Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition

TL;DR

This paper introduces a novel statistical method for adaptive window selection in count data, improving the prediction of M&A events and supporting strategic planning with data analytics.

Contribution

It presents an automatic procedure for detecting intensity changes in count data, enhancing forecasting accuracy and managerial decision-making in various business contexts.

Findings

Robust to aberrant behavior in data

Accurately forecasts M&A events in tested sectors

Guides selection of fixed windows for forecasting

Abstract

Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is robust to aberrant behaviour and generates accurate forecasts for the evaluated business sectors. It also provides guidance for an a-priori selection of fixed windows for forecasting. Furthermore, it can be generalized to other business lines, e.g. for managing supply chains, sales forecasts, or call center arrivals, thus giving managers new ways for incorporating statistical modeling in strategic planning decisions.