“It was after the downturn in 2010 when we decided to shed 60% of our customer base.” John Sammut, CEO of electronic manufacturing services provider Firstronic told members of the Manufacturing Leadership Council at a recent plant tour at his company’s Grand Rapids, MI, plant.

How many CEOs have you met that would walk away from 60% of their customer base? Regardless of the scale, this is a bold move. Done for the right reasons with the right focus, these pruning measures can position the business for the right type of growth. Here is my take on how Firstronic’s market insight, organizational alignment, and customer focus paved the way for a great turn-around story.

Profitability Analytics: Setting the Table for Success

The decision at Firstronic in 2011 was to reduce their customer base from 25 to 10 accounts. While no complex algorithms were required, they did build a model to score their customers. As shown below, the analysis weights a ‘Customer Favorability Score’ against ‘Annualized Throughput Dollars’.


The favorability score factors in about a dozen items such as volume, complexity, and lifecycle. Throughput revenue is the total revenue minus materials, which effectively captures the revenue for Firstronic’s core business.

The power of this evaluation is in its simplicity. If someone had presented the Firstronic executive team with a black-box of algorithms and optimizations, would that have inspired action? Probably not. But, in developing a common-sense model grounded in business metrics that execs could relate to, Firstronic was able to make a bold business move that arguably saved their business.

Leaders of companies with larger customer bases may dismiss the Firstronic story as ‘not-applicable’ to their business. This would be a mistake. When working with larger data sets, the first step in exploring your customer data is to find patterns, correlations, and possible groupings helping you segment them uniquely. Breaking down the complexities into manageable chunks makes the data easier to dissect and understand.

This is not to dismiss more complex approaches, but rather to appreciate the power of simpler models. At the end of the day, the most amazing, complex models in the world deliver zero value until someone takes an action on the insight.

After adopting this profitability model the next key for Firstronic was to operationalize and embed the model into their DNA. Not only did Firstronic act on the analytics by pruning back the customer base, they continued to evaluate their customers and new prospects with the same model.  If something didn’t look right, they would question and tune the model from time to time. For perspective, when Firstronic responds to an RFP, they spend more effort scoring their prospect than they do responding to the RFP.

While taking this data driven approach has been critical in positioning the business for success, Firstronic differentiates its business on people and customer insight. As Firstronic VP of Operations Steve Fraser stated to the ML Council, Our goal is to understand our customers better than they know themselves.”

This was evident in their words and the major investments Firstronic has made in their people, their enabling IT systems, and their customers. The Firstronic team spoke of multiple occasions where a Firstronic customer would come to them with an issue that Firstronic has already started working on. Firstronic often has solutions drafted up before their customers have a chance to draft an RFP.

Here are some of the other benefits Firstronic gain for this dedicated focus on customer success:

–          Happier customers and happier employees;

–          Fewer price negotiations when customers are happy;

–          Word of mouth advertising. Firstronic suppliers often refer companies to Firstronic.

In closing I’d like to give a big thank you again to Firstronic for sharing their story with the Frost & Sullivan Manufacturing Leadership Council. You demonstrated a great story on how a company can find success in aligning their organization behind a data-driven approach.