The methods for setting the advertising budget haven't really changed in the last 30 years. It seems that advertising as a percentage of sales is by far the most popular methodology (53% of companies use this approach according to the most recent study quoted). This corresponds to our own experience. It is surprising how frequently simple and very crude rules like these are used to make decisions on millions of dollars of advertising budgets by some of the biggest companies in the world. More scientific methods like econometric modeling have been around for decades but companies seem to struggle to incorporate them in the decision making process. Econometric modeling is often seen as a black box and too hard to understand. For these reasons many companies who even invested heavily in econometric modeling revert to very basic rules such as advertising to sales ratios when the actual budgeting decisions were being made. This is a real shame. The fact that rules such as advertising to sales ratios are easier to explain doesn't mean they are better for decision making. And it certainly doesn't mean that they should replace more scientific approaches.

1. Global Data
& Analytics
Marketing Investment -Setting the Marketing Budget
By Dimitri Maex
Managing Director Marketing Effectiveness
Ogilvy New York
Ogilvy & Mather

2. Setting the marketing budget
Exactly 30 years ago Harry Henry published an article in the Cranfield Broadsheet that
described 15 approaches to determine how much to spend on advertising. They are :
1. Intuitive / rule of thumb – “enough to do the job” based on experience
2. Maintaining previous spend, sometimes inflation adjusted
3. Percent of previous sales – backward looking, compounds failure (or rewards
success)
4. “Affordable” – what’s left after cost and profit requirements are met
5. Residue of last years profits – focuses on source of funds, not their use
6. Percent of gross margin – begs question of cost efficiency
7. Percent of forecast sales – most common method used
8. Fixed cost per unit of sales – like % of turnover
9. Cost per customer/capita – mostly business to business
10. Match competitors – assumes they are right
11. Match share of voice to brand share – like the above
12. Marginal return – direct response approach
13. Task approach : define objectives and cost out how to reach them – best in theory
but may require modeling
14. Modeling – the most sophisticated approach: not easy
15. Media weight tests – looks empirical but usually difficult to evaluate or replicate.
Simon Broadbent had a very similar list in his book The Advertising Budget (1989).
Others have built on these lists more recently.
Gullen (Gullen, 5 steps to effective budget setting – Admap 2003) categorized the budget
setting approaches in 5 independent schemes or “budget bearings” as he calls them :
1. Inertia bearings based on usual practices (ie last year’s budget maybe adjusted for
inflation)
2. Business bearings based on sensible ratios within the business plan (eg
affordability, advertising to sales/volume/margin ratios)
3. Media bearings based on the cost of fulfilling a sensible media plan (eg. Cost of
media plan to achieve goals)
4. Competitive bearings based on the spend of competitors (eg match share of voice
to share of market)
5. Dynamic bearings based on the observations of apparent effects of previous
advertising activity (eg. in market tests, econometric modeling)
Dyson (Dyson, How to Budget Better - Admap 2004) had 7 categories :
1. percentage of sales : the ad budget is a proportion of last year's actual sales, or
next year's forecast sales
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3. 2. objective and task : objectives are set (turnover, profit, growth) and the budget
required to meet these objectives is estimated
3. competitor : the amount spent by competitors is used as a yardstick; a version of
this is the well-known rule of thumb that share of voice should be at least equal to
share of market
4. affordability : the budget is the amount left after everything else has been
accounted for
5. historical : do the same as last year, with an adjustment for inflation
6. executive judgment : basically guesswork, but probably an informal use of one or
more of the above.
7. brand led : a more scientific approach that uses research data and econometric
modeling.
Green (Green, How much should I spend on advertising? - WARC 2006) says that there
are roughly 2 categories:
1. Rules based approaches that start with the financial goals of the brand (eg rules 1-
11 in Harry’s list).
2. Objective based approaches that start with what resources are available or assume
that what a company has been doing in the past will work in the future brand (eg
rules 12 and 13).
As you can see, the methods for setting the advertising budget haven’t really changed in
the last 30 years.
Dyson has an overview of some of the very limited research that has been done over the
years on what methods are used most often. It seems that advertising as a percentage of
sales is by far the most popular methodology (53% of companies use this approach
according to the most recent study quoted).
