In their efforts to anticipate, engage, and reward customers, marketers depend on predictive analytics. But predictions are a double edged sword. They’re scored against reality and marketers will live or die by them.
With a complex digital landscape, marketers are challenged with staying above the noise and making sense of an unprecedented amount of data, so it’s no surprise that predictive analytics tops their list for 2017. — CMO, Jan 2017
Predictive Analytics — Challenges
Many factors impact predictive analytics. When customers are sensitive to price changes, or when purchases are emotional then predictions become more uncertain and traditional statistics behave inconsistently. In these cases a triumvirate of statistical, econometric and rule-of-thumb models lead to more stable results.
In general, if you are in an uncertain world, make it simple. If you are in a world that’s predictable, make it complex. Your fancy predictive analytics work best on things that are already predictable. Rule-of-thumb methods are generally as good or better at predicting customer behavior… — Harvard Business Review, Oct 2014
Marketers are frustrated by efforts spent to access and integrate data and by the slow delivery of predictive analytics. Many analysts, although Excel gurus, aren’t trained in data mining or deep-dive analytics. They can’t manage complex data structures. So they create error-filled or incomplete analytics. And marketing executives lose confidence in both the reporting and data.
Another flaw in predictions thrown up by algorithmic analysis is the propensity to create self-fulfilling prophecies. Acting on algorithmic predictions, managers can create the conditions that ultimately realise those very predictions. — Phys.org, Feb, 2018
Marketers are also overwhelmed by the volume, velocity and complexity of data. Many aren’t sure of the data they have. They don’t believe they have what they need; and what they have, they question the quality. Or they can’t access.
Predictive Analytics — IDP
Predictions are Tileston’s stock-in-trade. “Taking the Sting out of Forecasting” — IT World Canada, Oct 2006
At IDP, through ‘invasive’ data management and ‘deep-dive’ data mining, we build rock-solid analytical data sets as close to the source data as possible. The closer analytics are to the source, the faster, more accurate and repeatable are the predictive analytics.
Effective use of customer and transaction data are competitive weapons. But, your data is challenging and you have limited resources. So, we manage large data volumes with complex structures from disparate data sources. We build rock-solid analytical data sets and we always collaborate with marketing to generate actionable predictive analytics.