E-Commerce Challenges in SEA that can be addressed with Data + AI
Andrew Martin, Head of Databricks, South Asia chats with Enterprise IT News about how the combination of data and artificial intelligence can help e-commerce in Southeast Asia.
EITN: The pandemic continues to push people online at an aggressive pace, accelerating the adoption of omnichannel shopping and fulfilment. This is most apparent in Southeast Asia, where research has revealed that digital retail in the region grew 85% year-on-year and is on track to see almost 80% of consumers go digital by the end of 2021, outpacing the likes of China, Brazil and India. (ref: Facebook and Bain & Company’s annual sync Southeast Asia report).
What are contributing factors to the 85-percent YOY digital retail growth?
Andrew: The boom in Southeast Asia (SEA) for consumer spending and retail, is driven largely by online channels. Compared to more traditional retail models, online formats have two distinct advantages that are empowering the accelerated growth:
Hassle-Free Digital Experiences & New Demand for More Engaging Purchase Journeys
One of the contributing factors to the increase in digital retail growth is the changing consumer behaviour brought on by the pandemic. During virus-imposed lockdowns, it was found that Southeast Asians spent on average an hour more daily on the Internet and social platforms, which in turn has shifted the average SEA consumer’s purchase journey towards social and online channels. Today, more than 80 percent of discovery, research, and evaluation of retail products by SEA consumers is done online, with social media and video as the predominant format for these phases in the purchase journey
Another contributing factor is the increasing consumer demand for end-to-end digital experiences. Starting from the first point of encounter online, to frictionless payments, flexible delivery methods and responsive after-sales service, customers today look for a hassle-free purchasing experience, and this on its own is empowering the accelerated growth of digital retail.
What is your definition of digital retail?
Andrew: Digital retail today can be defined as an omnichannel experience that allows consumers to easily manoeuvre and engage with a business’s platform, and in turn provides the information and education to encourage the purchase decision.
With uncertainty at the core of today’s decision-making, retail and consumer goods companies must focus on speed and agility of collecting and understanding data as a core business competency. These companies need to be faster at collecting customer data to meet new demand, be nimble about data analysis so that costs and supply chain inefficiencies are reduced and stay flexible when responding to dynamic market shifts.
Over the last two decades, India and China have become significant players in the digital landscape and are seeing a growing presence within the products software and hardware industries.
Why should we draw comparisons with China, Brazil, and India?
Andrew: Over the last two decades, India and China have become significant players in the digital landscape and are seeing a growing presence within the products software and hardware industries. Already in 2019, China’s digital economy is speculated to cross $5 trillion amounting to third of the country’s total Gross Domestic Product (GDP), while India’s current digital economy is estimated around $300 billion (12 percent of the GDP). Similarly, Brazil, the fifth largest nation in the world, is enjoying a booming e-commerce sector that expects 138 million online shoppers by 2025.
Despite the robust growth of these markets’ digital economies, the emerging potential of SEA’s fast-growing and large middle-class population that is estimated to approach 350 million people with a combined disposable income of US$300 billion, represents great opportunities to tap into an increasing appetite for ecommerce retail and experiences that can either leapfrog or at least keep pace with the other digital powerhouses.
EITN: How did you determine the four challenges to address – fraud, delivery theft, returns and customer service – that can help drive e-commerce profitability? How can data and AI help address these challenges?
Andrew: While expectations surrounding the customer experience have certainly risen to new heights, few retailers have invested in the right technology for meeting these new standards. These four customer challenges retailers face in SEA are not only common but have a significant impact on their bottom line. Here is how data and AI can address them:
Fraud: Fraud has become too commonplace. SEA stands to lose US$260m per annum to online fraud, with Indonesia, Thailand and Vietnam expected to be the most heavily affected. Losses associated with fraud soared to $56 billion in 2020 globally and accompanied a huge dip in customer confidence in the brand. Data and AI can help retailers get ahead of fraud and avoid financial and reputational damages, especially when it comes to proactive approaches. A modern data architecture that brings data together from across the business – from geospatial data to sales trends – can effectively enable anomaly detection at a massive scale to protect losses caused by fraud in real time with machine learning.
Delivery Theft: Logistic costs are high in most of SEA due to geographic challenges, concentration of economic activities in a few pockets and poor connectivity between various parts of the countries. The lack of adequate shipping infrastructures will make it difficult to deliver parcels within the promised delivery timeframe. This increases the risk of package theft, which is a significant operational burden on retailers, with global estimates of over a million packages being stolen or lost daily. Data and analytics can help logistics providers identify common sites of traffic accidents or package thefts and design their services around those. The global shipping industry is also using AI to enhance security measures, both within and outside of business grounds, with shipping carriers using drones to patrol the grounds around their warehouses to collect real-time information and data. Data and AI allows retailers to easily identify such hotspots and frame apt responses.
Returns: Without the tactile experience of bricks-and-mortar shops, consumers are returning their online purchases at an alarming rate. According to industry data, at least 30 percent of all products ordered online are returned compared to only roughly 9 percent bought in bricks-and-mortar shops. The ability to uncover trends in that data with the power of machine learning allows retailers to better understand customer behaviours, spot high-return items and act with data to minimize returns. To any e-commerce retailer, returned products mean additional shipping cost, which can constitute a significant portion of any retailer’s operating margin. Retailers can also incorporate predictive analytics using data and AI in their returns and reverse-logistics operations, to improve service levels with fewer queries and reported issues.
The global shipping industry is also using AI to enhance security measures, both within and outside of business grounds, with shipping carriers using drones to patrol the grounds around their warehouses to collect real-time information and data.
Customer Service: Customer satisfaction, customer retention and cost to serve are three factors that can define the long-term profitability for retailers. Although customer issues can be wide-ranging, many issues will be common to reoccur amongst customers. For example, Natural Language Processing (NLP) tools can be used to quickly analyse service call notes and easily identify the straightforward and most common issues. These can be tackled by blending digital and call center channels and driving self-service usage for common queries. Finally, businesses can arm their customer care teams with visual data snapshots of helpful customer insights – an at-a-glance view of context, key data points in their history and next-best-step suggestions while the customer is on the call. This is especially useful while dealing with an at-risk customer identified by machine learning (ML) models who has been routed to the retention specialist.
EITN: Are there other challenges that retail brands in SEA face today? How can data and AI help?
Andrew: Across SEA, countries are still battling uneven vaccination rates and are still dealing with outbreaks and lockdowns. While regional online retail has skyrocketed, SEA economies continue to rank poorly in being able to prepare and adapt to the pandemic’s changing conditions.
When looking at the retail industry holistically, one needs to be mindful that logistics, supply chain and last mile delivery are also as critical as the products and services sold and the user experiences provided. With SEA’s retail and consumer goods markets in flux, accurate forecasting that considers variations in day-to-day product demand and distribution will be essential.
Accounting for these shifting market conditions is often well beyond the capabilities of legacy, data warehousing-based tools. The retail industry and related organizations working with ever-growing, day-to-day digital data will need a centralized hub — a logistical control tower of sorts — to orchestrate the technology, tools and processes used to capture data across all stages of the supply chain.
The demand for more granular, timely forecasting, can be met with solutions that employ data insights derived from machine learning. These can power the retail industry to generate forecasts that move away from traditional linear models and historical-based algorithms, towards flexible inventory planning that can be precisely adjusted on an individual day and store level.