In this increasingly data-driven world it is becoming more important for businesses to be customer intelligent in order to reach their target audience, efficiently serve customer needs, and increase their bottom line. At Silicon Valley Research Group, we believe that good customer intelligence is built on three component parts: big data, social data, and primary research. However, strides in AI and Machine Learning technology are revolutionizing all three aspects of intelligence gathering and, consequently, the way in which we discover key customer insights.
What is perhaps the most startling aspect about machine learning is the extent of its ability to build on itself and become “smarter” over time. There are two primary ways an AI learns; an AI might be programmed through “supervised learning,” in which a programmer tells the AI what to do, and then there is the more powerful process of “unsupervised learning,” in which the AI effectively teaches itself by building a neural network. This neural network may become so sophisticated that, much like a child learning to walk then run, you could metaphorically put a ditch in the path of an AI and the unsupervised program will, all on its own, try to jump over the ditch to achieve its goal. Programmers can then effectively tell an otherwise unsupervised AI, “good job” or “try again,” which is generally referred to as “reinforcement learning.” The technology is impressive and its potential even more so. It’s no wonder more businesses are turning to AI and machine learning solutions.
The benefits of AI are most apparent in intelligence and data gathering. While humans are capable of analyzing big data and social data, AI can sort through high-volumes of data much faster and, generally, more accurately. Once an AI has been configured it can even make decisions about what data is relevant and then report trends, correlations, and differences. While AI is of course fully capable of reporting results of closed-ended surveys - such as in conjoint analysis or net promoter scoring - it is also able to then add a layer of interpretation, such as causational inferences and comparison between groups. Natural language processing AI in particular has some promising applications in sentiment analysis: open-ended survey responses and social media data can be searched for keywords and phrases without losing contextual meaning.
With so many applications for AI in customer intelligence, it’s easy to see that this technology will be revolutionizing the way in which marketers achieve insights.
Because marketers will be able to process higher-volumes of quantitative data with more speed and accuracy, they will effectively achieve a birds eye view of their market. AI will help marketers see patterns in customer behavior which allows the marketer to focus on what these behaviors might mean in the context of marketing goals. Using AI analysis of existing data sets, marketers can then create more informed, targeted primary research surveys to gain better insight to the “why” behind these patterns. This allows the marketer to focus on client suggestions and reading between the lines of customer comments.
At this point, you might be wondering why most customer insight hasn’t become fully automated. Well, that’s because AI does have its limits. We’ll be covering those limits and how this affects marketers in Part 2 of this blog.
Heather Carpenter is Marketing Manager of Silicon Valley Research Group, a global market research and strategy development firm focused on the needs of technology companies.
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