HIF has formulated a unique and proven method that enables enterprise businesses navigate the complexities of the predictive systems and achieve their business goals. Executives can now boldly invest in predictions and bring substantial impact to their businesses with high levels of confidence.
Rules-based systems, such as ERP, function at 100% accuracy rates. Risks caused by errors are almost none, as they are made to comply for four-nine/five-nine reliability rules.
On the other hand, prediction-based systems are much-less accurate and produce substantial and unavoidable errors.
Accuracy rates depend on the quality of input signals from within the data. High-quality data inputs depend on sound hypotheses and the valuable signals from within data that support them. Accuracy rates also decline over time and thereby requiring continually renewed data sources.
Errors encompass false positives and false negatives and can be fueled by tangible but inconspicuous costs and risks. Leaders, in some instances end up realizing the negative impact to their business ex post facto, and in others, will be lost in space and time with no clear indication of what happened. Errors can bankrupt a business.
Traditional metrics such as break-even units and payback period are no more valid in estimating ROI for an investment involving predictive systems.
The unique aspects of predictions – the dissipating nature of accuracy rates, the diminishing returns produced by incremental data, and the inconspicuous costs of error rates – demand an innovative way to allocate capital.
The HIF team will partner with business leaders to understand their business goals and key performance indicators stakeholders use to drive outcomes among the three strategic pillars - acquisition, retention, and cross-sell/upsell. This stage will help capture the key drivers unique to the business and help discover the inputs essential to estimating ROI in step 5 of the HIF Method.
Develop hypotheses that underpin the identified business problem. Evaluate a set of modeling options and identify modeling methods that can provide a range of solutions to the problem.
Mathematical, statistical, and machine learning methods considered would include linear and multinomial regression algorithms, complex classification algorithms, reinforcement learning for optimization, uplift modeling, structured causal modeling, among others.
Evaluate data maturity within the scope of the developed hypotheses. The business must own the necessary data, aka the sources of their truth, and/or be willing to invest in new sources of data that reflect the developed hypotheses. The HIF team will help the stakeholders identify key sources of data that may be necessary for the solution, if needed.
With the available data, the HIF team will develop a model prototype, capture and share with the stakeholders the deep insights and a set of early predictions the model produces. Model outputs produced in this step will be used as inputs in estimating ROI.
The HIF team partners with the stakeholders to record the cost components for the use case. Using the unique and complex HIF Formula, the HIF team calculates and estimates the return on investments.
Develop recommendations for the business use case along with Hindsight, Insights, and Foresights the stakeholders need to make intimately informed business decisions.
Strategic business decisions among the multiple go-to-market goals are made not in silos. We emphasize business leaders take a unified approach to modeling, predicting, and prescribing solutions among acquisition, retention, and other growth pillars.
AI/ML algorithms learn to yield specific predictions and inadvertently produce misclassification errors that result in unexpected costs and liabilities. We advocate businesses to invest incrementally by adapting to the learned lessons, to minimize the costs contributed by the errors, and to maximize the long-term returns on every invested dollar.
Business leaders need a language that addresses their immediate concerns without worrying about the involved technical complexities and parlance. We engineer business KPIs associated with model KPIs and demystify the complexities of mathematical modeling.
A lack of combined expertise in mathematical modeling and in business domains has been identified as a key reason for the lack of adoption of data science solutions in decision intelligence. We bring expertise in both and our clients have a trusted partner in us.
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