What happens if we run that campaign? Who’s going to be affected if we make this change? How do we know what good looks like?
Data can be a valuable asset, helping drive business effectiveness and decision-making. Yet businesses often fail to ask the right questions of their data in order to realise its ultimate potential.
Here are the top five questions you should be asking your Data Analysts in order to get the most out of your data.
1. What does this data tell you?
We’ve all been there, blinded by spreadsheets, looking at the same reports and numbers and thinking nothing looks particularly surprising, wondering where best to focus our attention. We’re often just too busy to go through everything in detail.
Challenge your analyst to draw out the key insights and highlight what’s important or interesting. Ask them to focus on areas where you can take-action – what would they recommend you do differently next time, or what are the key areas that need to be addressed.
Ask them to leave out what doesn’t matter – anything ‘as expected’ or unsurprising can be left in the appendix.
An executive summary, or a one-page summary of the key insights and recommendations can be a really effective way to draw attention to what’s important and save time reading through a lengthy presentation or report.
2. How can we measure relevance?
Without a benchmark or comparison group, numbers are often meaningless or misleading. Whether you are comparing the profile of a certain group of customers or the performance of a specific promotion or campaign, you need a relevant and robust comparison to truly understand what the data is telling you.
A benchmark will provide relevance and context and help to answer questions such as; “is what we’re seeing more or less likely than we’d normally expect?” or “do these customers behave or look different to others?”.
Benchmarks can come in different forms; in the case of a campaign it's likely to be a control group, for promotions it may be a comparative time period and for customer profiling you’d often compare to another group of customers.
Next time your Data Analyst is showing you some numbers or analysis, be sure to ask them to include a benchmark or comparison group to help you better appreciate the story behind the data.
3. Is there another way to solve this problem?
Analytics isn’t an exact science. Data constraints, like gaps in data coverage, can often inhibit what is possible.
However, analysis doesn’t always need to be 100% perfect and often 80% is good enough when looking at general trends and behaviors.
If the specific data you need isn’t available or is constrained, ask your analyst to consider what substitute could be used. Chances are they’ll know another way to answer the business problem that you haven’t considered, or another source or field of data that could be used as a proxy for missing information. Otherwise, it may be that you can integrate an external data source to enrich the data you hold. Be creative and work with your analyst to find a solution.
4. How will we measure success?
Proving the business value or return on investment of new initiatives is essential in order to ensure ongoing funding and support. However, often not much thought is given to how success will be measured and the data capture requirements to certify measurement is possible.
By including the analyst from the start, you can better ensure the appropriate data is captured and foundations are set up to make sure the results of future product launches, promotions, campaigns and other business initiatives are measurable.
5. What is the financial impact of that decision?
At the end of the day, businesses exist to generate revenue. Understanding the financials can help you to gain internal buy in for future projects, weigh up alternative options or prove the value of existing initiatives.
Ask your analyst, wherever possible, to determine the impact in monetary terms. This will mean considering elements such as the volume of customers affected, their revenue or change in revenue and attrition rates.
For future projects where it may not be possible to know the impact, work with the analyst to put together some likely scenario’s and hypothesise the potential impact - this can really help when deciding what projects to prioritise and undertake. You’ll be amazed at how different the story can be when you put a dollar figure against it.
Successful analytics is about setting the right framework, criteria and outcome. Bring your Data Analysts into strategy and planning process early so they can actively participate; to ask the right questions and ensure you get the best results.