When running a business, it‘s easy to get overwhelmed with the sheer amount of data that’s put in front of you. These days, the problem no longer lies with how or where to acquire more data; instead, it’s figuring out how to convert the raw data you already have into actionable insights. After all, data is useless without any meaning. Therefore, it’s about time you put it to good use through guiding your decision-making, providing critical insight about your consumers, and revealing steps you can take to enhance your business processes. On that note, here are four steps that will help you start converting your data into valuable business insights.
1 – Start with the right questions and track the right metrics
First things first, it’s imperative that you start by asking yourself the right questions before you potentially waste your time barking up the wrong tree. With this in mind, you need to have a clear destination in mind right from the get-go that you can use as a reference point to base your judgments.
What questions are you hoping that your data will answer? Don’t be afraid to get specific, as you want to really hone in on the specific purpose of this exercise. Here are a few examples:
- What part of my sales funnel is the least effective?
- How and where are customers engaging with my brand the most?
- What percentage of my website traffic actually fits within my buyer persona?
- Which of my lead generation sources have the lowest/highest conversion rate?
Following on from this, you must make a deliberate attempt to employ the metrics that will assist you in answering your original questions. Many organizations make the mistake of adhering to metrics that portray their firm positively; however, this is short-sighted and will only lead to further difficulties down the road. The purpose of this task is not to shine a favourable light on the work you have already done. Instead, you must maintain as much objectivity as possible by selecting the appropriate metrics to aid you in finding the most valuable insights – which are generally the ones that identify inefficiencies and areas that should be prioritized for improvement.
Finally, before you begin sifting through your data sets, you must state your ultimate objectives. While you’ve previously outlined the initial questions you want your data to answer, you must now specify what you want to do once you’ve discovered the answer. For the most part, this will correlate with your broader corporate objectives, such as improving sales conversion rates, increasing customer retention, or better understanding your buyer personas.
2 – Consider your data sources
Quality input is important, but it isn’t always enough to create actionable business intelligence. You need to consider how difficult it is for you to obtain your data regularly. Can you automate it, or does it require human intervention? If it does, how does this affect the value it will deliver? Suppose your data is scattered across your organization in different file types and various formats. In that case, you need to find a way to unify it so that you can integrate all of your data sources so that your interpretation will be more accurate. These days, if you are looking for tools that will help you integrate disparate sources, so you don’t miss out on gathering meaningful customer insights, then you are simply spoiled for choice.
Thanks to rapid advancements in cloud technology, there are a plethora of services on the market that will assist you with storing, processing, retrieving, and analysing your data – and the good news is that you don’t even need to install large servers on your premises. Simply check out some of the most reputable cloud solutions and run a side-by-side comparison of their features, such as Snowflake vs. BigQuery, to see which service best suits your needs.
3 – Segment the data
Once you’ve gathered all your data sources, it’s a good idea to break it down into smaller chunks so you and your employees can make sense of it. After all, raw data is difficult to interpret, so the easier you can make it, the better. When segmenting your data, bear in mind your initial hypothesis and the insights you’re attempting to obtain. If you’re segmenting client data, consider how segmenting may be used to highlight areas of interest.
If you run an eCommerce business, for example, consider segmenting consumers based on their transactional value to your organization by looking at historical transaction data such as how much they’ve spent, how frequently they spent it, and the value of the items they bought. This will enable you to delve further into the characteristics and habits of some of your most valuable consumers, potentially yielding some surprising results.
4 – Visualize the data
Following on from the previous point, it’s commonplace to use visuals with data in order to make the complex appear more straightforward. While data analysis might make a lot of sense to data scientists, it certainly won’t have the same effect on your marketers, sales team, or other stakeholders. After all, these are the people who will be putting your data to work, so you better make sure they understand what they are looking at. With that in mind, your data must be presented with context, which is why you must take the time to highlight any potential insights you have already derived. This will make it less likely that misinterpretations will be made, and it will help your workforce achieve maximum productivity.
Furthermore, when you have many distinct datasets, data visualization may help you better interpret the information by converting it into a language that is simple to grasp, process, and present. Data visualization also helps you see the big picture’ in your data by connecting the dots and discovering hidden patterns or trends. If done correctly, this graphically displayed data may assist your organization in gaining more relevant insights, allowing your staff to enhance business operations such as customer satisfaction, marketing decision-making, sales, and business strategy refinement. When all of this is taken into account, the outcome is a significant increase in company efficiency and the potential to increase revenue growth.
