Digital Marketing in practice (part 2)

In part one we defined our landscape and the first challenges encountered on managing the known customers .

We now want to investigate how to bring new customers to our e-commerce site and more importantly how to target , among potentially 7 billion customers, the ones that have the higher chances to buy our fantastic product.

Ideally we would like to place advertisement on :

  1. search engines (google, bing, etc..)
  2. social networks (facebook, twitter, snapchat, etc..)
  3. big content websites (msn.com, yahoo, news websites,etc..)
  4. inside famous apps that can host advertisement
  5. etc…

How do we contact all these different “information publishers” and how we can create a single “campaign” targeting all these “channels” ?

Here we have go into the DMP, DSP,SSP world, and see how these platforms can help us in reaching our objectives.

Let explain this with an example : go now to this yahoo page https://www.yahoo.com/news/ , you should see almost immediately on the top of the page an advertisement like this:

How and why this advertisement was placed there ?

The “publishers” like yahoo, have a so called “inventory” of places where ads can be positioned on each page , on different times of day or of the week typically. So they use a platform called SSP  to communicate to the entire world the following message : “I have these open slots/positions in my pages, who pays the highest amount to buy them and place their own ads?”

On the other side of the fence there is another platform called DSP where “marketers” can say the following : “I want to pay this amount of money to place my banner in yahoo pages “.

The process where “supply” and “demand” meet together is called RTB , real time bidding , and thanks to that, in real time it will be decided what is the advertisement appearing in yahoo website. If you want to understand this more in deep look at articles like this , but you understood that in this way we can have new customers that can reach our e-commerce site clicking on the banner.

But now another question comes up: is this banner the same for all the visitors? And if it has been displayed to 1 Million or 10 Million or 100 Million visitors what is the price that we have to pay?

This is the right time to explain the concept of audience: before going to the DSP and search for “open inventory” we first want to define who are the visitors or anonymous customers that we want to target in our campaign, in this way we can have an idea of how many of them in theory can see the banner.

But if these customers are “unknown” how do I target them? Here the final piece of the puzzle comes into play: the DMP . With DMP we can actually “purchase” (or better rent) from third parties anonymous customers profiles  that are based on browser cookies or smartphone device ids and pick only the ones that according to us are the best ones.

So for example we select them using simple filters in this way:

Once our audience is prepared, we can have an idea of how many of these potential customers we can reach and with this have an idea of the money we will spend if all of them will see our banner and hopefully click on it.

Now this is a pretty straightforward process, but it is not really super optimized…

In fact we already have customers in our e-commerce site (the so called first party data) and we already know who are the ones that are “our best customers”, it would make sense to find on the DMP platform the potential customers that are very similar to those ones, right?

This process exists and it is called look-alike modeling:

So now we know that we have somehow to integrate also the DMP world (that is already a world on his own with connections to DSPs and third party data providers) into our digital marketing landscape at least in two directions:

  1. unknown–>known campaigns integration
  2. known–>unknown look alike modeling and on-boarding process (bring our first party data into the DMP)

Be patient now 🙂 .

We will start designing our digital marketing landscape in part 3.

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