The artificial intelligence (AI) industry has been leading the headlines consistently, and for good reason. It has already transformed industries across the globe, and companies are racing to understand how to integrate this emerging technology.
Artificial intelligence is not a new concept. The technology has been with us for a long time, but what has changed in recent years is the power of computing, cloud-based service options and the applicability of AI to our jobs as marketers.
AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.
Advances in AI now mean product developers can create innovative and leading-edge products and services that, until recently, would not have been within reach of the average marketing budget.
These new products and services entering the market make AI adoption lower risk with a focus on delivering practical and immediately impactful results. Many past attempts resulted in expensive and custom-developed marketing technology projects that left their scars.
But before marketers commit to and execute their AI strategy, they need to understand the opportunity and difference between data analytics, predictive analytics and AI machine learning.
Marketing managers have readily engaged with data analytics, benefitting (and most likely suffering) from the mountains of data at their fingertips. This includes everything from user-tracking data on apps and websites, newsletter conversion rates and online advertising click-throughs, to CRM data analysis.
Data mining delivers vast quantities of data, often unstructured. Marketers are more familiar with interacting with data via dashboards that structure data to deliver analysis of commonalities, such as averages, ratios and percentages. The goal is to aggregate data in order to report a result, search for a pattern and find relationships between variables. Assumptions are made by humans, and data is queried to attest to that relationship. If valid, testing may continue on additional data.
For the sake of example, let’s say that AnyBank credit card loyalty program uses data analytics to determine that it has 10,000 middle-aged male members, and 1,000 of them have redeemed their accumulated points for golf. The data helps make the assumption that middle-aged males are more likely to golf, and therefore AnyBank’s marketing efforts focus on this segment.
Data analysis is descriptive since it is based on past events. It does not predict the impact of a change in a variable.
Data analytics leads naturally to predictive analytics using collected data to predict what might happen. Predictions are based on historical data and rely on human interaction to query data, validate patterns, create and then test assumptions.
Assumptions drawn from past experiences presuppose the future will follow the same patterns. “What/if” assumptions are informed by human understanding of the past, and predictive capability is limited by the volume, time and cost constraints of human data analysts.
Going back to our earlier example, AnyBank’s credit card loyalty program might use predictive analytics to determine whether they could increase reward redemption by 20% by spending 10% more on advertising golf to middle-aged male members. AnyBank could make the assumption that middle-aged males like to golf, so it markets to this segment and predicts that, based on past redemption rates from other specials, they will increase redemptions in-line with that result.
Predictive insights derived from data analytics are extremely useful to marketers. They can help predict campaign effectiveness, inform decision-making on collateral, geographic markets and demographics to target. But the more detailed the desire to target and segment, the higher the time and cost demands, making successful, hyper-personalized campaigning nearly impossible.
AI Machine Learning
Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously.
AI is a combination of technologies, and machine learning is one of the most prominent techniques utilized for hyper-personalized marketing. AI machine learning makes assumptions, reassesses the model and reevaluates the data, all without the intervention of a human. This changes everything. Just as AI means that a human engineer does not need to code for each and every possible action/reaction, AI machine learning is able to test and retest data to predict every possible customer-product match, at a speed and capability no human could attain.
For example, AnyBank’s credit card loyalty program could utilize machine learning to determine that 1,000 of its male members live near a golf course, have not golfed before but enjoy sports. It also determines that many women members in the loyalty program are equally likely to be interested. It also sets parameters for the golf season in certain climate zones, such as the Southern U.S. It further determines a microsegment to offer Saturday afternoon to men and women without young children, who can more likely take the time on a Saturday. The system also acknowledges other sports-loving middle-aged men and women who live near a golf course and have young children, which would prompt other, more family-oriented offers.
Complex analysis, such as the example above, can be done instantaneously with many more variables involved, allowing the system to rapidly learn. This learning can deliver microtarget insights that could not be realistically done by human analysts across a large population. These results can dramatically improve conversion rates, marketing return on investment and customer loyalty.
It’s not a matter of one or the other — it is imperative that marketers understand the benefits and limitations of each. Simplified down:
• Data analysis refers to reviewing data from past events for patterns.
• Predictive analytics is making assumptions and testing based on past data to predict future what/ifs.
• AI machine learning analyzes data, makes assumptions, learns and provides predictions at a scale and depth of detail impossible for individual human analysts.
CMOs are increasingly required to make decisions that have significant technology implications. Understanding the difference between data analytics and AI is all about choosing the right tools for the right job.
This article was originally posted on https://www.forbes.com/sites/forbesagencycouncil/2018/08/01/do-you-know-the-difference-between-data-analytics-and-ai-machine-learning/#2c0d4d435878