The Power of Data: How Leading Organizations Leverage Data As A Competitive Advantage

Business leaders completely understand the importance of data as a fundamental asset for their business growth. Although many have realized its significance, most of them still struggle to understand how it can be utilized to derive improved business outcomes, such as converting more prospects into customers, enhancing brand reputation, or gaining a competitive edge in the industry against other players.

Industrial competitiveness can be derived by many factors. But it has been observed that most of these factors can be controlled and manipulated by data collection and analysis. In this article, we will learn the factors that influence a company’s competitive edge in the industry, and how organizational data can contribute to improving competitiveness.

Outperforming Competitors with Data Initiatives

In the current era, consumers have a long list of options to choose from while looking for a product or service. Data collection and analytics can widely help an organization to set themselves as a differentiating player in the market.

Let’s go over the top three factors that influence a consumer’s choice while focusing on how data collection and analysis can improve the attractiveness of a brand against other competitors in the market.

Factor 1: Market need meets product offering

A product’s unique features and attributes distinguish it from its competition. If you sell the same product as competitors, with no additional unique value, there’s a high chance that your competitors may attract more consumers with value-added offerings. Predicting consumer behavior and understanding their requirements is an important step of earning a competitive edge in the market.

Data initiative to predict consumer behavior

There’s a certain pattern behind what consumers are purchasing in a market and what features they are looking for while making the buying decision. You can analyze market data to understand:

1. Which product features get more attention from consumers?
2. What needs do consumers fulfill with their purchases?
3. Which products do consumers usually buy together?

Factor 2: Competitive Strategic Vision

It is crucial to stay aware of competition and their strategic moves so that you can competitively align your decisions as well. Whether it is promotions, discounts, or pricing intelligence, it is important to infer this information from past data, rather than following gut instincts.

Data initiative for competitive decision making

Data analytics can help you to understand competition better in terms of:

• What promotional schemes and discount offers other competitors offer?
• What are the factors impacting your competitors’ pricing rates?
• How satisfied are your competitor’s customers with their purchases?

Factor 3: Improved Product Availability and Accessibility

Consumers nowadays expect fast product deliveries, as well as smooth omnichannel experience. Because of this, brands need to ensure that their inventories are filled with appropriate amounts and types of products as per market requirement. Similarly, marketing product information in an accurate manner, and enabling customers to access and order the same products from online as well as in-store channels is very important.

Data initiative to enhance product availability and accessibility

Data analytics can help you to answer questions like:

• What are the percentages sales in-store as compared to online?
• What are the most common locations for product deliveries?
• Where are consumers reading about your products/services?

The Power of Clean Data

For all the questions highlighted above, you can either guess the answers to them through gut instincts, or use accurate, reliable data of the past and make calculated future decisions. But it is a bit more complicated than this. Data that is collected and stored by many organizations is not in the correct and accurate format to be used for analysis, and it must be subjected to data quality management lifecycle before it can be utilized for such reasons.

A data quality lifecycle takes your data through a series of steps to ensure data usability and accuracy, such as data integration, profiling, scrubbing, cleansing, deduping, and merging. Self-service data quality tools have made it quite easier to automate data quality management with reduced time, cost, and labor investment. Managing data quality in time can enable real-time calculation of competitive measures, such as market requirements, consumer preferences, pricing and promotions, and product accessibility, etc.