Graphing Partnerships

Ben Chappell
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All signs point to partner marketing gaining in popularity and yet very little effort is being put into systematizing partnerships as a channel and helping companies find partners at scale.


I can spy on my competitors ad creative and targeting, I can subscribe to their email list, I can read their blog and even look at who links to their articles, and yet there is very little standard practice for figuring out who they have partnered with, how they have collaborated, and what results they have achieved. Click for click, partnerships are the least analyzed marketing channel. 


Imagine you knew about every partnership in your entire market and all adjacent markets. You would know exactly who to integrate with, who it was worthwhile to write guest posts for, who might be willing to co-market with you, who could resell your solution, and who to avoid partnering with because of a lack of traffic. Considering that even one successful partnership can be impactful enough to alter the trajectory of an entire company, having access to such a data set would be an incredible edge. 


One solution is to graph the relationships between all companies. That's right, all of them. 


First, let's imagine a hypothetical SaaS ecosystem where companies can collaborate in the following ways:


Integration (I)

Affiliate (A)

Reseller (R)

Co-marketing / Guest Blog (C)

White Label (W)

App Store / Marketplace (M)


Now let's say that in our SaaS ecosystem there are nine companies represented alphabetically as company (A) to company (I) with the following marketing relationships: 


  1. Company A co-markets with Company B and integrates with Company D
  2. Company E runs an affiliate blog that sells Company A and Company D's software
  3. Company G provides a white label solution that is sold by Company I, Company H, and Company C 
  4. Company C's CMO wrote a guest blog that was posted by Company D
  5. Company F hosts an app store where they resell Company D's software


We can illustrate these relationships in a directed graph where traffic flows in the direction of the connections and where connections are weighted by traffic volume.


We can also represent the relationships numerically where edges are notated as (start node, end node, weight, relationship):

G= (V, E) 


V= {A, B, C, D, E, F, G, H, I}

E = {

(A, B, 3, C)

(B, A, 8, C)

(A, D, 6, I)

(E, A, 7, A) 

(E, D, 2, A)

(F, D, 6, R)

(F, D, 6, M)

(D, C, 3, C)

(C, G, 3, W)

(I, G, 7, W)

(H, G, 9, W)

}

It's obvious that having such a graph would be useful if it could contain every company in an industry, but is it possible to build and how do we find these relationships at scale? 

Fortunately for us, companies leave a trail of digital footprints as they collaborate online. For example, affiliate relationships can be identified by backlinks that include affiliate tracking in their url, guest blogs can be identified by scraping blog posts and identifying if the author works for the company directly, integrations are usually announced publicly, and white labeled products almost always contain some reference in their html to the company that produced the original product.

Not only can we scrape indicators of these relationships at scale but there are all sorts of heuristics we can use to estimate the traffic they are producing. For example, there are plenty of tools that will help us judge an affiliate blog by its domain rating as well as estimates of organic SEO traffic for each specific post based on the keywords that they rank for and the search volume they receive. 


Now for a real world example.

Below we have taken a few of the top affiliate programs for sunglasses and graphed the top thousand affiliates that have promoted them. Note that the graph is dynamic and you can zoom out or click on any node to highlight its connections.

Looking at the graph, you may notice that only a handful of affiliates promote multiple brands of sunglasses. Intuitively, this could tell us that there is a lot of brand loyalty amongst affiliates in the sunglasses market and if we ourselves were a sunglasses provider it might make sense to target affiliates that have promoted complimentary products rather than affiliates that are already promoting a competitor. 

This is one very small example of what is possible if we take the time to start tracking partnerships and the impact it can have on our own marketing.

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