Big data collected from customer behaviour from all strata of the Internet has added valuable information to improve sales force and customer services. With the analytical power of Machine Learning, the mass of data can be transformed into thin causalities. For example, the Artificial Intelligence algorithms are able to review the complexity of Big Data and simplify the client’s information through accurate analysis of the buying journey.
Artificial Intelligence algorithms disturbing insight
Since its first public use in the late 1990s, Machine Learning continues to be discussed. AlphaGo, a computer program developed by Google DeepMind in London to play the board game Go, represents one of the most notable examples of deep learning; that is to say a machine now is able to independently analyze the amounts of data with extremely high performance.
If such a technological feat is exceptional, users are daily experience of small Machine Learning without knowing it. For example, when surfing the Amazon, LinkedIn, Spotify or Netflix, you will see these platforms automatically offer suggestions for you, and those suggestions are from the analysis results from your surfing behaviours.
Customer satisfaction – the Holy Grail to reach
Applied to digital marketing, the ambition of Machine Learning is primarily to facilitate the client’s life. For example, by helping a customer choose between several means of transportation or by offering the product or service that the customer most likely needs. To achieve this grail, the Machine Learning tools enable sales, marketing and customer service to better understand the customer journey.
Machine Learning also intends to promote the act of purchase. The solutions significantly increase the turnover of a merchant website and the conversion rate to personalized e-mail campaigns.
Big Data to Machine Learning
Deep learning allows discovering that the purchase intention may be correlated with action at the precise moment in the customer journey. With Machine Learning, we can target with a precision that is beyond human understanding. Deep mining opens up a huge potential for digital marketing to increase sales additional and processing leads, ensure the best possible customer and stem the loss of fidelity service.
Machine Learning – better tracking of customer experience
According to the survey on the state of the art of marketing published in 2016 by Salesforce, customer engagement is a top priority. 88% of the best performing marketing teams, the development of a customer experience management strategy is indeed seen as essential. Customer satisfaction stands out as the main criterion of success, even outperforming revenue growth and new customer acquisition. In this context, marketing email, mobile and social media offer become increasingly important for all marketing professionals. Machine Learning is thus emerging as an important ally.
From an operational point of view, most of the Machine Learning applications used today in digital marketing are subject to a pre-learning phase. For example, a large amount of data is processed at the upstream of the algorithm development to better guide research and more easily automate responses that will be offered to users. The objective remains to achieve are to deal with a clever combination between human intelligence and artificial intelligence. Machine Learning has already enabled a leap forward by using the ultra-segmentation profiles to refine tracking of customer journey.
Data sharing – the sinews of war
To support Machine Learning applications with top quality data, companies usually use the available information they collect through cookies, geo-location, social networking or loyalty programs (which typically collect data on age, location, purchase history …).
Contrary to popular belief, consumers are more inclined to share their data, but not at any price. This is demonstrated by the study “What is the future of data sharing” conducted in 2015 by the Center on Global from 8,000 people in five countries – US, Canada, UK, France, and India, to investigate the consumer mindsets for data-sharing and the power of brands. Following are the key findings from the study.
- Consumers understand which data are most sensitive, but they are willing to share it with companies in exchange for a product or service they value.
- Brand trust positively impacts consumers’ willingness to share data.
- Traditional offers and new, data-enabled benefits can be used strategically to influence people to share various types of data.