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Six characteristics of actionable analytics

WHAT ΜΑΚES АСTIONABLE INSIGHT Contextualised Insight should clearly show data around its priority. After surveying your customers, a driver analysis will tell you how important each topic or touchpoint has been to customer satisfaction levels. Insightful New is everything. For example, knowing that CSAT is low is non-insightful. But, knowing customers who considered their post-sale experience bad churn faster is insightful. Timely Fresh insight wins. Organisations frequently run voice of the customer programmes for a few months, then take months to analyse the data. The speed at which consumer needs change makes this insight largely useless. Granular While timely insights are important, a granular level of detail is the shortcut to a solution. When receiving customer insights, leaders across your organisation want to be able to understand the root cause of those insights so they can quickly curtail them. Statistically significant Qualitative feedback is awesome but it suffers from the 'small sample' problem. We suggest turning your qualitative customer feedback into something statistically significant using data tagging of topic and sentiment. Unbiased Any type of bias that has crept into your customer feedback analysis results should make you wary to report them. There are two main buckets of customer survey bias to avoid: selection bias and response bias. But, bias also depends on the questions you ask and how you ask them. RETAIN AND GROW YOUR CUSTOMERS WITH UNBIASED INSIGHT

Six characteristics of actionable analytics

shared by bengoodey on Dec 14
Companies often waste customer insight. They gather lots—through endless surveys—but fail to get their customer's voice heard across the organisation. What a waste of time. With this infographic...




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