by Mustafa Savas
SAN FRANCISCO – There’s no doubt that the more businesses know about their customers, the better an experience they can create. However, traditionally, gaining these insights is labor-intensive and lengthy for analysts.
It’s costly for major corporations and even more so for small businesses. So, it’s no surprise that many companies have started to deploy artificial intelligence (AI) to ensure that their customers’ experience is informed and convenient at any given point along the journey. In fact, Forbes recently reported that 75 percent of enterprises using AI and machine learning can enhance customer satisfaction by more than 10 percent.
AI-powered data analytics can give companies seriously useful insights in a fraction of the time that it would take a human analyst, all the while eliminating human bias and error. That’s not to say that data analyst jobs are going to become obsolete; rather, that their role in connecting the data with the real world will become even more important and nuanced.
Let’s take a look at how AI-powered data analytics can help drive business results, while revolutionizing the role of the data analyst.
Artificial or human intelligence?
The benefits of AI over human intelligence have been debated endlessly as automation creeps in to more and more workplaces across countless industries. Whether it’s retail, HR, or customer service – it’s almost guaranteed that at least some part of a business is either already enhancing their capabilities with AI, or planning to very soon.
In fact, Gartner recently reported that AI-derived business value will grow to $3.9 trillion by 2022. This is no different in the field of data analytics, with more than 40 percent of data science tasks expected to be automated by 2020.
However, one of the most important benefits that AI has over human employees (that’s often not as lauded as other advantages) is that the expertise gained by AI is not lost like it is when an employee leaves a company. This is crucial in the field of data analytics, where such understanding of historical patterns and the way datasets interact with each other is vital for company success.
A human data analyst will learn these important skills as they develop in the role – but there’s no guarantee they’ll stay with the company to enhance it with their know-how years down the line. However, employing AI to conduct data analysis means the technology – and the skills and knowledge it learns – will always be there.
In addition to this, AI that can analyse data eliminates the probability of human error and bias. Often humans subconsciously look for the results that they want, resulting in an unfair assessment of the true data landscape, and increasing the possibility of skewed insights.
Finally, perhaps the most obvious of the benefits AI holds over human employees: saving time. By automating so many aspects of data analytics, data scientists’ time can be freed up for high-value work that requires human attention and skill. For example, on average 80 percent of analysts time is taken up by data preparation — a process that AI is now developed enough to deal with.
AI for customer analytics in action
advent of AI to customer analytics has given brands the opportunity to gain insights into their consumer base that they would have never had otherwise. The technology has the capacity to research and analyze thousands of individuals, uncovering exactly what
their preferences and interests are, ultimately powering successful, targeted marketing campaigns.
example, a global sports brand experienced a surge in sales after using Kimola’s platform to understand which of its Istanbul stores they should remodel or redesign in order to appeal to soccer-lovers. In order to train out AI-enhanced platform with parameters,
our team delved into what really defines a soccer lover in terms of their profiles on social media.
platform then began to profile social media users, specifying 50,000 soccer lovers. This enabled our tool to research 50,000 known soccer lovers to find out where they spend their free time, where they shop, along with their general interests.
Based on the research, it was revealed that the most logical store to redesign for soccer lovers was not the city center site, as initially anticipated – rather, one in an area on the outskirts of the city. The sports brand remodeled this store with a complete soccer theme – and experienced a 210 percent increase in sales in that store as a result.
AI-enhanced data analytics can also help with location-specific analysis. Another brand that used Kimola’s platform wanted to discover the areas that their customers were most likely to be spending their time. So, the tool conducted a location-specific analysis of 100,000 social media users to find out where the company should be pushing their product more, and where marketing efforts might not be as successful.
As a result of this analysis, the brand decided to adapt product placement in stores where their customers were most likely to shop — increasing visibility by 42 percent (and subsequently sales).
AI is here to enhance – not replace – the data scientist
anyone concerned that AI will automate the role of the data analyst, the need not worry. This technology may be taking away from many tasks originally performed by data engineers and data analysts, but their roles are not sacrificed, rather augmented. AI is
reshaping the industry of data analytics and creating new job roles while it does it.
the job of the data analyst is to apply the results that the data analytics tools has produced. The new data analyst needs to be able to speak the same language as the technology, becoming an expert in it, so the tools can thrive in the hands of people that
are able to train and tune the AI.
so, a hybrid approach from the data analyst is essential: part Artificial Intelligence, part Human Intelligence. Only then can the best results and insights be achieved. For example, by AI pointing out anomalies in time series data, it means human data scientists
can have the confidence that they’re focusing on the right data and avoiding misleading insights.
The numerous benefits that AI can bring to customer analytics to help brands drive business results should now be clear. Companies should be embracing this technology that promises to deliver valuable insights, eliminate human bias and error, and free up data analysts’ time for more valuable and skilled work. And by expanding their capabilities with AI technologies, brands will have the chance to know their customer base better than ever before.