Digital marketing and big datapractices have quickly grown to become indispensable tools for reachingprospective customers and building brand loyalty.
The use of mobile phones todayis 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, needlessto say, the impact of data and its analysis techniques like big data will beimmense on modern retail. According to the National Retail Federation report (2016),”Too often people think simply collecting huge amount of data will lead toinsights, but the only way it will have an impact is if people start using andanalysing it using fields like big data”.Parsons, Zeisser, & Waitman (1998) had prophesized earlythat new forms of interactive media, which are currently being employedextensively by e-commerce retailers and digital marketers, represented both atremendous opportunity as well as a serious threat for marketers. Most Fortune500 consumer marketing firms fell short of achieving the potential of suchmedia. They also predicted that technological barriers were expected to fall,which they have now. Many types of attractive digital marketing opportunitiesnow exist for marketers, such as image ads, animated banners, VR video ads, andresponsive ads.
The relationship between performance of an e-business and theirdigital marketing activities were explored by Tiago & Tiago (2012). It wasfound that a majority of e-businesses focused on expanding their onlinepresence through internet sales. Yadav, Joshi, & Rahman (2015) researched the value ofmobile social media as a hybrid marketing tool. They found that continuousaccess to a user through mobiles & tablets depends on the user rather thanon advancement of technology, since it is the user that makes the decision fortheir device to be switched on and active. Since payment gateways are now fullyoperative on cellular devices, mobiles now act as crucial control centres forusers in the retail environment. Ahmad, Musa, & Harun (2015) measuredeffects of social media marketing on increasing brand health score.
Firmsengage with customers online and build active interaction with them using suchmedia, which is why it is imperative to be quick and active on online socialplatforms even long after establishing them.Big data is largely being used now-a-days in service analysisof retail stores. Adopting a different approach, Järvinen & Karjaluoto(2015) used big data web analytics for performance measurement in digitalmarketing. Analytics is a revolutionary step towards measurable marketing, withthree out of four marketers agreeing for its need in the current scenario.
Thegreatest benefit was noted to be the ability to track the number of usersvisiting websites, and the parity between traffic being brought to websites bydifferent marketing actions. Gaku & Takakuwa (2015) adopted a more direct research,coming up with a novel method to analyse retail store performance usingsoftware. Their method comprised of three steps:1. First, an informationgenerator is used to arbitrarily pick a specimen from a number of clients belongingto a sample, for evaluating information during a given time period (say, aspecific day). The samples belong to an extensive scaled data set consisting ofcertain promotions and offers.
2. Then, the agent’splans are put into an information table. This particular case made use of MS Excel.3. Lastly, a simulationmodel to look at and investigate the level of client benefit, with respect to theavailable information and the inputted agent plans.The procedure is successful in monitoring and predictingvarious factors, such as customer footfall in stores, the frequency of eachcustomer visiting a store, and the average time taken to service a customer.These results can then be used to identify profitable consumers, theirpreferences, and effectively segment them on a more microscopic level, specificto the retailer’s store.
Özköse, Ari, & Gencer (2015) classified the properties ofbig data as Volume (size of data set), Value (generation of results), Variety(number of sources of data), Veracity (accuracy and verifiability of data), andVelocity (rate of capture of data). They stated that interest in big data keepsgrowing every day. An actual challenge now is arranging storage for the massiveinflow of data into a firm’s systems. Voleti, Gangwar, & Kopalle (2016) intheir 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 majorstrategic goals of modern organizations today is to increase the number of rowsi.e.
unique customers (or in big data terms, adding more unique customer IDs, viacustomer acquisition methods) and achieving a higher number of transactions percustomer (which in mathematical terms, adds up to increased revenue per row). Oneof the key capabilities of a retail firm is the ability of the system to tracknew customers, and also to continue linking future purchases over time, evenafter the first visit. Loyalty programs are common today, and below thesurface, actually serve the purpose of such tracking. Apart from loyaltyprograms, users are also commonly tracked through other information such as creditcards, IP address, and registered log-ins. 2. Products: Product information in marketing always hasits 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 twodimensions.
