ARTIFICIAL INTELLIGENCE IN MARKET RESEARCH

1. ARTIFICIAL INTELLIGENCE IN MARKET RESEARCH Deep dive // November 24th 2015 Tom De Ruyck Managing Partner Steven Debaere Data Scientist Discussing 2 case studies

2. ARTIFICIAL INTELLIGENCE BIG DATA MACHINE LEARNING AUTOMATION PREDICTIVE ANALYTICS…. PATTERN RECOGNITION CONFERENCE TALK DEEP LEARNING | @tomderuyck @steven_debaere | @InSites

3. (Oxford dictionary) hype HYPES? extravagant or intensive publicity or promotion | @tomderuyck @steven_debaere | @InSites

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) | @tomderuyck @steven_debaere | @InSites

5. WE KEEP HYPING IT massive promotion implies massive adoption? | @tomderuyck @steven_debaere | @InSites

6. HYPE OR REALITY? 67% INTERESTING TREND, TOO EARLY TO TELL OR 23% GAME CHANGER (GRIT 2016) MUCH ADO ABOUT NOTHING | @tomderuyck @steven_debaere | @InSites

7. TEST BUILD LEARN SHARE | @tomderuyck @steven_debaere | @InSites

8. AI IN FUTURE-PROOF ENVIRONMENTS insight generation insight activation Consumer Consulting Board Insight Activation Studio & | @tomderuyck @steven_debaere | @InSites

9. FUTURE-PROOF? (Ray Poynter, 2016) communities impact gap insight generation insight activation 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) | @tomderuyck @steven_debaere | @InSites

10. AI ADOPTION CASESTUDIES insight generation insight activation &Minority Report Her | @tomderuyck @steven_debaere | @InSites

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12. PROACTIVE COMMUNITY MANAGEMENT Low quantity 1. Low quality 2. Participation Threat | @tomderuyck @steven_debaere | @InSites

13. BUILD AugJul Sep Oct Nov Past FutureFUTUREPAST DISENGAGEMENT PREVENTION PROACTIVE COMMUNITY MANAGEMENT | @tomderuyck @steven_debaere | @InSites

14. BUILD QUANTITY QUALITY Low High Low High Community Stars Passivists High- Potentials Annoyers PROACTIVE COMMUNITY MANAGEMENT | @tomderuyck @steven_debaere | @InSites

15. TEST IMPACT DETECTION ABILITY: 78% (QT) & 71% (QL) ACCURACY WHAT A T I FROM REACTIVE TO PROACTIVE MANAGEMENT SEE WHAT THE MODERATOR CAN’T SEE MODERATION TIME PROACTIVE COMMUNITY MANAGEMENT PREVENTION CAPABILITY: IMPACT CRM APPROACH | @tomderuyck @steven_debaere | @InSites

16. LEARN Increase adoption by trade-off between predictive accuracy, believability & actionability Volume and quality necessary preconditions Prediction model as pattern reveal tool Database Adoption Insights PROACTIVE COMMUNITY MANAGEMENT | @tomderuyck @steven_debaere | @InSites

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18. Low efficiency 1. Low effectiveness 2. Impact Threat INSIGHT ACTIVATION BOT | @tomderuyck @steven_debaere | @InSites

19. BUILD A SMART ASSISTANT INSIGHT ACTIVATION BOT FIND ME SOME INSIGHTS ABOUT PACKAGING Case 3 LET’S MEET THE CONSUMER Case 1 SHOW ME THE LATEST NEW INSIGHTS Case 2 | @tomderuyck @steven_debaere | @InSites

20. TEST A T I IMPACT INSIGHT ACTIVATION: ADOPTION & SATISFACTION INSIGHTS ANYTIME, ANYWHERE, IN YOUR POCKET CONSUMER-CENTRIC DECISION MAKING MARKETING MANAGER TIME WHAT INSIGHT ACTIVATION BOT | @tomderuyck @steven_debaere | @InSites

21. LEARN In addition to core- behavior, challenging to enable small talk Logic Map out human behavior for defining use cases Relevance Associating a personality and humanizing the chatbot for increased adoption Adoption INSIGHT ACTIVATION BOT | @tomderuyck @steven_debaere | @InSites

22. LET’S KEEP MOVING LET’S START EXPERIMENTING | @tomderuyck @steven_debaere | @InSites

23. ARTIFICIAL INTELLIGENCE IN MARKET RESEARCH Deep dive // November 24th 2015 www.insites-consulting.com Tom De Ruyck Managing Partner Steven Debaere Data Scientist Discussing 2 case studies Thank you for your attention!

