AI is no longer just a Hollywood vision of the world in 2050. Machine learning (ML), natural language processing (NLP) and robotics are already all around us, impacting our daily lives in unnoticeable ways, and transforming organisations’ ability to make smarter decisions.
The applications for these technologies are far-reaching. AI has been applied by Google and Facebook to counter growing concerns among marketers about sophisticated frauds perpetrated against their brands and potential association with undesirable content.
A notable example of how AI is being applied relates to more effective detection and removal efforts of violent and extremist content. In 2017, in response to rising awareness and frequency of offensive content, YouTube announced that they had succeeded in removing over 75 percent of a month’s worth of offensive content before it was flagged by a human.
This was only possible through increasing investment and focus on the application of AI, and ML/NLP specifically. This example shows how brand marketers, agencies and technology providers can leverage the power of AI and data to “engineer an advantage.” Luckily, a host of cost-effective, cloud-based solutions for storing massive datasets have emerged with the proliferation of data in organisations. Most importantly, even the non-tech savvy have access to better-automated technologies and simpler methods of data processing than ever before.
Making Decisions Enabled By Data A Top Priority
Regardless of industry or business type, using data to make better decisions, and automating this process through the use of technology, are top priorities for most companies today. In a study conducted by Altimeter Group on the state of digital marketing in 2019, 50 percent of respondents prioritised “investing in technology that enables real-time delivery and personalization of data” above all other initiatives, and 41 percent viewed integrating “multiple software systems to share customer data” as the most important initiative they would pursue over the next 12 months.
This shows that brand marketers are focused on bringing together all of their customer data residing in different areas of the business. A better-structured repository of customer and first-party data will enable them to deliver customised content in real-time, enhancing customer experiences and extracting more value from their marketing and advertising investments. As the importance of data becomes clearer for marketers, their next question is: How should I start this journey?
Adobe and Econsultancy research uncovered that starting this integration journey may not be as simple as it seems. Marketers were asked about the biggest challenge they were facing last year and 44 percent pointed to difficulty getting a holistic view of customers across all interactions. Additionally, 33 percent cited a lack of marketing technology integration as a key barrier to achieving this holistic view – which is essential to better marketing decisions.
The first step should be a comprehensive five-step marketing technology stack review. The review starts with making a plan. First, we conduct an audit of existing technology and perform discovery of essential data needed for marketing decision making. Next, an assessment of alternative marketing technology and data infrastructure needed to fill potential discovered gaps is completed. Finally, the marketing team develops a recommendation and action plan as to what the ideal marketing technology stack should look like, in order to make better marketing decisions.
Get An Edge With Algorithmic Design-thinking
In a survey of global marketers about digital media spending, including professionals in China, India and Singapore, Xaxis found that 71 percent agreed it has become more difficult to evaluate the effectiveness of digital media investments in recent years, and 81 percent said that it was essential for digital campaigns to have a direct correlation with business results. Instead, they are reliant on simple media metrics like CTR or CPM as indicators of media performance and marketing value. This is concerning because of the limitations of these metrics to measure and evaluate the true effectiveness of digital media, which then makes it difficult to translate into business outcomes.
When these metrics are being used to fuel the algorithmic features of today’s advertising technology, the resulting decisions about digital media investment are subpar and do not align with business goals.
