Data-Based Decision Making: Guiding Clients in the Right Direction

Marketers are bombarded with an almost constant stream of data every day. This overload of data is making knowledge management increasingly more important. Data can offer managers a wealth of information but processing overwhelming amounts can get in the way of achieving high-quality decisions. This is why the use of techniques such as data-mining, predictive modeling, and best customer profiling can all be used to facilitate better, more informed decisions.

Data mining, an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. In advertising, data mining is used to discover patterns and relationships about your customers in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Specific uses of data mining include:

• Marketing segmentation
• Direct marketing
• Interactive marketing
• Market analysis
• Trend analysis

The future of data mining is truly exciting for advertisers. It will continue to be a consistently profitable tool that will allow for advertisers to target customers with incredible precision while providing unique insights into the psyche of consumers.

Predictive modeling is used extensively in analytical customer relationship management and data mining to produce client-level models that describe the likelihood that they will take a particular action. The actions are usually sales, marketing and customer retention related. In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results. These predictors can include gender, age, purchase history, income, etc. By using predictive modeling, advertisers are able to best see where their money should go and how it should be used.

Best customer profiling is a comprehensive how-to guide on reaching your ideal consumers. It gives you a structured look at their goals in trying your product, the features and content that matters most to them, and the messaging that will help them find you in the first place. The most important aspect of best customer profiling is to assemble a customer persona and create broad descriptions of each ideal customer. Knowing how that customer typically feels towards other offerings available in your industry is vital when trying to pinpoint exactly how you‘re going to market to them. Identifying user goals for each consumer type and their most important features that may affect their willingness to purchase from you is another important facet when it comes to creating a profile. When looking at a customer’s current problem it is important to pay attention to the language they use to describe the problem so that it is not misinterpreted. Knowing where your client will find you is the final piece in completing a customer profile. When creating this list, take a close look at the other information you’ve gathered and assemble a list that makes sense for that customer. Knowing how a certain type of consumer will react to an ad is the main focus of best customer profiling and allows for advertiser to make the most informed decision about which group to target.

The ability to actively manage the research initiatives of your organization can increase your chances of generating success by making informed judgments based on the data you are able to obtain through these market research techniques. By facilitating decision-making, the capability to make the most monetarily beneficial decision for your organization has never been easier.

If you have any questions or comments about this post, or any previous post, please feel free to contact The Scream Team by clicking on the envelope to your right!

Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). “From Data Mining to Knowledge Discovery in Databases”. Retrieved 17 December 2008.
“Data Mining Curriculum”. ACM SIGKDD. 2006-04-30. Retrieved 2011-10-28.
Clifton, Christopher (2010). “Encyclopædia Britannica: Definition of Data Mining”. Retrieved 2010-12-09.
Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Retrieved 2012-08-07.

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