You’re tasked with analyzing Customer Experience data. How can you ensure your findings are accurate?

1. Set up a main KPI you want to track 2. Set a method to get your data, make sure this is not a single data point in time but it can be recurrent (Csat over time, weekly response rate) 3. Use analytical tools and methologies. Go to basic and make sure it is data significant 4. Organize, analyse and share 5. Provide insights, when sharing data from CX you're giving the customer a chair in the conversation, make sure you reflect that!

Most people get way too wide with their metrics. The problem is you then don't know what drives that metric. Instead, create narrow, finite KPIs that can be attributed to 1-3 actions.

By defining clear goals and metrics for customer success, you can ensure that everyone on the team is working towards the same objectives. This can help you analyze customer experience data more effectively and accurately, as you will be able to identify areas where you need to improve and make data-driven decisions to achieve your goals. By using consistent methods for data collection and analysis, you can ensure that the data you are analyzing is accurate and reliable. This can help you identify trends and patterns in customer behavior, and make data-driven decisions to improve the customer experience.

When analyzing Customer Experience data for GRC, accuracy is key. Start by sourcing data from reliable, diverse points to avoid biases. Ensure the data is up-to-date and reflects a broad customer spectrum. Use validated analytical tools and methodologies to process this data. Cross-verify findings with different data sets and, if possible, get a second opinion from a colleague. Consistently monitor for any anomalies or outliers in the data that could skew results. Document every step of your analysis process for transparency and future review. By adhering to these practices, you can bolster the credibility of your findings, making them a solid foundation for decision-making and strategy development in enhancing customer experience.

The more sources you use the better, Important though is that the data can be linked with a key so that they can be leveraged together. You need to understand the purpose and the expected outcome. Do the metrics and insights make sense when you compare them to your understanding, are the assumptions thought through and survive a consistency check? all important steps to ensure trustworthy assumptions from data.

By removing duplicate or inaccurate records, you can ensure that the data you are analyzing is accurate and reliable. This can help you identify trends and patterns in customer behavior, and make data-driven decisions to improve the customer experience. Providing customer records with additional data, can help you gain a deeper understanding of your customers and their behavior. You can make more informed decisions about how to improve the customer experience and achieve your goals for customer success.

Data cleaning is the process of refining your dataset to rectify errors, discrepancies, and the absence of values. This can include actions such as deleting duplicates, rectifying formatting discrepancies, or filling in missing data using suitable statistical techniques. Data validation involves scrutinizing your data against other references or industry standards to pinpoint outliers or irregularities, which may necessitate deeper examination.

You can either use qualitative data to support quantitative results, or use quantitative data to support qualitative results. Personally, I think there is more to be learned from qualitative data analysis. Behind the numbers, what are your customers saying? What comments do they leave, who do they reach out to and what do they say? Using a basic example, if a customer says their shirt was too small in a review, then look into the quantitative data to see if returns for that model are above average. If there are high returns, you may be able to correlate that to the comments. In that instance, simply viewing return numbers won't give you the corrective action you're looking for.

It's crucial to choose appropriate data visualizations tailored to your message. Different charts serve different purposes: bar charts for category comparison, line charts for displaying trends, pie charts for illustrating parts of a whole, and heatmaps for revealing relationships. Ensure clarity by labeling axes, data points, and legends without unnecessary clutter. Craft a narrative around your findings, emphasizing key insights and trends to inform improvements in customer experience. Adapt your communication style to your audience, using straightforward language for non-technical stakeholders and diving into details for technical audiences.

To enhance the accuracy and depth of analysis, it's advisable to employ triangulation by incorporating qualitative data alongside quantitative data, such as customer feedback or user interviews. Seeking peer review is crucial as it offers an opportunity to catch errors, biases, or explore alternative perspectives. Embracing an iterative approach in data analysis allows for continuous refinement, wherein initial questions evolve and deeper insights are unearthed as new data is examined.

Para ter uma análise mais acurada é importante analisar tanto dados qualitativos quanto quantitativos! Uma vez que esses dois tipos de dados e análises se cruzam, a chance de ter algo robusto e ambos se validarem é muito grande!