Predictive analytics is revolutionising how companies view and interact with their customers, but more importantly, predictive analytics help firms better understand the actions of their customers and develop a more effective plan to engage those customers. As companies continue to develop their use of predictive analysis, they'll find new opportunities to create and develop products based on the information gleaned from their customers' behaviour.
Predictive analytics gives businesses a unique perspective on customer behaviour. Traditional analytics will show companies what has been successful with their customers, while predictive analytics will provide an understanding of how those customers will act in the future and how that will impact their business. With predictive analytics, companies now have the ability to proactively manage their customers and tailor their offerings to meet the needs of their customers before they even arrive at the purchase stage.
Understanding predictive analytics and its implications for a business
It is crucial understanding predictive analytics and its implications for a business,
given the growing reliance on digital channels for all interactions. The current climate of constant connectivity and increased amounts of data have drastically changed the way businesses interact with their customers. Companies that leverage predictive analysis can take an informed approach to connecting with their customers, creating a stronger foundation for future interactions.
Predictive analysis is the next evolution of how companies connect with customers, which means companies should be prepared to invest in developing the proper technology and capabilities to support predictive analytics and take advantage of the numerous benefits of using predictive analytics as a means to grow their business.
Predictive Analytics is the ability to leverage predictive models to generate a meaningful prediction from an enormous amount of complex data and identify trends and correlations that are unrecognizable by the human eye. Organizations can use predictive analytics to gain insight into how customers will behave, to predict the demand for products or services, to identify risks associated with customer behaviour, and to find ways to operate more efficiently in their operations.
Understanding and using predictive analytics will change the way organizations operate and make decisions today.
Fundamental benefits of predictive analytics in business
- Improving the confidence of their decision-making process, by using predictive analytics to make data-driven forecasts and to support both tactical and strategic business planning.
This enables them to better understand when future outcomes may occur and what initiatives may be most important to pursue while appropriately allocating resources. For example, when leaders develop a revenue forecast for the next quarter or project how many customers may leave, they benefit from establishing certainty that their predictions are much more accurate than if they relied solely on “gut feeling.”
- Russian scientists have developed a technique for predicting customer purchasing behaviour, which consists of developing predictive models using data analytics and historical purchase history to predict when a customer will make future purchases. Data from these predictive models can be used to optimise marketing efforts and create personalised customer experience; therefore maximising the chances of customer conversion and retention. For example, Predictive Analytics Tools are used by internet retailers to create predictive models to anticipate customer behaviour and to provide incentives to encourage shoppers who would otherwise leave the shopping cart without completing their purchase. In addition, Predictive Analytics Tools are used by retailers and supply chain managers to develop forecasts which enable businesses to manage their inventory levels efficiently, avoid costly overstock or stockout situations.
- The ability to identify and mitigate risks using Predictive Analytics is very powerful. Predictive Analytics is used by banks to assess credit risk, predict the likelihood of customer defaults, and detect fraud before it affects customers. The use of Predictions, coupled with Claims History and Environmental Factors, allows Insurance Carriers to price their policies accurately, thereby reducing required loss reserves.
- Optimise operations' operational efficiencies, "Predictive analytics will improve companies' ability to make informed decisions through the use of predictive insights throughout their operations." Examples include using predictive maintenance by manufacturing companies for machinery so they can track their machines over time, predict but replace machines before they break down, reduce downtime, extend a machine's useful life, and ultimately improve production efficiency and increase revenues. Logistics companies are now able to use predictive models to optimize routing, delivery scheduling, reduce fuel costs, and provide reliable and timely service.
- Better Marketing and Customer Interaction. Marketing departments are increasingly embracing predictive analytics to comprehend the potential value of their leads, and determining the likely outcome of campaigns. Once past customer interactions are analysed at all the digital touchpoints a business has built an understanding of how future customers will behave. A company can therefore tailor their marketing, communication style and product offerings on an individual level, which enhances interaction and increases the customer value over time."
Creating a Data-Driven Culture
In addition to technological advancements, people and processes greatly impact how well any organization adopts predictive analytics. To succeed in this Data Era, organizations that are successful develop a training program for their employees so they can understand analytical results and how to use them appropriately. Additionally, successful organizations adopt a "break down the walls" mentality by allowing everyone access to data and creating processes that connect predictions to their bottom line metrics.
To successfully utilize predictive analytics in day-to-day operations, an organization's infrastructure must also be properly set up. A growing number of organizations are integrating analytical capabilities directly into their operational applications, reducing the need for multiple tools and dashboards and increasing the speed of decision-making processes.
The Role of Strategic Partners
For many organizations, developing an analytical maturity level is not achievable without the assistance of external experts. Strategic partners, such as BI consulting firms, help organizations achieve this level of maturity by providing their clients with: deep technical knowledge, industry experience and the ability to select the most effective predictive analytics tools and design predictive models for use; and integrate predictive tools into the organization’s core business processes while maintaining data quality and governance.
Businesses employ expert consulting services to speed up implementation of analytical technology and reduce risk of failure and enhance return on investment (ROI) from the analytical use of their technology.
Issues to keep in mind:
Though there are advantages to using predictive analytics, there are also challenges that come with it. Many organisations experience difficulties caused by bad data quality, disparate systems, and a lack of qualified personnel for the process. A key factor in ensuring predictive model accuracy is obtaining access to high-quality data. Many businesses will develop a data governance structure to accomplish this endpoint, standardising, cleaning, and preparing data prior to modelling.
Choosing a Technology Stack
The rise of new analytics platforms and AI technologies increases the need to align the capabilities of available tools and technology with the predictive analytics business objectives and strategic goals, such as how to leverage and scale the potential for predictive analytics.
Evolving Life Cycle for Predictive Analytics
Over the coming years, the development of predictive analytics will be influenced by advancements in artificial intelligence (AI) and machine learning (ML). This will allow for greater accuracy and insight when making predictions. With the growth of companies utilising real-time processing capabilities, predictions will not only be faster but also more relevant to the current situation, resulting in quicker and more accurate business decisions.
The businesses that will be successful, and have predictive analytics embedded within their core DNA, are those that see this type of data as not only being a back-end function, but an integral, real time driver for strategic and operations related business decisions, therefore they will utilize predictive analytics in real time.
Conclusion
As it stands, predictive analytics within the realm of business, is no longer a vision of things to come, but rather an indispensable piece of the current direction of competition and industry. Predictive modelling will elevate business decision making to a greater level of intelligence by allowing businesses to foresee an industry trend, reduce a risk, allow operations to operate much more efficiently, and by tailoring the customer experience inline with what predictive analytics suggests the customer might be interested in.
If businesses are to gain the full benefit that predictive analytics can provide, they must invest in the right products, people, and partnerships that will help to further develop their strategic based thinking with regards to data, and have true expertise in predictive analytics at every step of the business process.
In what we know today as our data filled world, the ability to predict is not simply a competitive edge, it is a requirement.