This corresponds to our own experience. It is surprising how frequently simple and very
crude rules like these are used make decisions on millions of dollars of advertising budgets
by some of the biggest companies in the world. More scientific methods like econometric
modeling have been around for decades but companies seem to struggle to incorporate
them in the decision making process. Econometric modeling is often seen as a black box
and too hard to understand. For these reasons many companies who even invested
heavily in econometric modeling revert to very basic rules such as advertising to sales
ratio’s when the actual budgeting decisions were being made. This is a real shame. The
fact that rules like as advertising to sales ratios are easier to explain doesn’t mean they are
better for decision making. And it certainly doesn’t mean that they should replace more
scientific approaches.
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4. The truth is that there is no single best approach to budget setting. All the approaches in
Harry Henry’s list have their pro’s and con’s (some have more pro’s than others …).
Therefore the best approach is to use multiple approaches to come up with multiple
budgeting scenarios. These different scenarios as well as the pro’s and con’s of the
approaches used to come up with them can then inform the ultimate budgeting decision.
This hybrid approach can easily be formalized in a budgeting process. Gullen outlines a
process for this in the article mentioned above. At Ogilvy we have developed a similar
approach wheich we deploy always tailored to the client’s existing budgeting process.
Goal driven budget setting – Funnel Allocation
One of the most important factors to consider when determining the marketing budget is
what you would actually like to achieve with the investment. The goal (or set of goals
usually) should determine the size of the budget, the types of programs you invest in and
the media mix you choose. This might seem very obvious. However time and again we
have seen budget setting and allocation happen in a vacuum, completely disconnected
from the marketing goals. Surprisingly, this is especially the case when more scientific
approaches are used.
This is why we have developed a budget allocation approach that takes the marketing
goals as a starting point. At the foundation of the approach lies a strategic framework
that is universally adopted and well understood – the marketing funnel. Marketing
funnels come in all shapes and forms but in general they contain a number of levels as
illustrated by the example in the figure below.
We don’t see the funnel as a linear journey every customer goes through – consumer
behavior is far too irrational and therefore non linear for that. We simply use the funnel
as a framework that captures the broad marketing objectives a brand needs to achieve in
order to grow revenue, market share and/or profit.
One of the most important investment decision a marketer needs to make is how to align
their marketing budget to their marketing objectives or using the funnel as a framework,
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5. how to allocate their budget across the different levels of the funnel. We do this through
3 steps :
• Defining the Funnel Revenue model
• Spend Get Analysis
• Optimization and Scenario planning
Defining the Funnel Revenue model : In this stage we first define what the funnel levels are.
This allows us to customize the framework to any company’s particular situation. We
then define the metrics that are associated with each level of the funnel. Once this is done
we determine how revenue is generated through achieving the different marketing goals
or to use the funnel terminology how the levels of the funnels are interconnected and how
profit gets generated throughout the funnel. This gives us the foundation for funnel
allocation.
Spend Get Analysis : In this step we define what we call spend/get curves for every level of
the funnel. These curves (often referred to as “response curves” in optimization
literature) formalize the relationship between spend at a level of the funnel and
performance as measured by the metric defined in the previous stage. We build the
spend/get curves using whatever data is available from in-market tests, econometric
modeling or competitive benchmarking. If no data is available we can even build them
through intuition and judgment using guided questionnaires.
Optimization and Scenario Planning : Once the Spend/Get curves are build they can be used
to run various optimization scenarios. The approach can be used to allocate a given
budget across the levels of the funnel or to determine what the optimal budget should be
(unconstrained optimization). We usually run various scenarios and we test the sensitivity
of the proposed solutions to any assumptions made while constructing the Spend/Get
curves.
The funnel allocation can then be used as an input for media mix allocations. The
advantages of this approach are :
• Goal driven : the approach starts from the marketing objectives. It thereby often
forces companies to be very explicit about their objectives which can be a benefit
in itself.
• Open : the user knows exactly what data and assumption are made in the
optimization and the sensitivity analysis will give the decision maker a good feeling
of to what extent assumptions are driving the outcome. This transparency is likely
to increase adoption of the recommendations that come out of the exercise.
• Flexible : the approach can use whatever data is available and forces the decision
maker to fill in the gaps with judgment,
• Collaborative : this is related to the openness of the process. One can easily adjust
some of the underlying assumptions and rerun allocation scenarios real time in
front of a group. This enables interactive budget allocation workshops that can be
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6. great for getting buy-in in the approach and the recommendations coming out of
the exercise.
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