Archive for the ‘Data Management’ Tag
The holy grail of CRM, the ability to leverage and monetize internal data, is now within reach of most medium to large enterprises. Low cost computing power, new software tools and sophisticated math skills have converged to enable high-level data analytics, a powerful capability that can drive incremental revenue, improve workflow efficiency and enhance customer satisfaction. Basically, advanced analytics uses special algorithms to comb through large databases of transactions looking for important causal relationships between variables that can be leveraged to improve the efficiency and effectiveness of a program or process. For example, Internet Services and Retail companies are mining millions of their transactions to uncover critical (and hitherto unseen) insights about consumer and supplier behavior. In other cases, some firms in the Consulting and Software industries are using observations from their own mountain’s of data (as well as the clients they serve) to launch new practices focusing on data management and consulting.
The business of helping firms make sense out of proliferating data is growing quickly. This industry, which includes leading IT players such as IBM, SAP, Microsoft and Oracle, has estimated revenues in excess of $100B. The markets is growing at almost 10% a year, roughly twice as fast as the software market as a whole.
IBM is a pioneer in the use of mathematical models to analyze huge data sets. IBM’s analytics business began as an internal project undertaken by in-house mathematicians, who wanted to learn how to maximize revenue per client by analyzing years of sales data. The insights discovered in their work prompted them to retool their sales teams by account size & industry and to tweak their service offering. The result was $1B in new revenues and better sales coverage. Not surprisingly, IBM concluded that others could benefit from these capabilities and they built an entirely new business analytics and optimization group within IBM Global Business Services to support it. To date, this group has already trained 4,000 consultants
And they are busy. IBM mathematicians are using high-quantile modeling in its workforce analytics practice to help clients make decisions about human resources issues such as how best to deploy their sales people. In other cases, their mathematicians are using stochastic optimization algorithms in their human resources and marketing practice areas to help clients find new customers and determine the right mix of experienced and junior programmers to staff large software projects.
Walmart generates reams of data through their Retail Link inventory management system. The Company is using sophisticated analytics to crunch this data in a myriad of ways, turning information into a powerful profit accelerator. In one impressive example, Walmart’s analysis showed that they should offload inventory management to their suppliers and not to take ownership of the products until the point of sale. This new strategy allowed the firm to decrease inventory risk, conserve cash flow and reduce its costs.
Like many telecoms providers, Cablecom has grappled with churn. Using advanced data analytics, Cablecom discovered that although customer defections peaked in the 13th month, the decision to leave was typically around the 9th month (as indicated by things like the number of calls to customer support services). To reduce defections, Cablecom offered at-risk customers special deals 7 months into their subscription. The results were impressive: customer defections fell from 20% of subscribers a year to under 5%, enabling the firm to save significant marketing acquisition costs while boosting customer satisfaction.
Regardless of your data management objectives and strategy, there is gold in those terabytes of data.
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