First, stores have hundreds and thousands of SKU’s and informationis now available about all of them, making the data set about products that havemany more rows. Second, the amount of information on each product is not alwayslimited to a small set of attributes, which increases the number of attributecolumns, expanding the whole product information matrix. Product information representedin such a two-dimensional matrix eventually enables a variety of downstreamanalysis methods. 3. Time: Historically,retail environments store data for analysis with segregation by time, down to amonthly, weekly, or even daily level.
But today, data in retailing comes with atime stamp that allows for continuous flow of data and measurement of customer behaviour,product assortment, stock outs, in-store displays and environments such that assuminganything static and in-mobile is at best an approximation. 4. Location: Theability of a plethora of contemporary methods that use GPS, to find out and usethe geographic location of the customer at any given point in time has openedup a whole new avenue for retailers. The customer’s geo-spatial location has aprofound impact on the effectiveness of marketing by changing what offers tomake, determining at what marketing depth to make an offer, to name a few. 5. Channel: Thecollection, integration and analysis of omni-channel data helps retailers inseveral ways: (i) understanding, tracking and mapping the customer journeyacross touch-points from decision making to purchasing (ii) evaluating profitimpact and customer lifetime value (iii) better allocation of marketingbudgets.Itis all but certain that the steady increase in consumers’ online purchaseintent will fuel future revenue growth across all B2B and B2C transactionalE-commerce sectors from retail through financial services to travel and more.
Analyzing the piles of information available in retail domain and devisingdigital marketing strategies that are customized to target customer can boostretail 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 marketingpractices influence modern day retail. The aimof retailers is to gain a competitive advantage by providing high qualityservice for customers and improving their customer base. The current conventionto achieve this is by scaling up the digital marketing practices and by theapplication of information technology. The development of information systemand digital marketing campaigns should be focused on the analysis of customer behaviour.
Sha & Guo-Liang (2012) found that most digital marketing campaigns find itdifficult to target the right customers. They adopted a method of case study tounderstand how to use innovative digital marketing practices in order toimprove retail service quality. They concluded that a digital marketing systemcan indeed effectively obtain information regarding customer behaviour andeffectively apply it to service clients in retail stores, and come up with a clearstrategy and direction for further implementation for digital marketing forretention.The retail industry is continuously affected by advances indigital technologies. On the one hand, consumers expect to find technology-equippedretail environments, on the other, retailers achieve advantages through the useof new tools for market expansion and research.
Pantano, Priporas, Sorace,& Iazzolino (2017) attempted to reach a clearer understanding of the impactof innovative forces in modern retail sector. Innovation trends in the sectorwere evaluated by analysing trends in the current retail market. The insights theygained give an overview in certain areas, again helping with the prediction offuture trends, and developing long-term strategies for digital marketing inretail. Bradlowet al. (2016) have highlighted the obvious ethical and privacy concerns thatcan arise from the use of big data for predictive and descriptive analysis inretailing. This can create a “boomerang effect” where the customer might end upfeeling ambushed due all the “hyper-localized” targeting being offered byretailers.
Additionally, self-regulation is required for firms that make use ofbig data, in order to avoid potential legal repercussions. Ecommerce’sinfluence on modern retail has taken several turns over the years. Some striking findings from research papersare as follows: · Rate of growth of online sales has slightly decelerated,but is still significantly high· The online sales growth rate for publicdepartment store chains declined from 39.3% in 2012 to 18.6% in 2015, while theonline sales growth rate for public specialty stores declined from 17.5% in2012 to 9% in 2016· E-commerce volumes are not sufficiently highto justify store closures – traditional retail still needs to have a foothold· Price-matching should not be a “one size fitsall” approach – analytical tools are available to help with segmentation ofcustomer price groups