For years, marketers and other business leaders have argued that it’s simply too time consuming and expensive for companies to conduct individual, targeted marketing campaigns.
Nonetheless, data analysis tools have become powerful to the point that companies can gain an incredibly rich understanding of the behaviors, needs, preferences, and the life cycle status of individual customers and cater to their individual characteristics – should they so choose.
While it still may not be cost effective to craft promotions for individual customers – especially for consumers of high-volume, low-value products such as grocery items where the returns don’t justify the efforts – there is value in targeting offers to clusters of customers with similar characteristics and values.
Indeed, companies can use data analysis to closely identify and target micro customer segments, including customers who share similar traits with respect to life cycle status, earnings power, and perceived needs.
It’s certainly a more effective approach than mass marketing, which is what all too many companies continue to do. Mass marketing not only wastes precious marketing dollars, it’s potentially harmful to customer loyalty.
If customers are bombarded with irrelevant marketing offers from a company, they’re likely to think – consciously or subconsciously – that the company sending them these offers doesn’t know a thing about their needs or preferences. This can frustrate customers and help fuel customer defection.
As author Doc Searls points out in a recent Wall St. Journal article, “Businesses today tend to herd customers as if they were cattle, but a revolution in personal empowerment is under way – and buying will never be the same again.”
Once marketers know the people they’re targeting, the rest of the marketing process becomes a lot easier. Consider footwear maker Skechers.
The company began sending out personalized email alerts to members of its Club SKX online shopping group who expressed interest in certain categories of shoes. After utilizing information about customer preferences and behaviors, the company has been able to generate a 24% email open rate and a 7.8% conversion rate, resulting in a 15X return on its marketing investment.
Companies can also use data visualization to identify prospects with similar characteristics to high-value or otherwise attractive customers. For example, a national electronics retailer with tens of thousands of customers can gather behavioral, transactional, and demographic information to help its executives identify which customer types in certain regions are most likely to make larger and more frequent purchases.
Companies can sift through this information using data analysis to help identify common demographic information and traits among certain customer sets. They can then use these findings to determine which prospects are the most likely candidates to respond to similar types of offers.
The use of data analysis for customer segmentation is also useful beyond identifying the right customers for the right offers. A growing number of telecommunications companies – in an industry that is notorious for high levels of customer churn – are increasingly using data discovery to identify which customers are at the greatest risk to defect as well as to identify the root causes for churn.
Data visualization can help make these types of insights tangible for business leaders and enable them to quickly identify emerging churn trends so they can act on this information as soon as possible.
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