Evaluating the Health of Client Relationships Through Data

A Global AM Law 200 Law Firm

An Overview

The Challenge

This law firm wanted to score their clients on quantitative and qualitative metrics to help them make better determinations about the health of their client relationships.

The Solution

2nd Watch built a table in Snowflake containing metrics important for evaluating a client’s risk and then scored the metrics.

The Outcome

Our client gained a full picture of how their clients trend over time, saving them hours of work when determining client risk and giving them the chance to bring in more revenue by salvaging “at-risk” relationships early on.


About the Business

This global AMLAW 200 law firm was founded more than 150 years ago and now has more than 30 offices, 1,700 lawyers, and more than $1 billion in revenue.


The Business Challenges

This law firm faced the inability to evaluate which of their clients are considered “risky.” Their goal was to analyze clients and score them based on a variety of quantitative (financial) and qualitative metrics. With this knowledge, the firm can make better decisions on whether to revive a relationship with a client or plan for a client’s departure.



The 2nd Watch Solution

2nd Watch built a table in Snowflake containing various metrics important for evaluating a client’s risk. These metrics ranged from revenue and profitability of the client, to length of relationship and active lawyers currently working with the client. All of the metrics evaluated were then scored based on their values. For example, a client with high revenue and profitability would be scored with very low risk, while a client with dwindling revenue and poor profitability would find itself with a higher risk score.

This data is refreshed daily and sliced at the monthly grain, allowing the firm to see clients’ trends month over month. This is especially useful for analysis regarding when a client relationship moves from “healthy” to “risky.”


The Business Benefits

Prior to this implementation, evaluating the risk of a client was not very structured or organized, and it often required evaluating several different metrics from different locations. The 2nd Watch implementation overcame those hurdles.

Additionally, our solution added several new metrics and data sources that were not heavily considered before. By taking a mix of regularly used metrics and new derived metrics, we enabled our client to gain a full picture of how a client trends over time. Not only will this save the client several hours when determining whether or not a client is at risk, but it can also bring in more revenue if a risky client is caught early enough and the relationship is salvaged.