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Thirteen Customer Experience Database Design Principles

[fa icon="calendar'] Jan 19, 2017 2:55:48 PM / by Matti Airas posted in Feedback Analysis, Customer Experience Management, Sentiment Analysis, Data Visualization, NPS, CES, data warehouse, customer experience, feedback categorization

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Extracting actionable insight is difficult. It takes a lot of work and requires serious thinking and planning. One of the most important things you need to do is to design and implement a CX database.


You, the CX professional, need to own this data. Don’t let BI or IT people set restrictions. Making compromises will greatly hinder your ability to do your work well. Good data is paramount!
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VERBATIM ANALYTICS Tip #8 Examine your feedback data from the customer perspective

[fa icon="calendar'] Nov 11, 2016 11:09:22 AM / by Matti Airas posted in Feedback Analysis, Net Promoter Score, Customer Journey, Data Visualization, text analysis, NPS, text analytics, open-ended analysis, net promoter system, dashboards, executive reporting

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Managers analyze customer feedback from "their" perspective. Often this view is touchpoint or function specific. Their job is to extract actionable insights that enable them to improve their own department’s performance. Your job, as a CX professional, is to analyze the whole customer experience .

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VERBATIM ANALYTICS Tip #7 Define the customer journey

[fa icon="calendar'] Nov 8, 2016 4:24:07 PM / by Matti Airas posted in Feedback Analysis, Net Promoter Score, Customer Journey, Data Visualization, text analysis, NPS, text analytics, open-ended analysis, net promoter system, dashboards, executive reporting

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It is easy to define the customer journey from top down: you plot the different touchpoints and set them in chronological or some other logical order. It is much harder to monitor and measure how different touchpoints are performing.

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VERBATIM ANALYTICS Tip #6 Design a four-layer insight distribution system

[fa icon="calendar'] Nov 3, 2016 6:41:02 PM / by Matti Airas posted in Feedback Analysis, Net Promoter Score, Data Visualization, text analysis, NPS, text analytics, open-ended analysis, net promoter system, dashboards, executive reporting

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Organizational layers like to consume information in different ways. Executives like static reports with KPIs. Managers need a dashboard with signals about problems or opportunities and the ability to dig deeper to find out the root cause for those issues. Frontline employees just want to get their jobs done. Analysts need to dig deep to detect weak signals, emerging trends, and do predictive analytics. That is why the reporting tools and the level of information in them need to be different for each organizational layer.

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Advanced Text Analysis Visualization Using Tableau: from Basics to Heatmaps

[fa icon="calendar'] Aug 3, 2016 3:09:46 PM / by Matti Airas posted in Feedback Analysis, Net Promoter Score, Data Visualization, text analytics, tableau

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Up to now, text analysis results have mostly been presented in the form of word clouds, but there are many other, often more powerful, ways to visualize the analysis results.

I am using a grocery store chain that has six stores in this example. They are running a  Transactional Net Promoter Score survey process.


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How To Visualize Text Analysis Results Using Tableau

[fa icon="calendar'] Jul 27, 2015 2:55:00 PM / by Matti Airas posted in Data Visualization

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Tableau is a great tool for visualizing structured data but how well does it work for unstructured data.

It took a bit of database tinkering to make the open-ended feedback analysis results suitable for Tableau. Tableau likes data in which each data record (in our case customer's survey reponse) is on one row. The challenge with open-ended customer feedback comes from the fact that almost all customer's talk about multiple service aspects or categories ('topics').

You could, of course, create a separate table for each customer response but this would create a large number of small tables. In order to avoid this our CTO came up with an elegant and simple solution: each 'topic' (mention in a customer response) is a separate row in the data table.

This creates another problem: the number of rows in the data table doesn't match the number of responses. (There is an easy remedy to this. Just create a new calculated 'measure': countd([Signal Id]))

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