“Data is the new oil!” – Clive Humby, dunnhumby
Behind the stage rhetoric, street campaigns and public debates; a team of around 100 analytics was assimilating voter preferences, analyzing voters’ registration data and online habits and were thus tracking potential voters thereafter following-up with events targeted at them. This was the 2012 US presidential election scene, wherein the Obama campaign ran on sophisticated data platform coded “Narwhal” that demonstrated utmost effectiveness in terms of raising funds and targeting the right voters (micro-targeting). The voters were not attributed to merely the geographic, demographic, gender categories. They were assessed as individual citizens demonstrating personal traits, preferences that could be banked upon by the presidential candidate to design relevant campaigns and direct them towards these citizens. Obama did just that and what the world witnessed was the impact of Big Data and analytics on US presidential election: A President was sold to the masses through targeted appeals.
The above serves as one of the many applications of Big Data and the corresponding analytics. The financial services industry has investment banks analyzing traditional databases for predictive analysis and gathering further insights on financial trading. The automotive industry has examples like Ford’s modern hybrid Fusion model that generates up to 25 GB of data per hour to analyze driving behavior, maintenance issues and to prevent accidents through its “smart intelligence”. Entertainment companies are using it to track customer trends in terms of their shifting preferences, engagements which guide them to deliver marketing, reputation-management programs. The video-gaming industry has been booming on massive data analysis as it helps in better designing of games that lead to gamer-addiction and retention through smart tracking of gamer performance and stickiness factors associated with it. Thus, all businesses across the world seem to be engaged in the act of conversion of the data-generated insights to commerce/revenue.
Also, the art of marketing has been suffused with a certain attribute namely “Marketing intelligence” that has been driving organizations towards an era of ad-hoc prices, products, promotions and advertisements. Mindless push strategies from brands have been overpowered by stronger pull strategies with their undercurrents being the insights generated from analysis of Big Data. An increase in the number of alternate channels of promotion and communication has shaped the contours of a marketing landscape that is tending towards behavioral targeting and in effect, targeted marketing.
A brilliant example of targeted marketing through Big Data analysis is: loyalty programs in retail. Research from Center for Retail Management at Northwestern University, US reveals that 12%-15% of customers are loyal to a single retailer and this small percentage delivers 55%-70% of revenues. This statistic compels retailers to direct their strategies towards more efficient stocking, planogram designing and deriving patterns from the large customer data in order to decide over geographical expansion. UK supermarket chain Waitrose uses its loyalty program data along with Visa card payment transactions to recognize the customer potential in certain geographies and thus strategically expand through new store locations. The traditional retail concept of driving retail store purchases has been replaced by insightful pattern derivation after the onset of digital technology and card transactions. There seems to be a switch from increasing market share of a particular product to attracting more customers per product. Earlier, if a particular SKU was seen as one not worth stocking due to poor sales, Big Data analytics now compel the retail chains to stock them after providing trends of loyalty attached to that SKU in that retail store. Thus, sacrificing shelf space so as to retain loyal customers is not a loss-making endeavor anymore and it would not have been practiced without the discovery of such an insight through Big Data analytics. And given that 80% of the 2.7 billion gigabytes of data produced per day globally is unstructured and a major portion of it rendered untapped, Big Data analytics eases the examination process of such volumes and reveals such interesting trends, correlations and impacts marketing in new ways as we see today.
“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
E-commerce websites have been shining through in the marketing landscape of opportunities owing to the avenues of advertising offered by the mining of Big Data. Google and Facebook use marketing tools carefully personalized and customized so as to target the right consumers for the websites and other companies.
The algorithms cross check these data warehouses’ data with companies’ data and through mapping algorithms, design advertisements in accordance with the specific target customers. A plethora of user presence on social websites like Facebook and voluntary sharing of information has also led travel marketing to the brink of a highly customized and personalized level deal delivery through effective gauging of customer preferences and linking the results of online travel agencies with hotels.
But given the nebulous attention it has received recently, Big Data can be stated to be a double-edged sword. Marketers might gradually shift to a short-term focus strategy of driving sales through “pay-per click advertising” which is majorly credited for the sale. Erosion of brand equity might take place due to such narrow thinking and the possibility of opportunity loss of other customer segments. Also, customers might be mentally “trained” to buy on the basis of price discounts leading to dissolution of brand loyalty. Societal impacts like individual privacy breach, ethical considerations regarding use of consumer data stamp a question on the limits of Big Data analytics. For example, Instagram’s user policy states that it might use photos for the purpose of other businesses that will pay Instagram without any compensation to its users. While in some cases where individuals respect privacy breach (customers asking Tesco for help in choosing healthy options thus providing it the incentive to use consumer data to track purchasing trends), Target’s “pregnancy-prediction” model provides a glimpse of how unsettling such a breach could be.
Broadly, Big Data offers convenience in terms of technical and strategic capabilities for organizations to derive value from the data they store. While organizations and customer can derive commercial and value benefits through it, it is imperative to debate important ethical questions related to security and consumer privacy and rally through the digital era with superior capabilities.