Guide for Constructing Effective Easy Answers Questions
Intended audience: USERS
AO Easy Answers: 4.3
Overview
Easy Answers solutions can be configured in two modes: a) Standard mode, and b) AI Enhanced mode. This topic focuses on the AI Enhanced mode which uses Generative AI technology from popular 3rd party Large Language Model (LLM) platforms, such as OpenAI.
Data is a critical resource in today’s business landscape, but to harness its full potential, users must ask the right questions. This guide is designed to help new users understand how to ask effective, actionable questions that lead to valuable insights from their data.
By following the principles in this guide, you will be able to:
Construct precise, focused questions.
Receive accurate data-driven responses.
Avoid common pitfalls in data querying.
Understanding Your Data Context
Before asking any questions, it’s important to be clear on the type of data you work with and what you're trying to achieve. Consider the following:
Data types: Are you working with sales figures, customer demographics, product performance, or other metrics?
Key metrics: What outcomes are you measuring? Revenue, profit, customer growth?
Objectives: Are you looking to optimize performance, discover trends, or assess historical performance?
How to Ask Effective Questions
Be Clear and Specific
Ambiguity can lead to incomplete or irrelevant data. Always be specific in your questions by including time frames, geographic regions, product categories, or metrics.
Avoid: "What were our sales?"
Ask instead: "What were our total sales for Q3 2023 in the Northeast region?"
Focus on One Question at a Time
Asking multiple questions in a single query can confuse the system and result in unclear responses. Stick to one query at a time.
Avoid: "What were the total sales last quarter and how did each region perform?"
Ask instead:
"What were the total sales last quarter?"
"How did each region perform in terms of sales?"
Use Data Terms and Metrics
Use measurable terms like "revenue," "units sold," or "conversion rate" to focus the query on the exact data you need.
Avoid: "How did we do last quarter?"
Ask instead: "What was the total revenue generated last quarter compared to the previous quarter?"
Provide Context to Your Question
Effective questions often include context to help refine the data being pulled. This can involve comparing time periods, regions, or product categories.
Avoid: "How were sales last year?"
Ask instead: "How were sales for Product A in Q4 2023 compared to Q4 2022?"
Ask Questions That Can Be Answered with Data
Ensure that your questions are factual and objective, not speculative or opinion-based.
Avoid: "Is Product A better than Product B?"
Ask instead: "Did Product A generate more revenue than Product B in Q2 2023?"
Specify a Time Frame
Including a time frame ensures the data is pulled from the correct period. Whether it’s a day, a month, or a year, time specificity is key.
Avoid: "When did sales peak?"
Ask instead: "When did sales peak in Q3 2023?"
Be Realistic About Available Data
Make sure you're asking questions based on the data you know is available in your system.
Avoid: "How many customers switched from competitors last month?" (if your system doesn’t track competitor data)
Ask instead: "How many new customers did we acquire last month?"
Use Comparative Questions for Insights
Comparing performance across time periods, regions, or products can yield deeper insights.
Ask: "How did Q3 2023 sales compare to Q2 2023 in the West region?"
Be Data-Driven, Not Hypothetical
Focus on questions that are based on actual data rather than speculative "what-if" scenarios.
Avoid: "What if we increased prices by 10%?"
Ask instead: "How did sales change the last time we increased prices by 10%?"
Ask Actionable Questions
Good questions should lead to actionable insights that you can use to make informed business decisions.
Ask: "Which product category should we prioritize in our next marketing campaign based on Q3 2023 performance?"
Types of Questions to Avoid
Vague or Broad Questions: These lead to ambiguous results. Always include specific details.
Compound or Multiple Questions: Ask one question at a time to get a clear response.
Hypothetical Questions: Data-driven systems can’t handle "what-if" scenarios.
Subjective Questions: Avoid asking for opinions or judgments; stick to measurable data.
Questions Without a Time Frame: Without a time component, it’s unclear which data to retrieve.
Example of a Poor Question:
Avoid: "How are sales?"
Examples of Effective Questions
What were the total sales for Q3 2023 in the Northeast region?
Which product generated the highest revenue in 2023?
How did our sales in California compare to last quarter?
When did we hit our Q2 2023 sales target?
Did Product A outperform Product B in terms of revenue in 2022?
Common Pitfalls to Avoid
Avoid vague or broad questions that lead to ambiguous answers (e.g., "How are sales?")
Avoid multiple questions in one query (e.g., "What are sales for Q1 and how many customers did we acquire?")
Avoid hypothetical or speculative questions (e.g., "What would happen if...?")
Conclusion
Asking effective questions is essential for extracting actionable insights from your data. By following this guide, you can construct clear, specific, and data-driven questions that lead to meaningful business results. Remember always to provide context, focus on one question at a time, and be clear about your objectives.