"Next generation analytics will expand beyond measuring and describing the past to predicting what is likely to happen, and optimizing what should happen, based on an increasingly varied set of data sources and types. Incorporating consumer interaction paradigms that make advanced analytics applications mobile, social and collaborative will empower a broader set of users — giving them more high-value insight for decision making at the time and place of every business process action. Organizations will need to put in place new processes and technologies to capitalize on this opportunity. A lack of skills will be the biggest challenge." ~Gartner
Analytics uses quantitative methods to derive insights from data and draw on those insights to shape business decisions. Predictive Analytics enables businesses to look towards the future and help answer questions like “What’s next?” and “What should we do about it?” This involves combing through past information to derive quantitative models and analyses that help to make predictions about the future. The goal is to learn from past mistakes and successes in order to know what to change and what to replicate. The core aspect of Predictive Analytics is that it provides a predictive score for each entity being analyzed (customer, employee, patient, product SKU, vehicle, component, machine, or other organizational / business unit) on the likelihood of a future business outcome - example, likelihood of a customer to purchase, or, an employee to leave. The scores help to "black-box" the complexity of analytics from business users and make it easier for them to digest, process, decide and act.
We deploy Predictive Analytics to help organizations improve business performance via faster and more accurate business insights that are easy for business users to digest and act upon - entering new markets, increasing profit margins, making processes more efficient, attracting / retaining employees, serving citizens more efficiently, improving customer service and customer engagement, etc.
At KMS, we understand that the accuracy of our insights and recommendations generated from analytics are driven by the quality, volumes, history and the level of detail of the data available for analysis. Hence the first phase of our Predictive Analytics methodology involves extracting internal organizational data, augmenting it with external data (from market, social media, internet, customer surveys, market research, mystery audits, website traffic, smart devices, sensors, etc.), analyzing the data to discover patterns, and then learning from these patterns to project future outcomes.
Our Predictive Analytics methodology can be applied to diverse business areas including
Improving ROI of key business functions.
Improving cross-selling / up-selling opportunities through personalized offers and experiences.
Maximizing productivity and profitability by aligning people, processes and assets.
Reducing risks to minimize exposure, loss and service outages.
Extending the useful life of equipment via predictive maintenance.
Decreasing the number of equipment failures and maintenance costs.
Focusing maintenance activities on high-value problems.
Increasing customer engagement and satisfaction.