- 23/11/2016
- Category: Commentaries
In the e-commerce world, businesses face multiple challenges related to the generation of revenues, maintaining the flow of customers, engaging customers, etc. Both marketing and remarketing strategies are important to address these challenges in order to make their business profitable. For example, in the case of unfinished transactions (abandoned carts), businesses want to ensure that customers comes back to their store when they are ready to purchase. To achieve this, businesses have attempted to reach out to their customers through conventional remarketing strategies, such as sending out occasional reminders on their incomplete purchases and abandonment survey e-mails. However, businesses have begun to realize that there are alternative strategies to marketing and remarketing that could guarantee a higher rate of purchases, which is by using big data.
The term big data refers to the use of predictive analytics, user behavior analytics, or other advanced data analytics methods to extract value from data sets that are too large for traditional data processing applications to process. It is a response to the demand for a new data management strategy to deal with the emergence of new waves of data from Open Data, Internet of Things, social network data, and other data-rich services.
When big data comes to play, abandoned carts is not only a potential revenue, but also becomes valuable customer data because the cart could provide information regarding the preferences and behaviours of online shoppers. With information regarding when and where customers abandon their carts, marketers are able to undergo predictive analytics on all four marketing P’s: price, product, promotion, and place. Basically, big data allows businesses to deliver better personalized interactions. One example is the “Customers who bought this item also bought” feature that actually resulted in a 30% increase in sales, because it helped shoppers to find more products. Furthermore, big data could improve the accuracy of their predictive services, which could help businesses set product supplies and predict demand.
You might have heard the news about how the retail giant Target was aware of a young woman’s pregnancy before her father did. The young woman had been shopping for Target’s pregnancy-related products online, and through the store’s predictive analyses they were able to deduce the young woman’s condition and send free pregnancy-related gifts to her door. This is an example of how big data is utilized for e-commerce, especially for remarketing and customer engagement.
However, concern has risen regarding the potential breach of privacy that big data holds. Customers have expressed their discomfort at the fact that retailers knows intimate details about their lives and habits, and could disturbingly predict very personal matters. There also lies the potential trading and misuse of customer data which the business owns. To address these concerns, strong regulations needs to be put in place so that customer data is solely used for the purpose of marketing. All criticisms aside, big data allows for a more tailored and efficient shopping experience which is beneficial for the customer.
References
Cavanillas, J. M., Curry, E., & Wahlster, W., New Horizons for a Data-Driven Economy A Roadmap for Usage and Exploitation of Big Data in Europe, Springer Open Publishing, 2016.
Docherty, P., ‘The Remarketing Report – Q3 2016’, SaleCycle (online), 27 October 2016, <https://blog.salecycle.com/stats/infographic-remarketing-report-q3-2016/>, accessed on 21 November 2016.
Kenny, E., ‘Abandoned Cart Remarketing Strategies: The Big Data Difference’, Boxever (online), 16 November 2015, <http://www.boxever.com/abandoned-cart-remarketing-strategies-the-big-data-difference/>, accessed on 21 November 2016.
Mallon, S., ‘5 Benefits of Big Data for E-Commerce Companies and Shoppers’, Smart Data Collective (online), 11 May 2016, <http://www.smartdatacollective.com/seanmallonbizdaquk/410001/5-benefits-big-data-e-commerce-companies-and-shoppers>, accessed on 21 November 2016.
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