AI-Enabled Data Analysis’ Big Role in the Quest for Better Product Recommendations
Data analysis is already a well-integrated tool for business that helps make sense of amassed information, turning it into actionable insights. However, the advent of robust AI technologies is now enabling businesses to intelligently drive analytics, empowering improved AI-led product recommendations to customers.
A number of factors have come together to create a “perfect storm” whereby AI is now accelerating, namely the general cost decrease of data storage, the development of open source frameworks, and the huge benefit of globally distributed computing power known as Big Data.
The final ingredient is the appearance of companies such as Latize, that are pushing the boundaries further by developing products to help businesses maximise their data insight potential through innovative AI-enabled tools.
Huge Promise for Finance
Financial institutions, in particular, are set to gain massively from AI-enabled data analysis. This is because the enormously disruptive changes experienced through AI adoption in other sectors such as digital start-ups and internet-dependent companies are now being recognised as the essential framework on which the future of finance rests. Both structured and unstructured data is everywhere, and the banking sector, for example, faces the triple pressure of tighter regulation, increased competition, and ballooning consumer expectation in digitised banking services.
Clearly, that data needs to be captured and put to work.
Adoption is on the rise, according to a survey done by Narrative Science and the National Business Research Institute, 32% of financial services companies indicated they already integrated AI technology into their working process. Functionality includes voice recognition, recommendation engines, and predictive analytics.
This adoption is helping customers to format better decisions via “augmented recommendations,” meaning they no longer need only rely on the financial advice from professional human experience but can opt to back up opinion with solidly performing data metrics.
Of note from the same survey is that only 12% of the tested group stated they weren’t using AI yet, and the reasons given ranged from fear of failure to uncertainty about AI product offering security and its ability to retain certain sensitive data as siloed sets. These are concerns that indicate professional reluctance and unfamiliarity with the rapidly evolving AI products in the marketplace.
How do AI-enabled Data Strategies Do This?
AI empowers financial institutions to streamline their operations and crunch real-time data in a way that’s previously impossible. For example, companies previously relied on quarterly corporate performance and weekly financial analysis used to drive customer product formulation according to the market changes within those time periods.
However, now that AI can effectively deal with increasing data sets and work those numbers in real-time, financial product offerings can be accurately modelled on immediate conditions, providing customers with the best possible investment opportunities and product recommendations based on micro-fluctuations in market conditions.
AI does this by leveraging multiple technologies simultaneously to propel greater results. For example, cloud-connected data sets can be processed at much greater volumes and at much higher velocities than before, all enabled by increasingly sophisticated machine learning algorithms.
And the key difference here is the iterative nature of AI performance. It’s never static at a single performance level. The technology evolves as it performs more data analysis. As higher volumes of data are processed, and more concrete results acquired, machine learning adapts this data to improve its own functioning. Semantic technology also helps speed up the AI journey, thanks to its ability to simulate how humans understand and process language and information, resulting in quicker and more accurate outcomes from the outset.
Man vs. Machine
While a premium is placed on professional experience, people cannot match AI when it comes to data analysis in business. Here’s why AI-enabled data analysis can lead to improved product recommendations:
- Faster – It’s not just the acceleration of business pace in a digital world, but the crucial ability to arrive at a solid, informed decision sooner. Real-time data insight allows real-time dynamic fluctuations to be made in critical decisions around pricing, margins, and reaction to market conditions. AI is simpler quicker.
- Agility – Multitasking is a prized human attribute, but it’s a normal state of behaviour for AI-powered programmes. The ability to evaluate multiple factors at a given moment to output the most appropriate decision pathway is crucial in weighing risk factors in recommendations.
- Non-intuitive – People rely on accumulated intuition to inform snap decisions, and while this is indeed one of the core values of human reasoning, AI approaches data interpretation differently. Without intuition, AI can look for non-intuitive patterns, such as multiple retail product purchases that are visibly connected in the data, yet not naturally understandable to a person because those product connections do not follow assumed behaviours. Also, in the absence of testing and historical analysis, intuition can be unreliable, making it no more than mere guesswork.
- Consistency – As we are forced to make multiple decision over a certain time, studies show that our decision-making quality decreases. This is called decision fatigue, and AI simply does not succumb to exhaustion.
Somewhat unfair as these AI attributes may sound, they are successful in either identifying effective product recommendations or simply corroborating human-led decision-making processes.
How the US Bank has Succeeded through AI-driven processes
The US’ seventh largest bank, US Bank, needed to improve its sales and customer engagement and opted to leverage AI and integrate it into its core operating procedures to achieve success. This was achieved by systemic AI analysis of customer data points to develop a scoring system that indicated appropriate product recommendations in the form of mortgages and asset management strategies.
Following this approach, US Bank was able to increase the original customer conversion rate of 4.9% to a healthy 15.2%, while allowing its staff to focus on understanding customers on a more personal level.
The Bottom Line
Human judgement, even when informed by considerable experience, is unable to perform at the same level as AI-enabled data analytics within real-time scenarios. Data-driven decision-making, backed by AI, is able to evaluate dynamic fluctuations within massive bodies of information to pinpoint specific solutions that work to given values and concerns. This approach will compound human-driven insights to provide improved results.
This leads to an improved method by which financial companies can identify appropriate client solutions while mitigating risk. Given that behavioural studies dating from the 1950s discovered that even simple predictive models would outperform human decision-making by avoiding the traps of assumption and intuition, it’s perhaps unsurprising that the financial sector is moving so quickly to develop data intelligence through AI adoption.
To help organisations maximise data analysis through AI-empowered tools, Latize has developed its intelligent data management platform Ulysses to enable AI-driven insights by harmonising data into understandable insights. This helps businesses make sense of their data, informing better product recommendations and helping them make the right decisions for today and the future. Contact us today to know how Latize can work with you.