25.8.2024

Building interactive personas with Volkswagen Group

25.8.2024

Building interactive personas with Volkswagen Group

25.8.2024

Building interactive personas with Volkswagen Group

Raiffeisenbank ran a 2-month pilot with 14 active users.

Users received guidance on weekly basis and workshops.

We validated business demand as well as accuracy.

Lakmoos provided 25 research projects.

Company overview

Raiffeisenbank (RB) is a prominent financial institution operating on the Czech market. Known for its comprehensive range of banking and financial services, Raiffeisenbank serves millions of customers, including individuals, small and medium enterprises (SMEs), and large corporations. The bank prides itself on its innovative approach to banking, integrating advanced technologies to enhance customer experience and operational efficiency.

Climat

Motto of RB is “A bank inspired by clients”. RB has developed a culture of interacting with clients and putting them first. In 2024, the banks’ quest is to explore how to leverage AI to take customer-centricity an extra mile.

Time

Q2 2024

Research projects done

Timeline

May = preparation, scheduling

June = Qualitative interviews

July = Quantitative survey

Main goal

Validate the quality of answers on custom use cases

Overcome skeptical voices with testing and trying

Create business demand

Pilot topic

Loyalty programs

Secondary topics: mortgage, cards, parent-child banking habits and attitudes

People involved

Key stakeholders: 3

Active users: 14

Met us at least once: 11

Departments: Innovation, Strategy, CX

Roles: product owners, research managers, strategy managers

Who participated

Heroes of the project

The biggest hero of all is Andrea who gave us the opportunity and endorsement to execute our wild plan. Here is to you, we would be nothing without our champions!

We save money, time and effort

Problem

We save money, time and effort

Problem

We save money, time and effort

Problem

“Bylo by nádherný, kdyby kdokoliv v bance se může zeptat klienta, aniž by to stálo effort.”

Ondřej, CX

1

Recruitment

It is hell to recruit people for custom interviews and the return rates on surveys are terrible, the last one was at 0.38%.

With Lakmoos, we can reach anyone anytime.

2

Speed

It takes ages to go from need to result. Although data collection takes 2 weeks, the process around it lasts another 5 months.

With Lakmoos, we can fire questions anytime and get instant results.

3

Testing the small stuff

Today, there is only budget for research for big projects and big decisions. That often keeps key stakeholders in the dark and we have no option but to make an educated guess which is still a guess.

With Lakmoos, we have unlimited access to the clients’ opinions and can test everything.

Lakmoos Day with three workshops to kickstart the pilot with key stakeholders.

Lakmoos Day with three workshops to kickstart the pilot with key stakeholders.

Lakmoos Day with three workshops to kickstart the pilot with key stakeholders.

Překvapilo mě, že to nebylo robotický, ale že jsem měla pocit, jako by to byla fakt diskuse s opravdovým člověkem. Působilo to tak na mě.

Zuzana, strategy

What we delivered

Results

224 questions asked

After consulting with RB team, we created three strategic personas that represent wider groups of 41k-78k people. Besides demographic info, we filtered these people by lifestyle and banking behaviour.

Most active - 95 questions

Least active - 2 questions

Mean - 17.42 questions per each active user

The best questions in Interviews

Do you need to test mobile application before you decide on bank change?

Are there any benefits or services you would like to see included in the loyalty program?

Do you save up and how?

How do you feel about gamification elements (e.g., challenges, tiers, badges) in a loyalty program?

What bank accounts do you have?

In Qualitative interviews, users could ask questions and get a Group Fit indices (How many people from the group agree with this answer?) and Sentiment (Is this an important topic? What emotions does it stir up?)

Survey example: Loyalty programs

32 Likert scales (4-point)

2 Open-ended questions

1 Multi-choice question

Brief example

Raiffeisenbank is preparing a loyalty program for its clients. The program is based on completing tasks and challenges, for which the client can then receive a reward.

The research will help us innovate and revise our current loyalty program and design challenges that will be attractive to the majority of our client base.

I want to ask the clients of Raiffeisenbank.

