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Evaluating the growing influence of E-commerce, Big Data

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Digital marketing and big data practices have quickly grown to become indispensable tools for reaching prospective customers and building brand loyalty. The use of mobile phones today is the primary and most important growth and traffic channel for valuable data, as well as a soon-to-be major revenue channel for e-commerce. Hence, needless to say, the impact of data and its analysis techniques like big data will be immense on modern retail.

According to the National Retail Federation report (2016), “Too often people think simply collecting huge amount of data will lead to insights, but the only way it will have an impact is if people start using and analysing it using fields like big data”.Parsons, Zeisser, & Waitman (1998) had prophesized early that new forms of interactive media, which are currently being employed extensively by e-commerce retailers and digital marketers, represented both a tremendous opportunity as well as a serious threat for marketers. Most Fortune 500 consumer marketing firms fell short of achieving the potential of such media. They also predicted that technological barriers were expected to fall, which they have now.

Many types of attractive digital marketing opportunities now exist for marketers, such as image ads, animated banners, VR video ads, and responsive ads. The relationship between performance of an e-business and their digital marketing activities were explored by Tiago & Tiago (2012). It was found that a majority of e-businesses focused on expanding their online presence through internet sales. Yadav, Joshi, & Rahman (2015) researched the value of mobile social media as a hybrid marketing tool. They found that continuous access to a user through mobiles & tablets depends on the user rather than on advancement of technology, since it is the user that makes the decision for their device to be switched on and active. Since payment gateways are now fully operative on cellular devices, mobiles now act as crucial control centres for users in the retail environment. Ahmad, Musa, & Harun (2015) measured effects of social media marketing on increasing brand health score. Firms engage with customers online and build active interaction with them using such media, which is why it is imperative to be quick and active on online social platforms even long after establishing them.

Big data is largely being used now-a-days in service analysis of retail stores. Adopting a different approach, Järvinen & Karjaluoto (2015) used big data web analytics for performance measurement in digital marketing. Analytics is a revolutionary step towards measurable marketing, with three out of four marketers agreeing for its need in the current scenario. The greatest benefit was noted to be the ability to track the number of users visiting websites, and the parity between traffic being brought to websites by different marketing actions.

Gaku & Takakuwa (2015) adopted a more direct research, coming up with a novel method to analyse retail store performance using software. Their method comprised of three steps:

1. First, an information generator is used to arbitrarily pick a specimen from a number of clients belonging to a sample, for evaluating information during a given time period (say, a specific day). The samples belong to an extensive scaled data set consisting of certain promotions and offers.

2. Then, the agent’s plans are put into an information table. This particular case made use of MS Excel.

3. Lastly, a simulation model to look at and investigate the level of client benefit, with respect to the available information and the inputted agent plans.The procedure is successful in monitoring and predicting various factors, such as customer footfall in stores, the frequency of each customer visiting a store, and the average time taken to service a customer. These results can then be used to identify profitable consumers, their preferences, and effectively segment them on a more microscopic level, specific to the retailer’s store.

Özköse, Ari, & Gencer (2015) classified the properties of big data as Volume (size of data set), Value (generation of results), Variety (number of sources of data), Veracity (accuracy and verifiability of data), and Velocity (rate of capture of data). They stated that interest in big data keeps growing every day.

An actual challenge now is arranging storage for the massive inflow of data into a firm’s systems. Voleti, Gangwar, & Kopalle (2016) in their research have exploited big data and its current uses in 5 dimensions, namely: customers, products, time, geo-spatial location, and channel.

1. Customers: Data is stored in the form of rows. In fact, one of the major strategic goals of modern organizations today is to increase the number of rows i.e. unique customers (or in big data terms, adding more unique customer IDs, via customer acquisition methods) and achieving a higher number of transactions per customer (which in mathematical terms, adds up to increased revenue per row). One of the key capabilities of a retail firm is the ability of the system to track new customers, and also to continue linking future purchases over time, even after the first visit. Loyalty programs are common today, and below the surface, actually serve the purpose of such tracking. Apart from loyalty programs, users are also commonly tracked through other information such as credit cards, IP address, and registered log-ins.

