Why do you need Predictive Analytics now?
Nearly 15 million gigabytes of data are generated every day. Now, more than ever, users are demanding direct access to trusted and accurate data.
This means the need for a holistic approach to manage and leverage information for business gain has never been greater.
Predictive and Business Analyticsare becoming a competitive edge for organizations. Once a “nice-to-have,” applying analytics is now becoming mission critical.
When the primary factors that drive an organization’s success are measured, closely monitored and predicted, that organization is in a much better situation to adjust in advance and mitigate risks. That is, if a company is able to know – not just guess – which nonfinancial performance variables directly influence financial results, then it has a leg up on its competitors. Predictive Analytics is a great tool to help you achieve that leg up on the competition.
In our experience, the best place to start with predictive analytics is in your day-to-day operations. Operational decisions are about a single customer or transaction. Examples include: “Will this customer remain with us or defect to the competition?”, “What offer should we make to this customer to retain them?” Operational decisions are the best place to start with predictive analytics because they are transactional, because other approaches to information-based decision-making don’t work well and because operational decisions align best with the potential of predictive analytic models.
Operational decisions relate to a single transaction. Every order placed, every service request and every call received by the call center requires a decision. Because of their nature, it easier to build effective predictive analytic models based on transactions. The sheer volume of transactions means you have lots of data to build predictive analytic models with. You’ll often have data for a specific customer that stretches across time also, with each new transaction adding more data. Predictive analytic models most often rely on analyzing the behavior of customers over time.
Finally, the volume of transactions also allows you to experiment. You can try different cross-sell approaches with different customer orders to see what works best. This data also improves the quality of predictions.
Another important factor in operational decisions that makes them ideal for predictive analytics is their alignment with the power of predictive analytic models. You can predict two key things with predictive analytic models: You can use prior behavior to predict how risky a customer is likely to be in terms of attrition, and you can use past interactions to predict opportunities to grow the relationship with a customer.
Insights into your data and your customers