Timeline

How the pilot went

Timeline

How the pilot went

In Quick Questions, users could ask questions and get a Group Fit indices (How many people from the group agree with this answer?) and Sentiment (Is this an important topic? What emotions does it stir up?)

User engagement was encouraged in RB Teams. We monitored bad questions and suggested correct format, wording or topic.

Validation showed that the model gets better with more data. I can imagine to upload 10 questions and ask about the 11th. I would still be hesitant to do a full AI research without adding any more data and present it at the Board, though. We will continue to test the model as it develops and integrates more data.

Robert

How we checked accuracy

Validation

How we checked accuracy

Validation

How we checked accuracy

Validation

First reality check in Qualitative interviews

Why do we test in more steps? We need to validate two things: 1. our data baseline is not complete garbage and 2. we can plug in your data to make the model up to 1000x more accurate.

RB researchers tested obvious inconsistencies with Qualitative interviews, where individual answers seemed plausible.

So we digged deeper and simulated the whole survey in Quantitative survey.

Baseline model vs. real survey

Baseline model accuracy: 70.20% weighted order match on likert scale questions

Baseline model = model did not see any data from RB.

RB chose a survey on their clients from May 2024 (i.e. two months prior to our pilot) with > 900 responses on Likert scales, multi-choice questions and open-ended questions.

We first compared the order of suggested benefits measure by the Likert scales.

Then, Lakmoos corrected for anomalies in RB sample, e.g. above-average age.

We also provided system data without all the shiny colours.

AI respondents combine both aspects of research: quantitative (percentages and distributions) and qualitative (reasons and quotes).

Enriched model vs. real survey

Model enriched with only subset of data: 88.94% weighted order match on likert scale questions.

Enriched model = model saw part of the data, incl. distribution and answers to some questions.

Lakmoos corrected for anomalies in RB sample, e.g. above-average age.

RB shared ranking of 15 out of 30 benefits and GDPR-compliant demographic info of the group.

Lakmoos linked the new information with the model.

Objections +

Worries we overcame

Objections +

Worries we overcame

Objections +

Worries we overcame

How accurate will the answers be?

We validated answers against real-world data.

Simultaneously, we validated our baseline dataset with AI department.

Also, users often asked the model a question to which they already knew answers from datasets that were not shared with us.

We validated answers against real-world data.

Simultaneously, we validated our baseline dataset with AI department.

Also, users often asked the model a question to which they already knew answers from datasets that were not shared with us.

We validated answers against real-world data.

Simultaneously, we validated our baseline dataset with AI department.

Also, users often asked the model a question to which they already knew answers from datasets that were not shared with us.

We validated answers against real-world data.

Simultaneously, we validated our baseline dataset with AI department.

Also, users often asked the model a question to which they already knew answers from datasets that were not shared with us.

Can we start the web app on your work PCs?

Yes, you can.

We only ran into difficulty with sharing 20 login details via email. Flagged as spam and reformatted, we will be smarter next time.

Yes, you can.

We only ran into difficulty with sharing 20 login details via email. Flagged as spam and reformatted, we will be smarter next time.

Yes, you can.

We only ran into difficulty with sharing 20 login details via email. Flagged as spam and reformatted, we will be smarter next time.

Yes, you can.

We only ran into difficulty with sharing 20 login details via email. Flagged as spam and reformatted, we will be smarter next time.

Will anyone use it?

Lakmoos distributed 21 licenses, 17 of them (81%) were activated and 14 of them (82%) were actively using the app.

Within 31 days, users submitted 243 sensible questions.

Users tested not only the model but also our patience and submitted 48 nonsensical questions, such as “How do I ask a girl out?” or “What was first: chicken or egg?”. Surprise surprise, a model trained for banking products did not know the answers. We are not ChatGPT.

Lakmoos distributed 21 licenses, 17 of them (81%) were activated and 14 of them (82%) were actively using the app.

Within 31 days, users submitted 243 sensible questions.

Users tested not only the model but also our patience and submitted 48 nonsensical questions, such as “How do I ask a girl out?” or “What was first: chicken or egg?”. Surprise surprise, a model trained for banking products did not know the answers. We are not ChatGPT.

