eringilliam: customer-data*

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  1. Businesses begin and end with customers, so your pool of customer data should not be ignored. Technology makes it easier to do business, with startups able to establish a lean and effective business model thanks to the volume of handy and affordable tools at their fingertips. But as your business grows, employs staff, and establishes multiple departments, the number of technologies and level of functionality required increases in size and complexity. This can leave many startups staring down the barrel of customer data with little time or knowledge of what to do you with it.

    Here are some tips to help you make use of the data you’ve collected to level-up the insights you have into the people that matter most – your customers.
    https://www.smartcompany.com.au/partner-content/articles/four-simple-steps-nail-your-customer-feedback-process/
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  2. Some people might say that the secret to a successful customer feedback survey campaign is quantity. If you send out enough surveys, you’re bound to get some responses eventually, right? Wrong.

    While this approach might have worked in the past, the modern customer isn’t eager to take time out of their busy day to take any survey. Today, to collect the best customer data you must ask the right questions while also providing the ideal customer experience.
    https://www.business2community.com/customer-experience/5-survey-tips-for-getting-better-customer-data-02161018/
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Mopinion: The Leading User Feedback Tool

Mopinion is a proud sponsor of Website Feedback News. The voice of the online customer is taking on an increasingly important role when it comes to improving websites and apps. So web analysts and digital marketeers are making more and more use of User Feedback Tools in order to collect feedback from the user. Mopinion takes it one step further and offers a solution to analyse and visualise user feedback results from your websites and apps wherever you need them. The real challenge for companies is not about capturing feedback, it is about how to make sense of the data.