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Quality Analysis Verses Quality Assurance

Customer service quality assurance and quality analysis are diverging. How do you structure them both in your contact centre?

With the advent of speech analytics and artificial intelligence in the contact centre – are customer service quality assurance and quality analysis beginning to diverge?

See our infographic for a detailed comparison.

Quality Analytics Verses Quality Assurance

Quality assurance versus quality analysis: what are the differences?

Quality assurance: we are probably all familiar with this far-reaching term. ‘QA’ has been a vital part of almost all industries since the dawn of the industrial revolution and the systemic division of labour, and is a vital tool is ensuring customer satisfaction at all stages of the retail chain.

Quality assurance at the level of customer service is perhaps the most elusive and indefinable of all arms of QA. It involves checking not material products, but the performances of members of staff, the satisfaction of customers, and the adherence of employees to the ever-shifting sands of data protection protocols, company KPIs and compliance to GDPR, FCA or ISO guidelines.

Quality assurance, in such cases, is about monitoring pre-existing standards in order to allow companies to optimise their internal processes and thus optimise the experience they can provide customers. Using tools such as mystery shopping, call recording or NPS, companies can use outsourced or in-house QA strategies to make sure that their employees are meeting company standards, and to what extent customers feel happy with their retail experience.

It can also be effectively used to manage underperformance, allowing companies to gain an insight into where they are failing to meet expectations. This is especially effective when using outsourced quality analysis, which allows for greater objectivity and the eradication of biases produced by familiarity or loyalty.

Quality analysis, however, is somewhat more of a complicated affair. If quality assurance is about monitoring extant processes in order to measure compliance and performance, quality analysis lies in interrogating the validity of those processes in the first place.

Take, for example, telephone purchasing. Quality assurance could see the advisor/salesperson pass with 100%, but still lose the sale. Quality analysis can look at processes and determine solutions by analysing customer journeys and examining ‘leaky pipe’ revenue loss, figuring out why, when and where over the course of a telephone call, customers tend to lose interest and terminate the sale.

This, of course, means that different skill sets are required to perform analysis and assurance effectively. When carrying out quality assurance roles, executives must pay unwavering attention to detail, work to rigid sets of client-dictated guidelines, demonstrate objectivity with a desire to calibrate results to ensure universal standards, and produce reliable and timely feedback.

Whilst quality assurance does call on some more flexible skills like the ability to measure soft skills and respond to tone of voice, it is more often quality analysis that requires a greater degree of critical thinking. Quality analysis roles need an individual who can understand processes and interrogate their bases as well as their execution, digesting large data sets in order to make sense of trends, anomalies and levels of standard deviation from the norm.

With these different roles and skills come different tools. Quality assurance relies largely on recording software of various types, with mystery shopping another important evaluative tool. Quality analysis tends to use more sophisticated systems such as machine learning and speech analytic software in order to extrapolate broader trends and better inform the user’s market research. However, it also has scope for individual users to identify problems and innovate solutions, with quality assurance then being used as a way to measure the success of these innovations.

Quality analysis, in this way, is highly analytical, and can be used not only to analyse quality assurance’s effectiveness but actively inform the criteria on which quality assurance is based. The two components of quality control are most effective when in a reciprocal relationship like this, combining the flexibility of service optimisation with the objectivity of empirically informed monitoring.