Introduction

How to measure social media return on investment (ROI) continues to be one of the most hotly debated topics in PR, marketing, and advertising. While there may be differing opinions on how socila media ROI should be measured, most of the literature regarding the measurement of social media investment agree that measuring the return on investment from social media spending is crucial to proving social media’s worth to an organization’s overall business goals and objectives. Rather than using return on investment to prove social media’s worth to an organization’s business objectives, I propose a method that captures the effect that a dollar spent on social media investment has on an organization’s revenue. After all, the goal of any for-profit business is to maximize profit, through increasing revenue and/or decreasing costs Thus, all social media goals, whether they be an increase in followers, increase in clickthrough rate, or increase in likes or retweets, should have an impact on the organization’s sales or revenue. Investing couneless dollars on social media ad spending means nothig if it has no impact on the business’s revenue.

Rather than using return on investment as a measure of social media’s impact on an organization, I propose a model that captures the effect a dollar increase has on a business’s revenue. It is by no means a novel concept, but it is a different proposition to measuring social media’s worth to a business’s overall goals and objectives. Using a fictional burger chain as our example, we can examine the effect social media investment has on the chain’s revenue. This method is more efficient than measuring social media’s impact on business objectives because it gets straight to the point. It doesn’t, for example, require monetizing social media goals (e.g. monetizing the number of followers gained). Rather, it directly captures the effect every dollar spent on social media investment has on revenue. The model is a multiple regression model where at least one of our explanatory variables \(x\) will obviously be the monetary amount spent on social media investment.

1. Literature

It’s imparative that we discuss one key definition before we delve into the literature regarding social media ROI measurement. When talking about social media investment, I am referring to the monetary amount that an organization spends on their social media marketing efforts. It does not mean how much time or effort they spend on social media (e.g. posts, retweets, likes, etc.). Henceforth, when referring to social media investment, it will always be denominated in a monetary unit (usually dollars, because USA, USA!). Quantifying social media investment for any organization is very simple to accomplish, since Facebook, Twitter, Google, etc. disclose the cost of advertising on their sites. So, in a way, think of social media investment as an advertising budget. Also, I will no longer be using the term “social media ROI” for reasons you will soon discover.

There are countless blogs, articles, and white papers out there each proposing their own unique way of measuring social media’s value to their organization’s business goals and objectives. Most of the literature I’ve encountered include the phrase “social media ROI.” The problem with this is many of these blogs and articles are misunderstood when it comes to the defintiion of return on investment. As a refresher, return on investment can be calculated as

\[ \begin{equation} \text{ROI} = \frac{\text{Gain from investment - Cost of investment}}{\text{Cost of investment}} \end{equation} \]

One thing most fail to realize from this equation is that both the numerator and denominator must be denominated in the same unit. That is, you can’t calculate “social media ROI” by calculating how many dollars were spent on social media investment and tracking goals such as reach, engagement, traffic, or conversion rate (which is what Brian Zeng proposes). I’m not saying Zeng’s approach is wrong, but it’s not, by definition, return on investment. It’s simply tracking the results of one’s time and energy spent on social media efforts. That’s well and good, but as I noted earlier, the goal of measuring social media’s impact on an organization is to prove its worth to the organization’s business objectives and goals. Executives care about reach, engagement, traffic, and conversion rate insofar as it has an effect on revenue or the bottom line.

Perhaps the best piece of literature I’ve come across regarding social media measurement is a blog post by Kevan Lee titled The Delightfully Short Guide to Social Media ROI. What I love most about Lee’s approach to measurement is that he seems to understand the definition of ROI. He suggests that the two key elements to track ROI are:

  1. Identifying monetary investment in social media.
  2. Attaching a dollar amount to social media goals.

These two key eleemnts ensure that that both social media goals and investment are denominated in the same monetary unit, and ROI can therefore be properly calculated. The first key element to Lee’s suggestion is fairly straightforward and easy to ascertain, as one can identify the monetary investment in social media investment by knowing the cost of what it takes to advertise on Facebook, Twitter, or any other social media platform. The second key element is where it gets a bit trickier. Assigning a dollar or any monetary amount to any social media goal is fairly subjective. First, he suggests choosing a social media goal such as new followers, signups for newsletters, downlaods of files, and time spent on an important webpage. Then, he proposes attaching a monetary value to these goals. Some of the methods the proposes are lifetime value, lifetme value multiplied by converstion rate, and pay-per-click costs. While not totally impossible, some of the data required to assign monetary values to these goals may not be practical for many organizations. For example, to calculate customer lifetime value, one would need data such as discount rate, profits and costs of retaining a customer, yearly retention rate, ond even referral rate, depending on which customer lifetime value formula one decides to use. For many companies, this data is fairly difficult to ascertain, as most companies, especially smaller ones, don’t even keep track of such data.

