More on networking-It’s more than “who” you know
In my last post I introduced the concept of networking being a three-dimensional issue. We talked about the three dimensions as follows:
- Who you know-identity
- How you know them-context
- How well you know them-depth
Let’s look at this in a different framework; again, the math and science kick in for the former engineer. I’ll use a three dimensional framework, similar to length, width and height:
For those of you who learn from visual cues, this box may help you see the three dimensions of networking. Let’s explore a few circumstances where you consider a need and where there would, or would not, be a fit for a networking request.
- You have an interest in company A and one of your best friends from a previous job works for company A. You obviously score high on the Who axis and the How axis fits well also. If the relationship was solid in the previous work experience (Depth), then this request will probably be a successful one.
- One of your best friends would like to meet the CEO of Company B. You do not know the CEO personally (low on Who, How and Depth), but one of your former bosses now works for this person (higher on Who, How and possibly Depth). Depending upon the nature of the request, this may or may not be a successful ask.
- A friend from church you met last month has an interest in your firm. She would like to be considered for a new role in your company and she has asked for an introduction to the hiring manager. In this case the Who is mid to high, How is low and Depth is low also. This may or may not work well. It all depends upon on how much emphasis and effort you provide as the introducing party. You will take risk on by going too far if she is hired and things do not work out, but there is always the risk of doing nothing and missing an opportunity. This presents a challenge that often arises when helping other network.
The diagram and scenarios should help you visualize the metrics of networking and the probability of success.
I’ll be back again soon to see if quantifying this model makes any sense.
What do you think?