State of predictive CX in APAC
Customers have very limited tolerance for lazy customer experience management. In fact, the 2021 CX Network Customer Experience in APAC report saw 95 percent of Asia-Pacific (APAC) CX practitioners surveyed confirm that customers are more willing than ever to switch brands when unsatisfied. To retain clientele, APAC companies must effectively predict what customers require and the access channels they prefer using.
In response, APAC brands are developing their proactive customer experience management capabilities with the help of machine learning and predictive analytics. In a recent survey by CX Network, 45 percent of APAC CX professionals say they provide proactive support to website visitors and 34 percent design experiences such as personalized webpages or workflows around key persona needs.
By analyzing historical data, like browsing histories and purchasing behaviors, predictive analytics can identify likely outcomes to support business decisions. Aside from the obvious benefit of needs forecasting, predictive analytics assists with improved operational efficiency and customer churn prevention. Predictive tools provide insight into the potential twists and turns of customer journeys. Equipped with this knowledge, companies can pre-empt customer needs and intervene where necessary to improve customer engagement and brand reputation.
Without predictive analytics, companies limit their ability to address issues before they frustrate customers. When applied incorrectly predictive analytics can annoy customers with poorly targeted experiences that can be deemed as too intrusive or ‘creepy’.
In this report, produced in collaboration with RingCentral, CX Network explores the state of predictive customer experience management in the Asia-Pacific APAC region.
“Almost 60 per cent of APAC businesses are increasing CX budgets because of 2021’s surge of 130 million new mobile users throughout the region.”
2022 Digital Trends: APAC In Focus study
Adobe
The current state of predictive CX in APAC
The acceleration of technology adoption from consumers in APAC is triggering an explosion in CX investment. According to Adobe’s 2022 Digital Trends: APAC In Focus study, almost 60 per cent of APAC businesses are increasing CX budgets because of 2021’s surge of 130 million new mobile users throughout the region. This digital-centric mobile landscape has become a competitive battleground for businesses, especially as many employees continue to work remotely due to the Covid-19 pandemic.
Technological advancements in the sheer processing power of artificial intelligence (AI) and machine learning (ML) systems are allowing companies to utilize the masses of customer data available to them. The global predictive analytics market size is forecasted to grow to US$$13bn by 2023, with APAC’s usage charted to grow by 23 per cent CAGR during that period.
Kyla Aldrich, product marketing manager at RingCentral says that, in general, most organizations in their region still have a long way to go before they can truly deliver on predictive or proactive customer experience, as they do not have the foundational elements in place.
“To be able to apply AI, machine learning and predictive analytics tools, you need to have a 360-degree view of your customers, where all your customer’s data is consolidated in one place,” Aldrich explains. “That data can’t just be historical; it also needs to be in real time, or you have very little chance to develop any proactive or predictive capabilities that can influence a customer interaction as it is happening, online, over the phone or in-person.”
An example of this advancement is the AI-powered image search functionality on the app of south Asian online retailer Lazada that not only provides a more convenient experience for customers, allowing them to submit pictures of what they are looking for, but it educates the brand’s inventory demand forecasting to help prevent stock shortages. The company also uses AI translation to serve customers in their language of preference.
Predictive analytics are crucial for increasing brand agility and productivity. Currently, only 25 percent of CX and marketing practitioners in APAC (Adobe) are confident in their organization’s agility to respond to disruptions and opportunities. Predictive algorithms observe historical data, current events and customer behaviors – their stimuli and their outcomes – to advise companies on how to navigate potential events in customer journeys. When launching a new contact channel, predictive analytics can advise on the allocation of additional human resourcing to field a spike in customer enquiries.
Also read: CX in retail 2022
Contact center performance is being boosted by the application of predictive analytics. By analyzing historical data on service issues (e.g., customers with issue A also have problems with issue B), contact center agents can use this intelligence to reduce repeat-call incidents, improve first-call resolution (FCR) rates, and enhance customer satisfaction.
A telecommunications provider in APAC recently employed predictive analytics in its contact center to improve FCR levels and increase call deflection. A thorough analysis of its data, including call volume, customer lifecycles and product mix, led to agent recommendations that cut annual costs in half, significantly reduced total interaction volume and deflected interactions to low-cost digital channels. Its predictive analytics also forecasts the likelihood of a repeat call from a customer, which guides agents’ service levels to improve FCR rates.
Investments are increasing in the field of experience management and technology, but brands must not neglect the need to bolster skillsets and cultures for predictive technologies to thrive.
Case study: AXA
Insurance company AXA worked with RingCentral to deploy a single CX platform to manage digital interactions from all channels. AI-based smart routing categorized incoming messages and pushed them to the right agent based on urgency and skills. Customer identities were also merged across multiple channels into a single profile to improve resource handling and eliminate duplicate conversations. This allowed AXA agents to have access to a complete, unified conversation history across all digital channels.
Since the deployment, the company has seen a 50 percent increase in monthly digital interactions easily managed by the team, 10 times increase in case resolution and a 50 percent reduction in average time to resolve issues. It has also provided AXA with a platform to integrate additional digital channels over time and introduce greater levels of automation through AI and machine learning.
“[Customer data] needs to be in real time, or you have very little chance to develop any proactive or predictive capabilities that can influence a customer interaction as it is happening.”
Kyla Aldrich
Product marketing manager at RingCentral
The challenges of predictive CX
CX Network’s recent research identified three key challenges holding back APAC companies from delivering predictive experiences for customers.
