Monitoring your business environment, especially your competitors is a basic business necessity. In fact, most companies do it at varied levels of detail and scope. But only a few companies leverage competitive data analysis to derive actionable insights - to enhance their competitive edge. In a recent study published by HBR, it was observed that only about 50% of the companies actually use the competitive data they collect for their managerial decision making.
Competitive analysis is a delicate art. If it is carried out without obsessive attention to detail, it can turn into useless data. With limited and restricted data sources available, analysts need to be cautious to choose the most relevant data for analysis. In this article we discuss 5 most important things to note to ensure high quality competitive data analysis.
Strategic Approach: Competitive data analysis should be a long term continuous process that is closely aligned to organisation’s strategy. Competitive analysis that is carried out with a short term objective based on ad-hoc requests will have limited impact. Generally, each function within an organization (Marketing, Sales, Product Development, Finance etc) collects parts of competitive data relevant to their team. If this is not monitored over a longer term, it does not provide clear insights for decision making.
Clear Structure: Since Competitive Analysis is very delicate, it needs a strong structure to get relevant insights. Competitive data generally includes only the organisation's own view of its relative performance which is a competitor-centered view and often discounts the customer-centered view. Due to the challenge of limited data sources, competitive analysis is often too broad in scope and is perceived as generic and lacking clear actionable insights. Detailed analysis of a specific product's relative performance in a specific target market will provide insights for decision makers.
Resources and Skills: In most cases, competitive data analysts take up this activity as an additional responsibility to their full-time roles. Due to the delicate nature of competitive data, a full-time focus is needed. Right competitive analysis frameworks need to be chosen based on the problem being solved. Full time resources with competitive data analysis skills will ensure the quality of data and extract actionable insights
Biases: Individual bias includes the biases and prejudices employed by the individual or small group of individuals. Individual bias is prominent as individuals working on competitive data analysis generally have an additional full-time responsibility in teams they are required to evaluate. Confirmation bias (cognitive bias that involves favoring information that confirms your previously existing beliefs) and Selection bias (sample obtained is not representative of the population intended to be analyzed) are the two most common Individual biases in competitive analysis.
Organizational bias occurs when factors such as culture, senior leadership, strategic focus and team organization influence data selection and data use to a point where data selection is no longer based on merit. Its influence can be particularly devastating because it usually has a wider reach than individual bias. Organizational bias is harder to identify and isolate. Use of full-time competitive data specialists for confidential data and external consultants for publicly available data can help reduce the biases and ensure high quality competitive insights.