11.09.2018
Data/Audience

4 ways machine learning can elevate your brand

Woman against wall

Consumers are more connected to their machines than ever before, so it's ironic that understanding real human behavior is what drives the most successful brands in their technology-based customer relationships. Consumers want to feel respected, and brands need a complete view of an individual's actions and feelings to meet and exceed expectations.

Highly loved brands deliver great products or services while communicating their value in effective and efficient ways. This is often accomplished by fulfilling the functional needs of consumers, especially on mobile devices, as we discovered in the Oath Brand Love Index. Brands that are able to target the right customer at the moment of intent create more meaningful connections with real people.

Machine learning can empower brands to make smarter ad buys to reach beyond the device. Finding customers where they are requires a diverse set of accurate data signals combined with sophisticated targeting and modeling capabilities. Successful brands know how to distinguish unique value from the buzz surrounding machine learning and use data to deliver exceptional customer experiences.

Here are four questions to ask yourself about machine learning as you look to maximize the impact of your performance advertising solutions.

How accurate and diverse are the data points?

The effectiveness of machine learning comes down to data. Quality data has become a focus area in the past year, with 84 percent of marketers citing accuracy as a critical concern. Highly accurate data points are a must, but so are diverse data sources from places like email, search, apps, user registration, content consumption and more. Data shouldn't come from audience alone, either—for the most accurate depiction, consider leveraging deep-site segmentation from both supply and demand.

Where and how is machine learning used?

It's important to assess areas and functions where machine learning is deployed by a DSP, since each tool does things differently. One might use machine learning in campaign optimization and forecasting. Others might use it in their modeling of predictive audiences, where machine and deep learning techniques predict purchase probability for certain demographics. And others do both. Foundational use cases can include:

• Performance prediction: Estimate the KPI rates.

• Control system: Maximize performance while meeting pacing and performance constraints by computing campaign-level bid adjustments.

• Forecasting: Predict the properties of a campaign's price-volume curve, which is then used to maximize efficiency.

• Bidding: Combine performance predictions and information from the forecasting system to enable optimal bidding.

Given its complexity, it's important for marketers to understand how machine learning is activated. By demystifying use cases and gaining clarity, they can make smarter decisions and more targeted plans.

How flexible is the technology?

Flexibility speaks to the quality of the technology. For example, can a system optimize bidding for both first-price and second-price auction dynamics? Keep in mind, bidding for first-price inventory demands flexibility and requires sophisticated prediction and forecasting of competing bids. Machine learning systems must be able to optimize for both brand and performance goals, which is why a DSP with malleable capabilities is increasingly important.

Does everything work together?

It's not enough for a DSP to feature the right algorithms. It needs those right algorithms to work in concert to determine the best strategy and optimal bidding tactics to deliver against goals. There must be connective tissue among systems so they can collaborate, learn from a campaign and create better performance. Harmonious technology is better able to make meaningful connections in the moments that matter with consumers.

Original article ran in Adweek on 11/09/18. View full article here.

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