Concerns over consumer privacy can be felt across the advertising ecosystem. Cookies will no longer be supported in Google Chrome in 2024 (opens outside ibm.com). Additionally, the GDPR limits how third-party data is collected and stored across the European Union. As advertisers embark on a cookieless future, they must learn to leverage new technologies such as AI and machine learning to maximize ROI and deepen customer relationships.
An overview of data science and targeted advertising
Data science takes a scientific approach to obtain insights from structured and unstructured information. Targeted advertising leverages these insights to deliver the right ad to the right person at the right time.
Although companies face challenges when creating targeted advertisements, these ads are highly effective. In general, consumers like relevant messages and advertisements. Fifty-six percent of surveyed consumers (opens outside ibm.com) expect all offers to be personalized.
In order to deliver these personalized experiences, advertisers must embrace new tools to connect with the right audience.
What are the advantages of targeted ads?
Targeted ads are a great strategy to ensure you maximize your advertising budget. Below are some benefits of this kind of advertisement.
More relevant messages
By leveraging data insights, your team can tailor messaging in real time, based on how consumers are engaging with your creative. By showcasing more relevant messages, you can better engage consumers, while improving your conversion rate.
Reach the right audience
Ensuring your advertisement reaches the right audience can be difficult in today’s advertising ecosystem. By using various data signals, your team can determine which users are more likely to convert, ensuring your ad reaches the right people.
Challenges in creating targeted ads
For marketers looking to create ads that reach the right audience at the right time, there are numerous challenges. These include the following:
Consumer concerns over privacy
Two out of three consumers (opens outside ibm.com) want ads that are personalized to their interests. Yet, nearly half of consumers are uncomfortable with sharing their data to create personalized ads. Additionally, 65% (opens outside ibm.com) of consumers worry that brands are collecting personal information without their permission. Seventy-four percent (opens outside ibm.com) also think that companies are collecting more information than they need.
What information consumers define as too personal can also vary across different customer groups and segments. For example: New parents are 70% (opens outside ibm.com) more likely to share their income than the average consumer, but may find other forms of data collection invasive. Wealthy and retired people, on the other hand, are less likely to share how much money they have. Companies need to collect information to deliver a better experience, while respecting user’s preferences. If they fail to achieve this balance, ads can feel invasive and damage the customer relationship.
A company’s advertising efforts can only be as good as the information it is based on. Without a proper way to ensure that data is accurate and updated, organizations will suffer from poor targeting capabilities. In fact, 44% (opens outside ibm.com) of study respondents estimated their company loses over 10% of their annual revenue due to poor quality CRM data.
If data isn’t reliable, it is difficult for teams to pivot their plan or determine which strategies are effective. For this reason, 91% (opens outside ibm.com) of respondents from an Experian study noted that data quality is a necessary part of creating a data-driven culture in the workplace.
The team lacks the right skills
Data analysis is a skillset that often requires companies to hire new personnel or train existing staff. Eighty-four percent (opens outside ibm.com) of organizations think a lack of data skills in the business hampers agility and flexibility. In today’s competitive work environment, 85% (opens outside of ibm.com) of organizations are trying to fill positions focused on these roles, including data analysts and data engineers.
Regulations can make data collection difficult
There is no unifying law around data collection and these regulations can vary from country to country and state to state. For companies looking to leverage data, it’s important to understand what limits these regulations place on your efforts. Companies might have to store data in a specific way or may not be allowed to send unsolicited emails.
The amount of data collected can be difficult to maintain
Data needs to be continuously updated to ensure accuracy. For companies serious about taking a data-driven approach, there must be a process in place to validate existing information and to ensure the data is clean, correct and usable.
It can be difficult to combine different data sets
Data can be structured and unstructured, making it difficult to combine the information into a usable data set. Blending these sets of data can be a time-consuming and difficult process. However, it is often a necessary step to ensure the information can deliver the right insights.
Establishing a process for data integrity
The first step in beginning with a data-driven campaign is to establish a process to collect data, so that it is high-quality and accurate.
Outline steps and responsibilities
In order to maintain high-quality data, companies must ensure that they have individuals or teams in charge of maintaining this information. These roles can include the following:
- Data managers: These individuals are responsible for maintaining the quality of the given data set.
- Data stewards: These individuals ensure the integrity of the data set is maintained on a day-to-day basis.
