How to ask the right question to your data

When a company is leading in a more data-driven direction, is it  mainly because they want to increase their revenue and/or lower their cost. 

But before you start incorporating your data as an asset in your company, is it essential to ask the right data questions. The number one rule is to ask questions where the answer will create value. This may seem obvious, but over 73% of all data science analysis isn’t profitable [1], which is all because the right questions haven’t been asked. 

This article will present concrete examples of data questions you can ask your data science team. The answers will give you the insight, knowledge, and understanding to make the best strategic decisions for your business.

Main areas data-questions answers

Almost every company there is starting to ask data-questions has the same idea of which areas the answers can have an impact. These are: 

  • Customer behavior and buying patterns
  • Tracking and predicting sales
  • Streamlining processes and operations
  • Budgeting and forecasting

And they are right…
Most answers to a data-questions can fit into one of the above areas. But that is just the tip of the
data-question-iceberg. 
As mentioned, you need to ask questions where the answers will give you value. That is why the questions you need to ask must be more specific. 

Some of the answers you will get will only result in minor business adjustments, while others will be the basis for a significant shift. In the end, all adjustments must improve your company and increase the bottom line. 

Soo, know that we got that in place. Let us start asking data science questions

Sales and marketing

The results of asking the right questions in the marketing and sales department are to get the insight to create a deeper customer understanding, redefine and optimize sales strategies, increase conversion rates, better and smarter outreach, lower Cost pr. Click, better performing remarketing, optimized funnels, which all, in the end, is increasing the profit for the company. 

Questions could sound like this: 

  • What is the ideal length of time between initial prospect contact and first follow-up?
  • Which sales rep collateral has the best and worse impact on conversions?
  • Which customer segments are most likely to promote is on social media?
  • What are the ideal cross-sell and up-sell opportunities per product and/ or customer segment?
  • How can we optimize retail display per location?
  • What is the best path to conversions based on each funnel stage?
  • Which marketing initiatives will maximize return?
  • Which prices can we increase, and by how much, and still retain customers?

Human resource

Your business result is a result of your employee’s work. By asking the questions below, you can get insight that will help you attract and retain the best talent for your business, maximize productivity and employee satisfaction, pinpoint recruitment efforts, forecast when an employee is about to churn, and therefore reduce churn expenses. 

Start asking: 

  • What do our employees value most at work? (You can add layers to this question by segment employees into groups like top 10% performers, by departments, By role, etc.
  • What are the similarities between our best performing or longest-tenured employees?
  • What are the similarities between employees who leave within 1-2 years?
  • How can we spot if a valuable employee is at risk of leaving?
  • How can we spot employees there are disengaged from the organization and their work?
  • What is the best combination of compensation and bonuses for the most motivative performance? 

Supply chain management

Are you looking for ways to streamline logistics, optimize predictive planning, eliminate hidden costs or achieve a correct picture of the profitability for each product? Then you should ask your data science department the following questions.

  • Where are the biggest holdups in paperwork and procurement?
  • What are the most significant causes of delay?
  • Which inspection errors occur most frequently?
  • How resilient is the chain to external forces?
  • What are our hidden inefficiencies, and which one should we focus on fixing to create the most value?
  • Can we eliminate any steps in the supply chain?
  • What are our most significant opportunities for additional supply chain profits?
  • How can we best protect margin when demand falls?
  • What will our product demand be the next year?

Business strategy

Changing the way you take business decisions from gut feelings to data-driven will help you improve many different areas in your business. Insight from the data questions can give you evidence-based strategy insights, making it easier for you to get stakeholder’s buy-in, a better and clearer business direction, and confident decision-making, which leads to faster growth. Isn’t it terrific? 

Start ask questions like: 

  • How can we reduce our expenses by 105?
  • How can we leverage our analytic project to produce the most significant impact?
  • How do we best combine innovation with performance sustainability?
  • What are our biggest value-creation drivers?
  • What are our five most significant areas for improvement?
  • What are the biggest threats to growth and scalability?
  • What should our primary focus be right now, the next quarter, next year?

Takeaways

The mentioned questions are just a drop in the data-question-ocean. You can ask questions about specific products, services, marketing initiatives, your competitors, etc. But the most important thing to know is – that we haven’t yet come across a question that data couldn’t answer. 

Asking data-questions is all about asking questions where the answer will give you insight that can be translated into valuable actions. 

Sometimes, the answers can positively affect multiple departments, even though the question was asked for a specific area. That means data will give you answers, but it is your job as a CEO, Business leader, or executive to translate the data answers into action that suits your company’s culture and work style. 

 

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