ByÂ RahulÂ Muralidharan:
The world is being deluged in a massive downpour of data. ‘Big data’ is the latest buzz word doing the circles. Big data refers to voluminous amounts of data that cannot be processed through traditional techniques. This data ranges from a few terabytes to many petabytes. Making sense of this big data and using it to drive business growth is an uphill task. The concept of using previous consumer data to predict consumer behaviour and sales has been prevalent from quite some time.
In the 90s, the point of sale scanners was to gather consumer insights and influence pricing. With the proliferation of the Internet, a humongous volume of consumer data is waiting to be processed to generate valuable nuggets of information. Marketing today, is faced with both the challenge as well as the opportunity to utilize big data to revolutionize the relationship between the customer and the business. A survey conducted by Teradata revealed that 71% of the marketers plan to design and implement a big data analytics solution in the next 2 years. Efficient use of big data analytics would ensure the success of data driven marketing. Big data helps marketers in several ways. It helps not only in retaining the existing customers but also to tap into new customers. Analyzing trends in big data can lead to more marketing opportunities and measure the effectiveness of advertising campaigns. They can micro target the customers with personalized products, campaigns and offers. Outlined below are 7 principles that marketers should keep in mind to ensure effectiveness while adopting a big data strategy.
1. ‘Strategy is Alohomora’: The key idea is to understand how big data insights can support the marketing strategy of the company. It needs to be aligned with the company’s vision and goals. This reduces complexity as data is now in the context of a specific strategy. For example, if Amazon wants to improve its website experience, it needs to look at the relevant data.
2. ‘Divide and Conquer’: Marketers must understand it is impossible to use all the data available. They need to profile the data, filter it into good and bad. After this, they need to divide and analyze the data according to their objectives for efficient results. For instance, if Shoppers Stop wants to launch loyalty and promotional offers, it must filter and study the consumer behaviour in similar scenarios, particularly that of the First Citizen Card Holders to effectively launch these offers.
3. ‘The pricey fallacy’: The advent of big data has made pricing quite transparent and competitive. Starting from Flipkart to MakeMyTrip, there is a growing trend to offer the lowest prices. Marketers must avoid this temptation and focus on enhancing the brand equity so that pricing can be in accordance to the quality of the products.
4. ‘United we stand’: Using big data strategically involves a cross functional collaboration. The 2012 BRITE-NYAMA Marketing in Transition Study reveals that nearly 50 % of the marketers acknowledge that a lack of sharing customer data within the organization acts as a barrier to effective marketing campaigns using big data. Marketing has to work in tandem with all departments especially with IT to ensure its campaign success.
5. ‘Cook the Cookie’: A common mistake perpetuated by most marketers is to place excessive reliance on ad cookies. Cookies serve as an effective tool to identify the target audience. For instance if your web search increasingly revolved around CAT coaching, your Gmail ad suggestions would pertain to CAT coaching centres and study packages. That is due to the ad cookies. However ad cookies suffer from two major flaws. Firstly, most customers remove cookies periodically, and secondly, on a multi user system, the identification of the target audience might be wrong. The solution lies in using in-flight campaign information so that the data is real time and accurate. Marketers should cook the cookie before using it.
6. ‘See the tree’: A potential problem that marketers face is that of selection bias. Sometimes they choose non-random samples of data from the data pool that leads to mistaken inferences. This is described as ‘seeing the forest and not the tree’. This percolates down the various layers of strategy and hinders the end goal of the marketing strategy.
7. ‘Be a Soothsayer’: Marketers should use predictive analysis to determine the customer behaviour and future buying preferences. Amazon has successfully used predictive data analysis and recommendation algorithms to suggest products to customers. This not only enhances the user experience but also helps generate more sales.