2. The Model

Kevan Lee’s proposition to social media measurement is perhaps the best approach to measuring social media’s impact on an organization’s business goals and objectives. However, the only gripe I have with Lee’s proposition is his suggestion of assigning a monetary value to an organizaiton’s social media goals. The reason being is that it can be extremely subjective and somewhat arbitrary, and it is more of an art form than a science. To truly capure the impact social media has on a business’s overall goals and objectives, why not directly measure the impact social media investment has on revenue? That is, what is the effect, if any, a dollar investment in social media ad spending has on revenue? Mathematically, this can be represented as

\[ \begin{equation} R = \beta_{0} + \beta_{1}I + \epsilon \end{equation} \]

where \(R\) denotes sales, \(\beta_{0}\) denotes revenue when social media investment \(I\) is zero, \(I\) denotes the dollar amount spent on social media ad spending, and \(\epsilon\) denotes an unobservable random error term.

The above equation is just a general form of the effect social media investment has on revnue. In reality, there are numerous factors that affect the level of a firm’s revenue, such as price of its goods, price of its competitors’ goods, costs of goods sold, etc. Thus, we can further extend this model to include any pertinent variable that might affect revenue. If we let \(x_{i}\) denote any variable affecting revneue, including social media investment, then the general form of our equation can be expressed as

\[ \begin{aligned} R = \beta_{0} + \beta_{1}x_{1} + \beta_{2}x_{2} + \ldots + \beta_{k}x_{n} + \epsilon_{n} && \text{for} & i=1,2, \ldots, n \end{aligned} \]

Those familiar with linear regerssion will recognize that this is a multiple regression model. For those less mathematically inclined, don’t fret. It will all make sense once we use an actual example. Our goal is to estimate the values of the \(\beta\)’s, commonly referred to as estimators or parameter estimates.

2.1 An Example

Bob run’s a restaurant called Bob’s Burgers. He wants to increase sales so he decides to advertise on various social media platforms. Using weekly sales, price, and social media investment data, Bob wants to know how much, if at all, his investment in social media is affecting his sales.

Table 1: Bob’s Burgers
sales price sm.investment
73.2 5.69 1.3
71.8 6.49 2.9
62.4 5.63 0.8
67.4 6.22 0.7
89.3 5.02 1.5
70.3 6.41 1.3
73.2 5.85 1.8
86.1 5.41 2.4
81.0 6.24 0.7
76.4 6.20 3.0
76.6 5.48 2.8
82.2 6.14 2.7
82.1 5.37 2.8
68.6 6.45 2.8
76.5 5.35 2.3
80.3 5.22 1.7
70.7 5.89 1.5
75.0 5.21 0.8
73.7 6.00 2.9
71.2 6.37 0.5
84.7 5.33 2.1
73.6 5.23 0.8
73.7 5.88 1.1
78.1 6.24 1.9
75.7 5.59 2.1
74.4 6.22 1.3
68.7 6.41 1.1
83.9 4.96 1.1
86.1 4.83 2.9
73.7 6.35 1.4
75.7 6.47 2.5
78.8 5.69 3.0
73.7 5.56 1.0
80.2 6.41 3.1
69.9 5.54 0.5
69.1 6.47 2.7
83.8 4.94 0.9
84.3 6.16 1.5
66.0 5.93 2.8
84.3 5.20 2.3
79.5 5.62 1.2
80.2 5.28 3.1
67.6 5.46 1.0
86.5 5.11 2.5
87.6 5.04 2.1
84.2 5.08 2.8
75.2 5.86 3.1
84.7 4.89 3.1
73.7 5.68 0.9
81.2 5.83 1.8
69.0 6.33 3.1
69.7 6.47 1.9
78.1 5.70 0.7
88.0 5.22 1.6
80.4 5.05 2.9
79.7 5.76 2.3
73.2 6.25 1.7
85.9 5.34 1.8
83.3 4.98 0.6
73.6 6.39 3.1
79.2 6.22 1.2
88.1 5.10 2.1
64.5 6.49 0.5
84.1 4.86 2.9
91.2 5.10 1.6
71.8 5.98 1.5
80.6 5.02 2.0
73.1 5.08 1.3
81.0 5.23 1.1
73.7 6.02 2.2
82.2 5.73 1.7
74.2 5.11 0.7
75.4 5.71 0.7
81.3 5.45 2.0
75.0 6.05 2.2

