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How can statistical inference help you evaluate the effectiveness of a new product or service?

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If you have launched a new product or service, you might want to know how well it is performing in the market. How can you measure its impact, compare it to alternatives, and identify areas for improvement? One way to answer these questions is to use statistical inference, a set of methods that allow you to draw conclusions from data.

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1 What is statistical inference?

Statistical inference is the process of using data from a sample to make generalizations about a population. For example, if you survey a random group of customers who bought your new product, you can use statistical inference to estimate how satisfied the average customer is, or how likely they are to buy again. Statistical inference also lets you test hypotheses, such as whether your new product is better than a competitor's, or whether it has increased your sales or market share.

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Samar Mohapatra

Senior Consultant
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While in PwC i used all available resources in the stats field for greater goodness but in the end I got fired I still admire PwC for its internal politics

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2 How to use statistical inference for evaluation?

To use statistical inference for evaluation, you need to have a clear research question, a suitable data source, and an appropriate statistical technique. For example, if you want to know whether your new product has increased customer loyalty, you might use data from your loyalty program, and apply a statistical test to compare the retention rates of customers who bought the new product versus those who did not. Alternatively, if you want to know how your new service affects customer satisfaction, you might use data from online reviews, and apply a statistical model to measure the relationship between your service and customer ratings.

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Adam Mullen

People Analytics | Leadership Development | Knowledge Management | MBA | Six Sigma Black Belt | KCS v6 Practices Certified | Proud Passamaquoddy | Aspiring Polymath
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Take care to use the appropriate statistical test for your data. The distribution and type of data (i.e. discrete vs. continuous) matters when selecting which statistical test you apply. For example, deciding on parametric tests versus non-parametric tests based on how closely your data resemble a normal distribution. Getting this right is one thing - of many - that improve the reliability of the output and resulting conclusions and decisions.

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Ibekwe Obinna

Customer Support Specialist | Tech Support Specialist/ CyberThreats Intelligence Analyst| Passionate about Problem Solving | Focused on Enhancing Customer Satisfaction /Cybersecurity
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Customer Surveys: Use statistical techniques to analyze customer survey data, which can provide valuable insights into user satisfaction, preferences, and perceived effectiveness of the new product.

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3 What are the benefits of statistical inference?

Statistical inference can help you evaluate the effectiveness of your new product or service in several ways. First, it can help you quantify the impact of your innovation, and provide evidence to support your claims. Second, it can help you compare your performance to benchmarks, such as industry standards, competitors, or previous results. Third, it can help you identify the factors that influence your outcomes, such as customer segments, features, or channels. Fourth, it can help you improve your decision-making, by providing insights, feedback, and recommendations.

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Robert Armstrong

Senior Director of Customer Support | Design, Scale and Optimize Enterprise Service Delivery Operations | Large Scale Service and Program Transformation | SaaS B2B B2C |
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Statistical inference is vital for predicting and estimating data with precision through confidence intervals. Take a simple CSat KPI for example. In Customer Support, measuring Customer Satisfaction (CSat) is common, but reliability hinges on survey numbers and response rates. Assessing trends or changes in CSat requires careful consideration of response rates and confidence intervals. For instance, a 10-point spike in CSat may seem positive, but with a 4% response rate, the low confidence interval questions its reliability, making it crucial to delve into data specifics and qualitative feedback to accurately understand such fluctuations.

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4 What are the challenges of statistical inference?

When using statistical inference, there are several challenges to be aware of. Data quality is a key factor, and you need to make sure that your data is reliable, valid, and representative of your population. Additionally, you should choose the right statistical technique for your research question and apply it correctly. Furthermore, data communication is essential; you need to communicate your findings clearly and in a way that is relevant, understandable, and actionable for your audience. It is also important to acknowledge the uncertainty, limitations, and implications of your results.

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Adam Mullen

People Analytics | Leadership Development | Knowledge Management | MBA | Six Sigma Black Belt | KCS v6 Practices Certified | Proud Passamaquoddy | Aspiring Polymath
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Be very cautious with data selection, particularly regarding common cognitive biases like selection bias, availability bias, and confirmation bias. Getting your sample right is also much more involved than a simple harvesting of data. Ensure your selection is as free from bias as possible (easier said than done certainly), that it accurately represents the population, and that you have enough of a sample size to actually detect the desired effect and so your confidence intervals are reasonably sized.

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5 How to learn more about statistical inference?

Statistical inference is a valuable skill for any product or service innovator, as it can help you evaluate your ideas and improve your outcomes. If you want to learn more about it, you can read books and articles, take online courses or tutorials, use software or tools that facilitate statistical inference, and practice your skills on real-world data sets. For example, Statistics for Business and Economics by Anderson, Sweeney, and Williams, The Signal and the Noise by Nate Silver, Udacity's Introduction to Inferential Statistics, Khan Academy's Statistics and Probability, Excel, R, Python, SPSS, Kaggle, or your own business data.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can statistical inference help you evaluate the effectiveness of a new product or service?

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1 What is statistical inference?

2 How to use statistical inference for evaluation?

3 What are the benefits of statistical inference?

4 What are the challenges of statistical inference?

5 How to learn more about statistical inference?

6 Here’s what else to consider

Analytical Skills

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How can statistical inference help you evaluate the effectiveness of a new product or service?

1

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4

5

6

1 What is statistical inference?

2 How to use statistical inference for evaluation?

3 What are the benefits of statistical inference?

4 What are the challenges of statistical inference?

5 How to learn more about statistical inference?

6 Here’s what else to consider

Analytical Skills

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