How a Smart(er) Contact Center can be game changing


Effective and efficient management of contact centers to deliver great customer service is a challenge in an omnichannel world of e-mail, SMS, web, voice, and in-person interactions. Rip and replace type initiatives for call center systems are a significant investment and risk for organizations (I have the battle scars). Can technology help organizations deliver a better customer experience (CX), reduce the risk of rip and replace and deliver in a cost-effective manner? Let’s dive in.

Contact Center Dynamics

Contact center agents get many low complexity calls that have the potential to be handled by bots or virtual agents. Within the mortgage business, examples of such calls include password resets, next payment due date, have you received my cheque, why did my bill amount change, what is the payment address, etc. Similar situations would apply to other service industries.

The average call duration is about 300 seconds (5 minutes), and an average call center agent spends 32 minutes every hour speaking to customers. The remaining time per hour is spent on post-call activities, meetings, training and breaks. So on average, a human agent handles about 6 calls per hour.

Google Cloud Platform Contact Center Artificial Intelligence (CCAI)

Google Cloud Contact Center Artificial Intelligence (CCAI) provides a platform which utilizes Google’s artificial intelligence (AI) and machine learning (ML) capabilities, including natural language processing and speech capabilities to support contact center agents while operationalizing voice bots and chatbots that can naturally converse with customers to understand the intent and drive call resolution with minimal intervention from an agent. CCAI also has the ability to integrate with an organization’s back-end systems to enable bots to perform various tasks, including identification and authentication, and provide relevant guidance and information to agents to achieve satisfactory call resolution.

Return On Investment

Running a call center is expensive, and it does not scale well during spikes – natural disasters, Covid, acquisition of a large mortgage servicing portfolio, contractual limits on the max number of calls that agents will handle, etc.

While CCAI is designed to be bolted on top of existing call center systems (removes the risk of rip and replace) a commonly expressed sentiment is “This is all great, but what is this going to cost, and how quickly can I recover the cost of my investment.” In other words, show me (hat tip to the State of Missouri!) the money and what is my ROI?

Having been in Corporate IT for more than 20 years and a buyer of technology products and services, I can relate to this sentiment. Costs vary for multiple reasons, including scope, existing organizational technology infrastructure, resources, awareness, maturity, etc., but below I make an attempt to do a back of the envelope calculation in the spirit of John Maynard Keynes’s maxim “It is better to be roughly right than precisely wrong.”

To be clear, this is not intended to be a proxy for a detailed financial model with parameters that will be unique to each organization (training, onshore/offshore resource mix, licensing cost, hiring, etc..). Think of this as a means to address the basic feasibility question – does this thing have legs, and should I invest more time and resources into fleshing this out?

Current State Costs

ItemQtyAnnual CostRemarks
Number of support center agents300$15,000,000Fully loaded cost of average agent assumed to be $50K annual.
Total contact center interactions3.39 MillionNA6 calls per hour, 40 hour week, 47 working weeks rounded
Cost per interaction in $4.4215 Million / 3.39 Million = 4.42

ROI Projection

ItemQtyAnnual CostRemarks
Total contact center interactions2.71 Million$12,000,00020% productivity gain, 3.39M X 80% = 2.71 Million interactions2.71 million interactions X $4.42 = $12 Million rounded
Ongoing additional costs$500,000Technology, additional specialized staff and training
ROI in stable state$2,500,000ROI = Existing manual costs less ongoing additional costs. Initial ROI will be lower than ROI in stable state due to   implementation costs.

Boiled Down

Folks love numbers – but the above basically boils down to the quantitative fact that at scale, it is highly likely that ROI is positive even if one layers in additional costs of training, hiring specialized staff, etc. Also, note that I’ve assumed a conservative productivity gain of 20%. Plug in higher productivity gains, and the ROI will correspondingly increase.

Fine Print (the lawyers made me do it!)
All assumptions and calculations are entirely hypothetical but nevertheless are loosely anchored to real use cases. This does not represent any price or effort commitments for any products or services from either Google Cloud or SpringML.

You anticipated this right! So here we go

SpringML was founded in 2015 and is a Google Cloud Partner. We have more than 250 implementations. Don’t let the ML in our name throw you off! We have chops in multiple areas, and are happy to partner with you on your cloud journey for a variety of use cases – cloud migration, data management, analytics, visualization, machine learning, contact center artificial intelligence, and document processing to name a few. Contact us, and let’s discuss how we can partner. Your volumes might be lower than the example here, but there still might be a positive ROI – we can get creative together.

Thought Leadership