complex in its implementation, not to mention that the technology is not completely developed yet. However, it has already shown great results in a variety of industries, most noticeably in the healthcare sector, where forecasting of patients’ health is not enough and there has to be a doctor’s interpretation of facts to determine possible outcomes of certain treatments based on the variety of factors.
Salesforce Predictive Analytics Platform at OMI
Predictive analytics tools provide businesses with the ability to capture customer data and analyze it, allowing managers and executives to improve decision-making processes and optimize business strategy accordingly. If you are interested in applying predictive analytics solutionsto your Salesforce CRM platform, then feel free to contact us at OMI. We are a team of first-class experts, specializing in Salesforce customization and optimization as well as machine learning services. With our CRM solution, your business will be able to produce more leads, provide better customer service and generate maximum return on investment for our clients.