Challenges in Leveraging Big Data Analytics for Digital Marketing
Internet and its associated technology are opening new avenues of businesses everyday. Given the massive growth and its increased importance, companies cannot risk themselves by not having a game plan and strategy in internet, digital marketing and big data domains. But many have yet to reap the ditgital marketing benefits that “big data” has yet to provide. Big data analytics in layman’s term is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.
Companies operating in servicing the customers are seeing internet, social media, big data and mobility as new doors to harness business opportunities. It has become a tool for companies to interact with customers. The forum provided by the Internet lets companies market to customers, sell their products, build brand relationships, and ultimately sell more products. One direct consequence of the Internet's transformation into a key communications and selling platform for companies is the rapid growth in digital marketing. Initially limited to search engine enhancements and banner advertisements, digital marketing now takes the form of rich media display ads, targeted e-mails, YouTube video clips, Facebook content and other campaigns. The second reason for the Internet's increasing importance to companies is its unmatched role as a gold mine of customer intelligence. Consumers spend hours every day on the Internet and leave behind large amount of information about who they are and what they seek. This opens the door to the usage of “big data” and advanced analytics.
While industries continue to collect all of this online information, so far, very few have been able to crack the “big data” code. The volume and veracity challenge is clutching down organisations, this is not enabling companies to generate the super-targeted communications to consumers they seek. One of the fundamental challenges is a structural flaw. In many organisations Digital marketing is being run by conventional marketing team. These marketing teams are not proficient in technologies in Big data, Analytics and Digital marketing topics. Their knowledge remains confined to bookish acquired knowledge. They are heavily dependent on the IT vendors and have to dance to the tune of Digital marketing vendors resulting in ROI going for a top spin.
Handling unstructured data and extracting/transforming it into structured data relevant to Digital marketing is the Key. Many struggle due to lack of technical competency and technical tools. Correct story and meaningful insights due to effective big data analytics enhance the decision making process in Digital marketing. It helps to decide a target and structure a marketing campaign.
Profiling online users is an effort to evolve digital marketing. This step takes companies towards unlocking “big data's” potential. This is not done adequately and often becomes a challenge. There is a correlation between ‘Internet user profile characteristics’ and ‘sales conversation rate’. The best way to capitalize on the correlation involves focusing the digital advertising campaign on the customer segments with the highest conversion rates. Team should ensure that the campaign reached a significant number of people. Such targeted marketing campaigns would be more fruitful than an average “random” campaign.
Looking further, another challenge is the adaptation of a’ company's online initiatives’. This needs tobe based on target Internet user profiles. Organisations need to take stock of the wealth of online consumer information that is readily available and develop highly targeted advertising campaigns to boost their marketing efficiency. In addition to profiling online users, “big data” can also be leveraged in a very pragmatic and operational way. Based on my experience, I suggest following four prong strategy to overcome this challenge.
1.Profile users. Take a large sample of Web users (e.g., several million) and collect their Web histories, using cookies or other forms of anonymous tracking. Then analyse the profiles of these users based on behavioural criteria. This profiling relies on algorithms and on semantic analyses of user Web histories.
2.Tailored advertising. Build a digital campaign that focuses on the customer segments they have identified as being the most likely to buy the considered product. Then, these segment-specific ads are pushed only to those segments for whom they were created.
3.Link to products. For a specific product, analyse consumer purchasing behaviours to identify correlations between the product and the profile characteristics of the Web users. Marketers should base the purchasing behaviour analysis on sales conversion rates, i.e., the percentage of people who actually purchase the product compared to the total audience who received the related display advertising.
4.Integrate algorithm. Finally, the company must integrate the algorithm into its digital advertising management IT tools. Once the algorithm is embedded within the various ad servers, the targeted campaigning becomes a part of the organization's day-to-day processes, focusing only and automatically on the Web users with the highest potential to convert.
A refined approach to digital marketing in the hands of technically competent IT team is the need of the hour.