Join speakers and colleagues to hear how to make your insight generation deeper, faster and even more valuable.

Compare different techniques for extracting insight from structured and unstructured data. Hear how data driven insight is uncovering new truths; forcing marketers to question previously held assumptions. Discover how to transform your market research capabilities with AI.

Join the event to:

  • Explore new applications of AI for insight generation
  • Compare techniques for extracting useful insight from unstructured data sources
  • Blend multiple sources of data to achieve deeper detail about your customers’ behaviours
  • Improve your data visualisation to convey simple data stories to  internal stakeholders
  • Develop a future proofed customer & marketing intelligence framework for total insight
Venue

Radisson Blu Edwardian
9-13 Bloomsbury Street,London,WC1B 3QD

No Biography Available

09.00 Registration & coffee

09.30 Opening comments from the Chair

Leigh Morris, Strategy Director, Bonamy Finch

 

09.40 MRS data analytics guidance

  • Data analytics support and standards provided by the MRS
  • Signposting for MRS data analytics resources

Camilla Ravazzolo, Data & Privacy Counsel, Market Research Society

09.50 Understanding customer lifetime value

MandM Direct is the fourth largest online fashion retailer in the UK. It owns one of the largest online user databases in the UK and relies on its web analytics and email data to generate powerful insights. To gain a clearer understanding of customer lifetime value MandM Direct partnered with Google to run a predictive analytics project which aimed to generate a score for each customer based on their expected lifetime value.

Hear how the customer lifetime value model was built and used to generate insight. Examine how these new insights have led to MandM Direct to better identify who their ideal customer is improving its customer acquisition strategy.

Nitin Gulati, Measurement Lead, Google UK

 

10.20 How AI could convert in-the-moment dinner stories into growth opportunities?

Streetbees was approached by a world-leading F&B that was looking for an intelligent grassroots level understanding of consumer journeys to dinner. Claims people made were different to what was actually consumed in the moment. So Streetbees created a bespoke Dinner Moments Growth Engine study collecting in-the-moment food stories from consumers across 3 markets (UK, US and Australia). To date, the Dinner Moments Growth Engine has collected 9,000+ photos, 150,000+ open text responses along with a further 120,000+ data points in addition to rich profiling data. Using unique ML techniques, this data is aggregated to reveal granular and holistic consumer journeys leading to different dinner choices and hence dynamic growth spaces.

Hear how this new tech-based approach to insight has enabled this F&B client to listen, observe, and learn from consumers in a way they never thought possible: in their world, on their terms.

Vidisha Gaglani, Head of Client Success, Streetbees
Dawn Cummins, Global Food CMI Director, Mars  

10.55 Morning refreshments

 

11.25 Blinded by technology – a future-proof CMI framework

Technology is everywhere, also in the world of Consumer & Market Intelligence. It makes doing research and getting to insights better, faster and cheaper. But, are we blinded by technology? Isn’t there more to having a competitive edge as a CMI team? 

Join Tom and Fiona for an exploration into insight and the future of data analytics. How can companies make the most of the data and insights they already have? Are marketing and innovation teams close enough to consumers?  Are organisations ready for a mindset of experimentation in a rapid changing world?

Tom De Ruyck, Managing Partner, InSites Consulting

In conversation with Fiona Hall, Head of Insights UK&I, AkzoNobel

 

12.05 Delivering insight by structuring unstructured data

Open end responses are a great resource for rich insights, but have historically been difficult to codify at scale.  Using NLP, Zappi is exploring quantifying qualitative analysis so that they can be automatically classified. This session will examine:

  • key challenges in extracting value from open enders at scale
  • how the Zappi model works (path of discovery, models used, training methods and feature engineering)
  • why an unsupervised approach was rejected
  • value delivered for clients

Steven Perianen, Data Science – Product Manager, Zappi
Jack Millership, Research Architect, Zappi
Justin Fisher, Data Scientist, Zappi

 

12.40 Lunch

 

13.55 Transforming complex data into money-making opportunities

Everyone knows data is valuable but how do you harness this data? How do you derive real business value? And how do you do this in a way that is compliant and good for your customers? Join DataCubed and its clients to examine:

  • Common misconceptions and myths of monetising data
  • Types of data outputs that are commercially valuable
  • Opportunities to use AI & machine learning to transform raw data into new insight
  • Opportunities to use NLP to interrogate data in user-friendly ways
  • Challenges of GDPR and how they can be overcome
  • Learnings from real-life client case studies

Helen Tanner, Consultant, Data Cubed

Richard Flemmings, Operations Director, 4 Earth Intelligence 

 

14.30 A detective’s story – how tiny clues revealed massive insights

Big data and advanced analytics are powerful tools for any data driven UX team – and we are no exception to this. But there are times when dry A/B testing or vast amounts of data deliver a pixeled image whose quality we could improve by using rather traditional approaches – such as good old qualitative work, desk research and so on.

This is a story about how small data (in Lindstrom’s terms) has saved the day by helping us translate numbers into action and unveil a massive opportunity we would have otherwise missed.

Beware – no AI or ML was involved!

Iulia Cornigeanu, UX Researcher, On the Beach

 

 

15.05 Speeding up NPS analysis with artificial intelligence

To acquire feedback on users’ experience Yahoo / Verizon frequently asks an Advocacy question (NPS), which includes an open ended question. Traditionally, these open-ended questions in text format are analysed and categorized manually by insights teams.
Recently, SKIM has proposed a faster and more reliable process of feedback using artificial intelligence. The specific algorithm inspects the structure of the language to understand what a given text is about. This Syntactic Analysis breaks up the text into a series of sentences and obtains the topic of the sentence. Hear how this new process is enabling Yahoo / Verizon to acquire faster feedback on its content.

Giacomo Sartori, Data Science Manager, SKIM

Michael Hetherington, APAC Director, SKIM

 

 

15.40 Afternoon refreshments

 

16.00 Extracting actionable emotion insights from unstructured text

This session will examine how combining ontologies, and keyword sets with complex rules, with the AI toolset can produce game-changing results in understanding consumer emotions and uncovering actionable insights from unstructured data.

Using a case study based on publicly available consumer text data of a major retail brand and their competitors, Pansensic will show the market research capability of using a Hybrid Text Analytics(HTA) engine to extract actionable insights.

  • Hear how Emotion Analytics provides critical insight beyond Sentiment Analytics
  • Determine what levels of analysis and insight-extraction are presently possible, and how automated and scalable they are?
  • Examining the present challenges and limitations to AI and what the future should look like
  • Learn from a real-world demonstration of the value in unstructured data

Paul Howarth, CEO and Founder, Pansensic

 

16.35 Making data science clearer with… data science

Modern marketing effectiveness techniques involve complex statistical analysis that a typical brand manager isn't well equipped to understand. This story is about making existing clever analytics simpler and easier to engage with.

Working together, Gracious Economics and Culture of Insight used R and R Shiny to convert econometrics results into something brand managers can easily understand and use. Approaching the problem from different sides of the Research, Data & Insights business, Gracious Economics brought statistical expertise, while Culture of Insight brought the tools to make that expertise more accessible. Both sides learned a lot - from planning how a client might use an online tool in their day-to-day planning, to actually building a tool that combined a simple user workflow with complex data.

Grace Kite, Managing Director, Gracious Economics

 

17.10 Closing remarks from the Chair

 

17.15 End of conference


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