Being successful as a marketer generally means you take the scientific approach to planning. The scientific approach is based on “observation – hypothesis – test – optimization.”
This scientific approach is music to the ears of finance because the finance sector lives for verifiable models and a good spreadsheet. Finance simply wants to better understand that you have a plan and you analyze the performance against the plan. This is what they do when it comes to capital expenditures and operating expenditures, and campaign management is nothing but a subset of these (which one depends on the campaign).
I have always tended to work well with the finance folks because this is how they think, and I believe marketing to be a heavily mathematical exercise. I believe in a good story, but I think a good story is driven by the data.
The reason the friction between marketing and finance folks comes up is because of the ongoing struggle between “how much SHOULD we spend” vs. “how much DO we spend.”
Show me a marketer who says their marketing campaigns are well-funded, and I will show you, well, no one. All marketers believe their teams should be funded with more budget, and the numbers tend to agree with them. But too often a marketer is unwilling to build a financial model and forecast out the ROI for what they can do.
I tend to love building those models. I see them as a cornerstone for a strong marketing effort. In a SaaS business, for example, the recommended marketing allocation as a percent of revenue tends to be somewhere between 4.5% to 15%.
Most of the time I see marketing budgets settle between 1%-2%, but in every case the marketers are citing the industry benchmark for why they should get more money when they SHOULD be building a forecast model.
Simply put, marketers should be willing to put their money where their mouths are. A strong, data-driven marketer in today’s world should be willing to build a model, evangelize it to finance and sales, and use that to plan out spend and establish a benchmark for optimization.
I like to build models that forecast the balance between awareness or perception vs. demand generation, and look at spend allocations by channel as a result. With the right model you can forecast everything and educate teams about how they should be allocating their time.
Too much of legacy marketing is guesswork or “gut feeling.” I don’t personally believe in gut. I like data.
I would even argue that forecasting is what makes a stronger relationship between sales and marketing, and not just finance and marketing. Sales is all about the ability to forecast pipeline. This is especially true in a SaaS environment. SaaS businesses are all about mapping a customer journey, crafting engagement, and dropping prospects into a funnel for development and close. If you have a fully fleshed out model that predicts impact by stage, then everyone is on the same page. Your model becomes the glue between sales, finance, and marketing.
If you build a good model, based on accurate assumptions, you end up with a more well-funded marketing program. At that point, it is on you and your creative mind to find ways to hit the numbers. If you do it right, your budget will rise. If not, then you have more work to do.