When you have more data than you know what to do with, finding effective ways to do something with that data can be a challenge.
For example, with more and more people using desktop, laptop, and mobile computing devices, all of which are generating more information, it makes sense that 90% of the world’s data has been created just in the last three years alone. And that’s not just a result of people creating data; it is because of algorithms and other software that track user behaviour and generate its own data about it.
In fact, on a daily basis, global computer usage now generates over 2.5 quintillion bytes of data, and that figure is only going to grow thanks to the proliferation of the Internet of Things devices. It’s a massive, almost incomprehensible amount of information. And only some of it is useful to your specific, relevant business interests.
So how does a business wade through the mountain of data gathered daily and find information that is actually useful in improving performance, profit, sales, or other operational factors?
These eight ways are a few of the things that a business can do to sift through the information and find what makes sense and is useful.
Big data, looked at without refinement, can appear to be a huge, unwieldy mess of random information. And taken in that context, there’s a real temptation to simply ignore it. However, ignoring big data is ignoring a key opportunity. Data science, for example, is a growing profession because of the value that comes from being able to screen, filter, and interpret what is useful in big data.
People who choose to ignore big data because of its volume do so at the peril of their own business. The first step to extracting valuable customer data is to acknowledge that good data is “in there somewhere” and to have a willingness to mine through it.
The key factor to making use of big data, once you acknowledge its potential, is to have the right tools and talent to wade into the sea of information and come back with data that makes the most sense to you.
You should seriously consider allocating some operational budget to analytics. Bigger companies may have entire departments dedicated to this job, but while that scale gets results, that’s not the only way to achieve useful analytics. Even small to medium enterprises can find an analytics solution, whether personnel or software, that works for their size and budgetary requirements.
People often think of data as a way to help predict the future, but some of the most important lessons that data teaches us have come from the past. A good long-term plan to use data from customer information is analyzing historical data.
Businesses should always be aware that some of the best data available are the sets that are gathered over time. For example, you should not be ignoring sales data from the last 5 to 10 years, even if it’s not part of your current digital infrastructure.
Use document management systems and archive this data if you must, but analytics put to the task of discerning past customer sales data can reveal incredibly useful information about customer behaviour and buying patterns. If you have existing data, even if it’s not digital, digitize it and make sure your software can use it. Under the right circumstances, it may be far more helpful than you imagine.
Extracting data from customer behavior and other datasets can obviously help to increase sales, but don’t forget that another way to make more money is to spend less. In addition to increasing sales, the right type of customer data can also help to increase efficiency and reduce waste, both of which can increase your revenue.
For example, your customer data is showing that more and more of your marketing response is coming from YouTube and Instagram, and less of it is coming from Facebook. This may be an indicator that for your product and target market, you can spend less—or cut down entirely—on your Facebook spending and devote more effort to the platforms that are getting good results. Let customer behaviour data help to streamline the way you run your business.
“Client churn” is a term used to describe the turnover rate of customers or clients from being regular purchasers to taking their business elsewhere. It is another area where big data, combined with analytics, can make a big difference.
This process is about looking at your user data and arriving at conclusions that help explain why some customers remain loyal, and others support your business for a specific period and then leave. You may find relationships between drops in customer retention and when you stop supplying certain products. Alternatively, you may find certain sales or times of the year give you a big spike in customers, but they don’t stick around afterwards.
Analytics focused on the specifics of your client churn can help you understand how to retain more customers and thus enjoy more consistent profits.
Up to 80% of the data generated today is video, documents, and images, much of which is posted on social media. Analyzing social media and using the unfiltered, unsorted data that appears there can be enormously helpful not just in finding out where your market is, but also in what a market wants or is looking for.
It’s important to recognize that content that appears on social media can be symptomatic indicators of other trends and factors to capitalize on, for the businesses that are paying attention. So, don’t just view social media as a channel to reach out to your market, understand that it can also be a window into what that market may be looking for, and you can get ahead of the curve to provide it for them. Do not ignore trends, take advantage of them.
Data access should be shared across your company. While it is understood that knowing something your competitors don’t can be a valuable edge, this does not apply within your business itself. If you know something about a customer’s needs or behavior, but someone who could actually use that data does not have access to the same information, that hurts your company.
You should always ensure that digital data is shared and accessible for those who need it. Someone who handles customer support, for example, should be able to share a customer’s complaint and specific problem with any department that might actually be able to help solve the problem or provide deeper insight into the issue.
Information is only useful when people who know what to do with it can get to it. If your finance department is locked out of certain financial data, that’s a layer of interference making things less efficient.
Contrary to popular fear-mongering, automation does not automatically mean that a human’s job is on the line and that someone is about to get laid off. Automation is not just applied to physical, mechanical activities, but it can also be applied to data analytics.
Some of the most useful insights you can get about customers will not come from doing a line-by-line analysis of each bit of incoming data, but instead from trusting software and algorithms to sift through the data and find only what is relevant to you and your needs.
Automation can even be used to free up your human resources for more analytical activities that are better suited to their talents. Processing payments, for example, is largely a rote, mechanical process that software can assist with. With fewer routine operations to deal with during the day, your human staff can concentrate on more ambitious, innovative activities.
More data is being generated on a daily basis than any human or group of humans can feasibly keep track of. However, there is also a wealth of useful, actionable insight hidden in the depths of that data. It is only through strategy and sensibility when choosing the right data, analyzing it, and putting it to the correct use that a business can improve its revenue.
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