AI in MR: Hype or Reality
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Presented at ESOMAR Congress 2017
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AI in MR: Hype or Reality
- 1. ARTIFICIAL INTELLIGENCE IN MARKET RESEARCH Deep dive // November 24th 2015 Tom De Ruyck Discussing 2 case studies Steven Debaere Data ScientistManaging Partner
- 2. ARTIFICIAL INTELLIGENCE BIG DATA MACHINE LEARNING AUTOMATION PREDICTIVE ANALYTICSâŠ. PATTERN RECOGNITION CONFERENCE TALK DEEP LEARNING
- 3. (Oxford dictionary) hype HYPES? extravagant or intensive publicity or promotion
- 4. UMBRELLA TERM Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider « smart » (Bernard Mahr, Dec 2016, Forbes)
- 5. WE KEEP HYPING IT massive promotion implies massive adoption?
- 6. HYPE OR REALITY? 77% INTERESTING TREND, TOO EARLY TO TELL OR 23% GAME CHANGER (GRIT 2016) MUCH ADO ABOUT NOTHING
- 7. AI IN FUTURE-PROOF ENVIRONMENTS insight generation insight activation Consumer Consulting Board Insight Activation Studio &
- 8. FUTURE-PROOF? (Ray Poynter, 2016) communities impact gap 50% OF MARKETERS BELIEVE MR LEADS TO CHANGE IN ATTITUDES & DECISIONS 45% OF MR PROJECTS LEADS TO CHANGE 5% 70% 2016 2026 as % of MR budget (MR Impact Study, InSites Consulting, 2014) insight generation insight activation
- 9. AI ADOPTION CASE STUDIES &Minority Report Her insight generation insight activation
- 12. Low quantity 1. Low quality 2. PARTICIPATION THREAT market research online communities
- 15. âWe see what they seeâ
- 16. DETECT THE UNTAPPED POTENTIAL OF RESEARCH COMMUNITIES 3 years 150,000 posts 7 Million data points Text Mining Natural Language Processing Behavioral analysis
- 19. âThe future can be seenâ
- 20. THE BENEFIT OF ARTIFICIAL INTELLIGENCE & PREDICTIVE ANALYTICS 1. % 2. Risk indicator Output ï PREDICTION MODEL FOR MEMBER DISENGAGEMENT PREDICT TIP! Use the prediction model as a proactive alarm system
- 21. AugJul Sep Oct Nov PREDICT PREDICTIVE ANALYTICS PAST FUTURE
- 22. QUALITY QUANTITY Low High Low High 37% 32% 41%4 40% 87% 79% 72% 91% 36% 19% 21% 24% 91% 93% 87% 84%69% 61% 94% 17% ï Low quantity risk ï Low quality risk 1 2 14% 27% PREDICT FUTURE RISK
- 23. 50% 100% 78%71% âŠ. Low Quant Low Qual PREDICTIVE ACCURACY PREDICT RANDOM CHOICE BAD GOOD
- 26. âMurder can be preventedâ
- 27. prediction model framework CRM info prevention suggestion 1 2 HUMANMACHINE MACHINE interpretation action 3 PREVENT QUALITY QUANTITY THREE-STAGE APPROACH IDENTIFY CONTEXTUALIZE FINALIZE
- 28. Badges Secret room Motivational emails PREVENT 1-on-1 attention Financial payout Refresh ⊠ENGAGEMENT STRATEGIES TIP! Donât reinvent the wheel. But focus on personalization and use existing methods for the right participant at the right time
- 29. PROACTIVE COMMUNITY MANAGEMENT MOTIVATIONAL EMAIL CAMPAIGN CASE STUDY
- 30. CASE STUDY QUANTITY QUALITY Community Stars Passivists High- Potentials Annoyers Increased quantity impact of mail with âfunctionalâ participation motivation. Go! Increased quantity impact of email campaign. Avoid motivation. Human moderation needed! No community impact. Give them a break!
