Using the PPDAC Cycle to Guide Contact Centre Strategy

“Numbers don’t talk, we given them a voice and inject definition. Communication is a key part of the problem-solving cycle”
William Elder
Head of Business Intelligence
Tuesday, December 8, 2020
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“Numbers don’t talk, we given them a voice and inject definition. Communication is a key part of the problem-solving cycle”

Diving into intricate problems can unveil deeper underlying issues, the process of abstracting and solving statistical problems helps identify potentially larger challenges. Using the correct decision and strategy through deep analytics on problem-solving helps guide businesses down their desired road. The PPDAC cycle is what I think of when I’m faced with a range of problems.  

How can this be used in the Contact Centre?  

A challenge for any Contact Centre is having the right amount of staff available at the right time.

  • Looking at historic data will help to analyse and establish a successful contact strategy for customers across each platform, allowing us to draw data led conclusions for any recommended improvements.
  • A problem can be resolved using data such as a schedule that results in agents working at the same time each day or staff allocated onto the wrong platform at the wrong time, both resulting in customers waiting in extended queues at peak times – this can be reviewed using data to ensure customer contacts are answered as quick as possible

  • A resolution plan might be to spread the staff out to have the right people working on the right platforms at the right time. Whilst the plan might be a sound approach to solving the problem, continual new data and analysis is still required before finalising rearranged agent schedules

  • Continual contact data being recorded will indicate how well the plan is performing. The new data being recorded is the testing phase and necessary to verify the plan's success rate.    

  • The analysis and conclusion stages support the correct deployment of any new staff scheduling times in accordance with customers’ needs and identify other challenges, such as the requirement to staff sufficiently at peak times or make available certain platforms at peak times.  

The cycle never ends, improvements are essential to growth and efficiency and removing reoccurring problems, that can easily be solved but often ignored, can go some way to improving any contact centres overall performance. This process of problem-solving can be applied to more than the contact centre.

How does this apply to everyday analytics across the business?

  • To begin with, an analyst needs to develop a good understanding of what has happened previously, particularly around how data was obtained

  • In the early stages (Problem), an issue is often loosely defined. It can start with a very vague idea about what the problems are and the impact they are having. The Analyst will attempt to turn these vague feelings into much more precise detail and the ultimate goals or requirements using data to present very specific questions.
  • The Analyst will then consider the data needed (Plan) considering source of data, amount of data needed, examining things that can be measured using the data and how to go about achieving the plan.  
  • Extracting or gathering the data is then undertaken (Data). The data is stored appropriately and then whipping it into shape (data cleansing). Data analysts are always involved with data cleansing as Analysts almost always discover problems with uncleansed data during analysis

  • The final stages (Analysis and Conclusion) are about stress testing the theory, creating the conclusions and then presenting the findings. There is always a back and forth involved during analysis, tentatively forming conclusions and doing more analysis. The formation of conclusions typically involves the analyst and an executive who delivers the conclusions, decisions and guides the strategy.  

Thank you to The University of Auckland, Department of Statistics for great insight into the use of the PPDAC cycle in problem-solving (www.stat.auckland.ac.nz/~wild/d2i/)  

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