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Six Easy Steps for Measuring Marketing

By: Guest | June 20, 2011 | 
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This guest post is by Adam Boatsman.

I should preface this blog post by telling you I’m an accountant.

I know, I know, what the heck does an accountant know about marketing? Maybe not much, but I do know how to measure.

Measurement is an ongoing topic at Spin Sucks; one that Gini Dietrich and the team of guest bloggers discuss at length.

Which is great! They know marketing. I know accounting.

Together we can help you on your quest for the Holy Grail: The ROI.

Before we begin, however, allow me to explain the Regression Analysis, which is something we use for many of our clients. It measures overall marketing spend, either as time or resources, relative to sales.

It establishes three things:

  • Strength of the statistical correlation.
  • The actual financial correlation.
  • ‘When’ you should see the return to know if you’re successful.

You can use this tool to determine:

  • The actual return-on-marketing spend to each audience.
  • The average rate of return-on-marketing.

Six Easy Steps for Measuring Marketing

Don’t worry. If the above explanation doesn’t make sense, I’ll make it more clear for you. There are six easy steps you can take to begin measuring your efforts today. If you take this to your CFO, you’ll instantly begin earning respect as an investment instead of an expense.

1.  Determine the Theory

Typically, the theory is that if you spend more on marketing there is likely a corresponding increase in sales. Statistics are powerful at either disproving correlations (A does not cause B) and proving correlations (A caused B). My theory is that marketing TIME and MEETINGS correlates to sales. Unfortunately we don’t track that, yet. D’oh!

2.  Get Your Data

You’ll need a large data set. I suggest at least three years of data. In the example below, we gathered sales by customer segment and the associated marketing spend.

3.  Build Your Model

  • Install the Analysis Toolkit in Excel.
  • Create column X – Sales. Enter your 36 months of sales (for an individual customer segment).
  • Create column Y – Marketing spend for each of the same months.
  • Marketing spend: In marketing it’s generally accepted that when you spend $1 it takes a month to a year to see the corresponding sales. I advocate looking at a three-month minimum correlation. Start three more columns called ‘Month 1, Month 2, and Month 3.’

It should look like this:

 

 

 

4.  Perform Regression Analysis

Using the Excel Regression Analysis, go to work.

  • The ‘all data’ should start at the third month assuming you are using three months.
  • ‘X’ is the sales column.
  • ‘Y’ is the array that starts at the third Month 1 column.
  • Set the ‘Constant is Zero’ checkbox.
  • Click the ‘Residual’ checkbox.
  • The output area can really be anywhere in Excel.

5.  Analyze Results

R Square means the ‘strength of the correlation with 1 being the best. Anything  more than 75 percent is enough to create your position.

Return: If you sum the coefficients, you get a pretty good (albeit conservative) estimate of the actual return. In our example, the coefficients sum to $5.50 – so rounding up we get a $6 return for every $1 spent, with an 86 percent likelihood this will occur. This was confirmed by simply dividing sales by marketing spend for the year, for each year and looking at the average.

Results and timing: Divide an individual coefficient by the sum of the coefficients (do this for each of your three – or however many months you used) and get a rough breakdown of the ‘marketing yield’ in each month for a $1 spent in each month.

6.  Use the Data

  • Measure the effectiveness of your marketing. If, on a rolling twelve months, you find that you are BEATING your historic returns, or, if you are lower than your historic returns you have a strong or a bad campaign respectively.
  • Predict sales. If you know that there’s a good correlation between marketing and sales, you can peg your sales to marketing spend. A note of caution: in statistics there is always a deviation. As a whole your predictive model will be good, however, in a given month it will vary up or down.

This is a powerful tool that takes the guesswork out of marketing gambles. You still have to decide whether or not a campaign will be effective, but at least now you have the tools to measure it.

Adam Boatsman is a founding partner of Boatsman Gillmore PLLC, a Charlotte-based CPA firm that specializes in improving cash flow and reducing risk for closely held business owners. Boatsman Gillmore more is an Arment Dietrich client.

8 comments
wabbitoid
wabbitoid

Great stuff, people need to think this way if they are going to prove themselves! The only thing I want to add is that there is often a time delay with advertising, so what we spend this month may not come in for months. Design of Experiments is very important if you are looking for correlations. I recommend either constant effort or constantly increasing effort if you are going to find a correlation - an effort that has a big splash and then decreases (like, say, book publishers) will often give you a mush of data for months that is impossible to make any sense of.

Adam Boatsman
Adam Boatsman

Thanks for all the commentary folks. I'll knock out responses with this comment.

With statistics, remember the old saying - 100% of all people that eat carrots will die. What statistics is really good for is either proving out good 'gut assumptions' or proving that your good 'gut assumptions' really aren't as good as you thought they were - you need to keep digging for better assumptions.

As some of the comments have indicated - sometimes you do need to do a little deeper digging. For example, if Company A purchases Company B, of course sales increase. However, so would the likely sales growth goal and associated marketing spend as both may have had the same marketing / pr as a % of sales.

In observing our clients, what I can say is that those that cut marketing first typically have had sharper sales declines than those that kept marketing relative to sales and measured the effectiveness of the campaign - meaning that those that continued to spend and measure effectiveness continued to either keep (or sometimes increased) their marketshare of a diminished pie (e.g. realtor example).

Adam Boatsman

www.makeanimpactcpas.com

Adam | Customer Experience
Adam | Customer Experience

Nice post Adam! I think I was just teleported back to finance class. I would agree with @rebeccadenison about considering other variables, and I would add, particularly macroeconomic conditions. It would be like a realtor looking at her sales in 2006 versus 2009 and only looking at her marketing spend when analyzing it.

Still, very valuable information. Thanks.

jonbuscall
jonbuscall

Great stuff! This is the kind of post I like to see. I'm rubbish at all this kind of stuff so it helps to get an insight into how people with a real head for figures approach these issues. Thanks immensely.

rebeccadenison
rebeccadenison

These are some great (and simple!) steps to start measuring marketing more effectively.

I have to disagree about using R as a means to measure causation, though. R is supposed to measure correlation or a relationship between two sets of data. But we all have to be careful using something like this as cold hard proof that our marketing is driving sales. What about advertising? PR?

Or say for example your marketing spend increases because your company has been acquired recently and the budget is bigger. Sales may go up too, but can you really say it was all because of marketing? Likely not. The acquisition may have helped, too.

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