"Big data" is a term used to refer to data that is far too large and complex to process and analyze by manual means or with basic software.
Currently, big data is used mostly in sales and marketing, the financial industry, and IT departments. You have probably seen examples of big data analytics software, such as Google Analytics, Salesforce, and HubSpot. Sales and marketing teams use these tools to dig through massive data sets and identify patterns in the data. Business analysts (BAs) look at these patterns and try to determine what might have caused these patterns to form. For example, let's say ACME Inc. just created a new, fun color for their product. Over 1,000 people had requested this color over the past quarter, so they did a test run to see how it would sell. Over the next 30 days, the analyst noticed a major boost in sales of this product, but also hundreds of new customers.
This is a grossly simplified example of how big data can be used. In practice, big data often involves analysis of hundreds, even thousands of data points that the analytics software compares to find patterns.
For those not as well versed in big data as BAs it can be challenging to know where to start just to open your data. Additionally, many businesses collect data, but struggle to derive value from it. This post will help you understand ways to identify the valuable patterns in your data on a very introductory level, and show what to do with that information to help your business grow.
Analytics software and its limitations
Contrary to what some people believe, analytics software isn't an oracle. It can't tell you how to find the keys to the kingdom or to the holy grail. All analytics software can do is dig through data and identify patterns - it's up to you to figure out what those patterns mean.
What that means is this: You have to know what you're looking for and be able to match it with one or more patterns that your analytics software has identified. This may sound easy, but in practice, it can be profoundly difficult to do.
Start with a single data point
What is the primary kind of information you want from your data? If it's gross sales, then write that down. Now, define why this particular data point is so important. Go ahead and write the usual answers down just to get them out of your head. Keep writing until you run out of ideas. Take a break and go do something else for a few minutes. When you come back, write down whatever comes to mind about why gross sales are so important.
Take a look at your gross sales data for the last few quarters and see how it has changed. Try to identify a trend in the data to see if sales are moving upward, downward, or if they are steady.
What does this trend tell you about your gross sales? What other questions come to mind as you think about this?
- Did you have more customers in any particular quarter? Why?
- Did you have fewer customers in any quarter? Why?
- What kind of relationship is there between customers and gross sales?
- More customers = Higher gross sales (this indicates volume as a primary indicator of gross sales performance)
- Fewer customers = Higher gross sales (this may indicate customers looking for your high-end products, a potential KPI)
- More customers = Lower gross sales (this may indicate customers are purchasing low-price items, a potential source of loss in sales revenue)
- Fewer customers = Lower gross sales (sales volume determines gross sales)
Expanding on the questions you ask
You can already see how asking questions about customers can yield some important insights into the data. Expand on that by taking a look at product or service sales. Identify your top selling product and make note of its price. Next, identify the least purchased product and its price. Take another look at your customer sales data. What did they purchase? Why?
Why this is an important exercise
The idea behind asking and answering these questions while looking at the data is this: To begin making sense of what the analytics are telling you, it's important to develop some skill in matching your customers and products to the patterns in the data. You don't need to spend several hours a day poring over spreadsheets of data. Rather, you just need to get a feel of how to look at your data and make sense of it in a practical way. Spend a little time each day to look at your data and make note of the questions that come to your mind. Over time, you will find that you are able to quickly look at a page of charts and identify the patterns behind them. This will give you much-needed insight that you can use to improve your sales and marketing strategy.
Being able to look at visual representations of data and quickly make sense of them is a valuable skill that goes far beyond sales strategy. This skill can also help you identify customer service issues, problems with your product, and a host of other valuable information that currently lies hidden in your data.