How Bots And AI Are Blowing Away Traditional Customer Service ROI Measures

As a customer service leader, you’re likely aware of the longstanding, standard measures of customer service ROI — CSAT score, customer LTV, and cost per contact. Yet, what’s different in those measures as we approach 2020 are the values you should expect to achieve around each. They should be going up, and yet there’s little information that quantifies by just how much. So, it is in that context that we are providing you with the basis for revised customer service ROI expectations going into 2020.

All ships will rise with chatbots

Recent data from Juniper Research found that the adoption of chatbots across multiple B2C industries will deliver business cost savings of $11 billion annually by 2023, up from an estimated $6 billion in 2018. Juniper says that the cost savings will come from reductions in the time organizations must dedicate to managing customer service inquiries.

The research credits chatbots with being able to dramatically reduce response and interaction times so much, that in just three sectors, consumers and businesses will combine to save more than 2.5 billion hours by 2023.

The key enabler is the customer service bot, driven by artificial intelligence (AI). In fact, Juniper cautioned that the full promise of such customer service ROI will only happen when bots are combined with AI to create the most personalized experiences possible.

A modern customer service platform can deliver on projected ROI

In next-gen customer service technology (which is actually used today), the customer service bot is AI-driven for both web and in-app messaging user experiences, and these ‘smart bots’ automatically and instantly respond to a user’s initial message. Since end-users receive an instant response 24/7, you can support them more easily and with fewer resources. This is helping to blow away traditional ROI expectations.

For example, McKinsey reported just a few years ago that 29 percent of what customer-service representatives do has the potential for automation. Yet, the number is much higher than that when using AI-driven bots. Data from Helpshift customers alone shows that organizations can selectively incorporate intelligent bots and automation into the customer support process (via asynchronous messaging) to automate over 50 percent of customer/user support interactions. The number grows even higher in certain markets, particularly when serving in-app customers. For example, Helpshift customers using the platform for in-app gaming support have demonstrated that 70 percent of incoming issues have been fully automated. 70 percent is more than double McKinsey’s projections.  

As a result, you should be able to revise your customer service ROI projections when you adopt a modern customer service platform that uses AI-driven bots. Realistically, if your customer service and marketing teams have developed a robust knowledge base, you could take your overall cost-per-contact or your pure cost-per-agent and multiply that number by the extra rate of deflection. So, if you were deflecting 30 percent of tickets for in-app users and could deflect 70 percent with the right platform, then your increase in efficiency/deflections results in that additional 40 percent multiplied by your baseline cost.  

The ever-important CSAT

In addition to savings delivered by ticket deflection, AI-driven bots also deliver in terms of CSAT. When these bots are used in messaging-based support, the average CSAT score jumps to 4.1 on a scale of 1 to 5. That’s far greater than the CSAT scores for live chat (3.6), email (3.3) and web forms (3.0).

Higher CSAT brings home further cost savings related to customer long-term value (LTV). So, for your own company, you can calculate ROI from improved CSAT by multiplying the typical LTV of a customer (which your marketing team should have at their fingertips) by the volume of customers you expect to lose if you do NOT deliver strong customer service. For example, globally, the average value of a lost customer is $243. Therefore, if the average organization loses just 5 percent of its 100,000 customers to bad customer service experiences per year, then their opportunity cost totals $1.2 million per year in lost revenue (100,000 x 5% x $243).

Automation with highly effective agents lift customer service ROI even further

As described above, asynchronous messaging delivers a 4.1 CSAT score, on average. That represents increases over live chat, email and web forms of 14 percent, 24 percent and 37 percent, respectively. Even better, your customer service ROI grows even more, because your live agents become reserved for only the most complex issues or situations in which you may want a live agent to up-sell or cross-sell a customer.

This means you can strategize for even higher ROI. In research on actual customer transactions published in the Harvard Business Review, customers with the best experiences spent 140 percent more than those with the worst experiences. Put another way, it’s significant when you consider that a 5% increase in customer retention can increase a company’s profitability by 25% or more, according to Bain and Co. That makes sense, as a Microsoft study found that 96% of respondents say customer service is important in their choice of loyalty to a brand.

Now, if your VIP customers will represent a far higher percentage of future revenue than other customers and you know who they are, the revenue opportunity grows even higher. That was difficult information to incorporate into customer service transactions 5-10 years ago. But today, AI and bots know which tickets to quickly escalate to live support, based on a lot more context than before, including the issue type, the identity of the person raising the issue, their value, and the other context of their inquiry.


AI-driven bots are changing the revenue and cost savings that companies traditionally rely upon when developing customer service ROI estimates. The game has changed, as a modern customer service platform can transform both the efficiency of customer service and the value-add potential of each live agent transaction.

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