Every strategic manager who engages in understanding the industry should have in their toolbox Industry analysis templates. Industry analysis involves looking at the structures of the market, the players and the forces of competition both internal and external. Therefore, strategy consultants, marketing research analysts, investment analysts, etc. will appreciate such templates. The rules make the difficult task of information retrieval, the order of that information and the meaning of information with regard to the industry very clear.
With Industry analysis models, organizations can make more intelligent decisions, improve strategy formulation, and find new avenues for growth quicker. They help companies pinpoint dangers and new improvements effectively so that adjustments can be made in advance to head off defeats in the market. Every entrepreneur writing a business plan, corporate strategist defining a corporate strategy, and, an investor analyzing market risks understands that Industry analysis hedge funds are key to every winning game plan of any player who dreams of being successful in the long run.
The Current Landscape of Industry Analysis
Industry analysis templates has been written about a lot, but mainly in the narrow light of existing tools like Porter’s five forces, SWOT analysis, or value chain analysis. There is a growing consensus that such frameworks are useful but insufficient since most of them depict industries, casting them as if they were stationary without showing interesting economic dynamics such as the introduction of technological disruption, attention shift towards customers, or increased circulation of information. A number of these resources are outdated, still promoting the ideas that can be of use where the business environment is less competitive. Predominantly, materials present industry analysis as an activity of so-called reporting rather than reactive change seeking. Many, in addition, focus unproductively on the description of what industries ARE rather than what makes them TICK.
Introducing Adaptive Strategic Mapping (ASM)
Adaptive Strategic Mapping (ASM) represents a fundamental shift in how we approach industry analysis. It recognizes that industries are not fixed but constantly evolving ecosystems influenced by interconnected factors, including technological advancements, shifts in consumer preferences, regulatory changes, and, crucially, the cognitive biases that shape decision-making within those industries. ASM isn’t just about identifying competitors; it’s about mapping the relationships between these entities – and the underlying assumptions – to anticipate future shifts.
What is ASM?
ASM utilizes a multi-layered mapping process:
- Layer 1: Strategic Nodes: Identifying key players – competitors, suppliers, customers, regulators – along with their core strategies.
- Layer 2: Cognitive Influences: This layer focuses on the mental models, biases, and narratives driving behavior within each node. For example, in the automotive industry, this might include the “status symbol” bias or the “fear of missing out” (FOMO) influencing purchase decisions.
- Layer 3: Resonance Dynamics: Mapping the interactions and feedback loops between the strategic nodes and their cognitive influences. Identifying areas of potential dissonance and conflict.
- Layer 4: Predictive Modeling: Using the ASM map to forecast potential disruptions and opportunities based on shifts in cognitive resonance.
Example: Analyzing the electric vehicle (EV) industry through ASM might reveal that a dominant cognitive bias is “fear of range anxiety,” driving consumer behavior despite technological advancements.
Here are previews and download links for these free Templates using MS Office Suit of Applications.
We are going to upload more templates so please keep visiting.
Cognitive Resonance Analysis (CRA): Decoding the Mental Landscape
Cognitive Resonance Analysis (CRA) is a methodology designed to quantify the strength and influence of these cognitive biases within an industry. It moves beyond simple qualitative assessment to incorporate objective metrics, leveraging data analytics and behavioral economics principles.
How does CRA work?
- Identify Key Biases: Based on research and industry knowledge, determine the most relevant cognitive biases impacting the industry. Examples include confirmation bias, anchoring bias, availability heuristic, and loss aversion.
- Data Collection: Gather data related to the biases – marketing spend, customer feedback, social media sentiment, search trends, market share fluctuations.
- Quantify Resonance: Develop metrics to measure the “resonance” of each bias. This could involve analyzing sentiment scores, tracking the frequency of specific keywords associated with the bias, or monitoring changes in consumer behavior linked to the bias.
- Visualization & Mapping: Represent the CRA findings visually, highlighting areas where cognitive resonance is particularly strong or where shifts are occurring.
Example: Within the fashion industry, CRA might reveal that the “social proof” bias – driven by trends on social media – disproportionately influences purchasing decisions for fast fashion brands, leading to rapid inventory turnover.
