Predictive Analytics:Challenges and Opportunities

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Analytics adoption can be implemented at various levels, including tracking and preventing errors, data integration, predictive modeling and personalized modeling. While there has been significant progress from a data mining and research perspective, challenges and opportunities remain. This report seeks to throw light on how automation and AI have changed analytics in marketing. Here are some noteworthy points.

  • Understanding Where Automation Delivers Real Results

60% of respondents stated that their analytics solutions produced insights based on data without analyst involvement. A further 80% of those reflected upon the time saved as a result. Hence the analysts could be redeployed elsewhere to focus on trickier tasks or the insights can be used to find opportunities elsewhere. Automation and machine learning shall be critical for the sort of thinking that requires many calculations done quickly and in a predictable way.

  • After Automation

Time has always been the most precious resource of a business. Streamlining the marketing and analytics functions has always been hugely valuable. But is this exercise driving out innovation and originality? With so much determined by machines and algorithms, do brands risk losing the essence of innovation that created them?

  • Dealing With The Routine And Complex

Automation means different things to different people. For organizations that operate on defined processes and rules-driven decision making, automation saves a lot of time. This is because a trickle down effect of automation is created allowing other systems to take appropriate action without human intervention. In real time environments such as programmatic advertising, the automated processing of information into insights along with preferred actions is essential to realizing opportunities before they pass by.

  • Can Creativity Be Automated

The advertising community is closely looking at the question of whether automation and machine learning can actually create ad executions. Till now it supplied humans the insight with which to build their creations. So, the idea that AI would be integral to developing creative, is it still a pipe-dream? Maybe not. Apart from some grunt work or speedy number crunching to get the the right ads to audiences in real time, does automation have anything to contribute to the innovative side of marketing. In a discipline that is described as a marriage of art and science, can science replace art?

  • Automation And The Limitations

Although there is a sense that analytics will never fully be automated, the feeling remains that strong marketing is the smart marriage of art and science in today’s data based environment. Humans still have an advantage over computers, similar to that which comes from intuition and synthesized recognition. But the biggest problem with automated analytics may be human in origin, and is associated with scenario planning. Given the information around any scenario, a computer can effortlessly come up with the relevant insights. It’s just that humans cannot prepare the machines to anticipate every possible scenario. Humans lack the number crunching abilities of computers, but benefit from years of programming that contributes to strategic success.

  • Machine-based Innovation And The Pitfalls

With regards to speed and efficiency of automation, marketers may be in danger, but relying 100% on computerized data outputs to inform future direction is dangerous. Some executives interviewed warned that an over-reliance on data to substantiate decision-making was hindering innovation.

  • Paralysis By Analysis

Automation builds a data-driven culture because it allows for quick reporting, complete analysis and optimization of existing channels. This use of data however, can slowly suffocate innovative organizations. An official states “A company may be innovative and forward thinking, and surely grows on the back of that success. But when you’re a big player, you have to take care of the day-to-day responsibilities: make sure the direction is right , guarantee growth and so on. As a result, the ideation process is left behind.” It is clear that the use of automation as a solution to an existing problem rather than a solution for anything not happening right may seem faster, and the results more tangible, but automation is not going to deliver on all aspects. “Companies have many people analyzing reports trying to identify tactical performance gaps and opportunities so they make predictable adjustments. As a result, companies hire a lot of people who like trying to think like a computer. If that can all be automated, the goal should be to recruit different types of thinkers” the official added.

  • Automation Should Be Omni-Channel

Immediate challenges for developers of analytics automation is in creating solutions that move into an omni-channel environment. Out-of-the-box solutions are limited in their scope of operations. Designed to optimize one channel or only one page at a time, out-of-box solutions move towards multi-channel optimization. Automation is to be optimized in a way that allows a brand to maintain a consistent omni-channel experience across all channels and even extends to customer service and interactions.

  • Understand Questions Before Answering

Executives have repeatedly reinforced the old marketing adage of ‘garbage in, garbage out’. Automation in analytics will only be as good as the premise it is set up to work toward. Marketers must understand its limitations and potential to fully benefit. For some, it is a matter of plug and play to speed up number crunching and use resources elsewhere. Other organizations may find that incorporating automation in their analytics process requires a overhaul of departments and the HR function. In some cases the whole culture of the company could change.

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