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For years now, big companies have enjoyed the bottom-line benefits that come from mass data collection and analytics. It’s how Amazon knows which products to push while you’re browsing their site. It’s how Netflix can figure out what you might want to watch before you even know yourself. And it’s even how large hotel chains power dynamic pricing schemes to maximize per-room revenue.
Small businesses, however, are still behind the curve when it comes to data and analytics. According to the most recent data, only 55% of North American businesses use big data analytics — and the majority of those that don’t are small firms.
And while it’s tempting to write that off as a result of the financial disparity between small and large businesses, there’s also something else at play— a knowledge gap. The fact is, there’s been a protracted shortage of qualified data scientists and analysts, making it hard for even large firms to meet their talent needs. And in that kind of hiring environment, small businesses don’t stand much of a chance.
But that doesn’t mean they’re helpless.
Small businesses can close the gap by building up their analytics capabilities from within. And to do it, there’s a simple strategy they can follow. Here’s what it is.
Conduct a Data Inventory
The first step of the process is for the business to examine what types of data it already has access to. That will indicate what kinds of analytics are already within easy reach. And more often than not, small businesses have more available data than they realize. Common types of data small businesses should already have include:
- Sales data
- Customer location data
- Website performance data
- Marketing response data
- Social media data
From those data types alone, any small business can build a robust data analytics operation that will get bottom-line results. But to get to that point, there are a few more things they’ll need to do.
Set Goals for Analytics Processes
Using the results of the data inventory as a guide, the next step is for the business to decide what it needs to get out of analyzing it. For example, they may wish to improve their sales performance by extracting trends from their sales data. Or they may wish to evaluate their marketing performance to change their marketing budget allocation for the following year.
Whatever the goal, however, it’s important to make certain that it conforms to the following characteristics:
- Specific – the goal must be a single specific outcome
- Measurable – the goal must include a measurable outcome
- Attainable – the goal must be realistic and within the realm of possibility
- Relevant – the goal should be something related to the data to be analyzed
- Timed – there must be a deadline to get results
If it wasn’t obvious, those characteristics form an acronym — SMART. It’s a system taken from the field of project management, but it works just as well for an analytics program.
Build the Requisite Skills
Depending on the data set chosen, the next step is to build the skills necessary to analyze and interpret it. In this case, it’s typically a good idea to choose an employee that already deals with the source of the data. So, a marketer for marketing data, a salesperson for sales data, someone from finance for financial data, and so on. That way, the person chosen will already have the correct frame of reference for the analysis they’ll eventually undertake.
Once the right person is chosen, the next step is to get them the training they need to do the job. The good news is that there’s no shortage of online coursework available to help them learn what they need to know. And, if the business covers any costs involved — as it should — the employee will likely be glad to put in the work. After all, analytics skills are quite valuable in today’s job market.
And the business doesn’t have to stop at entry-level training. They can also build their employees’ skill sets up as far as necessary, even up to Master’s level analytics degrees. Doing so will help them to sidestep the current talent shortage while improving their own employee retention at the same time. In that way, they can grow a useful analytics operation that understands their business and has the exact skills the company needs.
Choose an Appropriate Analytics Platform
With the right data, a defined goal, and the needed skills in place, there’s just one thing left to do. It’s to choose an analytics platform that’s suited for the task at hand. And small businesses are spoiled for choice in this area. Some of the best options — like Google Analytics for tracking and analyzing web data — are even available for free (or close to it).
But for a small business that’s looking to build an analytics team that will handle multiple data types, it may be best to choose a more robust platform that can scale as their needs do. And again, there are dozens of options available at every price point. To make the decision, the business decision-maker can even solicit the advice of their newly-trained employee. That way, they’ll end up with the perfect marriage of skills and platform capabilities to yield the best results.
Ready to Embrace Data
At this point, a small business that followed the strategy detailed above should be ready to start analyzing data and trying to achieve the goals they’ve set for themselves. And as they do, they’ll gain additional experience dealing with data analysis and learn to trust their results to make better decisions. That’s all there really is to becoming a data-driven business. All that’s left to do is to embrace the data and see where it takes them.
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