Part 1: Feedback 2.0
AI in Event Management
The humanoid robot "Myon" explores Messe Berlin: the research project from the Neurorobotics Research Laboratory (NRL) at Berlin University of Applied Sciences has several neural networks on its "body" that are modelled on a biological nervous system - and can make the robot react to its environment using machine learning. Photo: Messe Berlin
The humanoid robot "Myon" explores Messe Berlin: the research project from the Neurorobotics Research Laboratory (NRL) at the Berlin University of Applied Sciences has several neural networks on its "body" that are modelled on a biological nervous system - and can make the robot react to its environment using machine learning. Photo: Messe Berlin
In event management, obtaining feedback has long been more than just a chore. Traditional surveys are still the tried-and-tested evaluation method in event follow-up. With the support of artificial intelligence (AI), they can now be used to find out not only what participants think, but also how they think.
The almost usual invitation to a feedback survey that lands in the mailbox after a conference - and is safely ignored. Or which, after clicking on the link with lengthy questions, endless options and text fields, tests patience and is eventually closed again: With poor survey design and a high dropout rate, event organisers forfeit their valuable opportunity to collect feedback in a systematic and structured way. And thus provide a reliable basis for assessing whether and how their quantitative or qualitative goals have been achieved. After all, feedback is nothing less than the backbone of a (future) successful event. It provides answers as to which of its elements work(ed) well and where there is potential for improvement: It provides information on how relevant content and how competent speakers were, whether the catering and networking opportunities were convincing, where waiting times or technical problems caused disruption and which participants' expectations or needs were met - and which were not. In addition, positive evaluations increase the attractiveness of subsequent events and strengthen the reputation of event professionals, while negative feedback can always trigger learning processes or identify new trends.
The initial ebx.lab presence meeting in October 2023 focussed on how the use of AI tools and technologies is reshaping processes - and thus heralding the next generation of business events. Photo: GCB German Convention Bureau e.V.
At the first ebx.lab Presence Meeting 2023, the working groups identified the fact that evaluation and strategic planning are usually still carried out using simple participant surveys and based on intuitive performance indicators as one of the pain points in their organisations. Representatives from convention bureaus, DMOs, hotel and trade fair companies, providers of platforms for hybrid and digital events, as well as Deutsche Bahn and Deutsche Telekom, accepted the GCB German Convention Bureau e.V.'s invitation to discuss process innovations using artificial intelligence as part of the "strategic innovation workshop for new events, brands and experiences".
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From data analysis to real insights
As an essential way to collect valuable data from participants of your events, proven tools such as Google Forms, Jotform or Formstack face the challenge that the large amounts of data - depending on the size of the event and the number of parameters queried - are difficult for human analysts to master:lab community agrees that the large amounts of data - depending on the size of the event and the number of parameters queried - are difficult to manage.lab community agrees. Patterns and correlations are therefore often difficult to identify. This is where new algorithms and comprehensive data models will come into play in the future: they can help to extract relevant information from a variety of unstructured data sources and gain meaningful insights into the preferences and opinions of event participants.
Artificial intelligence (AI)
"Artificial intelligence" is a generic term for machines or computer systems that exhibit human-like intelligence. Various sub-areas are responsible for recognising and processing language, solving problems, making decisions and learning from experience:
Machine Learning (ML)
Machine learning focuses on developing algorithms and statistical models without having to be explicitly programmed for each task. Instead, such a model "learns" autonomously by analysing large amounts of data in order to recognise patterns and regularities. Machine learning systems can then apply these to new, unknown data. Typical applications are e-mail spam recognition, facial recognition or recommendation systems. Deep Learning (DL) Deep learning is a special subcategory and advanced form of ML. It is based on artificial neural networks, which are modelled on the nervous system of the human brain and in turn consist of several layers of neurons. Depending on the information received, connections are made between these neurons. Self-directed machine learning is particularly effective when processing unstructured data such as images and text. Typical applications include image recognition, medical diagnostics and chess computers. Natural Language Processing (NLP) The aim of natural language processing is to enable computers based on deep learning to understand, interpret and generate meaningful human language. Typical applications include chatbots, voice assistants on smartphones and machine translation tools.
