I was having coffee the other day with an entrepreneur I respect greatly. We were discussing the challenges of small businesses in using analytics and she struck a chord with one statement.
“The small businesses that don’t have the cash flow to use analytics will always struggle with the cash flow to use analytics.”
While there is a truth to her statement, I disagree that these businesses will “always struggle” with the cash flow to use analytics.
Here’s why…
Limited Analytics
First and foremost, many businesses are already using analytics whether they believe they do or not.
Nearly every off-the-shelf website platform, social media application, and Software-as-a-Service subscription comes with some form of built-in analytics.
The question is whether those tools give you all the information you want to know. For example, my email service provider (ESP) tracks who opens each email I send out. They provide me with in-depth delivery, open, and click-through rates as well.
What my ESP doesn’t tell me is which customer segments are engaging with my email marketing. I need to combine my ESP data and sales database to see which segments are engaging.
It is useful for me to identify email engagement by customer segment. So, I perform this analysis monthly. However, this might be more complicated than your business needs at this point.
Maybe you only need to know basic conversion rates across all customers because you haven’t created separate marketing messages yet. It’s perfectly reasonable to limit your analytics to what is most important to your business now.
Most businesses will not use generative artificial intelligence (AI) models to deliver products and services. And it is very unlikely that these same businesses would need to develop a custom AI model of their own.
By limiting the scope and scale of the analytics you implement, you can limit your cash flow requirements. Over time, you will be able to expand your analytics to evolve with your business needs.
Different Paths to Implement Analytics
Regardless of the status of your business, there is more than one way to start or expand your analytics unit. The two primary models focus on different levels of staffing experience and oversight.
If you want to go the Data Scientist route, then you should hire highly experienced staff. You provide them with specifications and a budget and let them build a system to deliver what you want.
You will pay a lot for a data scientist. If you made a good hire, you won’t need to provide a lot of oversight on their work.
In contrast, junior analysts are less expensive than data scientists, and you can limit your initial cash flow requirements. However, junior analysts will be less capable of working on their own without explicit instructions.
The drawback of hiring less experienced analysts is that you will need to provide greater instruction and oversight to them.
Somewhere in the middle, you can elect to contract with analytic experts to set up and implement specific projects for your business. You can often make great progress at a moderate cost by focusing on targeted analytics. And, if your contractors are conscientious, they will provide documentation and transition support when you bring full-time analysts on board.
These are only a few of the many different paths you can take to implement or expand analytics in your business.
Restricting Analytic Overhead to Conserve Cash Flow
How do you implement analytics with limited cash flow? As outlined in the two sections above, there are many ways you can limit the amount and complexity of work being done. You can also limit the type of infrastructure you use for analytics.
The core components of your infrastructure are your data storage, data processing, and analytic applications. And while there are many options to choose from, your best bet is likely to be a combination of paid and open-source applications.
A full review of different options for storage, processing, and analytics is beyond the current article. However, I want to note that many cloud-based data storage and analytics solutions can be very reasonably priced. Generally, these services will charge a small fee based on the volume of data being stored or analyzed so that costs scale with your business.
Where analytic applications are concerned, your biggest decision is likely to be between open-source or commercial tools. Open-source tools like Python offer a free application with an immense amount of power built in. The biggest drawback is that Python is a scripting language and does not have a point-and-click interface. You will need to have someone who can write code available to execute your analysis.
Commercial tools like SAS, Stata, or SPSS can be costly but typically also have point-and-click interfaces available. A user can choose to write code, but it often isn’t required if you know the analysis you want to perform.
In the end, getting the right combination of data storage, processing power, and analytic applications requires knowing details about the specific use cases for the analysis. Just know that it is possible to set up an analytic solution for a reasonably low price.
Consider Your ROI for Analytics
Regardless of how you set up your analytics infrastructure or what use cases you pursue, you should keep an eye on the return on your analytics. Specifically, how much do you increase revenue or reduce expenses based on the information obtained from your analytics?
Tracking your analytics ROI is important for understanding what you get from your efforts as well as tracking cash flow. Each analytics decision changing revenue or expenses has an impact on cash flow.
As you begin refining your strategic decisions based on data analysis, your cash flow should improve. When this happens, you will have numerous options for how to leverage the increased cash flow.
Conclusion
Do you have the cash flow for analytics? More than likely you do. However, you may need to scale your expectations to match your financial position and the position of your business.
If you are concerned about having to spend large sums of money to establish useful analytics, breathe easy, my friend. You can do quite a lot without hiring an entire team and building expensive data platforms.
If you’re interested in taking the next step but aren’t sure where to turn, find an expert to help guide you. You can schedule a call with F1 Analytics (Link HERE), and I’d be happy to discuss your needs.
