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If you've had conversations about media mix modeling or predictive analytics, those conversations probably didn't last long. In pre-pandemic times, the promise of multisensory attribution (MTA) was considered the holy grail of marketing. Done right, it will help marketers understand the value of each user interaction point across devices, platforms and more.
MTA was a great and worthwhile idea, but it didn't reach its full potential until iOS14. A series of rules related to privacy and cookie expiration got it out of the dock. With this in mind, marketers looking for analytics and performance benefits are looking at predictive analytics in a new way and finding better solutions than ever before.
In this article, we'll take a look at predictive analytics: what it does, why it works (and doesn't) now, and why it could save the market in the future.
What is predictive analytics?
Predictive analytics has been around for a long time (since the 1940s and Alan Turing!) For marketing, it uses statistical models to predict what customer lifetime value (LTV) is expected by customer segments, and how to reach potential customers based on demographics and behavior. . . It's about where the advertising budget is spent and what the expected results will be.
A few years ago, from a marketing perspective, predictive analytics was very useful for high-budget, slow-growing projects like television and radio. Today, major advances in machine learning, artificial intelligence, and affordable storage and simulation solutions have helped make predictive analytics more flexible and efficient. Providers such as Pecan.ai and Channel Mix have emerged, offering fast-growing solutions to complement the offerings of giants such as IBM, Microsoft, Neustar, Oracle and others.
Related: Why industry leaders are turning to predictive analytics
Why wasn't predictive analytics removed earlier?
This was important for all kinds of non-marketing applications (weather forecasting, financial market modeling, etc.), as well as for high-value companies that could waste time and money dealing with big data models in the marketing world. As digital marketing takes over the speed and flexibility of campaign optimization and customer insights, the idea that you can access all user interaction data across all channels (e.g. Google, Facebook, LinkedIn) has been abandoned. On Pinterest. , tick tock. , Snapchat, programmatic, etc.) and use each device, and you'll understand the exact buyer journey and relevant LTVs for individual users.
You don't have to be a CMO to know that Apple's iOS 14 decision and a number of current and future privacy regulations (GDPR, CCPA) are changing the way we think about data. What marketers today complain about as "signal loss" means that platforms like Google and Facebook have lost significant ability to track user behavior (and conversions) in favor of cookie-based tracking that allows marketers to understand the behavior of individual users. outside
In short, the MTA chimera that promised a better road evaporated. Meanwhile, advances in machine learning and artificial intelligence have lowered the barrier to entry for predictive analytics, making it a more agile practice essential to responding quickly and efficiently to media data. Digital in real time.
About the topic: How analytics can help your business see the future
Why are you a good bet?
As an agency founder, I've worked with dozens of brands (an industry-wide issue) from small and medium-sized businesses that are constantly investing in Google and Facebook. These brands suffer from the law of diminishing returns, which ultimately reduces their effectiveness (I estimate that many brands tested by our organization have lost 30% or more in value due to lack of diversification). Perhaps just as important, and as iOS14 shows, it makes them extremely vulnerable to events that disproportionately affect their primary advertising channels.
On the other hand, brands adapting to a healthy mix of channels and touchpoints, including new platforms such as digital outdoor air (DOOH) and connected TV, are poised to reach their audiences. Where they want to participate and at a low cost. Commitment fee.
Related: CMO, AI and revenue forecasting with predictive analytics
Predictive analytics does not use behavioral data or individual demographic data to understand the customer (which was considered a drawback some time ago). Its models use big data to estimate potential LTV and potential point values for attributes and actions at scale, which aligns with many digital targeting options.
The last point I want to make about predictive analytics is very important: because it does not rely on personal identifiers or cookies, the analytics option will remain even as more privacy-focused regulations emerge at the national, national, and international levels. . fold
How to get started with predictive analytics?
While the barrier to entry is much lower than in the past, to unlock the potential of predictive analytics, brands must go beyond signing a contract with a third-party vendor.
The model's power lies in understanding CRM (Customer Relationship Management) data from platforms like Salesforce, HubSpot, and Adobe. By learning about your customers and prospects, predictive analytics can understand the characteristics of your most valuable customers and help you find more of them.
Predictive analytics relies on internal resources (or an agency if you don't have one) to turn data into actionable insights and visualizations. You can have the most accurate data, but if you don't have the right talent to interpret it and use it in the next steps to scale and optimize your campaign, you'll pay for lost opportunities.
Do your research to understand your internal data situation, talk to different providers to see what they offer, and assess the resources you need to implement your data into your campaign. It's still a modest increase, but staying ahead of your competitors will give your marketing campaign a significant, long-term advantage.