While virtually every company collects first-party data about its customers, many brands are now inundated with data and look to artificial intelligence (AI) techniques to derive actionable insights from it all.
This strategy leverages machine learning and predictive modeling tools to make predictions about consumer behavior, which empowers marketers to engage more meaningfully with their most valuable audiences.
In many industries, market forces influenced by the pandemic accelerate the need for AI-driven insights. Here, we’ll discuss how machine learning techniques can help your brand stay resilient through tightened marketing budgets, changing consumer behavior, and global economic strain.
The Realities of Marketing During Coronavirus
The current unprecedented economic and social conditions market forces brought on by COVID-19 force individuals, organizations, and even entire industries to embrace digital business in new ways.
Consumer shopping habits are changing, digital advertising is increasingly competitive, and e-commerce is essential. For example, a retail brand faces the following new realities:
- 74.6% of US consumers are avoiding shopping centers due to the pandemic¹, with 37% expecting their spending at online marketplaces to increase in response to COVID-19
- E-commerce web traffic for both necessities and discretionary spending has risen dramatically, with demand sharply increased in some areas (such as food ordering, hygiene products, and clothing) and reduced in others (such as event tickets and travel)²
- US linear advertising revenues dropped by 20% in the first half of 2020, while digital ad revenue is expected to increase by 4% by the end of the year³
These statistics illustrate the changing terrain of the digital ad and commerce space, especially for fast-moving consumer goods.
While it may be tempting to cut ad costs altogether, studies have shown that in previous recessions, brands that maintained or increased advertising presence outperformed those that went dark.4 As a result, many brands feel pressure to derive meaningful insights from consumer analytics data, cut through the noise, and wisely optimize ad spend.
Predictive Modeling in Strategic Marketing
AI turns data into insights through a process called predictive modeling, or predictive analytics, which uses algorithms to predict outcomes. These algorithms are trained using anonymized customer data. Models are built to predict the consumer behaviors that a brand is seeking to either drive or prevent. Here are some examples of predictive modeling in action.
1. Understanding High-Value Customers
A combination of data enrichment and predictive modeling can derive powerful, actionable insights from your current customers.
For example, when you identify the defining characteristics of your highest-value customers using predictive analytics, you can target audiences of prospective customers that share these traits (commonly known as lookalike audiences). These case studies take a deep dive into a few examples:
- Helping a rideshare company grow its business by understanding its best customers
- How a global food delivery service identifies high-value target audiences
High-value customer insights have immediate relevance for ad campaigns because they help define and refine target audiences, including ones that expand beyond your current customer base. Advanced targeting is associated with a higher return on your advertising investment because it allows you to focus on the groups most likely to spend.
The benefits of understanding your best customers don’t end there. These insights can inform brand partnerships, business strategy, customer experience decisions, and more, empowering you to maintain and grow high-value customer relationships despite economic uncertainty.
2. Solving Attribution Challenges
Connecting online and offline customer behavior at scale is nearly impossible without some form of advanced customer intelligence. Whether you drive offline business with digital ads or use offline campaigns to generate leads for online business, it’s difficult to know which cross-channel campaigns are most engaging.
Brand marketers and data scientists can work together to answer the attribution question with applied predictive modeling. For example, a location visitation attribution (LVA) study, also known as a footfall analysis, reveals the efficacy of digital ads in driving offline behaviors like visiting a brick-and-mortar store.
Explore how different brands have leveraged similar techniques:
- How a luxury retailer connected the dots between ad spend and consumer engagement
- How an automotive company drove foot traffic and optimized digital campaigns
- How a global technology leader measured ad effectiveness on female millennials
Leverage these insights for your brand to cut back underperforming campaigns and increase your advertising ROI, even in a competitive environment.
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