Ask 5 people what Artificial Intelligence (AI) is and you will most likely get 5 different answers. Demis Hassabis, the British AI researcher and neuroscientist calls it the science of making machines smart. Simply defined, AI is a set of technologies that helps pull out patterns, trends and insights from huge amounts of data and uses these to make predictions. If you’re thinking that sounds a lot like the definition of predictive analytics, you’d be right! Machine learning (ML), a type of AI most relevant to marketing and analytics practitioners – and which powers some of AI’s most impressive capabilities – uses patterns in data to make predictions, and then uses more and more data to improve those predictions over time.
Predictive analytics as used in marketing, uses historical data and the assumption that the future will follow the same patterns to predict what might happen. However it requires human intervention and interaction to query data, validate patterns, create assumptions and then test them. ML is different and goes exponentially further in that it is able to make assumptions, test them and learn autonomously and continuously without human interaction at a speed, scale and depth of detail that is impossible for human analysts to match.
It is intuitive then, that AI and ML technologies can play a significant role in boosting the effectiveness of marketing analytics. They can help create more robust models, extract more value and insights from available data, combine insights from multiple data sets, and make much more accurate predictions about marketing outcomes.
Broadly, AI can be used to automate and improve analytics processes that are not just time-consuming but also prone to human oversight or error. Here are some examples.
Improving Data Integrity for Modelling & Analytics
Ensuring that the raw data ingested into an analytics system is correct and error-free is critical to maintain the integrity of insights generated from it. Many analytics solutions still rely on manual data ingestion. While automating data ingestion is a first step, ML technologies can be leveraged along with this to significantly help with data quality control by identifying incorrect data and capturing outliers.
Simplifying, Accelerating & Improving the Modelling Process
The modelling process in marketing analytics requires at least some degree of data science expertise, domain knowledge and training. The iterative nature of modelling also makes the process time consuming, with outputs varying based on the expertise of the modeller. AI and ML can be used to automate several steps in the modelling process. Combining AI search techniques, statistical ML techniques and Deep Neural Networks can help significantly reduce modelling time, offer do-it-yourself (DIY) options to clients and ensure uniform and consistent results irrespective of users’ expertise levels. In short, it can make the process faster, better and cheaper.
Benchmarking for Models and Predicted Results
Marketing analytics is about measuring and predicting the influence of specific marketing activities on KPIs such as sales, revenues or brand awareness Over time, analytics yields benchmarks on how much various marketing programs like digital marketing, social media advertising, discounts, offers or promotions have impacted KPIs in different industries, verticals, regions, seasons or times of the year and more. AI can be leveraged to parse huge sets of past campaign data at scale and depth. Comparing this with predictive models for planned activities helps determine whether the results are within acceptable benchmarks, or if the models need to be tweaked. This saves an enormous amount of time and trial-and-error iterations for analysts and marketers.
Real-Time Tracking & Optimization of Campaigns
Typically it takes data collected over at least a year or two to analyse of how different marketing programs or campaigns are influencing sales. But AI technologies like Deep Neural Networks and Deep Learning can read vast amounts of data on a weekly or monthly basis and discern patterns that help to explain the factors that are impacting sales or other KPIs. Being able to track performance in near real-time at this level of granularity also makes it possible to optimize programs and campaigns on-the-fly for maximum impact. This is especially useful for digital marketing and advertising where real-time optimizations based on AI insights can deliver immediate performance improvements and sharper results.
Customising Reporting and Insights Generation
Another area where AI has a role to play is reporting and insight generation. Technologies such as Natural Language Processing and Natural Language Generation can help significantly reduce time and effort and improve effectiveness of reports and insights for clients. For example, different stakeholders have different preferences in terms of reporting format and content. Also, the “so what?” from marketing analytics results is dependent on the consultant and business user. AI and ML can help auto-generate reports for different client users based on preferences and past usage. Recommendation engines can also use a prescriptive dashboard to automate the “so what?” and guide decision making accordingly.
These are just a few ‘low-hanging’ examples of how analytics firms can embrace the potential of AI. Analytic Edge is already incorporating AI and ML technologies into various aspects of its solutions to provide faster, smarter and better results for its clients. As the business and marketing landscape becomes more complex and dynamic, and the amount of data available to marketers continues to increase, AI will have a greater role than ever to play in making sense of the data and extracting genuinely valuable insights and predictions from it.
