The answer to how much you should spend on analytics is, drumroll… It depends.
It would be great if the answer was as easy as X-percent of your marketing budget should be set aside for analytics, but of course, different circumstances will inevitably mean that percentage will vary. That said, there are meaningful guidelines that you can follow to help inform your analytics spend.
The “one percent rule” for data analytics budgeting
Some people say you should plan to spend one percent of your media budget on data analytics. While that may be a good starting point in some cases, it doesn’t really scale very well across all media budgets. For example, if you’re spending $500,000 in media, 1 percent ($5,000) is not going to get you very far as an analytics budget. On the other hand, if you spend $50 million in media, you probably don’t need to spend 1 percent ($500,000). The sweet spot for the “one percent rule” is probably for media spend in the range of $5-10 million. With lower media budgets, you’ll need to spend a higher percentage to get good data analytics, and conversely with higher budgets—you may be able to spend a lower percentage and still get the data intelligence you need.
As a case in point, one of our clients spends about $25 million in media, and the cost of the Callahan Intelligence Platform is about 0.5 percent. For that spend, we provide comprehensive data analytics: We gather, store, visualize and analyze all business and marketing data. Spending more than half a percent might provide additional insights from the data, but there is definitely a point of diminishing return where upward of 1 percent would be an unnecessary expense.
So how can you determine an analytics budget?
The only way to get to a definitive budget is to do the hard work of defining the scope of your specific data-analytic needs. Starting with basic marketing reporting (digital and traditional media, social media, web analytics, email metrics, etc.) requires less of an investment than more comprehensive business-focused analytics. Of course, the return on your analytics investment will be higher when you focus on business measurements as well as marketing metrics. At Callahan, we strongly believe in the value of that kind of comprehensive data analytics program. It’s what we call our front-end analytics approach.
The next question to ask is what data resources can be leveraged that already exist in the organization. For example, if there is a finance team or strategy team with existing data infrastructure, that reduces the need to start from scratch on the business side. If business data is organized and readily accessible, the marketing data can be added on top of it. However, if you have to build it all, it’s obviously more complex, and costlier.
Then you need to consider technology. You may already have some technology in place that can be leveraged or supplemented (internal sales or CRM databases, web analytics, etc.). If you’re building your own analytics tech stack, learn how to select the right marketing analytics technology. Or, you may choose to explore working with an outside vendor for a comprehensive analytics tech solution. Beware of analytics systems that are sold as turn-key solutions, but in reality, require significant investments of ongoing human capital to prove truly useful.
Finally, speaking of human capital, there’s staffing. When building an analytics team, you’ll start by looking at your data analytics objectives and consider your existing resources. We covered this topic in depth in our podcast on how to build an analytics team as well as in this post.
The bottom line for your marketing budget
In summary, expect analytics to cost about 1 percent of media spend if your media budget is $5-10 million (the percentage should increase if you spend less, or decrease if you spend more). To get more specific than that very broad framework, you’ll need to do the hard work of calculating the cost of all the required components:
- Scope: Simple marketing metrics or comprehensive business analytics?
- Resources: Available internal resources, new internal resources or outsourcing?
- Technology: Already in place or a new tack stack?
- Staffing: How large the team should be or will the work be outsourced?
Also consider that with the right analytics system, many processes can be automated for significant cost savings in time. For example, our Intelligence Platform has saved our clients six-figure amounts in a year by automating data-gathering and reporting processes that were previously done manually. Our clients aren’t adding costs, they are reducing costs by eliminating repetitive human tasks while getting better, more consistent results.
That’s the beauty of the right analytics solution: You should spend relatively little to learn a lot, and gain insights that have significant business impact. In fact, the best response to how much you should spend on analytics is zero. Insights from a well-implemented analytics system can return improved business outcomes many, many times over.