Notes de l'éditeur

Current conference talk Buzzword alert! What is all this buzz? Is this a hype?
A hype is ….
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 67% of marketers that say AI is an interesting trend, too early to tell or much ado about nothing, allow us to assume they didn’t even start adoption or experimentation. Let’s hope the 23% put there money where their mouth is.. But let’s 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; Let’s 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 need to build, measure & learn We have to start experimentation to what we can do with it, how it can affect us or how we need to adapt to embrace the benefits? We, at InSites Consulting already started this experimentation process; In two AI adoption projects, we have been through the build, measure & learn phase Today, we are here to share
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
Casestudies: online research communities Minority report, to predict future participant behavior, in order to prevent destructive behavior from impacting the community Isight Activation Studio Galvin, the personal assistant for consumer insight activation
Challenge of Member disengagement for CCB’s Low quantity  if the member does not participate enough in the topics that are organized in the community low quality  if what the member says doesn’t say anything at all. As a result, if those types of behavior occur frequently, the community may not be able to deliver useful insights anymore. Therefore, as we want to sustain ongoing ccb’s on the long-term, it’s crucial to battle member disengagement. The moderator can do a pretty good job, but it still requires extra energy that could be better used for analyses & insight generation motivation
We adopt AI in a community context: Predict member disengagement using prediction models Moderatore uses these models to become automatically alerted of expected unconstructive member behavior, so preventive actions can be taken How does it works? We construct prediction models using using machine learning on the data-rich environment We identify patterns in historical data to explain future events. The more intuitive explanation is that it tried to find habits. Human behavior can be actually quite predictable, this is the same for a community context. Now, we have our own « minority report » in a community context where murder is a threat for the world Tom Cruise lives in, member disengagement is the same for moderators & structural ongoing CCB’s
Delete? Enough time? Now how, can we make this more practical in a community context. By combining the two dimensions (quantity & quality) & their respective activation levels (low & high), we can come up to a four quadrant framework This allows you to identify easily the future performance of each member. Now, we can classify each member into one of the quadrants & see the future of each member
With this project, we aim to maximize capabilities to identify & prevent member disengagement, while maximizing moderator convenience The impact: Automational: automatic detection of destructive behavior leads to time reduction in community moderation Transformational: through the prediction models, we can go from reactive (when it’s already to late) to proactive community management (prevention) Informational: through the prediction models, subtleties & destructive community patterns arise that lead to destructive community behavior
Process learnings: Data preparation is time-consuming, but key for prediction quality Objective is to enjoy benefits of AI in a community context & to maximize adoption; Therefore, we gave up predictive accuracy through explore other models that are more justifiable & comprehensible for the moderator Prediction model as pattern reveal tool Models show that narcissistic moderators & negative community environments increases chances of member disengagement
Challenge of user assistance for Insight Activation Environments Who is my consumer? Which insights define my consumer? Insight inspiration for new projects? The Marketing department overloaded with consumer insights; in meeting & urgent need for insight? Guiding users through your insight database
Galvin is connected to the insights database, having the right information at hand to help marketers. It uses Luis AI to understand language It uses cortona to become an intelligent personal assistent Both powered by AI technologies
With this project, we aim to maximize user assistance in the insight The impact: Automational: because it’s a 100% digital chatbot, the marketing manager’s time can be spend more wisely Transformational: the chatbot for market research impersonates your personal market research assistent Informational: access to insights anytime, anywhere, right availble in your pocket

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Notes de l'éditeur