- 37. SIMULATED CHAT WITH CONSUMER « What are your favorite hobbies? » IMPERSONATE
- 41. INSPIRE DAILY FRESH INSPIRATION « There is a new trend in âŠ. »
- 43. âYou have a meeting in 5â
- 44. ASSIST THE RIGHT INSIGHT AT THE RIGHT TIME « This strategy direction doesnât fit with our consumers »
- 45. LETâS KEEP MOVING LETâS START EXPERIMENTING
- 46. ARTIFICIAL INTELLIGENCE IN MARKET RESEARCH Deep dive // November 24th 2015 www.insites-consulting.com Discussing 2 case studies @steven_debaere@tomderuyck Tom De Ruyck Steven Debaere Data ScientistManaging Partner @InSites
Hinweis der Redaktion
- Current conference talk Buzzword alert! What is all this buzz? Is this a hype?
- A hype is âŠ.
- Whenever we talk about smart tasks being carried out by the computer, we can use the umbrella term Artificial Intelligence
- Because we all have been hyping Artificial Intelligence, this means we have been massively adopting it Right?
- A recent industry report shows that we are not yet in the wide adoption phase: The 77% of marketers that say AI is an interesting trend, too early to tell or much ado about nothing, allow us to assume they didnât even start adoption or experimentation. Letâs hope the 23% put there money where their mouth is.. But letâs pause for a second; All the conference presentations about the huge possibilities of AI for MR say us that pioneers/trend watchers see something in it? Numbers show us that the majority of the industry is still awaiting or does not know what to expect The only way forward is to stop talking about the possibilities, but move to the next phase; Letâs bring in help Hollywood to insipire us. Shia Labeouf, please, can you give us advice of what our industry has to do with AI?
- We adopt AI in two future-proof environments Insight generation Insight activation
- Why future-proof? Insight generation: Communities as % or MR budget Insight activation: Impact gap of insights
- We have two case studies for which we adopted AI We also got some inspiration in Hollywood, because we can explain them to you very easily through the movies Minority Report & Her
- Letâs start with the first case study & a short fragment to recapitulate what the movie was about
- To make the link of the movie with the case study, let me make a cheezy analogy, where murder is a threat for the world tom cruise lives in, member disemengagement is a threat for online research communities When aiming to organize a research community on the long-term, member disengagement is a fundamental threat for the community viability: - When members donât participate enough in the community, low quantity, the moderator may not have sufficient input to derive consumer insights from - When members contribute low quality arguments, low quality, the moderator may not have qualitative input to derive consumer insights from.
- So proactive community management consists of three principles: detect, predict, prevent. Letâs go into detail on each principle.
- The first principle is Detect
- In the movie, you see that Tom Cruise relies on oracles to help him to combat crimeâŠ
- In research communities, moderators are most of the times on their own & have to rely on themselves and their own effort to manage the community. But this kind of crazy. On the one hand, communities are data-rich environments. But, we use only this data to derive consumer insights from, so we are not really doing much with it. On the other hand, already available technologies that allow to effectively exploit data and get more out of it like text mining have huge potential benefits So why not adopt these techniques and use it on community data to derive better community insights to support the moderator in community management? Thatâs what we did. In our research project, we have been working on huge sample from multiple brands.
- First, how do we measure member disengagement? We identified two dimensions to be important: quantity and quality⊠We can measure the quantity level by calculating the % of active topics the member engages in. We can measure the quality level by counting the amount of used cognitive words per post. Words like âbecauseâ, âthinkâ reflects the effort that has been put into the text. Second, how can we make this practical for the moderator? By using a cut off value to distinguish between high & low activation levels and combining the two dimensions, we can come to a four quadrant framework. This allows the moderator to classify the community member, asses the risk of member disengagement today and identify four different profiles of the community participant.
- Okay. Thatâs it. Now we can detect member disengagement. But letâs take it to another level Why only look at the present, when we can look into the future? Why only detect, when we can predict?
- In the movie, Tom Cruise can see the future. The oracles predict murder, before itâs actually taking place.
- We can also do this in real life, as has been proven by many succesflu use cases ranging from facebook to the obama campaign. Pedictive analytics & artificial intelligence can be used to predict future events. We can adopt this in a community context, by creating prediction models to predict member disengagement, low quantity and low quality. The output is a probability that reflects the risk a member will demonstrate disengagement behavior in the future
- How do we do this? We use historical data and use machine learning techniques to identify patterns in data of the past that explain future behavior. In our setting, we have been using a simple statitical classification model by means of logistic regression. The intuitive explaination is that we try to find habits that explain future disengagement behavior. Human behavior is very predictable, this is the same for the community context?
