The term predictive analytics refers to the use of data, statistical algorithms, and AI and machine learning to provide the best assessment of future outcomes, trends, behaviors and events based on historical customer data. Brands often use predictive analytics to better understand market trends, customer shopping behaviors and more.
Brian David Crane, founder and CMO at Spread Great Ideas, a digital marketing fund, told CMSWire that predictive analytics, as a computational model, uses AI to analyze past performance and existing models to forecast future possibilities and retrieve actionable insights that can be implemented in a business. "It can uncover hidden patterns and user behavior that companies can implement to improve productivity and profitability."
This article will look at the advantages and shortcomings of predictive analytics, and how the practice is changing in order to keep up with the evolution of technology.
Predictive Analytics: The Key to Actionable Insights for Brands
By 2026, Gartner predicts that 65% of B2B sales organizations will transition from intuition-based to data-driven decision-making through the use of technology that unites workflow, data and analytics. Predictive analytics makes it possible for brands to obtain actionable insights from data that has been collected from all of their channels.
“In recent years technological developments have further broadened the scope and reach of predictive analysis,” said Crane. “Nowadays, location-based data and geospatial predictive analysis are used to design customer offers based on where they are and their buying behavior.” Crane predicts that adaptive learning is the next frontier, where continuous data is streamed through AI computational models to understand human behavior and model proactive solutions that engage consumers on the go.
Ryan Fannon is the director of product management at UPS Capital, a financial services division of UPS. Fannon told CMSWire that between the 260 million instances of porch piracy that happen each year, rising instances of delivery fraud, the diversity of goods sold online and extreme weather, shipping resolution practices have become increasingly complex. As a result, the logistics industry turned to predictive analytics to uncover address-related data to more accurately determine high-risk, average-risk and low-risk delivery areas.
Predictive analytics are able to help Fannon’s business determine the elements of risk that are the most likely to occur, which allows it to minimize its client’s risk. “This data is providing merchants with insight on the risk level of specific deliveries and if customized safeguards (i.e., merchant or customer elect insurance, alternative delivery options, or requesting a signature) are warranted,” explained Fannon. “Such measures can protect a business’s bottom line and a customer’s delivery, offering peace of mind for both parties.”
Fannon suggested that although predictive analytics is currently being used by luxury and technology merchants, as data services become more accessible, we can expect address risk analytics to be used by retail businesses, large and small.
According to Bob Czechowicz, senior director of innovation with GS1 US, an authentic barcode provider, predictive analytics are already in use by retailers. “Looking at the retail segment — there are opportunities for retailers to take advantage of, to influence demand. Using predictive analytics to guide and influence consumer purchase decisions ladders up to new ways to increase revenue for brand owners and other stakeholders,” said Czechowicz.
“Instead of resting on analyzing sales, predictive analytics offers an opportunity to identify patterns that lead to highly personalized customer engagement strategies.” Czechowicz told CMSWire that in this new data-driven era, it’s no longer just about the operational supply chain getting the product to the consumer, rather, it's the whole retail ecosystem that needs to be considered in order to deliver a targeted consumer experience.
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Why Predictive Analytics Are Falling Behind?
When it first became available, predictive analytics was hailed as the panacea that would help brands leverage their first-party and third-party data. Many leaders have come to the conclusion that predictive analytics was overhyped in an industry that should have known better. Although the larger brands may have been in a position to have the massive volumes of first-party data that were needed to obtain actionable insights, smaller businesses had to resort to using lower-quality third-party data to fill in the gaps. As early innovators of the popular generative AI models have learned, to obtain quality results, you must have quality data.
It’s not just the IT sector that has been affected by the shortcomings of predictive analytics. Aviation Week in February published an article about how predictive maintenance tools are falling short for the aviation industry. Because predictive analytics are beginning to be a normal part of most industries' technology stacks, many businesses are running into the challenges of Big Data.
The problem isn’t that the technology doesn’t exist that can sort through both structured and unstructured data. According to a 2020 report from IDC, AI/ML functionality is being used to locate and extract data from unstructured documents with nearly 95% accuracy. It’s not a lack of data, but rather a lack of the right data.
Businesses are suffering from data overload, and the data is constantly coming in from all of a brand’s channels: its website, mobile app, contact center, brick-and-mortar storefront, chatbot, IoT and more. Big Data is increasingly and overwhelmingly vast and plays a role in the creation of data swamps, which very quickly become difficult to leverage. Being able to locate the right data in place will be vital for brands to effectively implement predictive analytics.
It’s not only big data and data swamps that are a hindrance to effective predictive analytics — siloed data also continues to be a problem. A 2021 report from the Association of Public & Land-grant Universities found that data silos are a very real challenge for institutes of higher education, acting as "a strong impediment to enhancing the use of data for decision-making." The report also indicated that data is often siloed because of technical infrastructure limitations, aka legacy applications, that do not link disparate data sources.
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Barriers to Effective Predictive Analytics
It’s not just data and technology that are barriers to the effective use of predictive analytics. Because humans are part of the process, they are also part of the problem. “Unknown variables can alter or affect the end results,” said Crane. “The most unpredictable is the human element and its shifting psychological stance based on flawed assumptions or errors in human perspectives.” Crane suggested that another challenge is the relevancy of the data, e.g., limited data or erroneous data used as inputs to extract future predictions.
“Predictive analysis also takes a lot of resources, time and data-gathering efforts to get at actionable and practical insights,” Crane explained. “Even if business analytics can predict future outcomes, buying decisions, and behavior correctly, most analysis is based on demographic data. This is still vague compared to the deep-end information that can be sieved from personalized data.” Crane told CMSWire that the massive data sets that have been collected from various sources can also vary in quality and format, making it challenging for the analysis model to read and analyze them correctly.
Final Thoughts on Predictive Analytics
Predictive analytics can be an effective way for brands to obtain insights about future consumer trends, customer behaviors, spending habits, usability and more. Although an overabundance of data and data silos remain a problem, AI and machine learning are being increasingly used to surmount these problems, allowing brands to not only deal with all this data, but to locate and use the right data.