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Data is often said to be the most valuable commodity in today’s economy. This is especially true in the e-commerce space, where raw customer data can be shaped from raw numbers into actionable insights.
When utilized effectively, data can be used to fuel AI and predictive analytics functions. Both of these can overhaul business operations and boost productivity, and business owners know this. In fact, 93% of e-commerce sellers believe that AI can drive long-term cost savings, but only 23% of Southeast Asian companies are transformative in their AI adoption.
In a fast-paced e-commerce market, having the edge of predictive analytics can be crucial and could be the differentiator one needs to stand out among the crowd.
What is predictive analytics?
Predictive analytics can be defined as the use of data to create models that can predict future trends and events. Predictive analytics can be especially useful in making informed decisions in inventory management, marketing campaigns, and product development.
Both historical and current data can be used to create an algorithm that can forecast future consumer trends and behaviors. While it is impossible to predict the future with certainty, predictive analytics combines statistical models and machine learning to identify future opportunities or risks.
How can predictive analytics help grow e-commerce?
Predictive analytics can help e-commerce marketers understand their customers’ motivations, which is crucial in growing a business. A study by McKinsey found that companies that use data-driven marketing have the potential to increase net sales value by 3-5% and marketing efficiency by 10-20%.
The use of predictive analytics is a data-driven approach to create a unique and personalized campaign for each customer. This campaign is designed using customer interactions, interests, preferences, and behaviors.
Source: Bridging the AI Gap: Online Seller Perceptions and Adoption Trends in Southeast Asia by Lazada and Kantar
Consumer data can be used to offer a personalized product selection and recommendations and targeted promotions, leading to greater customer engagement and loyalty. This can take the form of marketing messages that speak directly to the consumer about what they are interested in based on their past behavior, including how long they look at a certain product, browsing history, and purchase history.
Predictive analytics and AI in Southeast Asia
Most (68%) online e-commerce business owners are familiar with AI’s opportunities.
However, while almost seven in ten sellers are aware of AI, they reported only integrating AI into 47% of their business operations. Actual adoption of AI stood ten points lower (37%), meaning that sellers are likely not fully adopting AI.
A study completed by Lazada and Kantar Singapore found that Indonesia and Vietnam are leaders in AI adoption in their e-commerce market, followed closely by Thailand and Singapore.
Here is a breakdown of where each country stands on predictive analytics integration:
- Vietnam: was rated highest on familiarity with artificial intelligence.
- Indonesia: has seen success with adopting social media marketing, customer segmentation, and content creation–all of which can provide data to use predictive analytics.
- Thailand: is the most skeptical about the usefulness of AI, with 80% of surveyed respondents saying they still had reservations about its usefulness.
- Singapore: has a strong infrastructure that has encouraged the adoption of AI features such as AI-generated marketing content and chatbots.
It is clear that while some countries in Southeast Asia are further along in AI adoption, the region is far behind in terms of investment in new technologies. According to research by Kearney, the region is behind countries such as Canada, the United States, and United Kingdom by about two to three years.
What is the future of predictive analytics in Southeast Asia?
While it seems e-commerce sellers are aware of the opportunities and capabilities of AI and predictive analytics, there is still a substantial adoption gap. This is especially true in Southeast Asia, where only one in four sellers has integrated some sort of AI into 80% of their operations.
Perhaps one of the largest barriers to adoption is the perceived cost of AI: 64% of sellers say that a high cost and time investment is difficult to overcome.
However, it is likely that this will change as AI becomes more accessible and the capabilities of predictive analytics continue to evolve. The adoption gaps across the AI field will continue to close as more businesses integrate AI into their operations—and realize the need for these technologies to stay ahead of their competitors.
