This is a guest post by TrueBlue. In their own words, “Founded in 1989, TrueBlue provides specialized workforce solutions, including staffing, talent management, and recruitment process outsourcing (RPO). In 2020, the company connected approximately 490,000 people with work.”

At TrueBlue, we offer solutions that help employers connect with workers worldwide. Every day, sales teams at our 500-plus locations offer our customers job quotes. These quotes show our staff the hourly rates they should charge and what the gross margin might be on a bill rate.

As part of our work, our sales professionals use a concept called lockout, which is the process for approving sales orders below standard margins. As our company has grown, these approval requests have skyrocketed. We have more than 850 people bidding for potential customers at any time, but only a few dozen managers can approve lockout requests. The number of requests that managers had in their inboxes was increasingly overwhelming and took time away from more important daily tasks. They wanted a way to avoid the process altogether by standardizing job rate information.

In this post, I discuss the steps we took to solve our problem using data analysis and Amazon QuickSight.

Identifying regional pricing differences

To begin, I looked at hourly worker rates across all our locations and added state tax data and other information. That gave me our customer billing rate, plus the overhead to calculate the gross margin. Through my research, I discovered that regionality is important in determining different rates and margins, and that pricing isn’t consistent overall.

Our sales leaders wanted to take this to the next level and figure out the gross margin they would need to maintain a specific hourly billing rate. I could only see 7 months of information, but it amounted to nearly 1 million rows of data. We needed a fast, easy way to use spreadsheet software to find what we were looking for.

Using QuickSight to give sales teams better pricing data

In 2020, we decided to go all in on AWS to create a new data lake and invest in other business intelligence (BI) solutions. After speaking with the AWS team, we learned that QuickSight, a powerful BI service that runs on AWS, could give us the detailed filtering and analytical capabilities we needed.

We used QuickSight to create a new customer job quoting engine for our sales teams in 40 of our branch offices. This solution provides our team with the price quotes that optimize profit margins and the data to calculate the precise charge in each market, all of which can be quickly accessed on their laptops. Now, the lockout requests are disappearing because the sales teams have the information at their fingertips and don’t need to ask for approvals. And because our sales leaders don’t have to read through countless emails every day, they can focus on more value-added tasks.

The following diagram illustrates our solution workflow, which sends data from AWS Database Migration Service (AWS DMS) through a data pipeline to Amazon Athena for analysis, and ultimately to QuickSight.

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Boosting customer retention and acquisition by 3%

With the data we’re getting from QuickSight, we can present our customers with more accurate pricing and billing information. As a result, we’ve increased new customer acquisition and retention. Our sales teams are closing phone deals at rates 3% higher than an internal sales control group. We’ve also seen an 11% increase in gross margin for the market in which we’ve used the job quoting engine the longest. Applying the data we have now is really making a difference in our business.

And with the live data powering QuickSight, we’re able to increase our margins. Every time we pay someone, our pricing is updated based on real-time regional data. The solution is always adapting to market conditions, so we can give customers nationwide a price with detailed market segmentation. For example, they can see why we’re charging more in the Midwest than in the South.

Being more transparent with customers

Our frontline sales teams can be more transparent about pricing with potential customers because they have better, more accurate pricing data. When a salesperson is on the phone with a customer, they can view the data in QuickSight and accurately explain what’s going on in a specific market. The pricing information is no longer an estimate; it’s completely accurate and up to date, and we can talk more confidently about what’s driving the cost, such as local conditions or risk ratings.

Another advantage of QuickSight and AWS is the agility and speed they give us. With AWS services, we can control how quickly to roll out the solution and who gets access. And we have more flexibility with AWS, so we can change things as we go and create better, faster tools for our internal teams without relying on a time-consuming, cumbersome development process. We can try things tomorrow that would have previously taken us 6 weeks to get into production, giving salespeople the new features they ask for quickly. And as a rapid prototyping vehicle, QuickSight is perfect for defining the next generation of job quoting packages that we’ll create for our customers.

Our job quoting tool isn’t just helping our frontline sales employees, it’s also benefiting staffing specialists, branch managers, market managers, and even regional and senior vice presidents. They can all see pricing averages and trends (as in the following screenshot), and select the data for specific markets or TrueBlue branches.

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Conclusion

The downstream implications of our new job quoting tool powered by QuickSight are huge. Now conversations are happening at the right level, with the right kinds of customers driving more value for our business.


About the Authors

Robert Ward is the Senior Director of Technology at PeopleReady. His teams are responsible for delivering data science and machine learning solutions, strategy and data insights, democratized data, and business analytics solutions. PeopleReady is modernizing how the North American staffing industry connects people with work. Robert Ward is driven to craft innovations for desired outcomes.

Ryan Coyle is the AWS Account Manager for TrueBlue. He has partnered with TrueBlue on their digital transformation efforts since the beginning of 2020. In this function he has collaborated with them to close on-premises datacenter facilities, develop and deliver new products to market, and deliver data driven results to TrueBlue business units.

Shivani Sharma is one of the Account Managers supporting TrueBlue. She joined the team July 2020 where she partners with TrueBlue to drive and collaborate on their transformation initiatives.

Categories: Big Data