- Data scientists: This role is responsible for finding insights from a particular data set.
Since many employees produce data in some form, there must also be steps for how each type of data is handled internally. For example: Sales and marketing teams should have clear instructions on how to implement leads into a CRM system. These steps should be consistent no matter the department or location.
Ensure your campaigns aren’t biased
Unconscious bias is prevalent in today’s advertising campaigns. Advertising bias can distance customers from the brand and create a negative experience for consumers. In fact, 62% of those surveyed (opens outside ibm.com) are concerned about the prevalence of bias in AI and machine learning.
Campaign bias can be the result of bad data collection or the algorithms themselves. In order to mitigate bias, teams must understand how their advertising algorithms work and investigate results proactively.
Partner with companies that prioritize clean data
Organizations need to find technology solutions that can help them use data efficiently. Thirty-nine percent (opens outside ibm.com) of respondents from a global data management research report from Experian are also looking for speedy, flexible access to data that can be scaled as needed. By partnering with a company like IBM, organizations can be sure that the information being used is accurate and up-to-date.
What data is needed in targeted advertising?
Targeted advertising can leverage a wide range of data to ensure that the right ads are served to the right people. These can include:
- The content of a web page
- Weather data
- Location data
- Behavioral signals
Companies should use a wide range of information and data to make decisions about their advertising budget and which strategies to invest in.
How is data science used in targeted advertising?
Data science, machine learning, and AI can be used in various ways to reach a target audience. These include:
Contextual ads use various data signals, including the content of a page, weather signals and location to determine the right target audience. Instead of relying on third-party cookies, AI is able to leverage these factors to determine the best time to serve an ad to an end user.
Weather plays a big role in decision-making because it has such a profound impact on the decisions people make. By leveraging accurate weather forecasting information, brands can reach consumers at the right time. Additionally, IBM Watson Advertising Weather Targeting can account for geographical differences. Fifty degrees in Massachusetts may feel different to a Bostonian than 50 degrees feels to someone local to Miami.
Companies can easily identify their current customers, but may face more challenges when finding new audiences. Predictive targeting uses insights obtained from data science to determine when ads should be displayed to a user. IBM Watson Advertising Predictive Audiences uses 15,000 data signals to effectively reach this audience.
Dynamic creative optimization
Dynamic creative optimization (DCO) is able to determine which message will best engage users based on various information, including device, location, weather and date. IBM Watson Advertising Accelerator can understand real-time customer engagements to optimize campaigns across OTT, video and display.
Examples of data science applied to targeted ads
L.L.Bean uses DCO to deliver the right ads
L.L.Bean partnered with Watson Advertising to increase awareness around its athleisure line of products. IBM Watson Advertising Accelerator, a dynamic creative optimization technology solution, extracted insights on which creative resonated with audiences based on consumer reactions, DMA, device type and time of day. This led to the following results:
- 76% lower cost per site visit vs brand benchmark
- 68% lower cost per order vs brand benchmark
- 48% increase in online orders from beginning to end of campaign
Weather data leads to better targeting for Walgreens
It’s no secret that healthcare spending fluctuates as the seasons change. Allergies, the flu and the common cold are all impacted by the weather. In an effort to reach these consumers, Walgreens partnered with IBM Watson Advertising and achieved the following results:
- 379.98% lift in store traffic with Native Ads
- 11.29% lift in store traffic with Rx Total Flu Messaging
- 24.39% lift in store traffic with Cough & Cold Symptom Messaging
Frequently asked questions
How do targeted ads actually work?
Targeted ads use insights from various sets of data to determine which user is most likely to take action on an ad. Targeted ads have traditionally relied heavily on cookies to showcase relevant messaging. However, due to increased concerns surrounding privacy, targeted advertising now leverages AI and machine learning to make accurate predictions.
How are targeted ads so accurate?
Targeted ads are accurate because the machine learning algorithms behind them constantly take in data. As new information is added, these algorithms are able to make better predictions on who to target and which message works best.
What technology is needed for targeted advertising?
AI and machine learning algorithms are used to deliver the right ad to the person at the right time. These algorithms incorporate a large amount of data sets to determine the best outcome. As campaigns run, these algorithms can leverage new insights and information to adjust the strategy automatically and at scale.
How is data science used in marketing?
Data science can be used across a wide variety of marketing functions, including targeting, creative, customer segmentation and conversational marketing.
The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions.