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Prolog Leung, Head of BI & Digital Analytics, Sa Sa International Holdings
In Big Data era, the game in eCommerce industry has totally changed - no matter how large your business is, business decision-making now heavily relies on data interpretation to enable the best fit of consumer needs. Big data plays a strategic role as in providing multi-perspective clues for Data Analyst to measure consumer behaviors in a more customized way.
In past decade, digital marketing mainly relied on primary data to retarget consumers through various media channels, such as DSP (demand-sided platform) and RTB (real-time bidding). Now the game has changed - DMP (data management platform) solution is one of the powerful primary data sources, it enables marketers to drive traffic to website and further improve conversions by merging owned and paid media into a single view for more precise consumer behavior analysis. Through DMP, marketers can adopt a more accurate consumer-centric approach and hence identify specific target audiences.
Apart from primary data, marketers now also need to wisely leverage secondary data as market trend information to best define competitive edge. Net, the magic of Big Data comes from a good match between primary data (internal own data) and secondary data (external data).
So, where can we get the most relevant and instant secondary data? It is within arm's reach:
1. Competitor tools such as comScore and similar web for viewing traffic of competitor websites.
2. Social listening with hot keywords from social network for getting a sense of the hottest trend in town without investing in a decent research.
3. Tmall BI tool for getting the sales figures in Taobao and Tmall (as long as you are the online store owner of Tmall) to understand the top products in the market.
Through DMP, marketers can adopt a more accurate consumer-centric approach and hence identify specific target audiences
With both the primary and secondary data ready, Data Analyst's business sense now plays the most determining role to fully turn these big data into something strategic and actionable to drive the business. Here is a real case example of an Online Retail Shop to demonstrate how a Data Analyst plays an important role:
As an Online Retail Shop, the business objective is to secure its market leadership by offering the most popular products. To achieve this objective, Data Analyst would conduct gap analysis by leveraging market research report (secondary data) to identify top selling products in market, then set as benchmark to compare with internal inventory database to see if the shop is already carrying these trendy products. If not, Data Analyst would pass those "missing products" to merchandising department for sourcing. If the shop is already carrying these products, Data Analyst would then make use of internal sales reports (primary data) from these trendy products to compare our sales performance against the market. If we are under performing, Data Analyst would first review and adjust the prices if necessary (given pricing is the most critical driver in online retail industry). Undoubtedly, website traffic and conversion would also be reviewed by web analytics tools such as Google Analytics and Adobe Analytics to ensure the issue is fully addressed. Big data could lead to information overload if using it without tying with business objective and following a logical analytic process. Having said so, a logical analytic progress may not necessarily be related to complex statistical knowledge, or mathematical manipulation. Big data is full of variety, what matters is how you can smartly connect the dots, step-by-step, to crack the business barriers and ultimately reach the business goal.