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Marketing ROI – How to accurately evaluate campaign performance

Marketing ROI – How to accurately evaluate campaign performance

Accountability and marketing ROI go hand in hand

Marketing ROI is becoming the number one metric for evaluating campaign success. And for good reason: Marketing spend is increasingly under pressure for greater accountability.

For this reason, campaign profitability analysis is essential. So is the need for optimization. ROI is often equated with profitability and traditionally stands for “Return on Investment”.

But if you search for the definition of marketing ROI on the Web, you’ll notice that we don’t all share a common view of what ROI actually looks like.

Not only that but the definition marketers might use may have nothing to do with one that your CFO has in mind. That’s why measuring ROI can become a source of friction and cast doubt on the true contribution of marketing to the bottom line.

Marketing ROI – a commonly-used definition

The definition of marketing ROI generally refers to gross ROI. It looks at total sales made as a result of a given campaign, compared to the total cost of the campaign (media, creative and other direct costs).

The formula is simple:

Total sales ÷ campaign costs = ROI.

ROI can be expressed as a percentage, or as a multiple. A campaign with sales of $ 1,000 and a cost $ 100, has an ROI of 10X or 1,000%.

$1000 ÷ $100 = 10X

A dollar revenue for a dollar in costs would have a 100% ROI or a 1X multiple. Nothing lost, nothing gained.

10X is not always good, and 1X is not always bad

It is important to put this metric into perpective. Depending on the nature of the campaign, a 1X yield may be excellent or crappy.

  • For example, in the case of a customer acquisition campaign, the acquisition cost often exceeds the value of the first purchase. So, a neutral, or even a negative ROI is to be expected.
  • In the case of a campaign sent to your most engaged customers, there’s something wrong if your ROI doesn’t reach 10X or even 50X.

But beware: sales do not equal profits

The challenge with this commonly-used definition is that it does not take into account the profit margin of the product sold. Most goods have a cost. The cost of raw materials, R & D, packaging, etc. should normally be taken into account in the Marketing ROI calculation.

For example, let’s say that a product that has a cost of 40% of the selling price. That’s 40 ¢ per dollar of sales. In that case, every dollar of sales actually only generates 60 ¢ in revenue. Therefore, a more accurate marketing ROI calculation should be based 60% off the sales amount.

So if we take this into account for the previous example:

Campaign creates $1,000 in sales

Product cost is $400

The net revenue generated is $600

With a campaign cost of $100, the net ROI is now 600% or 6X (instead of 100% or 10X)

So, if the campaign results in $ 1 in sales per dollar invested, the Marketing ROI is negative. Once you subtract the cost of the product, the net revenue is only 60¢ and so you would lose 40¢ profit for each dollar in sales generated.

Sometimes Gross ROI serves us well

Even if it is not totally accurate, the calculation of gross ROI is often useful. It makes it possible to compare campaigns amongst each other, without having to calculate the profit margin of each product sold or determine a blended margin.

However, when campaign performance is marginal, a net ROI analysis allows us to make better decisions about resource allocation and serves to raise a red flag for campaigns that need to be optimized.

Related content

ROI and Effectiveness – Do You Truly Understand the Difference?

A/B Testing – How to implement a winning strategy

A/B Testing – How to implement a winning strategy

Yes, we can learn to love statistics and A/B testing

I am a big fan of optimization and A/B testing. But that was not always the case. When I was studying for my MBA at HEC in the 1980s, the most challenging courses I enrolled in was Statistics and Probabilities. This course, loaded with obscure equations, was meaningless gibberish to me. And I couldn’t see how it could be useful in real life except maybe for surveys.

Ironically, a few years later, I found myself starting a career in direct marketing, an area where everything was based on statistical analysis, probabilities and confidence intervals.

I quickly learned the value of optimization testing, as well as some of the pitfalls that novices face. Here are some tips to help you better implement a testing strategy. Never fear, we’ll try not to bore you with statistical concepts and equations.

Foster an optimization culture

The most successful companies understand that small gains of a few percentage points can add up to big gains when compounded year after year.

They also understand that the marketer’s gut feel can only go so far.  And that our personal biases sometimes lead us to make the wrong decisions. After all, we are not representative of our customers. We must objectively validate our assumptions, through optimization testing. And to maximize our chances of success and growth, testing must become part of your DNA and your corporate culture.

Give yourself the right to make mistakes

The first step towards an optimization culture is to accept that some tests will give disappointing results. We must give ourselves the right to the error. We must also accept that our current practices may not be the best and that there is always room for improvement.

On the other hand, do not excess by multiplying frivolous tests, because each underperforming test means that your sales will be impacted.

Prioritize the A/B testing that will have the greatest impact

In this sense, we must test the hypotheses that are most likely to have a major impact on the commitment and sales. Among these, we can test:

  • Targeting a campaign to identify niche markets that are more receptive to the product / service
  • The elements of an offer (the price and the expression of this price, the incentives – deals, gifts, free delivery, contests, etc., the duration of the offer and the conditions of sale)
  • The structure of the emails, the type of content, the relative position of the elements – in particular the CTAs.
  • The graphic processing of emails including imagery and emotion emanating from it.

But do not overdo it, like testing one word vs. another, the color of a button, and so on. Use your good judgment.

Your optimization test must respect the rules of statistics

When performing optimization A/B tests, an estimate is made of the proportion of customers who will be likely to have a given purchasing behavior. If we repeat the test several times, we will observe a similar result, but different. The dispersion of these results around the mean is what is called the standard deviation.

What you need to know is that the larger the sample used in your tests, the smaller the standard deviation. So the more the result of your test is reliable. Make sure you are working with samples of sufficient size so that you can validate if the result of your test are actually different (better, or worse) from what you normally observe.

Also make sure you only change one variable at a time. You cannot change the target population, the product price and the creative concept and expect a statistically valid result.

Measure the profound impact of your tests

It is not enough to dwell on engagement rates (opening and clicking). You must also look at the conversion on the site (newsletter sign ups, potential leads, bid solicitations, purchases and customer retention) and the long-term impact on the health of the database.

It is an issue that is measured over several months, even years, and you have to be vigilant. We have already seen situations where a test before stimulated a very large increase in the recruitment of new clients, but poor quality clients whose loyalty was very low.

Create a results directory accessible to all

The knowledge that results from these optimization exercises is a competitive advantage and the result of these efforts must be preserved and made accessible to all stakeholders in the marketing team, or even senior management.

Create a secure directory that groups the results of each test. After just a few years, you will have accumulated valuable strategic information that will allow you to make better decisions. This will allow you to always go further, but also not to test the same hypotheses again. After all, repeating the same gestures while expecting a different result is the definition of madness.

Some statistical analysis resources

Confidence interval calculator

• Sample size calculator

Do you want to implement a successful optimization strategy? Contact us to discuss it.