DSII. Part 2

Consider these steps that illustrate how AI’s core definition applies to marketing: 1) Systems gather large-scale consumer data. 2) Systems use insights to tailor promotions or recommendations. 3) Systems learn from outcomes, refining future decisions. In what logical sequence do these steps typically occur under AI-driven marketing?

• 1 → 3 → 2

• 2 → 1 → 3

• 3 → 2 → 1

Answer explanation

The process begins with collecting data (1). Then, as outcomes (campaign performance) become available, systems learn from those results (3), enabling them to customize future marketing efforts more effectively (2).

Which of the following statements best describes Machine Learning (ML) in the context of marketing?

• A set of static rules that only updates its parameters when required by last-minute trends

• Improve automation through experience, enabling more refined campaign insights over time

• A manual process of categorizing consumer data using human-led tagging

Answer explanation

Machine Learning relies on analyzing large amounts of data to continuously learn and adjust predictions or decisions without constant reprogramming, making it especially valuable for evolving marketing strategies.

Natural Language Processing (NLP) provides digital marketers the ability to ________.

• Understand and generate human-like text, enabling chatbots and sentiment analysis

• Visualize internal server data with numerical outputs for the creation of stereotypes that assist marketing profiling through general categorizations

• Replace all creative staff in marketing campaigns to optimize costs and increase outputs

Answer explanation

NLP focuses on comprehending and producing human language, empowering chatbots, virtual assistants, and real-time sentiment analysis that inform marketers about consumer opinions and communication strategies.

Match each Key AI Technology with its principal marketing application:

• Computer Vision

• Identifying objects, logos, or product usage in social media images

• Recommendation Systems

• Customizing product suggestions based on user behavior

• Dynamic Pricing

• Adjusting product/service costs in real time

Which key concept is primarily responsible for analyzing past and current data to forecast future customer actions, such as churn or lead conversion?

• Dynamic Pricing

• Predictive Analytics

• Basic Keyword Matching

Answer explanation

Predictive Analytics uses historical and real-time data to anticipate consumer behavior, enabling more proactive and targeted marketing interventions (e.g., churn prediction, lead scoring).

AI-driven Dynamic Pricing involves real-time cost adjustments based on ________.

• Supply, demand, customer profiles, and competitor strategies

• Random assignment of daily price tags without rationale

• Occasional seasonal changes only, ignoring user behavior

Answer explanation

AI-based dynamic pricing examines multiple variables (e.g., demand surges, competitor rates, user browsing patterns) to continually optimize the right price at the right time for revenue growth and customer relevance.

Why does Computer Vision significantly enhance brand monitoring on social media?

• It detects objects, or brand mentions providing real-time insights into brand exposure

• It strictly filters out visual data, focusing on textual hashtags

• It only applies to audio-based platforms that combine image and audio altogether

A core function of Predictive Analytics in marketing is estimating future customer behaviors, such as ________.

• Which users are most likely to convert or churn in the near future

• Removing all unnecessary user data from consideration for global-directed campaigns

• Manually splitting consumer groups with glimpses of algorithmic assistance

Answer explanation

Predictive analytics employs historical and real-time data to infer probable future outcomes, helping marketers focus on leads or customers with higher propensity to buy, respond, or leave.

When discussing Dynamic Pricing, why must marketers balance AI-based price changes with customer perceptions?

• Consumers do not prefer static prices instead of prices that respond to real-time conditions

• Overuse of demand-based fluctuations can cause backlash or feelings of unfairness

• Dynamic Pricing never relies on factors like competition or user profiles

In the realm of Recommendation Systems, why might a platform incorporate a hybrid approach that merges content-based filtering with collaborative filtering?

• To guarantee that all user interactions are ignored, focusing on executive assumptions rather than data patterns

• To overcome the limitations of relying solely on user similarity or item features

• To ensure recommendations are uniform for every user, simplifying the search experience

Answer explanation

A hybrid approach addresses cold-start issues, sparsity, or over-reliance on either user-based or content-based signals. By combining data sources (e.g., item attributes plus behavioral similarities), the system can produce more accurate results across varied user scenarios.