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Contact Center gets a COVID-19 Redefinition

Contact Center gets a COVID-19 Redefinition

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Post-Covid-19
  • Competitive Differentiation


Publication | Update: Oct 2020
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Contact centers became the front and center of a company and transformed from a cost burden into a revenue generation accelerator.

Why?

“Because our core business is now remote, it is based on delivery [efficiency], and we can’t interact face to face with our customers. So, we have to focus on omnichannel delivering the right experience using AI not just to offload workload but to improve customer experience,” said Sami Ammous, vice president for East Asia and the Pacific at Avaya.

According to Winston Thomas of CDO Trends, one area that saw tremendous interest, as a result, was the idea of public cloud contact centers. With social distancing, lockdowns, and other measures restricting employees’ movement, the contact center was now operating remotely. A public cloud contact center makes business sense.

“The onboarding process is simpler; it’s quicker. So, the public cloud tends to be less customizable, has lower costs, and, in general, scales well, both low and high. So, it really depends on the organization, what their challenges are, and how they would like to address them,” Ammous added.

Ammous also noted that hybrid cloud-based contact centers are also gaining traction. “And that predominantly happens where you want to maintain customer information on-premises for security and data sovereignty purposes.”

Moving your contact center to a public or hybrid cloud may make economic sense, but it has challenges.

For one thing, business process outsourcing (BPO) (or specifically outsourced call center companies) see it as additional complexity. “Because if you think of an outsourcer, they need to make sure that their infrastructure is integrated and connected to their customers’ infrastructures,” said Ammous.

The complexity arises from the business setup, and not necessarily because of the cloud infrastructure. Usually, you have a customer on one end, and the BPO on the other. In the middle lies the telco.

Now, the cloud provider is a fourth element that goes into play. BPOs say it’s just too complicated to go public cloud. So, what is very attractive to those BPOs is the private cloud concept,” said Ammous.

However, Ammous argued that BPOs will eventually see public cloud setups as an advantage as their business becomes more seasonal or time-driven. “So, if they sign a contract, they need to ramp up quickly. If they lose a contract, they need to ramp down quickly,” he said.  

“Security needs to be looked at a little differently. Access control needs to be looked at a little differently. The ability to spin up and down needs to be looked at differently,” said Ammous.  

For example, companies will need to look at session border controllers (SBCs). Virtual private networks (VPNs) are not suitable for remote contact centers as they do not differentiate between “voice and non-voice traffic.”

This is an issue because, in a cloud environment, “everyone is technically remote.” It makes VPN unreliable and increases the need for companies to consider SBCs. It promises to transport voice without loss of quality, “both from a service provider to the data center and from the data center to the agents who receive customer calls,” Ammous explained.

Cloud contact centers will evolve quickly as they move to the front and center of an organization. The main reason is the data. They will now collate enough data on customers to shape responses and create new revenue streams.

Ammous already sees this happening before the pandemic. He noted that many were using chatbots, “AI-infused applications” and conversational IVR to drive contact center efficiency. “Conversational chatbots can only exist if you have large amounts of data to train the model.”

Making your public cloud contact center become your key differentiator will matter as customer demands shift, and companies fight for survival.

Public cloud contact centers allow companies of different sizes to quickly ramp up their customer servicing capabilities. Besides, building the same capabilities in-house will be prohibitively expensive, especially in the current lean climate.

Avaya is partnering with cloud platform providers like Google to drive AI-driven cloud contact centers. “They call it CCAI (Contact Center AI), and it’s something that we also partner with them on. So, we infuse our solutions with Google’s logic,” said Ammous. He added today’s APIs and cloud services enable companies to build cloud-based infrastructure for their contact centers and drive business outcomes, and not the other way around. 

The concept of public cloud contact centers is now gaining ground fast as companies shift the business model.

For example, we see the fast emergence of telemedicine. It is not new, but its biggest obstacle before the pandemic was data regulations and established money-spinning practices like writing prescriptions. COVID-19 changed this by making regulators more open to data sharing, and hospitals becoming more focused on treating COVID-19 sufferers.

“Now, they’ve linked those solutions to an online pharmacy. So not only do you get your prescription, but you can also order it on your phone to get delivered. It also means for doctors, they do not have a clinic full of patients waiting, and they can just see people when they need to. Face to face interaction is now dedicated to stuff that is quite serious,” said Ammous.

Even cultural habits are changing. Ammous noted Japanese companies, famed for paper-intensive work processes and in-office attendance, are now looking at remote working. For example, businesses like Ikea are offering flexible delivery options, whereas, in the past, they had a single delivery charge for using their van.

“Companies will realize that they need to maintain customer service. They need to respond to phone calls, emails, chat, webchat, and whatever new channel comes up in the contact center space. People will also ask, ‘why should I come to your branch? I’m calling you on the phone, please help me on the phone.’ It will become a competitive differentiator.”

Source: CDO Trends

 

 

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

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  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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