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Data governance
Many organizations are hindered by data sets of low integrity that are scattered in silos throughout the business. Without conquering these data management problems, predictive CX programs are relying on inaccurate or outdated information on customers. As a result, predictive initiatives are at great risk of delivering frustrating interactions, like reaching out to the wrong customer with inaccurate product information or failing to reflect the customer’s preferences. Poor data governance procedures also have the potential to expose customers to predictive communications considered as “creepy” or too invasive, such as when an unexpected action unsettles a user.
Data-centric hygiene projects, such as centralizing repositories, mining data lakes and driving data integrity, are intensive but crucial foundational tasks for predictive CX success. Huawei’s Global accounting and reporting senior director, David Wray urges brands to have enterprise-wide naming conventions and taxonomy for data points. “It is incredible the number of companies that do not have this consistency.” He explains: “This set-up leads to an extremely inefficient environment, and a significant loss of potential value, because the data has to be cleaned to a point where it can be mapped and connected to other related and relevant pieces of information [for predictive CX initiatives].”
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Skillset shortage for predictive CX
Despite the investments made in big data and CX, it remains a challenge to find people with the necessary technical know-how and background to transform customer data into actionable forecasts. The integration of predictive CX systems often requires employees to adapt their usual ways of working. RingCentral’s Aldrich says that disparate systems and applications, which are exacerbated by having to accommodate legacy on-premises technologies and long-term contractual obligations with existing service providers, hold many APAC brands back.
Aldrich also explains that as organizations have been operating for so long with this disconnected infrastructure and customer data sets, advanced CX capabilities might reside in a niche group within the business or for a particular service offering but are not broadly applied.
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Quantifying the ROI of initiatives
To access the budget and resources needed to create predictive customer experiences, persuasive business cases must be made. CX practitioners are expected to quantify exactly how a predictive CX investment is going to unlock business benefits. As well as delivering quantitative evidence, Sidney Madison Prescott, global head of intelligent automation at Spotify, advises CX practitioners obtain the support of an executive sponsor at the proof-of-concept stage.
“This drives trust in the program’s potential from your wider stakeholders and opens the door towards a world of opportunity in terms of engagement levels and passion from the workforce,” Prescott notes.
Also read: CX in APAC 2022
As data becomes more ubiquitous and management methods become more advanced, APAC brands are leveraging their progress to enhance the convenience of customer experiences. As such, predictive experience programs are expected to play a significant role in the future of customer interactions.
Unified and structured data sets with stable data governance processes and smooth integrations that draw on various sources act as a firm foundation for organizations to base predictive customer decisions. Music streaming platform Spotify predicts customer needs using the data-fueled BaRT (Bayesian additive Regression Trees) machine-learning algorithm. The algorithm provides music recommendations to customers based on behavioral data.
BaRT is smart enough to avoid recommending songs a customer listens to for less than 30 seconds and models are retrained each day based on collected interaction data.
Healthy data architecture is empowering predictive recommendations that can increase customer purchase sizes. For instance, forecasts on which customer segments are most likely to convert on a particular up-sell or which product bundling suggestions are best for a cross-sell. By drawing on past-purchase behavior, e-commerce giant Alibaba provides predictive reminders that ask customers if they forgot to include a routine purchase of theirs in their basket. Algorithms can use Voice of the Customer (VoC) data to forecast future scores and customer reactions. The insights on common pain points in customer journeys can be used to build intervention boundaries into digital journeys. If a user starts to near a common point of friction, notifications and prompts can be triggered with helpful tips to nudge the customer away from the issue. If necessary, human agents can intervene to prevent customers from churning.
Airline Air Asia’s website offers proactive support through its AI, multilingual chatbot ‘AVA’ which opens up with relevant prompts and assistance for users according to the webpages they are viewing.
As well as using automated threshold triggers, global tech brand Microsoft has managed to reduce its number of customer complaints by deploying predictive projects to improve the performance of frontline staff. For instance, algorithms flag cases at risk of escalation or customer churn and predictive systems provide the best-known solution for that situation.
“A lot of our work in the crisis and escalation department has moved from being reactive to problems, to being proactive about the customers’ experience,” says Microsoft director of customer service Michelle Huenink.
Automated workflows that streamline issue resolution save time for both company and customer, as well as lowering the cost-to-serve by minimizing manual customer support tasks and deflecting inbound enquiries. These savings are useful business benefits to highlight in ROI business cases for predictive CX initiatives.
“A lot of our work in the crisis and escalation department has moved from being reactive to problems, to being proactive about the customers’ experience.”
Michelle Huenink
Microsoft director of customer service
Final remarks
APAC’s strong growth in technology adoption will continue to fuel the appetite for sophisticated experience management techniques such as predictive CX. Predictive AI-infused systems empower brands to anticipate customer needs and address issues with ingenuity. For instance, by mining usage patterns manufacturers can predict downtime and send spare parts to users as part of preventative maintenance programs. These systems can also educate brands’ business decisions around new products and prevent customers from switching to competitor brands.
Although the benefits are undeniable, the challenges around master data management, culture and quantifying ROI are firm obstacles blocking the adoption of proactive experience management models. Nevertheless, to stay ahead of the competition and satisfy customers, APAC brands should embark on addressing these challenges so they can capitalize on the power and evolution of predictive experience technologies.
“To be able to apply AI, machine learning and predictive analytics tools, you need to have a 360-degree view of your customers, where all your customer’s data is consolidated in one place.”
Kyla Aldrich
Product marketing manager at RingCentral