The rapid development and deployment of cheaper, and more intuitive Artificial Intelligence is revolutionising fields as diverse as medicine and manufacturing — and, yes, even marketing.
For years now, Marketers have relied upon online tools to streamline every step of the customer journey: from engagement to conversion. These appliances evolved in conjunction with increasingly sophisticated marketing stacks, to organise the flow and use of these tools, and optimise the entire process as efficiently as possible.
As most marketers know, building the optimal B2B marketing stack requires both a broader and deeper knowledge of the technologies available, and the varying benefits, niche advantages, drawbacks, and costs attached.
Now, this trusted arsenal of tools must integrate a new technology.
AI has evolved to the point where it is not only possible but also increasingly cost effective to automate tasks, and make decisions traditionally carried out by humans, with ever-greater efficiency and efficacy.
If data-driven marketing represented a fundamental step change, the advent of machine learning represents the next great leap. Potentially the most impactful and profound transition in the field since the emergence of online marketing itself.
Just as data analysis has become crucial and indispensable to everything from lead generation to customer relationship management, so too will the sophisticated insights and strategies drawn from AI.
Watson, from IBM is one such AI system. In December of last year, IBM announced that, after demonstrating its extraordinary advantage in fields as varied as research, finance, and education (not to mention the niche of game shows, where Watson first made its name), IBM was training its sights on marketing.
To demonstrate Watson’s marketing prowess, IBM presented an example where Watson attempted to comprehend the underperforming sales expectations of a well-known retailer. In short, the kind of deep dive analysis – connecting seemingly disparate data sets – typically performed by experienced human minds. Of course, this is precisely the sort of task for which Watson was developed.
In the case study, Watson realised that mobile traffic was high for the retailer, but that it was concentrated in only a few locations. This insight allowed the company to identify which regions were underperforming and respond accordingly – with discounted pricing, new incentives, or a more precise sales strategy targeted to specific areas.
Perhaps the greatest marketing advantage conveyed by AI is that it makes greater use of something marketers have known for decades – long before data-driven tech changed the game – but still struggle to realise to this day, namely: there is no such thing as a one-size-fits-all approach. Sophisticated machine learning can achieve personalised strategies, which are both nuanced and accurate, and that better target, convert and maintain buyers.
And here’s the rub. Human beings no longer have the monopoly on the “human touch.” That is to say advanced algorithms possess the ability to take a holistic view of the customer’s journey, to provide anticipatory customer service, based on predictable needs, and to adjust to answer a buyer’s unique challenges and needs.
AI systems not only absorb and digest reams of data about customer interactions at every point of contact – and at speed, but return agile and intuitive personalisation that once applied at every stage of the marketing cycle, when the pace of interaction was more humane.
AI may be more artificial than the human operative, but it is far more intelligent. It can already deliver actionable insights the moment potential customers are ready to move from prospect to a buyer. It can track real time marketing and assess the most effective strategies. And display the predictive powers traditionally held by humans, with increasing precision.
No longer the stuff of science fiction, AI has reached the point where it can realise cost-effective and accessible solutions for any marketing strategy.