“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Kim Brown, founder and CEO at Centrally Human.
Marketing is going through an unprecedented evolution with the convergence of data and technology. Automation and AI are realities, data is coming from every conceivable touch point and audiences are engaged with brands more than ever.
Yet underneath this digital marketing transformation, there is one thing that hasn’t changed and is holding the industry back: budgets, specifically the budgeting process.
It’s critical to acknowledge the disconnect of annual budgeting in a digital world. It doesn’t work, and it has to change.
The general process is still rooted in this notion of static data and campaigns with specific start and end dates. Always-on channel specialists, like those in search, social or programmatic, have to convert streaming data into batches and project against a future chunk of time.
This recommended budget usually gets changed – inflated when a leader wants to spend more online even if data indicates low or decreased demand because the size of the pie is set regardless of data showing high demand. Either way, digital ends up operating with mismatched resources because of an unsuitable funding model.
Why Old School Budgeting Doesn’t Work For New Age Marketing
The overarching challenge with the process is that it forces programmatic marketing into a reserve media model. Programmatic and reserve media, however, just don’t work in the same way.
The static budget model creates unnecessary constraints on digital programs that restrict performance. Brands either lose out on opportunity because the budget is too low, or there’s underperformance because the budget was too high or designated for the wrong tactic. The scenarios are the same: Overarching expectations were created against a past reality that has changed.
Unfortunately, once the budget is done, digital marketers are stuck. With excess funds, they’re left trying to find decent ways to spend or give it back, jeopardizing future ability to get a higher budget next year, even if demand increases. If there is too little budget, marketers spend valuable time trying to cut the worst of the best, explaining the underprojection and planning the next budget assuming demand stays high or increases at a projected rate.
Finally, all of this is compounded by the reality that overall budgets are typically fixed and contain more stable reserve-media budgets. Will marketers pull from TV to pay for search or fight for a total budget increase that accommodates digital budget variances?
This budget model doesn’t work in other data-driven industries. In restaurants, for example, a new eatery can project customer flow and demand, but it’s all guesswork until it opens and learns real-life customer patterns and demands. The old media budget model resembles a bank giving that restaurant a one-time, lump-sum loan for 12 months with the hope it works out as it does on paper. The new data-driven budgeting model is like a bank supplying a revolving credit line that the restaurant owner can manage and throttle during the ups and downs of business.
The Solution: Data-Driven Budgeting
Data-driven budgeting optimizes fund allocation that better aligns with the way digital media actually works. It maintains the requirements for budget controls and accountability while becoming more responsive and adaptive to real-time opportunities.
There are a few ways to create data-driven budgets.
One way is to look beyond one-year budgeting and work with leadership to create a two-year marketing budget. This allows spend to pace with incoming data and performance. It gives decision-makers more flexibility and confidence to allocate funds based on what the market and performance data indicates outside of a 12-month window. It also facilitates increased focus on managing digital programs without as much distraction from annual rebudgeting. A 10-month underspend won’t cause panic because the balance can be used in months 11 through 15.
Another kind of data-driven budget rewards performance. Leaders need to make a conscious decision to give equal attention and praise to highly efficient, smaller-budget digital tactics. Praise performance marketers and find ways to incentivize and capitalize on their expertise. The programs aren’t as sexy, but that outward positive reinforcement sends a powerful signal that business results are more important than budget size.
An optimization budget is another data-driven option. Everyone has a test-and-learn budget, but who has a budget for those lucky times when consumer demand unexpectedly spikes? Yes, it involves the possibility that it doesn’t get used that year. It’s invaluable because it pre-approves trigger rules that dictate the release of funds. It takes about a week to develop the rules, and it saves at least that much time and increases revenue potential. It also increases team confidence and focus knowing those dollars are set aside to capture profitable audience interest on demand.
Digital is still new, and there is more data, technology and know-how than ever before. Reframing the budget process to match a real-time, data-driven world will unleash the powerful potential of digital marketing.
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