2. Products: Product information in marketing always has its own set of attributes and levels, in order to define the product. However, in today’s data rich environment, the product information has expanded into two dimensions. First, stores have hundreds and thousands of SKU’s and information is now available about all of them, making the data set about products that have many more rows. Second, the amount of information on each product is not always limited to a small set of attributes, which increases the number of attribute columns, expanding the whole product information matrix. Product information represented in such a two-dimensional matrix eventually enables a variety of downstream analysis methods.

3. Time: Historically, retail environments store data for analysis with segregation by time, down to a monthly, weekly, or even daily level. But today, data in retailing comes with a time stamp that allows for continuous flow of data and measurement of customer behaviour, product assortment, stock outs, in-store displays and environments such that assuming anything static and in-mobile is at best an approximation.

4. Location: The ability of a plethora of contemporary methods that use GPS, to find out and use the geographic location of the customer at any given point in time has opened up a whole new avenue for retailers. The customer’s geo-spatial location has a profound impact on the effectiveness of marketing by changing what offers to make, determining at what marketing depth to make an offer, to name a few.

5. Channel: The collection, integration and analysis of omni-channel data helps retailers in several ways: – (I) understanding, tracking and mapping the customer journey across touch-points from decision making to purchasing – (II) evaluating profit impact and customer lifetime value – (III) better allocation of marketing budgets.It is all but certain that the steady increase in consumers’ online purchase intent will fuel future revenue growth across all B2B and B2C transactional E-commerce sectors from retail through financial services to travel and more.

Analyzing the piles of information available in retail domain and devising digital marketing strategies that are customized to target customer can boost retail sales by over 25% on an average, in the short term (Bradlow, Gangwar, Kopalle, & Voleti, 2016).Sha & Guo-Liang (2012) in their paper discussed ways of how digital marketing practices influence modern day retail. The aim of retailers is to gain a competitive advantage by providing high quality service for customers and improving their customer base. The current convention to achieve this is by scaling up the digital marketing practices and by the application of information technology.

The development of information system and digital marketing campaigns should be focused on the analysis of customer behaviour. Sha & Guo-Liang (2012) found that most digital marketing campaigns find it difficult to target the right customers. They adopted a method of case study to understand how to use innovative digital marketing practices in order to improve retail service quality. They concluded that a digital marketing system can indeed effectively obtain information regarding customer behaviour and effectively apply it to service clients in retail stores, and come up with a clear strategy and direction for further implementation for digital marketing for retention.The retail industry is continuously affected by advances in digital technologies. On the one hand, consumers expect to find technology-equipped retail environments, on the other, retailers achieve advantages through the use of new tools for market expansion and research. Pantano, Priporas, Sorace, & Iazzolino (2017) attempted to reach a clearer understanding of the impact of innovative forces in modern retail sector.

Innovation trends in the sector were evaluated by analysing trends in the current retail market. The insights they gained give an overview in certain areas, again helping with the prediction of future trends, and developing long-term strategies for digital marketing in retail. Bradlow et al. (2016) have highlighted the obvious ethical and privacy concerns that can arise from the use of big data for predictive and descriptive analysis in retailing. This can create a “boomerang effect” where the customer might end up feeling ambushed due all the “hyper-localized” targeting being offered by retailers. Additionally, self-regulation is required for firms that make use of big data, in order to avoid potential legal repercussions.

Ecommerce’s influence on modern retail has taken several turns over the years. Some striking findings from research papers are as follows: – Rate of growth of online sales has slightly decelerated, but is still significantly high· – The online sales growth rate for public department store chains declined from 39.3% in 2012 to 18.6% in 2015, while the online sales growth rate for public specialty stores declined from 17.5% in 2012 to 9% in 2016· – E-commerce volumes are not sufficiently high to justify store closures – traditional retail still needs to have a foothold – Price-matching should not be a “one size fits all” approach – analytical tools are available to help with segmentation of customer price groups

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