Customer surveys and focus groups can help organizational leaders better understand their customers’ needs and preferences.
However, the responses offered by customers in these forums can often be biased as a result of how the survey questions are couched or how the respondents believe the questions should be answered.
One effective way that decision-makers can get a better handle on what customers want and need is by using predictive analytics and data discovery tools to examine the feedback and sentiment customers share regarding their experiences with companies – both good and bad.
Consumers tend to be more candid in the feedback they share in social media channels as well as in customer service interactions about their perceptions of the strengths and weaknesses of the products and services they use.
Consumer feedback can offer decision-makers insights on new features that customers want from existing products.
For instance, an analysis of sentiment shared by customers of a particular brand of iced tea in social media channels and in recorded contact center interactions reveals that a high percentage of customers are complaining about how the tea is packaged.
Customers grumble that the containers are typically filled to the top, making it difficult to pour without spilling. Further analysis reveals that customers would be willing to pay the same price even if the bottles contain a little less tea.
By reducing the portions in the bottling process, the beverage company is able to improve customer satisfaction. Meanwhile, data discovery reveals that its customers are also more likely to buy that brand of iced tea more often with the new packaging, thus increasing customer lifetime value.
Predictive analytics and data discovery tools can also help business leaders identify new products or services that would interest customers.
For example, feedback shared by customers of a restaurant chain reveals that many customers would like the ability to contact a local restaurant using a mobile app to identify anticipated wait times and pre-order appetizers as they’re en route to the restaurant.
Organizational leaders can draw on these insights to craft these capabilities into new or existing mobile apps. The use of predictive analytics can also help determine the type of payback that’s expected from investing in mobile apps, including changes in revenue and customer loyalty.
- Please join us today, Thursday, August 14, 2014 at 11 a.m. EDT, for our complimentary webcast: “Analyze Your Customers’ Actions: Market Basket and Customer Analysis with Spotfire.” In this webcast you will learn how Market Basket Analysis can help you leverage POS data to understand sales patterns, customer preferences, and buying patterns to create targeted and profitable promotions.
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