However, following John McCarthy, who coined the term "artificial intelligence" back in 1955 in a funding application for the Dartmouth Conference - the first conference on artificial intelligence: "Once it works, nobody calls it AI anymore", AI-supported survey tools are already able to support data-driven decision-making for the development of future strategies and a well-founded evaluation of the success of past events through machine learning and advanced algorithms. What's more, they can significantly accelerate this process: Used during an event, they enable organisers and planners to implement improvements immediately. Surveys can also be designed to be more interactive and engaging than traditional questionnaires. For example, they can use voice or chatbot interfaces to personalise survey responses for event participants and thus increase the response rate. When integrated as a gamified element, such as knowledge surveys modelled on AhaSlides or TEDME, they can be used to test how well the content conveyed in workshops has been understood. Equally important is the ability of AI systems to use machine learning to analyse historical data and make predictions about how future events might go by applying it to new data. If potential problems are addressed in advance, this enables fully proactive event planning. Artificial intelligence can even help with creating a survey and formulating clear, precise questions for the audience. The Fillout AI Survey Maker, for example, generates a customisable survey based on an input prompt that describes the objective, the parameters to be queried and the general event context such as the occasion or format as detailed and precise as possible. A similar process was recently integrated by SurveyMonkey Genius with its "Create with AI" tool. This tool is based on OpenAI technology and uses machine learning to automatically analyse text. This is intended to ensure that more meaningful responses minimise potential bias. Another prominent example of such a user-friendly implementation is the "Create with AI" tool from SaaS company Typeform, which is known for its visually appealing, interactive forms that can be automatically branded with the company logo, colours and information when the company website is entered. Typeform makes it possible to simulate a conversation reminiscent of a chatbot by skilfully arranging questions and answer options. The Survey Creator from SurveySparrow goes one step further and combines natural language processing (NLP) with real-time responses for completely conversation-based surveys.
Feedback in dialogue
This idea has also been the focus of a Heidelberg-based start-up company since last year: "If you want to understand what the participants at an event really want, then you have to talk to people. And then you also have to listen to what they have to say. Traditional questionnaires can't do that. And that's why we let people speak as freely as possible," explains its CEO and co-founder Dr Oliver Völkel. Together with his co-founder Dr Tobias Moldenhauer, he is of the opinion that predefined answer options such as multiple choice often do not accurately capture the true essence of the opinions of event participants. And valuable insights can be lost as a result.
Sci-an, Scientific Crowd Analysis, is the name of the project by the two PhD astrophysicists, which focuses on open questions and free response options and has so far made two tools available for this purpose: the voice survey tool and the chatbot survey. "Filling out" the questionnaires is reminiscent of a WhatsApp chat in which you reply to a message via voice message or text input - except that the recipient does not have human neurones in this case. The respondents' answers are structured using a data pipeline and analysed in several languages: "In addition to individual AI modules for speech and text processing, a lot of classic statistics are also used here. This also allows quantifiable statements to be made from open answers and these can also be combined with other closed answers," explains Moldenhauer. These AI tools also make it possible to determine the emotional tone of an answer and thus recognise subtle nuances in sentiment. For example, the word "exciting" can be understood both positively (in the sense of "interesting") and negatively (in the sense of "tense"). Similar to its competitor tools Survicate or Zigpoll, the AI can recognise such subtleties, interpret them accordingly and thus monitor the mood of the participants - i.e. how positive, neutral or negative they think about various aspects of the event - and report back to the organisers or planners in real time. For Völkel and Moldenhauer, the future - or rather the present - of feedback surveys lies in greater personalisation and taking people's natural expressions into account. However, sci-an is also in no way inferior to its famous competitors, the AI survey tools from Qualtrics or Formstack, when it comes to complex data analysis with the creation of recommendations for action: In addition to the sentiment evaluation, the artificial intelligence independently finds hidden patterns and trends, analyses the average response time, the number of conversation words and presents these in a clear dashboard. From this, it uses a SWOT analysis to develop short, medium and long-term actionable advice to improve the impact of the event.
What’s important to the two developers, Völkel points out, is “to make the process as simple as possible for everyone involved: no app, no download.” All sci-an users need to do is select the survey type "Voice/text" or "Chatbot", customise the questions to be asked from a template and, in the case of the chatbot, define a "personality" with three clicks. By sharing the survey link, the AI co-pilot takes the wheel. In this way, "the assistant is trained to follow up on superficial answers and to ask about the participants' experience step by step," continues the Heidelberg native: "The teams on site don't have to worry about anything" - except possibly reacting dynamically to the real-time answers. Instead of a boring and long list of questions, surveys with AI support can therefore contribute to an interactive experience that gives the feeling that opinions really count. As the ebx.lab community often still lacks a "strategy or guidelines for dealing with AI in organisations in the German-speaking business events landscape", it recommends collaboration and mutual inspiration with other players from the industry or beyond industry boundaries to start with - and emphasises the particular importance of partners from the fields of technology, science and start-ups. The future is in the starting blocks.
Graphic: tw tagungswirtschaft
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