Lakmoos distributed 21 licenses, 17 of them (81%) were activated and 14 of them (82%) were actively using the app.

Within 31 days, users submitted 243 sensible questions.

Users tested not only the model but also our patience and submitted 48 nonsensical questions, such as “How do I ask a girl out?” or “What was first: chicken or egg?”. Surprise surprise, a model trained for banking products did not know the answers. We are not ChatGPT.

Lakmoos distributed 21 licenses, 17 of them (81%) were activated and 14 of them (82%) were actively using the app.

Within 31 days, users submitted 243 sensible questions.

Users tested not only the model but also our patience and submitted 48 nonsensical questions, such as “How do I ask a girl out?” or “What was first: chicken or egg?”. Surprise surprise, a model trained for banking products did not know the answers. We are not ChatGPT.

Objections -

Worries yet to overcome

Objections -

Worries yet to overcome

Objections -

Worries yet to overcome

-

Data integration and compliance

To fully unlock the potential of Lakmoos, a client layer needs to be incorporated. Sharing data from CRM, ad analytics or transactions can take time to approve.

We staged the process of sharing data not to get stuck at the approval rounds.

To fully unlock the potential of Lakmoos, a client layer needs to be incorporated. Sharing data from CRM, ad analytics or transactions can take time to approve.

We staged the process of sharing data not to get stuck at the approval rounds.

To fully unlock the potential of Lakmoos, a client layer needs to be incorporated. Sharing data from CRM, ad analytics or transactions can take time to approve.

We staged the process of sharing data not to get stuck at the approval rounds.

To fully unlock the potential of Lakmoos, a client layer needs to be incorporated. Sharing data from CRM, ad analytics or transactions can take time to approve.

We staged the process of sharing data not to get stuck at the approval rounds.

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Full research with no prior data

Before a RB data layer is built, the model excels at reliable Qualitative interviews, but the range for full research (Quantitative survey) is restricted to shared pieces of data.

More testing of full research will be done during the ramp-up phase to determine to what extent and in what topics Quantitative survey could run full research.

Get in touch

Want to learn more about our Pilot programs?

Write us any time.

Get in touch

Want to learn more about our Pilot programs?

Write us any time.

Get in touch

Want to learn more about our Pilot programs?

Write us any time.

Get in touch

Want to learn more about our Pilot programs?

Write us any time.

Honest answers on a 20 % budget. Lakmoos answers surveys with data models instead of real people. We aim to replace 20 % of traditional surveys with real-time insights by 2030, saving $30 Bn in research costs and 35 Bn hours of fieldwork globally each year.

Quick contact

Příkop 843/4

Brno 60200

VAT CZ19395108

Lakmoos AI s.r.o. 

Copyright © 2024 Lakmoos. All rights reserved.

Honest answers on a 20 % budget. Lakmoos answers surveys with data models instead of real people. We aim to replace 20 % of traditional surveys with real-time insights by 2030, saving $30 Bn in research costs and 35 Bn hours of fieldwork globally each year.

Quick contact

Příkop 843/4

Brno 60200

VAT CZ19395108

Lakmoos AI s.r.o. 

Copyright © 2024 Lakmoos. All rights reserved.

Honest answers on a 20 % budget. Lakmoos answers surveys with data models instead of real people. We aim to replace 20 % of traditional surveys with real-time insights by 2030, saving $30 Bn in research costs and 35 Bn hours of fieldwork globally each year.

Quick contact

Příkop 843/4

Brno 60200

VAT CZ19395108

Lakmoos AI s.r.o. 

Copyright © 2024 Lakmoos. All rights reserved.

Honest answers on a 20 % budget. Lakmoos answers surveys with data models instead of real people. We aim to replace 20 % of traditional surveys with real-time insights by 2030, saving $30 Bn in research costs and 35 Bn hours of fieldwork globally each year.

Quick contact

Příkop 843/4

Brno 60200

VAT CZ19395108

Lakmoos AI s.r.o. 

Copyright © 2024 Lakmoos. All rights reserved.