The exponential expansion of data volume, velocity, and variety — and the ability to make sense of it all — has proven to be a game changer.
Listen to a CFO or a CMO at an industry event focused on analytics or Big Data and they will often tell you the same thing: They need to improve the use of analytics in forecasting.
Make no mistake, C-level executives are deeply interested in the past. Their key performance indicators all depend on accurate knowledge of what has worked and what has not. The exponential expansion of data volume, velocity, and variety — and the ability to make sense of it all — has proven to be a game changer. Sophisticated, statistics-driven models have led to an era of descriptive analytics that reveal cause-and-effect relationships formerly hidden from view. Basically, firms are able to use data rather than instinct or “their gut” to effectively answer such questions as “What happened and why?”
Nevertheless, advanced descriptive analytics have thus far largely been deployed to analyse cause-and-effect relationships at the process level. That is, they examine how specific actions generated observed outputs. Although this approach has proven extremely useful for establishing best practices in hindsight, it is still backward-looking, leaving decisions and planning about the future largely based on manual analysis.
Forward-looking organisations are now using analytics to automate estimates about the future. Through a combination of historical data and cause-and-effect models derived from descriptive analytics, they are able to quantify answers to essential questions related to everything from price increases and churn to productivity and recruitment to procurement and financial performance. Known generally as predictive analytics, this approach enables forecasters to run simulations and test many different potential actions in a short period of time.
Despite the immense potential of analytics for strategic decision making, it is still largely used to make tactical decisions. Offices of strategic management (OSMs) rarely deploy analytics of any kind. It is true that many use some form of Business Intelligence (BI) to monitor KPIs, often as part of a Balanced Scorecard. But the operational focus of descriptive and predictive analytics has often meant the OSM was isolated and never developed core analytics capabilities. OSMs have therefore not developed ways to integrate advanced analytics into their workflows, internal processes, and modelling of future scenarios.
This needs to change. Predictive analytics has already shown immense benefit for those that use it, particularly in terms of identifying new revenue opportunities. The most apparent examples can be seen in the retail space. Amazon, Linkedin, WallMart Online, Marks and Spencer, Tesco — they all track customer purchases and buying behaviour, using the data to then predict the next primary purchase. Amazon has even bragged about how it ships items to local delivery centres before customers even know they need them. Retail banks have also deployed analytics that combine demographic information with transaction and credit details to identify which customers might be ready for an investment account or mortgage services. Power companies have used advanced analytics to optimize grid management and customer billing. And, in an upcoming post, I will detail how a telco used analytics to examine how and why a special offer in one revenue stream had an unexpected impact on other revenue streams. As implied, the ability to create automated, real-time predictions has enormous strategic implications in terms of managing customer relations and determining what products and services to develop for them.
And these example just scratch the surface of what predictive analytics can do. It has also proven effective at risk minimization, fraud prevention, procurement and supply-chain optimization, healthcare delivery, and HR and staff development, to name just a few. Yet, in our experience, it remains underutilized at the strategic level, meaning scores — and perhaps hundreds — of opportunities to increase growth and reduce costs are being missed.
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For more information about the role of Analytics in your organization’s strategy execution management process, download Palladium and Synergy Consulting Group’s White Paper: The XPP and Analytics.