The Benefits and Limitations of AI in Customer Data Solutions

Artificial intelligence (AI) is becoming an increasingly-important business tool as more businesses start to harness its power. Being able to perform hundreds of complex tasks at once and contribute to analysis-based decision-making is invaluable in areas like customer data platform software. The caveat, however, is that AI, in its current form, still has some pretty massive limitations, especially when it comes to cleaning and organizing ‘messy’ data inputs. Therefore, organizations should weigh the advantages and disadvantages of artificial intelligence prior to deciding whether or not to invest in it. Below is a breakdown of the benefits and limitations of AI when used in customer data solutions.

AI: The Ultimate Data Analysis Tool

According to the Harvard Business Review, the average data scientist spends about 80% of their ‘valuable time’ finding, cleaning, and organizing data. This is time that could be spent performing data analysis, formulating actionable insights, and contributing to organizational decision-making. While artificial intelligence and machine learning (ML) may not be capable of completely automating the customer data analysis process, they can be invaluable tools when used in the right situations.

Improved Customer Personalization

In a 2017 consumer research study, 41% of U.S. consumers switched brands due to poor personalization and lack of trust, costing these organizations upwards of $750 billion in lost revenues. With a well-integrated AI customer data platform, companies can create more accurate customer profiles and, therefore, retain more of their revenues and client base. 

AI is able to enhance a business’s customer data solutions, and its personalization capabilities, in two distinct ways. Firstly, AI-supported customer data platform software allows CMOs and data analysts to quickly and easily experiment with different models during their behavioral analysis stage. For example, if the churn rate of a webpage did not improve based on the simplification of the webpage, then the data analyst could simply switch the model and try again. With AI, results are gathered so quickly that more time can be spent applying the customer data model to profile properties than on actual calculations. Similarly, AI customer data models can unify a business’s data inputs and create one singular database that is updated with real-time information. The more recent a data set is, the easier it becomes to track, analyze, and predict customer behavior and, ultimately, construct accurate customer data profiles.

The goal of a CDP (customer data platform) is to create a unilateral perspective of an individual customer. With more live data, organizations can update and maintain their customer profiles to more accurately reflect their consumer’s interests, needs, and behavior. The more accurate your customer profiles, the easier you can segment your audience, and the more consistently you can generate effective, actionable insights. Strategies that are fueled by these effective, AI-generated insights will be able to improve customer personalization and increase its return on investment.

High Return on Investment

Accenture, one of the world’s leading AI marketing consultants, addressed how AI investments in certain industries are expected to boost revenues by 30% for the next four years. An ROI of 30% is a staggering figure for any project, but for one as interchangeable and scalable as AI, this could present serious long-term gains for a given business. The biggest reason for this level of return is AI’s inherent efficiencies. Machine learning technology allows organizations to apply basic-to-complex analysis and computation to potentially thousands of tasks at once. For enterprises with multinational operations and millions of data inputs, AI is almost a necessity. It can coordinate, organize, and distribute data in an unprecedented capacity. The caveat is that this may only apply to certain industries, markets, and businesses based on data type. Customer data may not yet be one of these segments.

The Costly Gaps of Poor Artificial Intelligence Applications

There is a reason why 86% of COOs find it difficult to secure funding from management for their AI projects. AI’s utility is only as great as the quality of models it uses and the data it is fed. The challenge for AI customer data platforms is trying to evaluate thousands of data inputs that aren’t easily interpreted by modern machine learning algorithms. As the Marketing AI Institute points out, “the disparate disconnected systems that form the marketing stack can be a major obstacle.” The number of platforms organizations currently use in their marketing department alone can vary for content creation, content hosting, demand generation, analytics, sales, etc. The fundamental issue with disconnected systems and platforms such as these is that it becomes almost impossible to assess all behavior from an aggregate versus individual use basis. Trying to overcome this disconnect can result in data-gathering delays, which then limit the recency and accuracy of your data. But just because AI has its limitations doesn’t mean it can’t be useful or even integral.

A Moderate Solution

While AI may not be able to perform some of the more technical and complex actions of trained human professionals, its best use may be as a partner instead of an employee. A 2020 Harvard Gazette article highlighted the discovery that researchers at the Beth Israel Deaconess Medical Center made, where an AI-powered diagnostic program was able to correctly identify breast cancer biopsies 92% of the time. This was 4 percentage points shy of the 96% accuracy rate that their oncologists were able to operate at. While marginal, a difference this large can mean the difference between life and death for potentially hundreds of patients every year. The more surprising result to come out of this study, however, was that when machine learning algorithms and human expertise was combined, the percentage accuracy increased to 99.5%. Though the parallels between customer data management and oncology are few and far between, the data analysis approaches are relatively similar. In both cases, doctors and data analysts are evaluating real-time data, comparing it to past results, and generating actionable insights that they can use to inform their future strategies.

If a COO is looking to utilize the power and utility of artificial intelligence with their customer data platform software, it makes sense to adopt a balanced approach. Balanced, in this case, means that it is possible to limit the instances where AI is relied upon and there is efficient investment in human intervention. AI’s biggest challenge in the customer data space is being able to coordinate and organize industry data that is historically ‘messy.’ Fortunately, through a balanced, more moderate approach, you can circumvent these obstacles and maximize your AI investment.

By partnering with Enabled Concept, we can help you organize and restructure your data inputs to complement not only your CDP, but also your AI-powered customer data solutions. For more information on how we can elevate your company’s data analytics capabilities, contact our team today to book a consultation.

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Ron Bisaccia

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  1. […] is tracked and recorded, it may come at the expense of poorer personalization. In a recent study, 41% of U.S. consumers expressed that they had switched brands in the past due to poor personalization and lack of trust. […]