Serving Customers with Predictive Analytics
Data is not just the new buzzword of how we manage our businesses. It’s the air that drives our sustainability.
Increasingly, the marketplace will push to have better prediction tools at lower cost to integrate into all levels of business decision making.
A particular financial service company I know was decades ahead of the marketplace in CRM, customer centricity and data integration across all touch points. Forward thinking algorithms developed from the integration of marketing offers & outcomes across channels plus customer servicing enabled offer optimization, fed by rich statistical models developed in SAS code and transformed to customer offer sequences for individual channels and rules engines.
However, to truly achieve the complete customer centric vision, many brilliant strategies were shelved to wait for affordable technology to catch up. The problem was how to truly make this real time (or near real time) incorporating activities across the multitude of customer touch points and channels. Making me an offer for a product service that I’ve since purchased or enrolled in or declined is hardly customer centric and certainly not cost effective.
But now, what an exciting time we live in!
With the proliferation of deep algorithms embedded in software and utilities, cycle times are reduced and the performance dependent upon the quality of the data pumped into the software. Good enough can prevail but only if it gets it right more often than not.
Increasingly, companies are turning more of their decision making away from gut marketing into intelligent data driven strategies where statistical models (traditional and otherwise) and marketing automation algorithms run more of the relationship. Driven by the need to customize content and increasing costs in traditional channels to get it right.
Add to this the possibility that your eligible customer universe is finite, or that you’re best customers aren’t really that loyal.
With the wealth of opportunities available via cloud hosting, the ‘as a service’ genre is ever increasing from software as a service (SaaS) to platforms as a service (PaaS) to big data as a service (BDaaS) and the ability to leverage predictive powers of that data is predictive analytics as a service (PAaaS).
PAaaS embodies traditional analytic capabilities, meaning the ability to take large amounts of data to analyze via traditional methods (statistical modeling programming) but more often than not, it refers to the ability of leveraging machine learning and automated insights, reporting and visualization.
Catalyzed by the proliferation availability of cloud solutions and machine learning algorithms, the mainstreaming and democratization of predictive analytics has moved from customer data-driven analytics residing in the walls of large corporate dominance (with significant tech and staff investments) to the mainstream accessibility of mid and small market.
Predictive analytic tools and features will be a basic component of software releases, with key automated algorithms & visual reporting as standard features. Added value features, driven by the depth of Big Data available, will factor into the type of predictions, insights and reports available and will allow the software to be leveraged by Big Data hogs and lean data collectors alike.
In tandem will be growth in Predictive Analytics as a Service (PAaaS), providing a steady stream of suppliers providing data interpretation and consulting.
Ultimately, even the easier to use and digest information is still reliant on the reality check of leveraging and measuring the right data inputs and ensuring you’re taking action against the right measures. Big data can be unwieldy and noisy and thin data can be highly predictive. The job of predictive analytics solutions is to optimize the data available for the greatest productive impact.
Data – the ingredient to business sustainability.
Pamela Veraart is the SVP, Analytics Practice Head at Consultants 2 Go.