- [Instructor] Marketing mix modeling
is a technique developed in the 1980s
to match spikes and dips in sales to marketing spend.
It works particularly well for
measuring the attribution of channels
like TV and social media,
where sales happen through repeated exposure to your brand,
rather than via a direct click on an ad.
This template works through a simple example
of marketing mix modeling,
but once you understand the concepts,
you could work with a professional
to build more advanced models for your business,
that fully captures the ROI of your social media campaigns.
So how does marketing mix modeling work.
So the main thing you need
to build a marketing mix model, is your data in this format.
You need sales, or how many conversions,
or leads, or whatever you're trying to predict
in the first column here.
Then you need different variables
that you think will be predictive of sales.
In our case, we've chosen
how many times we posted on social.
So we want to establish the ROI of social media.
How much we spend on advertising,
and then whether that week was a promotion or not.
Because we've noticed higher sales when we run a promotion.
And then all of these split out by time.
So in this case, it's weekly,
but you could have daily or monthly.
And what the model does is try and predict
the sales revenue that you're going to hit.
So if you look at sales over time,
what explains these spikes and dips.
Well, if we look at social posts,
it's actually pretty well correlated
with how many sales we got.
When we posted more, we got more sales,
and when we posted less, we got less sales.
Let's add that into the model and try and predict.
So if you're predicting with
just the number of social posts,
we actually get pretty close to predicting
the full amount of sales.
But it's pretty far off.
Now, if you look at posts and advertising,
you add one more variable in,
it actually explains a little bit more of the model.
It looks a lot more accurate in terms of prediction.
Finally, if we add in whether
it was a sales promotion or not,
that line matches pretty closely.
So now we can be confident that
the model is predicting accurate revenue,
but how is that useful.
Well, it's useful because what you can get out of the model,
once you find a model that's predictive.
What you get out of the model is the coefficient.
The coefficient tells you,
what is the return on investment for that channel.
So for example, if we look at social posts,
we're making $68.85 for every social post that we put out.
That's on average.
And for advertising, we're making $2.75
for every dollar we spend on ads.
If there was a promotion on that week,
we made an additional $356 in revenue.
And if none of those things were happening,
if we didn't post on social,
didn't spend any money on ads, or didn't run a promotion,
then we'd expect to make $2,204.
That's the baseline.
Once you have these coefficients,
you can project forward, and then make assumptions
in terms of what your sales are going to be in the future.
So it becomes really, really powerful.
You can figure out how much you need to spend,
or how many times do you need to post
in order to hit your goals.
If you look at the contribution percentage,
it tells us the relative importance of different channels.
Social posts, in this case,
drove 16.55% of all of our sales.
We can also spread this out over time,
so we can see where social posts are more or less important.
You know, here we made a lot more money from social posts,
and then on some of the weeks where we didn't post as much,
didn't make as much.
One of the main things you're looking at
when you're building a model is, how accurate is it.
And you can see, this is one measure of accuracy,
the R-Squared.
It's 0.86.
Typically you want this to be above 0.8, or even above 0.9,
if you're going to make decisions based on the model.
Now, this is a pretty basic example,
and marketing mix modeling is a deep topic.
But hopefully now you understand the basics,
you can really get a second opinion on the impact
social media is having on sales.
You won't have to rely just on analytics,
which might not capture all the value of
people seeing the posts in their feed,
then maybe searching for the brand
or coming through another way,
other than clicking on the post.
And that's really, really important
to measuring the overall value of social media.
You can't measure social media ROI
if you're just looking at last click analytics.