Why This Ugly Duckling Of Marketing Might Just Save Your Business

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They were already having conversations about mixed media modeling or predictive analytics, and those conversations didn't last long. In the pre-pandemic era, the promise of multi-touch authentication (MTA) was considered the holy grail of marketing. Done right, it helps marketers understand the value of each user touchpoint across devices, platforms and more.

MTA is a lofty and worthy concept, but it didn't reach its full potential until iOS14. Many laws focus on privacy and privacy. Today, marketers seeking excellence in analytics and performance are taking a fresh look at predictive analytics and finding solutions that are more effective than ever.

In this article, we'll take a look at predictive analytics: what works, why it works now (and exactly what hasn't worked before), and why it may deliver on its promise for the future of marketing.

What is predictive analytics?

Predictive analytics has been around for a long time (since the 1940s and Alan Turing!). In marketing, it uses statistical models to predict customer lifetime expectancy (LTV) based on customer segments, how leads are generated based on demographics and behavior, where advertising budgets are spent, and what results to expect.

A few years ago, from a marketing perspective, predictive analytics only really worked for expensive, slow-growing channels like TV and radio. Today, machine learning, artificial intelligence, and significant advancements in affordable data warehousing and modeling solutions have helped make predictive analytics efficient and effective. Vendors such as Pecan.ai and Channel Mix have emerged to offer fast-growing solutions that complement the enterprise offerings of giants such as IBM, Microsoft, Neustar, Oracle and others.

Related topics: Why industry leaders are turning to predictive analytics

Why is predictive analytics still not used?

It is great for all types of market applications (weather forecasting, financial market simulations, etc.) and for corporate accountants in the market world who can benefit from massive, slow data models. But as digital marketing evolves with speed and efficiency at the forefront of campaign optimization and customer understanding, the idea is that you have all the data about user engagement across all channels (eg Google, Facebook, LinkedIn, Pinterest, TikTok, Snapchat, software, etc.). .) ) rejected. And each device, and you can understand the unique buyer journey and LTV of each user.

You don't need to be a CMO to know that Apple's iOS 14 decision and current and future data protection regulations (GDPR, CCPA) have changed the data landscape. Today, what marketers call "signal loss" means that platforms like Google and Facebook have lost the ability to track user behavior (and conversions), and the cookie-based tracking that allows marketers to understand the behavior of individual users is at risk .

In short, the MTA's dream that promised a better way is about to evaporate. Meanwhile, the rise of machine learning and artificial intelligence has lowered the barrier to predictive analytics and the simplicity of practice needed to respond quickly and effectively to digital media in real time.

Related topics: How predictive analytics can help your business see into the future

Why is it a good thing to do?

As an agency founder, I've worked with dozens of SMBs and enterprise brands that continue to invest in Google and Facebook (an industry-wide concern). These brands will eventually suffer from the law of diminishing returns (I suspect many brands underestimated the results of our agency reviews by refusing to double their spend by 30% or more. Perhaps just as importantly, and as iOS14 shows, expose yourself too much ) . Events that disproportionately affect your primary advertising channels and leave you vulnerable.

On the other hand, brands are adapting a healthy mix of channels and touchpoints, including new platforms such as Digital TV Out of the Home (DOOH) and Connected TV, to attract and reach their niche audiences. participation fee.

RELATED: CMO, Predict Your Profits with AI and Predictive Analytics

When it comes to understanding customers, predictive analytics doesn't use individual behavioral or demographic data (which has long been seen as a shortcoming). The model uses big data to measure attributes and metrics with LTV potential compatible with many digital targeting options.

My final point about predictive analytics is an important one: because it does not rely on personal identifiers or cookies, it becomes a permanent option for analytics as privacy-focused regulations apply at national, national, and global levels. levels.

How to get started with predictive analytics?

​While the barrier to entry is much lower than in previous years, brands are still looking to take advantage of predictive analytics outside of third-party contracts.

The power of the model is to understand existing customer relationship management (CRM) data from platforms such as Salesforce, HubSpot, and Adobe. Get customer insights and lead data. Predictive analytics help you understand the characteristics of your most valuable customers and get more out of them at scale.

Predictive analytics relies on internal data (or in-agency data if you don't have that capability) to provide insights and actionable insights. You can get the most accurate data, but if you don't have the experience to turn it into the next steps to develop and optimize your company, you will have to pay for possible losses.

Do your research to understand the state of your internal data, talk to different vendors to see what they offer, and measure the resources you need to use your data in your companies. It's still a nice improvement, but staying ahead of the competition gives you a clear and long-term advantage in your marketing campaign.

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