The ideology of data by Sasha Gryjicic, @SashaG, Dentsu Aegis Network, #BigDataTO #BigData #AI #Intelligence
Notes from the #BigDataTO conference in Toronto
- Data marketing and artificial intelligence are headed in the wrong direction
- Marketing is the pursuit of convincing someone they need something, marketing is a commercial outcome to propel the broader economy forward, marketing uses media and communications to convince, largely based on human language
- Data is a digital expression of something in the world, organized and stored in many ways. We are finally getting the external world to use a single language but we can’t read this language. Humans don’t read binary code or extrapolations of code.
- Data violates the notion of scarcity and data is almost always out of both time and space context for necessity. Data is necessarily incomplete and it is not that thing itself. Data has inherent biases, is super messy, and contradictory
- We use data to optimize things that have already happened, or we generalize what we learn from data to engineer more of those outcomes, e.g., when managing an online store, we optimize data to get optimal business outcomes but this doesn’t help us learn why or what drives the decisions
- Intelligence is the ability to gather, category, organize inputs, store, reflect, and respond to them. For humans, intelligence is innate, structured, organized, and process oriented. We have a fixed capacity of intelligence and are creative with it. It is not the result of external stimulus.
- Language is the best way for humans to get access to our intelligence. It’s the language we use when we think. We talk to ourselves more than we talk to others.
- The AI we’re building is like automated statistics. We brute force relationships and create a black box of intelligence. We don’t understand why a computer makes certain decisions because we cannot hold enough variables in our mind to understand. Are algorithms intelligence or optimization? We are drifting further from understanding what intelligence really is. It’s not AI at all.
- We’re accelerating the fatigue of positive reinforcement. We’re following bad after bad. We’re heading away from language which is the only way to understand ourselves.
- Intelligence seems to include morality, the ability to store and reflect and take decisions based on reflections.
- We need to back away from disorganized data. We need to pause and relfect on what we see in the data to understand ourselves better. We need to dive into our own intelligence better. Reflecting on something is more important that acting on something.
Live note taking at the #IIeX conference in atlanta. Any errors or bad jokes are my own.
How the classic fairy tale inspired the mobile ad strategy by Vuk Pavlovic, True Impact (Winner of Best New Speaker at #IIeX Europe)
- What are good guys? Give to others, honest, helpful, kind, polite. What are bad guys? Uninvited, rude, inconsiderate, force their will, vain, self-serving. Which of these reflects your brand?
- Brands need to humanize the customer and not treat them like eyeballs with a screen. The mobile environment is personal, their own social network, with their friends, in their bedroom. We need better relationships with brands that are this close to us.
- Ads need to be seen – attention, be relevant – receptivity, and be chosen.
- They tested ads during games. The ads were presented only when they person actively stopped the game to get help.
- Ads viewed during a more convenient time got more view time, more cognitive engagement
- People ignore pop=up ads but they do pay attention to ads that play at a convenient time. These ads also perform better after the game is finished.
- Ads viewed by choice get a 40 second view compared to 9 seconds for interrupting ads. Heatmaps show people are less likely to be looking for the X Close button
- Annoying ads have more engagement and motivation because they are seeking the X Close button
- Need to consider the person on the other end of the phone. Don’t force them to change the rotation of their phone. If their phone is vertical, then play the ad vertical.
How Home Depot is optimizing the shopper experience by Dan Braker (Brakethrough research) and Brendan Baby (Home Depot)
- Inverted pyramid – customer sits at the top of the pyramid, front line associates, field support, corporate support, CEO
- Use a blend of in store eye tracking, qualitative shop alongs, exit surveys, employee interviews and more to give nagivation behaviours, reasons for behaviours,, experience metrics, operational issues, concept screening
- Asked shoppers on arrival at the store if they would do their shopping trip with eye tracking glasses. Measure area of interest, time in the area of interest, count of shoppers touching or holding a product, time touching or holding a product.
- Path tracking watches the path they walked through the store, where do people spend much more or less time, is it due to interest or confusion
- Can measure pupil dilation for engagement measures, can also measure voice pitch analysis if they talk or ask questions
- Don’t overlook the employees in your research, they know how shoppers navigate, when shoppers need help
- Need to use emerging and raditional approaches to maximize learnings
- Changes to store elements should be thoroughly tested before roll out
Leveraging Artificial Intelligence to do Real-time fan research during NASCAR’s biggest race by Brooks Denton (NASCAR) and Andrew Konya (Remesh)
- Time with friends, cooking and eating, arguments about strategy, social media, ad consumption all together equals the experience
- Asked a set of questions throughout the race, like a live bulletin board, to collect qualitaive data. Choose a few responses that best reflect the full range of responses and match those with segments and demographics
- Build a distribution of opinion for each answer, create a consensus for each answer
- Sometimes they show the live responses to people answering the questions to increase engagement and other times they don’t show the other resposnses to maintain research rigour
- Viewers want split screen commercials, the data proves this and now they can bring that data to the broadcast partners
The automation of behavioural science by Aaron Reid (Sentient Decision Science)
- Some associates are hard wired (attractive person, babies) or learned (police cars, spiders)
- Can you differentiate fear of spiders and spiders using sweat in the hand, do you sweat more for one or the other
- Automation is a major trend in survey design, push button question types and dashboard reporting, full study design is becoming automated, tracking analysis is automated, regression analysis can be automated [I really hope that a person monitors all of these things because humans creating data are not robots]
- STICKY does eye tracking online not in the lab, it may not be great right now but we improve so quickly that it’s worth it to get in early
- We need to automate the science so that cientists can wok on theory, discussion, ideas not button pushing. This gives us time to work on the importat parts. Gives you time to increase empathy for people and brands.