L15: Evaluating the future of SMM and the use of AI Flashcards
What is AI?
Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.
What is ML?
Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.
What is Deep Learning?
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data
AI AND STRATEGIC MARKETING DECISIONS
Multiple intelligences of AI for different tasks:
Marketing Action
Marketing Research
Marketing Strategy
Three types of AI:
Mechanical AI
Thinking AI
Feeling AI
· Mechanical AI is designed for automating repetitive and routine tasks good for standardization
· Thinking AI is designed for processing data to arrive at new conclusions or decisions good for individualizing
Feeling AI is designed for two-way interactions involving humans, and/or for analyzing human feelings and emotions good for personalizing relationships
AI FOR MARKETING RESEARCH
· Mechanical AI (Data Collection): Helps companies to automate data collection about costumer, competitors, and the market for repetitive, routine, high volume of data.
· Thinking AI (Market Analysis): identify competitors in a welldefined market or outside options in a new market, derive insights for a product’s competitive advantages. E.g., predictive analytics to predict volatile market trends and customers’ heterogeneous preferences.
Feeling AI (Customer Understanding): to understand existing and potential customer needs and wants, for example, who they are, what they want, and what their current solutions are. Involves emotional data about customer sentiments, feelings, preferences, and attitudes
AI FOR MARKETING STRATEGY
AI FOR MARKETING STRATEGY
· Mechanical AI (Segmentation) - Slice the market into pieces:
o various mining and grouping techniques, have the strength of identifying novel patterns from data. Very flexible in determining size of segments
· Thinking AI (Targeting) - Choose the right segments to focus on:
o choosing the right segment requires domain knowledge, judgement, and intuition. Recommendation engines and predictive modelling can recommend various potential targets for marketing managers’ final verdict..
· Feeling AI (Positioning) - Finding a competitively advantageous position for the product in customers’ minds:
is more about speaking to customers’ hearts, Feeling AI, such as feeling analytics, can help to develop compelling slogans.
AI FOR MARKETING ACTION (COMMUNICATION)
· Mechanical AI (Standardization):
o Automating various repetitive, routine, and data intensive functions of promotion
§ For example, automating advertising media planning, scheduling, and buying; key- words researching, automating social media posting.
· Thinking AI (Personalization):
o Content creation and personalization. For example, AI content writers can facilitate the generation of ad or post content. Content can be personalized and optimized to different customer profiles at different locations and different times.
· Feeling AI Relationalization:
can be used to track real-time customer response to promotional messages (like, dislike, disgusted, funny, etc.) and then adjust what to deliver and what to emphasize in both media and content
Current limitations of AI
- Mechanical AI:
Non-contextual data, Machine to machine interaction - Thinking AI:
Opaque AI, AI biases - Feeling AI
Technology unreadiness, Customer unreadiness
Ethical considerations of using AI:
TRANSPARENCY VS. TRUST
Problem of transparency (e.g., GDPR): Unrealistic that consumer understand what data is being collected and what is happening to it
Trust becomes an important part of treating data respectfully, securely, and solely for the purpose of serving the customer better
Inclusive approach to ethics training, security protocols, and data handling should be on every organization’s radar training for employees
Ethical considerations of using AI:
HIGH TECH IS WATCHING YOU
Surveillance economy = “the unilateral claiming of private human experience as free raw material for translation into behavioral data.
These data are then computed and packaged as prediction products and sold into behavioral futures markets — business customers with a commercial interest in knowing what we will do now, soon, and later.”
Algorithmic biases
- The lack of diversity in programming communities may be reflected in how algorithms are written and deployed.
- Algorithms may be trained with data sets that themselves reflect existing biases.
–> Data is not neutral
The future of AI
- Immediate future
- Omni social presence
- The rise of new forms of social influence (and influencers)
- Privacy concerns - The near future
- Combatting loneliness and isolation
- Integrated customer care
- Social media as a political tool - The far future
- Inceased sensory richness
- Online/offline integration and complete convergence
- Social media by non-humans
SOCIAL MEDIA AT PRESENT
Platforms…
Platforms: that provide the underlying technologies and business models making up the industry and ecosystem. Dominant business model has involved monetization of users (audiences) by offering advertising services to anyone wishing to reach those audiences with digital content and marketing communications.
SOCIAL MEDIA AT PRESENT
Use cases…
- Use cases: How various kinds of people and organizations are using these technologies and for what purposes.a. digitally communicating and socializing with known others, such as family and friends,
b. doing the same but with unknown others but who share common interests,
c. accessing and contributing to digital content such as news, gossip, and usergenerated product reviews.
Expand our perspective beyond the narrow communicative aspects consider how consumers might use it.