Leveraging Data Analytics for Competitive Advantage

When seeking to gain a competitive advantage, data analytics can really fall in to only one of three areas: accurate, misleading or irrelevant. What about inaccurate, you ask? Inaccurate data is misleading and therefore inaccuracy is also not relevant.

Customer data is captured by either passive (click rates, POS data, etc.) or active (customer feedback, surveys responses, etc.) means and is used extensively in business to gain a competitive advantage. Keep in mind that the larger the sample pool and the longer period of time the data is gathered, the more accurate an analysis will be. This article focuses on the benefits of leveraging data analytics for competitive advantage rather than on the types of data gathering techniques.

Types of analytic solutions and models:

Segmentation: divide customers into homogenous groups.
Classification: including decision trees, logistic regression, etc for predicting customer behavior
Forecasting: used to predict future metrics based on historical data, e.g. sales forecasting.
Basket Market Analysis: Customer purchase data used as a basis for segmentation.
Explanatory Modeling: Used to determine why something happens.
Predictive Modeling: Used to classify customers, or prospects
Decision Modeling: Used to guide decision making for a particular situation.
Customer Value Analytics: in conjunction with Segmentation and classification, it is used to identify customer type and priority.

Benefits of Data Analytics

In terms of survey analysis, the following is a list of benefits to using data analytics for competitive advantage along with the analytical solutions and models used:

  • Tailored offerings to fulfill specific customer needs.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling 
  • Develop new products or services or determine product and service features that are important to customers
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis 
  • Better customer relationship management.
    • Customer value analytics, classification, explanatory modeling, customer experience analytics, Exception analysis
  • Find, attract and retain the best customers
    • Customer value analytics, classification, explanatory modeling
  • Distinguish preferred from marginal customers.
    • Customer value analytics, classification, explanatory modeling 
  • Customize marketing plans to specific markets.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Identify new market opportunities.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, explanatory modeling
  • Develop insight into changing customer requirements,
    • Customer value analytics, classification, explanatory modeling
  • Target pricing
    • Customer value analytics, classification, explanatory modeling, predictive modeling, market basket analysis 
  • Determine which customers are open to cross-selling.
    • Segmentation, classification, forecasting, predictive modeling, decision modeling, market basket analysis

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