How to Decode Consumer Behavior for E-Commerce
E-commerce in the Asia-Pacific (APAC) region represents about half the global market and will accelerate at a rate set to outpace the rest of the world. This is thanks to two-speed growth: a combination of emergent economies, such as Indonesia, Vietnam and the Philippines, adding new e-commerce consumers to the market and mature countries like China projected to continue with strengthening sales.
By 2021, the APAC market is expected to be worth US$3.5 trillion in e-commerce sales with 80 percent of that, around US$2.7 trillion, coming from mobile devices (m-commerce). This makes the region highly attractive, which in turn means marketers will be competing with brands that have emerged and succeeded within the region as well as global brands looking to secure a slice of the action.
Thanks to e-commerce giants Alibaba and Amazon, there is plenty of data that tracks leads from search to purchase. Research shows distinct patterns of consumption across Southeast Asia; while weekends score the highest amount of traffic on e-commerce sites, most orders come through during business hours on weekdays, except for Singapore where peak order time is 10pm weekdays. This suggests people are discovering and researching products when they have more time, then purchasing on a weekday.
Data about consumers and buying behavior is everywhere, but you need more than data to compete – you need insight. How can artificial intelligence (AI) help you segment users so you can drive a more targeted campaign? What can AI do to support you to identify the right market, shape the right campaign, and run it at the right time and on the right channel for more effective results?
From Data to Effective Campaigns
Used effectively, AI tools such as Appier’s AIXON data science platform can combine, sort and analyze data from multiple channels including a brand’s own platforms and external channels to deliver actionable insights. You can then use these to design and implement an effective campaign, from introducing your brand to a new audience to increasing your customer base with high-value prospects.
Essentially, AI finds patterns and threads of information invisible to even experienced marketers. To increase the probability of conversion, AI can find out users’ interests from their behavior on other sites and channels to determine the type of campaign that is likely to attract them to your site and convert them from visitor to customer.
The timing aspect is also key to AI’s effectiveness as it enables marketers to predict consumer behavior at a granular level and can therefore run the right campaign at a time when the user is ready to purchase.
This also works as a re-engagement tactic. AI can identify behavior that indicates a customer is about to move on so marketers can run a specific campaign to remarket to them, such as a special offer on the customer’s favorite product.
With the right AI tools, marketers are able to evaluate campaigns before running them and that is a powerful feature at a time when competition is high and brands require better ROI. AI can also predict conversion by modeling response rates. Are you ready to unlock potential lookalike audiences, achieve better conversion and realize better returns through AI?
Download our latest white paper ‘Detail for Online Retail: How AI Can Help Marketers Decode Consumer Behavior in E-commerce’ to find out more about how AI can help improve your understanding of e-commerce consumers and provide actionable insights for your campaigns.
WE ARE HERE TO HELP
YOU MIGHT ALSO LIKE
Marketing has always been highly dependent on data. And in today’s rapidly moving world, the importance of managing vast quantities of diverse data from disparate sources is growing. “There are three main issues when it comes to data management for marketers,” explains Dr Min Sun, Chief AI Scientist at Appier. “The first is data quality – the maxim of ‘garbage in, garbage out’ is critical for marketers. Once data quality is assured, the second factor to consider is the usefulness of the data. What data is valuable and which data sets can be used together? Finally, companies need to ensure they have robust governance in place. These cover the legal and corporate obligations including jurisdictional frameworks, such as General Data Protection Regulation (GDPR) in Europe, the Personal Data Protection Act (PDPA) in Singapore and others.” What Is ‘Good’ Data? There are several dimensions to data quality. Dr Sun says that issues such as consistent errors and inconsistent noise in data can significantly decrease the usefulness and value of even huge data sets. However, a more subtle issue can come even when the data is completely correct, but the assumptions underlying the selection criteria are biased or skewed. As a
Q: What is GDPR? The General Data Protection Regulation (“GDPR”) is a new legislation by the EU parliament that lays out requirements for data collection, storage, and usage practice. This new law is meant to replace the 1995 EU Data Protection Directive (DPD) to significantly enhance the protection of the personal data of EU citizens and increase the obligations on organisations who collect or process personal data. Q: When is the GDPR coming into effect? The GDPR will become fully enforceable on May 25, 2018. Q: Who does the GDPR affect? Although the GDPR is an EU regulation, the territorial scope of GDPR is potentially far wider as it can also apply to non-EU businesses in certain cases. Businesses that market their products to or monitor the behavior of people in the EU are required to be GDPR compliant. Q: What constitutes personal data under the GDPR? Any information related to a natural person, defined as ‘Data Subject’ in GDPR, that can be used to directly or indirectly identify the person. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address. Q. How is
Algorithmic bias in artificial intelligence (AI) and machine learning (ML) systems has come under scrutiny over the last couple of years. While much of the focus is on the negative consequences of bias, marketers can also use bias in positive ways. But in order to do so, the bias needs to be recognized and understood. And that starts with understanding the origin of it. Dr. Min Sun, Chief AI Scientist at Appier, explains, “AI bias basically means AI or ML is making decisions with a certain bias towards a specific outcome or relying on a subset of features. A common example is a facial recognition system that has been trained with mainly Caucasian people. As a result, the system cannot make accurate judgements about people from different cultural groups.” The model is making decisions from a set of features that is not representative of the data it is meant to be making decisions with or it performs badly on some types of data the model has not seen during training. Where Does AI Bias Come From? AI and machine learning systems are trained using sets of data that are acquired using various mechanisms. The data that is used to