Advancements in Technology Play Both Sides of the Fence
It’s interesting to note that the same technology advances that’ve empowered buyers have also provided companies the opportunity to embrace and leverage the power of information. Organizations can now effectively understand and address not only consumers, but commercial customers as well. The ability to obtain, organize, analyze and store huge amounts of data about their markets and customers is the driving lever. Once appropriate systems are in place, the advantage of deploying predictive analytics to create competitive advantage becomes clear. Enterprises will have the means to win, retain and serve customers in outstanding ways. And that’s the bottom line for every business – serve customers in ways that surpass your competitors.
More Users Engaged, More Value for More Companies
Today, more organizations are capitalizing on this discipline because tools are quickly being adapted to accommodate a breadth of users. While large organizations will require data scientists to do the heavy lifting, business professionals and application developers (as highlighted above) can also employ today’s tools. And the more people who use predictive analytics, the more value the organization will glean. Here are a few examples:
- Data scientists require powerful, flexible tools. The demand for data scientists is huge, but not bigger than the demands being placed on data scientists. They are being pushed to build more models, more accurately, in less time. To be highly productive, they need tools that speed the process and have the might to analyze gargantuan data sets. Once the analyses execute, the insights must be operationalized wisely and efficiently. This can happen through custom APIs, Predictive Model Markup Language (PMMLs), or other methods for creating scoring engines and embedding predictive actions in applications.
- Application developers must apply insights to enhance software experiences. Not every organization will have data scientists, but most can benefit from predictive analytics. To address this, there are new tools intended for less credentialed users. These tools use modern development interfaces familiar to application developers who have experience with integrated development environments, such as Eclipse or Visual Studio. Further, smooth integration of these insights into apps is promoted via web services, APIs or PMMLs.
- Business analysts must explore and consume reliable predictions. As the demand for predictive analytics becomes more pervasive, options are being created for even the most naïve users. Accordingly, there are tools that provide “one-click predictive modeling.” This simple to execute process runs a series of algorithms against the data and finds the prediction with the highest accuracy. To provide even more options to non-data-knowledge types, there are an increasing number of exchanges and marketplaces for prebuilt predictive applications, such as Azure Machine Learning Marketplace.
Six Steps towards Winning with Predictive Analytics
While the tools and techniques can serve a myriad of goals, they should never drive the analysis. The lifecycle of effective predictive analytics begins with the obvious – by asking specifically what business problems require solving.
- Interested in attracting new customers? Who isn’t? New customer acquisition campaigns supported by targeted response modeling could bring in more customers for the same marketing cost.
- Is avoiding risk a top priority? Determine which customers have a high likelihood of defaulting by executing models on claims and risk of loss.
- Want more profit from a particular set of customers? Companies can increase profits from current clients by analyzing cross and up-sell targeting models.
The Pivotal Role of Culture and Leadership
The power of data is real and it’s here to stay. But to fully leverage predictive analytics and apply it to winning in this volatile, high-tech global economy; organizations must turn the lens within. Leadership style and the underlying culture are sure to make or break the effort. Continuing an old-time hierarchical, top-down leadership model is the best way to get the least value from this technology. The problem is lack of velocity. Since speed to market is everything, the previously tried-and-true process of synthesizing data and communicating to top management, then waiting for them to create directives to send back down the chain is impractical and backward-thinking.