Have you wondered, why only some companies are phenomenally proactive, agile and competitive, in reacting to their customer’s preferences and needs?
And how, only some companies are able to make smarter decisions on which product or services would be consumed, by each of their customers.
Interestingly these companies develop an undeterred loyalty for their products and services as well as magnetically attract new clients, with similar preferences. They leverage their historical data as well as the universal data available from new sources viz. social media, credit cards, machine sensors and other online activities.
High performing companies recognise that in order to stay ahead, they must continue to extract value from their data. They view their data as a strategic asset which continuously grows in wealth. To extract this wealth they inculcate a data driven culture, making their decision-making framework scientific, predictable and logical. In the process they eliminate errors, associated with human biases.
Forward thinking companies declutter the complexities impacting their decision-making and improve their success ratio using analytics. They classify their decision-making approach depending on their business objectives and goals.
In its simplest form, they categorise their decision-making needs into Descriptive, Diagnostic, Predictive and Prescriptive, and adopt the best-fit analytical model to meet their business needs. These analytical models are not exclusive of each other, but are rather mandated to coexist together. Thereby providing companies with a full spectrum for making decisions.
They use Descriptive Analytics to analyse their past data and get answers to “What has happened”. For instance, number of sales calls made by each team member, in a given time period; or performance reports on their products, revenues, customers and markets.
They use Diagnostic Analytics to analyse the past data and understand “Why did it happen”. For instance, knowing the root cause for a high Insurance claim; or drilling down to know why the sales performance has been low; or to know why their churn rate in customers is high; or why the market share for their products and services has declined.
To differentiate, compete and make smarter decisions, these companies leverage advance analytics viz. Predictive and Prescriptive. Understanding when and where to adopt, and apply advance analytics, is key to making smarter decisions.
They use Predictive Analytics to build their predictive model, which helps them forecast future outcomes. Based on their past and current data, statistical methods, algorithms and machine learning techniques they are able to detect patterns, trends and exceptions in the data sets, and predict future scenarios of “What will happen”.
They are able to respond faster, be it a change in competitive environment, change in market or regulatory conditions. Thereby increase their speed, accuracy and consistency in making decisions about their customers, products and markets.
Predictive analytics has helped them gain a deeper understanding of their customers, and enabled them to forecast their future behaviours, based on their past events. For instance what is the impact to revenues and profitability if a product price was raised by say 5 per cent; or what is the likelihood of a client defaulting based on his credit risk score; or what are the incremental revenues from a marketing campaign that is targeting the right customers with a right product mix.
Creating a predictive model involves following some fundamental steps viz. Defining business objectives and goals; Preparing and consolidating historical data; Identifying predictive patterns within the data sets; Building the prototype with well represented samples from historical data sets and validating their outputs; Selecting the final model after evaluating suitable algorithms; Deploying the model followed by regular updates; Refining the model to keep it relevant with new data sets.
When building data analyst teams, identify analyst who possess both a deep knowledge as well as creative skills to analyse your data. They must bring in the industry domain experience of knowing what to analyse in the data and must also be able to identify relationships between data sets and their predictive patterns.
High performing companies use Prescriptive analytics when they need to know “what should be done”. Prescriptive and predictive analytics work together to forecast future scenarios. They recommend “what should be done” based on “what would happen”. With prescriptive analytics, companies get the best possible recommendations, make smarter decisions and have better control on their future business outcomes.
Managing the numerous variables and analysing their patterns can be daunting task in the Big Data era. This clubbed with an uncertain economic environment makes it a compelling case for companies to make smarter decisions, quickly and accurately, supported by deploying advance analytics in their IT environments.
The writer is the Executive Vice-President of Dubai-based TransSys Solutions. He can be contacted via Twitter @Stephen_Fdes
