The Boston Consulting group’s famous matrix framework, identifies business lines to invest in, liquidate, harvest or maintain. There’s a different spin here when executing an account-based analytics strategy. We begin with the account and then identify how we are serving them. It’s a key distinction from a business or product prospective to optimize your B2B revenue model.
Account-centric firms begin with their named accounts, then look to see how their services, people and products are aligned to answer those account needs, then optimize. Some firms like to call this a “bottoms up” vs. a “top down” analytics approach.
Just as creating the BCG framework to measure the performance of business lines, we can do the same approach from the account prospective. What accounts are your stars, cash cows, dogs and in-betweens?
The goal with this insight is to help make decisions to reallocate/optimize workforce endeavors.
At times, many B2B firms mistake a highly-engaged client as a star client. However, that highly engaged client, AKA temperamental client, is extracting profit and keeping your high performing teams away from other clients willing to innovate your offering and pay you more for your B2B product and services. In the data, the famous 80/20 rule may apply. 20% of your accounts represent 80% of your revenue.
Until someone sees the numbers, this type “Work Drift,” can occur. Pedaling fast but going nowhere, years can past before the ship is righted. The idea behind Account-Based analytics is to monitor and quickly optimize the workforce to the most wealth-driving activities.
OK, here’s this gist of this analytics module framework, we can use a portion of a previous post, workforce alignment from an account prospective here. Since we know how workers are spending their time, now we can add the numerator, “outcomes” of those endeavors. We’ll focus on two outcomes for this exercise: pipeline and revenue growth. We’ll base the time series analysis on 60 days. But depending on your firm’s cycles, you may want to toggle.
For this insight module, we’ll need below data sets:
- Productivity data (Account Effort Score)
- We’ll need processed data from our original post here representing “Account Effort Score.”
- CRM Data (Account Velocity Score, what we’ll build)
- We’ll need a timestamp data of revenue and weighted pipeline as of 60-days back per account. We’ll need the same figures as of today.
- Calculate Account Velocity Score for each account (a zero score is the worst for this exercise)
- Total Active Contract Value today * 50%
- Weighted Pipe Changes: (Weighted pipe today – Weight Pipeline 60days ago) *.2
- Revenue Changes: (Subtract present contract revenue-60daysago contract revenue) *.3
Add the weighted score per account. Scatter Plot the results. A general output would end up looking like below:
Dogs: A lot of effort(higher Account Effort Score), but little return(lower Account Velocity Score).
Cash Cows: Little effort but high-velocity score.
Stars: High Velocity and Effort.
Questions: Low account effort and low account velocity.
For a quick analysis perspective, Dogs would be the 1st to look at. But maybe there’s something more to the account that’s not seen in the data? Are we simply investing for long term rewards that are attainable? Or should we reallocate our efforts to stars and cash cows?
That’s the quick gist – As always, just trying to find right, not act right. Welcome feedback.