Analytics Informed Strategy
(6 minute read)
Advances in digital analytics have transformed many operational areas of their business but their impact is yet to be felt in areas of business strategy. This blog draws from research from McKinsey to outline why advanced analytics can play a key role in strategy.
The transformative power of digital analytics in areas of business operations from customer service to manufacturing is well established. Advanced analytics has the potential to do the same in areas of strategy. Advanced analytics helps executives to:
- Calibrate strategy by reducing bias in decision making about resource allocation
- Identify new, hidden pockets of growth opportunities
- Spot early trends in real time ahead of competitors
- Anticipate complex market dynamics by simulating multiple impact factors
To gain best use of advanced analytics, they need to be front and centre of strategy and applied to analytics-informed strategic decision making.
Reducing Bias in Decision Making
Daniel Kahneman and Amos Tversky observed that often planners underestimated time and cost requirements for projects while overestimating outcomes. Labelling this the ‘planning fallacy’ they suggested this was influenced by experience, intuition, and the ‘inside view’ rather than an objective, evidence-based ‘outside view’. They suggested an alternative corroborative procedure called ‘reference class forecasting’ which complemented the inside view with data on real world outcomes. This methodology has been applied successfully in several project management contexts over the last two decades but contemporary thinking indicates that it can just as well be applicable to strategy. Subjective experience can be complemented and triangulated with objective outside evidence to reduce bias in decision making.
Identifying New Growth Opportunities
Advanced analytics are effective at identifying new growth opportunities that are hidden and otherwise hard to spot. Opportunities can arise in areas of new industry segments or acquisition targets or brand fits, or they may be in areas of new products or services or offerings for existing products. Sophisticated methods like network analysis, natural language processing etc. can be deployed to trawl through vast amounts of internal and external data to identify opportunities which can be built into strategic planning.
Spotting Early Trends
State-of-art AI can analyse vast quantities of publicly available information in real time including web pages, search queries, patent filings, news sources etc. Pattern finds amongst these disparate data sources can provide executives with early warning advantage of spotting emerging trends, thus bypassing the long gestation period of commissioning market research and waiting for results. Advanced analytics can work with not only quantitative data like patent filings, web searches etc. but also with qualitative material such as comments, opinions, online content etc. to perform ‘sentiment analysis’ which gauges public consumer sentiment on products in real time.
Anticipating Complex Market Dynamics
Advanced analytics enable simulating and mathematically modelling numerous scenarios by working out trade offs and assumptions in various strategic decision making choices. By approximating real world behaviour through simulation, analytics and modelling methods like systems dynamics, Monte Carlo analyses, and various ML approaches, enable executives to amass a range of complex market and competition situations and view the possible outcomes of their potential decision making choices in a relatively risk free environment. Simulations and modelling can be particularly useful in analysing systems with many independent, unpredictable variables that cannot be studied at aggregate levels. By manipulating numerous decision-making rules simultaneously, these simulations can predict ‘behaviours’ that cannot be perceived through traditional decision-making models.
The power and potential of advanced analytics demands a more central, leading role in strategy and analytics-informed decision making.