We see 75 weekly observations of Bob’s Burgers sales, price, and social media investment data, which are all denominated in dollars. So, is Bob’s investment in social media having any effect on his weekly sales? Let’s find out using the model

\[ \begin{aligned} \text{sales} = \beta_{0} + \beta_{1}\text{price} + \beta_{2}\text{sm.investment} + \epsilon \end{aligned} \]

First, let’s look at some summary statistics of Bob’s Burgers’ data.

##      sales           price       sm.investment  
##  Min.   :62.40   Min.   :4.830   Min.   :0.500  
##  1st Qu.:73.20   1st Qu.:5.220   1st Qu.:1.100  
##  Median :76.50   Median :5.690   Median :1.800  
##  Mean   :77.37   Mean   :5.687   Mean   :1.844  
##  3rd Qu.:82.20   3rd Qu.:6.210   3rd Qu.:2.700  
##  Max.   :91.20   Max.   :6.490   Max.   :3.100

The mean sales, price, and social media investment of Bob’s Burgers are $77.37, $5.69, and $1.84, respectively. Let’s check out the distribution of each variable.

Now let’s see if there’s any relationship betwen Bob’s sales to the price of his burgers and his investment in social media.

While it would seem that there is a negative relationship between sales and price (as there should), it’s not apparent that there is any relationship between social media investment and sales. However, that does not necessarily mean that social media investment has no bearing on Bob’s sales. We won’t know for certain until we actually construct our model.

2.2 Model Results

The results of our model are

## 
## Call:
## lm(formula = sales ~ price + sm.investment, data = bobs)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -13.4825  -3.1434  -0.3456   2.8754  11.3049 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   118.9136     6.3516  18.722  < 2e-16 ***
## price          -7.9079     1.0960  -7.215 4.42e-10 ***
## sm.investment   1.8626     0.6832   2.726  0.00804 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.886 on 72 degrees of freedom
## Multiple R-squared:  0.4483, Adjusted R-squared:  0.4329 
## F-statistic: 29.25 on 2 and 72 DF,  p-value: 5.041e-10

According to our results, both price and social media investment are statistically significant, meaning they both have an affect on Bob’s sales that isn’t purely based on random chance. Our model then becomes

\[ \begin{equation} \text{sales} = 118.91 - 7.91\text{price} + 1.86\text{sm.investment} \end{equation} \]

Interpreting the results of our model is fairly straightforward. Bob’s sales decreases by $7.91 for every dollar increase in price, all else being held constant. As for his social media investment, it increases his sales by $1.86 per dollar increase in social media investment, all things held constant. The fact that social media investment is statistically significant is important in proving that Bob’s investment in social media actually has an impact on his sales that isn’t a result of pure random chance.

It’s also important to note that a marginal increase in social media investment does not always lead to a $1.86 increase in Bob’s sales. That’s just the average. To get a range of values by which sales could increase, we can use a confidence level of 95% to get interval estimates by which sales could increase, all things being held equal.

Using a significance level of \(\alpha=.05\), we estimate that a marginal increase in social media investment could lead to an increase in sales ranging from

\[ \begin{equation} [$0.50, $3.22] \end{equation} \]

Using this model, we’ve statistically proven the value of social media investment on Bob’s overall business goals (i.e. sales). We didn’t need to track conversion rate, followers, etc. and assign some arbitrary monetary value. We simply got to the point and captured the effect social media investment has on sales.

Conclusion

Measuring social media ROI will continue to be a hotly debated topic. Everyone will have their own way of proving social media’s value to their organization, and it will vary depending on the organization’s business objectives. However, proving social media’s worth to a business shouldn’t be complicated. The goal of any business is to maximize profit, and to prove social media’s worth, one simply needs to measure the effect it has on a business’s revenue. It doesn’t need to be any more complicated than that. The method I proposed is by no means an innovative, novel concept. It is, however, straight to the point and relatively easy to implement. Using a fictional burger chain as our example, we were able to directly observe the effect the chain’s social media investment had on its sales. This method doesn’t measure ROI. It doesn’t use the formula for return on investment. Rather, it uses a model that captures the effect a monetary increase in social media investment has on sales, which, at the end of the day, is what most executives care most about.