Step 1: Find Patterns in the Data
This deep dive into the first step of the Marketing Optimized Framework posted previously.
Why
For more effective campaigning, get a better understanding of who to target and what assets are proven to work best
Depending on the maturity of processes, how marketing decides on which companies to focus marketing dollars on varies. Some may not even coordinate with Sales teams, some may try to target the universe and let the constraints of what contacts are in the database determine what companies are targeted
Sure, it is simple and easy to get into a rhythm of build content, deploy, and it hits who it hits based on some segmentation rules base on contacts... but it's not effective because alignment with sales is happenstance, not a conscious effort
Marketing and Sales teams need to be in lockstep to have real sustainable growth
What
Look for patterns in your customer Companies and the Contacts
Examine what content helps them progress (i.e. from new contact to Opportunity (Pipeline) or from Opportunity (Stage10), etc)
How To
Ideally use Data science, machine learning (ML) specifically to model these for best results
Humans typically can only see simple patterns across a small set of variables
Feature selection for the models need to include seasoned Marketing and Sales teams to help add context to the data
Can also help with finding derivative fields that could be used in modeling
Later in the series, can deep dive on how to do this with examples... comment below if this is of interest
Recognizing not all marketing teams have access to Data Science resources or there may not be enough data available to model
Rules-based models can be used with less accuracy
rules are a distillation of experienced sales and marketing team members of who buys
The reason these are often less accurate is these are built on anecdotes (from a select set of individuals) and may not represent what is happening broadly
Companies
Segment the universe of companies into tiers based on data from past deals that made it deep into the final stages of the sales process
Here best fit can have a couple meanings (i.e. will spend the most or simply will buy from us)
Don't boil the ocean, you don't have a large enough marketing budget to do it effectively, pick where you fish and be ready to be opportunistic if am unexpected stray prospect comes into your path
Be sure to coordinate with sales on this list (recommend this to be a quarterly activity)
Work backward from Sales goals and marketing contribution expectations using past conversion data to figure out roughly how many companies you need to target
You will need to decide how long to keep Companies on the Target list before replacing with others (this can be a combination of intel received (i.e. known competitor purchase etc) and time
Buying Teams and Contacts
This is the group of people involved in the purchase process. Depending on how the sales team is organized to sell and how the prospect company is enabled to buy.
This might be one group per company country headquarters (simpler approach and encourages larger deals)
Or could based on sites, for example, each District has it's own budget for this type of product and there isn't an HQ entity that would make the buying decision (the rules involved here are much more complex and would likely need some human intervention
We can predict Buying teams from past Deals (i.e. Contact Roles table on the Opportunity)
This data can also tell us the roles and titles of Contact that are typically involved in deals
Content
Again, looking at past deals and the contacts related to the deal we now look for patterns in Content consumed (clickstream data)
First, we want to see who are look-a-likes based on what they are consuming and what the sales team has identified their buying role as
This gives us an understanding of title, department, and level (perhaps by company size/industry) and their Buying Team Role
Next, we see what "works" with the group as a whole and for each role
Here we look across their engagement history to look form patterns in content consumed
You will likely want to put a time box on this based on what stages you are trying to understand
To understand what contributes to Pipeline creation you could time box from the date you see the company as being in-market (i.e. from Intent data) or for simplicity sake use a fixed window of before Deal creation
You need to do this exercise for all the stages you really care to understand which content worked
You will also want to aggregate content in terms of the Asset/Offer separate from delivery channel/mechanism
Tools
Here are some tools I have had exposure to, comment below if there are others... share what kind of user it is good for and what it can do for analysis of Audiences and Content.