- Now we can use the output of the two prediction models, to classify each member into a certain quadrant. Here, we transform the framework to give insights into the future behavior of each participant The moderator can use the prediction models and this framework, to identify what the future profile of each participant will be
- With respect to the accuracy,we see that we achieve pretty good results Well, by analyzing our models on unseen data, we can evaluate the quality of the prediction models For Low quantity, we can make correction predictions in 78% of the cases, while making correct classifcations for low quality 78% of the times; To clarify these numbers, by knowing that randomly guessing has a 50% prediction accuracy, you see that our models already perform better than that. A moderator may perform better than our models due to expert knowledge, but in fact it does not really matter.
- A moderator can never beat the model when scaling to the whole community or making fast predictions. So overall, itâs better choice to rely on the models for this phase. The moderator is more important in the third phase
- Okay letâs summarize. Until now, we saw that we can not only detect member disengagement, but we can also predict. But why predict, when you are not doing anything with it? Why not take actions on your predictions, so you can anticipate on whatâs likely going to happen, so you can prevent expected negative impact?
- Thatâs what they also did in Minority report, they prevented murder from actually happening?
- And thatâs what we also can do in a community context. We created a three-stage approach, where we combine the strengths of the moderator and the machine: identify The prediction model predicts for each member the future profile The framework classifies each member into the quadrant so the moderator can identify the future behavior Contextualize: Community data and crm info must be used to contextualize the identified future profile Based on this info, suggestions can be done for the corrective action Finalize Now the moderator comes into place, by interpreting all the previous information So he can finalize and take the right action, so we can personalize the prevention action
- You may wonder, which actions do we have to take? But donât worry, you have 10 years of industry experience to help you with that. The only differences are: Instead of using a one-size-fits-all aproach for all your members, to personalize, using the framework & Instead of using it in a reactive way, in a proactive way Use the toolbox that you already have, but personalize the action for the right participant at the right time!
- Mails laten zien Case study
- Passivists: Take other more severe actions than email campaign > Give them a break Annoyers: Reduce quantity increase by avoiding email campaign > human moderation needed High-potentials: Increase quantity by anticipating on functional needs
- A man who falls in love with an AI Who knows this movie?
- Now to make the link of the movie with the case study, where the lack of affection is a threat for the world Joaquin Phoenix lives in, lack of using insights is a threat for insight activation environments Low efficiency: when you are not using or looking for insights the right way When you know the insights exist, but it takes too long to find because they are hidden in all the reports or powerpoints Low effectiveness: or when you are not using the right insights for the intended purpose When you identify an insight, but itâs not the right insight for your intended purpose Implication: you are not really constructively activating the insight within your business
- Galvin consists of three important use cases
- The first use case is « impersonate »
- In the movie, you see that Joacquin Phoenix connects with the AI system, Samantha
- Well, as a CMI manager, you can connect with the AI system, Galvin. With Galvin you can have a conversation with a real consumer. Itâs a simulated conversation, but it feels real.
- Because Galvin uses predetermined consumer segments & all these insights he has on that segment to impersonates a consumer and have a real conversation with you. Choose a persona, ask him to tell a little bit more about his life, or ask him specific questions.
- The second use case is « inspire »
- In the movie, you see that Samantha helps Joacquin Phoenix wake up and inspire hem with news
- Well, Galvin can do the same Galvin can be your coach. With daily fresh & new inspiration on the topics that inspire you. When you wake it or whenever you consult him, he inspirses you with insights he think you may find interesting for any new topics. Galvin coaches you to create a consumer-connected mindset.
- The second use case is « assist »
- In the movie you see that Samantha helps Joacquin to manage his life
- Well, Galvin can do the same and be your personali assistant. When you are in a meeting and/or in urgent need of a specific insight? Galvin will provide you the right insights anywhere, anytime. Just ask what you need, he will do follow up questions to make sure you have the insight you are looking for