In business, knowledge is power, and the knowledge of what will happen in the future is a super-power. When data analytics, statistical algorithms, and machine learning come together, this super-power, also called predictive analytics, becomes a capability that can have a huge impact on business decisions and results.
The use of data as a basis for business decisions has had its own maturity cycle. In the early years, the focus was on reports, graphs, and dashboards with visualizations. All these helped business managers to know what had happened in the past. The objective of developing predictive analytics in business was to guide decisions based on what is likely to happen in the future.
Historical data and the outcomes they generated are used to develop predictive models. These models are then used to predict outcomes for different or new data. The predictions represent a probability of a target variable — for example, sales or revenue — by estimating the significance of a set of input variables. Predictive analytics in business have already shown dramatic results for organizations in many different sectors including banking and financial services, oil, gas and utilities, retail, governments and the public sector, healthcare, and manufacturing.
With predictive analytics, business leaders are looking for answers to questions such as: what do our customers really like? Why is demand fluctuating the way it is? Which regions are going to be the growth hotspots?
Let’s look at the top three ways in which predictive analytics in business make a real difference to organizational results.
1. Increase sales
The top-line benefit that every business in the world is chasing — sales — can grow with the application of predictive analytics. Analytics help understand consumer behavior, preferences, trends, and market research findings, so that products, offers, and advertising can be designed to get the best market response.
As consumers interact with the brand over a multitude of digital platforms, a huge amount of data is generated every moment. This provides valuable insights, such as what people are searching for, what they are they spending more time on, what is on their wishlist, what they are putting in the shopping cart, or what they are doing just before abandoning it.
With the application of predictive analytics on this data from user analytics, brand managers can predict what will be the result of changes in product, website design, mobile app features, online store offers, and much more.
Predictive analytics also helps create recommendation engines — i.e. “people who bought this product also bought”, which enables cross-selling and higher revenue realization from the same customer.
An analysis of a customer’s shopping pattern helps to define a propensity to buy certain products. This enables the brand to create more and more personalized offers, which leads to faster conversion to sales.
2. Optimize production and distribution
Predictive analytics are used very effectively in quality assurance during production. For instance, the probability of a particular raw material or sourced component being defective, or the likelihood of equipment breaking down, can all be known beforehand. This helps production lines continue to run smoothly. Sourcing, maintenance, and production scheduling decisions can be taken so as to ensure quality, minimize equipment downtime, and maximize return on capital employed.
Furthermore, IoT devices are increasing in numbers in production machinery, warehouses, and vehicles. The huge volume of data that IoT systems create can be used to build predictive models and make the right decisions about stocking, distribution, and routing. Predictive maintenance for vehicles can increase their lifespan and also improve the reliability of distribution operations.
3. Reduce risks
When banks are evaluating loan or credit card applicants, or insurance companies are evaluating potential customers, or companies are shortlisting vendors, dealers, or partners, it is essential to select the right profiles. Selecting the wrong persons or companies could expose the organization to a host of risks, such as bad loans or poor quality vendors.
Predictive analytics can look at historical data to see which profiles have been suitable and which haven’t. Then predictive models can be built. When data about new applicants is added to the model, the likelihood of defaults or other issues can be estimated.
A similar approach can also help identify candidate profiles for recruitment. A data-driven approach can identify candidates more likely to fit well into the organization and stay for a longer time.
In other words, predictive analytics helps to identify the right profiles, increasing the chances of success, and reducing the risks of financial or reputational damage.
Machine generated predictive analytics must be used along with human insights in order to get the most meaningful results. Many organizations have reached this inflection point in the journey to creating a data culture. This makes them capable of reaching decisions faster, making smarter choices, and staying ahead in the competitive race.
