How Automated Machine Learning helped reshape the future of Marketing
Posted by Ramla Jarrar on March 26, 2019
In the midst of the Digital marketing era, it is crucial for marketers to evaluate their marketing performance. The marriage between marketing and data science provided a whole range of possibilities for decision-makers. However, the information age is swift and dynamic. Trends have shorter lifespans than ever before. Media channels explosion made it harder to track the different paths to purchases characterised by a multitude of customer touchpoints.
Nowadays, technology is at a peak and the lives of everyday individuals are getting busier by the day. It is known for a fact today that the average attention span may not exceed 10 seconds at best. And this makes the marketers mission even harder. Not only it is a matter of choosing the right content and campaign but also the message has to empower each target’s individuality and “speak to their soul”. Machine learning inclusion in marketing provided a short-term solution and gave birth to a plethora of unexplored possibilities. Following this stream of thought, we are led to question the effectiveness of machine learning at understanding and possibly predicting individual patterns of behaviour, as well as the presence of a better solution for marketers in the near future.
Automated Machine Learning as the missing ingredient:
Being ahead of its time is now a marketer’s must have competitive advantage. Statistical data science models granted predictive models that proved to be accurate for the most part. The conflict that arises at this stage is that these models are formulaic, and thus cannot take into account the unpredictability of the human being. This is where machine learning enters the stage. Machine learning adds another layer to the mix as it is mutually programmed by human minds and learns through human behaviour.
At the beginning of the 21st century, primitive artificial intelligence algorithms took very long to program because of the technological limits and the recency of the concept. This caused many AI programs to become obsolete before they are even ready for implementation.
After the introduction of Automated Machine Learning (AML) in the 2010s, processes escalated to the next level. Marketers can now use their machines to be provided with tailored “what if” sales scenarios and constantly changing models with over 80% accuracy rate. The distinction can now be made between simple attribution models and machine learning models.
The Many Uses of Automated Machine Learning:
- Enhanced Inventory management: lead management consultants McKinsey & Co predict that 45% of jobs will be automated in the next 20 years. Marketing is just one area which has already been disrupted by artificial intelligence. From airlines to small local businesses, machines can now define relevant competitive prices for every product range as well as predict seasonal changes and upcoming trends. This in turn allows for more effective waste management and accurate demand prediction, and this contributes to better resource allocation.
- A faster, more effective marketing pace: IBM Watson's artificial intelligence product for instance is already enabling small marketing teams to create detailed marketing campaigns in less than a day instead of the usual weeks or months. And that allows companies to start investing more in creative skills and funnel team efforts into coming up with state-of-the-art ways to make their products more distinguishable.
- Redirect marketing campaigns: AML allows for o-ptimised marketing mix by enabling marketers to monitor which sales promotions, incentives, and campaigns have reached which prospects using which channels. An optimisation engine that uses automated machine learning continually tries to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. The recommendation feature of Amazon is an example of how their e-commerce site is using machine learning to keep the customer buying and predict their needs.
- Lead scoring: AML helps trace back initial marketing campaigns and sales strategies. And that’s through using relevant data from the web procured by user activity. Predictive models by AML can better predict ideal customer profiles and behaviour. Each lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritise time, sales efforts and selling strategies.
- Reduce customer churn: AML streamlines intervention models and risk prediction to reduce customer churn. Traditional approaches are simultaneously time-consuming and cost-ineffective, companies in high-churn industries such as telecommunication companies are turning to automated machine learning. An intervention model helps marketers witness how the level of intervention could affect the amount of customer lifetime value (CLV) and the probability of churn.
- Enhanced customer experience: using AML learning chatbots and customer experience automation still looks as a futuristic idea for many users. Automated Chatbots are 24/7 available, easy to set up and incrementally learn to cope with customers. Chatbots are also able to deal simultaneously with hundreds of requests and to resolve issues in the sooner of delays.
- Personalised Advertising: By 2020, the combined effect of marketing technology improvements namely real-time personalised advertising across digital platforms and optimised message targeting accuracy, context and precision will increase both product and service sales effectiveness and B2C-based channels. Qualified Lead generation will also increase, potentially reducing sales cycles and increasing win rates.
AML allow for faster predictions by automating many of the processes applied by data scientists. Significantly alleviating the needed work. AML looks into historical data to find patterns and combines the different touchpoints to measure their impact.
Through the emergence of AML, the sales funnel became clearer. Marketers now allocate their budgets more efficiently and garner better sales than ever before. Marketing Attribution shows you what channels are driving sales and which are not.
With the second decade of the 21st century approaching, the marketing industry is presented with a multitude of choices for where it can keep advancing. Although many speculations are presented daily, the roadmap remains blurry. How would Automated Machine Learning further affect the future of marketing? Would smart data science provide us with hybrid alternatives for traditional marketing techniques?
MASS Analytics' Team