Organisations have been battling for decades to document the ROI of their contact centre or to justify the implementation of a new artificial intelligence solution. Typically, the focus is on quantifiable elements such as revenue generated or cost savings, and more narrowly on the average handling time (AMT) of calls, emails, etc., especially its decrease. This is known as the hard ROI.
However, the measurement of soft ROI is increasingly required in addition to these strictly financial indicators.
The increasing value of soft ROI
"Solving the AI ROI problem. It's not that easy", wrote* Anand Rao, Global AI Lead PwC US in July 2021. It is all the more tricky to prove when it comes to valuing the soft ROI. The stake would be to isolate the contribution of data processing by AI to the ROI, in particular the company's financial results (increased margins-lower costs). Although it is not possible to take exclusive credit for organisational gains, reputation, etc., it is possible to measure a positive contribution to the ROI. However, it is possible to measure a certain number of concrete gains that will contribute to the computation of the return on investment. The soft ROI can be defined as the return on investment, which is not quantifiable or measurable specifically in euros. A few indicators are interesting to consider: the increase in brand awareness, brand familiarity and the commitment of employees and customers.
Evaluating customer satisfaction
Customer satisfaction is assessed by means of several methods: NPS (Net Promoter Score), CSAT (customer satisfaction score), but new ones are appearing that somewhat upset the predominance of the NPS. What the CES (customer effort score) measures, for example, is the effort made by a customer to satisfy a request, whatever it may be. The objective is fairly simple: to make it easy and efficient for customers to contact the company. This is done through the responsiveness and contactability of the customer service. All too often, the finger is pointed at the difficulty of the processes linked to the customer journey. Users want more and more real-time answers, whatever the day and whatever the time. Regardless of the medium or channel used, the important factor is the speed of response. Self-service or self-care is therefore very popular.
Let's take the example of the automation of responses to incoming messages by AI. The e-mail medium is currently under-exploited** in customer relations. However, it is possible to initiate an automatic and personalised response to a simple request, via e-mail. The AI will thus identify one or more requests (intention or interaction) and send a personalised response, with or without an attachment (a certificate, an access map, a brochure, etc.). Resolving a request from the first contact contributes significantly to this customer satisfaction, which is useful for both loyalty and recommendation. There is even an indicator to measure this: the First Contact Resolution Rate*** (FCR).
Measuring employee engagement and satisfaction
Creating a satisfactory working environment will clearly have an impact on the commitment of the teams. This working environment also involves efficient tools that will ease the day-to-day work of advisors, by lightening the load of certain tasks that have little added value. Let's take the example of email processing once again. In the case of a more complex request, the email will be routed to the most competent advisor, and depending on the intentions acknowledged, a draft response will be offered. The advisor can personalise his or her response based on the context or history of conversations with that customer. This solution will enhance the advisor and make his or her daily work easier. This is an important point in reducing the turnover of teams and the cost of this turnover. In a sector where recruitment is not so easy, the time saved by not having to select, on-board and train a new employee is precious. It should even be financially quantifiable by human resources.
These two soft skills can be enhanced by many others, such as the more general one related to business transformation. Indeed, streamlining and modernising decision-making processes through automation is one of the challenges of AI, notably in the area of customer relations. This automation is complemented by another benefit: the use of data**** - and in particular unstructured data - to better understand and exploit data that will inspire informed decision-making and make the data actionable.
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