Lazada, the Singapore-based retail e-commerce giant, is using big data to improve the customer experience (CX) of their shoppers by analysing their profiles and retail shopping habits. This allows them to target their marketing and advertising campaigns at those who are likely to be most interested in the offering - an enormous return on investment when tracking spending. They are also able to make better product recommendations to their users, and improve their product range if there is substantial demand in a particular category.
Lazada CEO Martell Hardenberg believes using big data is like a dating service, only it matches people with products instead of other people. For a user interested in laptops, the average time between them viewing the product on a webpage and actually making the purchase is 15 days. This time can be better utilised to help guide customers to make buying decisions, says Hardenberg. Lazada also sees a lot of new users signing up before a major sale (up to ten times the usual). By figuring out what they would like to buy, the customer’s experience can be improved vastly by providing them with recommendations of products that are discounted on the day of the sale. On average, Lazada captures 100 GB of data every day.
Problems faced when dealing with digital execution
Big data is still in its infancy, but Lazada faces no shortage of talent, thanks to Singapore's university system that generates a large number of STEM (Science, Technology, Engineering and Management) graduates every year. Interest in the field is growing, with the big data industry growing at 23.1% every year since 2014 and projected to continue that way till 2019, when annual spending is expected to hit AUD$63.2 billion, according to IDC.
But this may not be enough to fully exploit the potential of big data for retail. The skills shortage is estimated to be 21%, compounded by shortages in budget allocation (45%). 34% of the time, there are other priorities which take precedence. To add to the challenge, 46% of the time, there are difficulties integrating technology and this may have something to do with the fact that 43% of the total pertains to lack of technology. Simply put, database mining technology as we know it today is at best rudimentary, unable to take on big data. 29% is attributed to poor quality data and company culture/lack of management support has to do with 16-19% of why big data isn't being exploited to one's advantage.