Using Big Data to Retain Your Best Agents and Create an Energized Workforce


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In an earlier column I introduced the “4 Foundations” that companies need to put into place in order to become Me2B Leaders and deliver outstanding customer experience. The 4th Foundation revolves around an “Energized workforce”, meaning that unless your agents, other customer-facing staff, and what we call “customer support staff” (essentially everyone!) is energized and empowered and able to engage with the customer, then you cannot have great customer experience. These Foundations and the 7 Customer Needs are laid out in our recent book Your Customer Rules! Delivering the Me2B Experiences That Today’s Customers Demand1.

These are the core elements needed to produce an “Energized workforce”:

  1. Right hires
  2. Right role models
  3. Right rewards
  4. Right metrics
  5. Right paths
  6. Right empowerment

With customer service agent attrition at “epidemic levels” in many companies, especially offshore outsourced operations (often over 100% per year), it’s essential to figure out how to predict retention of your best agents. Keep in mind that whenever you lose one of your “best agents” you will probably need to hire three replacements to cover the productivity loss, and still not be able to keep quality levels at par – more cost, worst results.

What used to be hit-or-miss and a good dose of mother-in-law stories to remedy agent attrition can now be replaced by some of the insights we gleaned from Me2B Leaders such as Apple, Starbucks, and Vente-Privee. These companies told us that all of the 7 Customer Needs and most, if not all, of the 39 Sub-Needs applied equally to create great experiences for their employees. Let’s review what to do step by step:

Step 1 = Figure out who are your best agents

This first step might seem simple but it’s not! You need to figure out the balance between speed/quantity and quality performance, often in a balanced scorecard that juxtaposes several elements that define speed/quantity such as contacts handled per hour and sales per hour, and the elements that define quality performance including internal QA scores, post-interaction c-sat results, FCR, and perhaps also customers’ subsequent purchases as a clear metric for how well the agent performed. Using this scorecard will produce your top 15-20% of agents, those able to produce excellent results while working quickly (and the 80-85% of your agents who are not the best ones, but let’s pick that up in a future column).

Step 2 = Find patterns that profile your best agents

After identifying the top 15-20% performers, it’s important to figure out their profiles, backgrounds, and processes followed. One insight we obtained is that many of the “best agents” don’t do what they were trained to do; instead, they brought the right temperament to improvise and adapt their responses to different customer needs (see also the old concept of “Situational Service”), often creating their personal knowledge sources and “cheat sheets”. In many cases their first supervisor helped to mold their balanced performance, hence our emphasis on the “Right role models”.

Step 3 = Analyze why you are losing your best agents using Big Data

Forget about getting much value from exit interviews, or chasing after your best agents after they have left – too little and too late. We have found that it’s far better to:

  • Collect a wide range of operational performance data across your agents who are still working with you and those who turned over, both best agents and the rest of your agents. Include demographic data such as previous customer service agent experience, whether or not they were introduced to your company from another employee (sort of a “Net Promoter” applied to agents), commute distances and time, and educational levels.
  • Track the number of supervisors that each agent has per year, who provided the initial training, amounts of ongoing training (and how often training is cancelled due to capacity shortages), and other key factors that we all know affect employee engagement.
  • Add to these “hard data” results from post-interaction customer surveys and from your periodic employee surveys, but be mindful that response rates will be low and responses skewed, so some results might not be too reliable.
  • Ask new questions based on the 7 Customer Needs and 39 Sub-Needs for Me2B leadership, for example focusing on “You trust me” and “You make me do better”, which are often ignored by standard employee surveys.
  • Create discrete segments of your agents using regression analysis to reveal how these segments view their jobs and their connection with the role that you have asked them to perform.

By following these three steps you will be able to predict using Big Data which agents are more likely to stay, and which ones are more likely to attrite. When you then compare these predictions with the profiles for “best agents”, you can create intervention programs at the individual level to shore up their frustrations and enhance their positive performance and feelings about the company, and how they are being treated. Applying these interventions and tracking their success will them feed back into the Big Data as a form of “machine learning” to refine the model.

When we perform these Big Data projects we always find surprising results such as some agents and agent segments crave overtime and additional income, but that mandatory overtime only appeals to one segment; reducing the rate of customer satisfaction feedback isolates agents and they don’t feel as connected; and promoting supervisors or moving them around damages agent education and community. Attacking these and other results of your Big Data analyses can go a long way to reduce unwanted and damaging agent attrition and to help you retain your best agents.

(For further reading you might want to consider respected HR communities and tools such as Total Rewards2 and the Gallup Human Sigma3 work.)

1. Your Customer Rules! Delivering the Me2B Experiences That Today’s Customers Demand (Wiley 2015). These are the 4 Foundations to deliver great Me2B customer experiences:

  1. Customer oriented culture
  2. Streamlined processes
  3. Integrated channels
  4. Energized workforce

Here are the 7 Customer Needs that Lead to a Winning “Me2B”Culture:

  1. “You know me, you remember me”
  2. “You give me choices”
  3. “You make it easy for me”
  4. “You value me”
  5. “You trust me”
  6. “You surprise me with stuff that I can’t imagine”
  7. “You help me better, you help me do more”

2. Total Rewards is one of the programs promoted by World at Work.

3. Human Sigma is one of The Gallup Organization’s approaches.

Bill Price

Bill Price is the President of Driva Solutions (a customer service and customer experience consultancy), an Advisor to Antuit, co-founded the LimeBridge Global Alliance, chairs the Global Operations Council, teaches at the University of Washington and Stanford MBA programs, and is the lead author of The Best Service is No Service and Your Customer Rules! Bill served as's first Global VP of Customer Service and held senior positions at MCI, ACP, and McKinsey. Bill graduated from Dartmouth (BA) and Stanford (MBA).


  1. Agree with all of this. These data streams, along with targeted employee commitment research (commitment to the organization, commitment to the product/service value proposition, and commitment to customers), can be leveraged to identify ambassadors (and those who can be bootstrapped through training, incentives, etc. to become ambassadors).


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