Data is what takes the uncertainty out of business. It's what allows entrepreneurs to understand how much money they can expect to make. It's how they figure out the cost of a new customer, the amount of revenue they need to generate to make deals profitable. It's also how they anticipate the value of a specific input in the context of a predictable output.
In other words, how much money can they reasonably expect to generate with an investment of $100 in ad spending with a sophisticated suite of data analytic tools? There's an actual accurate answer to that question.
Digital technology has created a world where every action creates a small footprint. Now, with artificial intelligence available and widely affordable, it's easier than ever for business owners to improve their operations through data. Many of these skills can be developed in business administration courses. Others can be picked up the old fashioned way. Through experience.
In this article, we take a look at the significance of analytics.
Analytics refer to a business's ability to analyze the information they've collected. At this point in the world of big data, it's an almost universal requirement for all departments of a business.
It can be used to perfect customer interactions, improve efficiency, boost security, and mitigate risk. It's not magic; it's simply pattern recognition.
It's also not something that happens instantaneously. For example, a business that has been in operation for six months does not actually know its customer lifetime value. It simply hasn't racked up enough experience to have achieved a reliable figure for that metric.
To that end, data refinement is an ongoing process for which there's never any true end. Analytic understanding can be endlessly enhanced with greater levels of sophistication.
For example, a new home service company might develop, after a few months, a general idea of how much ad spend they need to invest in to produce a single customer. However, that same company, three years down the line, and looking at the exact same data figures, may now be able to understand how much ad spend they need to commit to, what service areas provide the highest return on investment, what their average customer lifetime value is, and what characteristics inform their ideal customer profile.
It's the same set of numbers, but with enough time and refinement, more and more insights can be derived from it.
Through this information, businesses are able to forecast with extreme accuracy what their financial quarters will look like.
In this way, they are able to proceed with limited risk, even in situations where there is not the more tangible certainty of, say, a salaried paycheck. The business owner nevertheless understands what to expect and why to expect it.
That's the basic utility of data in a nutshell.
Many business owners hear that they should be using data. They don't know what it means in a practical sense. How sophisticated do they need to be? Should a professional be hired?
To that end, it really depends on the size of your business. If you're running the hypothetical home service company that we described above, you'll most likely have a few basic statistical categories. To keep an eye on, which will again be refined and extrapolated over time.
At the most basic level, you should receive generated insights from your CRM and your marketing platforms. Facebook ads, LSA, AdWords, all produce automated campaign statistical categories that you can actually learn how to understand on your own, in most cases without professional assistance.
The more niche the data points get, the more you'll need some form of support to parse out all of the important details. In LSA, you'll get a breakdown of your average cost per lead. It'll be up to you to determine your close rate. That's the number of... That's the percent of leads you convert into customers and the average lifetime value of a customer.
But there's a handoff from LSA or other marketing platforms to your CRM, where you process the majority of customer interactions, including revenue produced. So, to understand the complete lifestyle, life cycle of a customer, you'll need multiple data sources. From marketing channels, you learn how much it costs to get a customer. From your CRM, you find out how much you make from a customer. And through these various data points, you drive profit.
Not only does this tell you how you need to strategize, but it will also eventually lead to even greater insights, possibly in the form of ideal customer demographics or services, or in identifying services that are more worthwhile than others.
The more intricate your business is, the more heavily you need to rely on data. If you're running a company with multiple departments, every single one of them will produce its own data set. For example, a brand with a designated customer service department will have its own stats.
An HR department will have statistics on hiring demographics. Sales will have its own statistics. And every department will still need to be able to hand off information to each other. This is often achieved through what software people call a single source of truth. Basically, a singular location where information from various departments can be consolidated for general use.
There are so many analytics-based software programs that making specific recommendations would be disingenuous at best, impossible at worst. Most probably, you'll find that there are very specific programs designed for your area of business.
For example, if you own an e-commerce brand, there will be CRMs designed particularly with your needs in mind. There are general solutions available, Salesforce, HubSpot, Stacks, and so on, but many people are better off with a niche tool.
Going back once again to home service, a lawn care company might use Copilot for its CRM, a cleaning service might use ZenMaid, and so on. The more bespoke the tool is, the more refined the data categories will be.
From there, you can consider other ways to optimize data utilization. For example, AI assistance to generate rapid insights. Professional consultations at the organizational level to take your business to the next level. Maybe even a full-time data specialist if you grow large enough.
There is no single way to integrate data into your business. Rather, it’s an iterative process that thrives through refinement.
