Data Gurus Podcast | Insights on Business Strategy, Mergers and Acquisitions, Market Research & Data Collection

Welcome to another exciting episode of the Data Gurus Podcast! Today, Sima is happy to welcome Anton Umnov, the VP of Consumer Insights and Partnerships at Drop Technologies Inc., as her guest for the show.

Anton’s journey to where he is today

Anton started working in the consumer insights and data space about ten years ago. He spent a long time at Nielsen Data Analytics, where he had many engagements on the client-side with various experiences across multiple categories. Then, about a year ago, he joined Drop.

Data analytics and consumer research

Anton loves data analytics and consumer research. He particularly enjoys the intersection between data and research.

Drop

Drop is a consumer app with about four million users based primarily in the US and Canada. It is a downloadable platform that focuses on driving loyalty rewards and value for the clients, users, or consumers. Apart from getting great deals, shoppers can also get rewarded for doing surveys and playing games.

Why Drop is different  

You can connect the information from your bank account to Drop. That will allow you to unlock better deals and offers from brands and retailers automatically. So you do not have to scan receipts or use loyalty cards.

Drop users

The majority of Drop users are millennials and Gen Z. In most cases, the users are willing to connect their bank accounts to Drop for a seamless experience and better deals.

Security

Security is one of Drop’s primary concerns.

A younger audience

Although the big retailers already have many shoppers, they still work with Drop as clients because they think of Drop as having a younger millennial audience and consider it a user acquisition gain.

Retailers

Smaller retailers use Drop to attract new customers after launching a new brand or product. For the bigger retailers, it is all about the loyalty play. Drop can share a lot of data with the retailers because they have their customers’ banking information. They can see exactly where the customers were shopping in the last six months. That allows them to prove to the retailers that the customer that they attracted is incremental for them.

Share wallet switches

At Drop, they can see the share wallet switches, which allows them to provide all their partners with additional analytics that help them evaluate the success of their campaigns. It also allows their partners to see if they gained any volume over the last few weeks and where their sales originated.

Data by category

At Drop, they sell data by category. They are currently tracking about forty different categories. Those include food delivery, CPG, financial services, and travel. They also go to specific retailers and brands to see who is winning and losing and why.

Launching surveys automatically

At Drop, they see spending transactions almost immediately. So they can do an NPS-type survey, or a more complex one, right away.

A hybrid product

They have a hybrid product on the market that has quite a large amount of transactional data. They also talk to people to find out why they switched to another brand or retailer.

Two pillars

At Drop, they have two pillars that work hand-in-hand. They have an app to maximize the number of points and rewards that their consumers will get. They also have the data side of the business that works in conjunction with the app itself because the more data they monetize on the market from working with different brands, the more value they can provide for their users in terms of rewards.

Market research analytics

Market research analytics is a big part of Drop’s overall business. It is one of their key pillars for 2021 and beyond.

Expanding

Drop is trying to expand outside of traditional CPG and pressure retail. They are also focusing on some of the newer industries, like food delivery and crypto.

Stimulus tracker

Via their Stimulus Tracker study, they tried to help the banks and hedge funds understand how people were spending the stimulus money that was deposited into their accounts.

A surprising finding

They found that right away, many people invested the money they received on home improvements. Many consumers also invested some of their money in stocks and crypto. That was a surprising finding for the team at Drop because their audience is mainly millennials.

A joint approach

They used a joint approach incorporating both transactional data and survey research for the study.

Data and insights 

Drop spent their first year making sure that their data made sense. Then, last year, they started putting some of their insights on the market.

The biggest obstacle

For Anton, the biggest obstacle is trying to find a product-market fit that combines data and surveys.

The challenge

Drop has coverage on forty industries. That makes it challenging for Anton to decide on which industry to target and focus on.

The future vision

Drop is expanding. They are doing some work in the UK currently. They will be focusing primarily on the US and also on Canada for this year and the next.

The goal

Their goal is to maximize different revenue streams, rewards, and points for their members.

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In this exciting episode, Sima is excited to have Nitzan Shaer, the CEO, and Co-Founder of Wevo, joining her.

Nitzan’s background

Wevo is the fourth company that Nitzan has either started or joined early on. He loves the thrill of joining a team of like-minded people in the early days of a new venture. Skype was among the companies that he joined early on, which were all in the space of trying to help consumers solve a big problem.

Wevo

The context for Wevo got formulated while Nitzan was with Skype. He started thinking about other industries where the cost-per-minute was very expensive, and reducing the costs would make them more efficient.

A nightmare

At Skype, they wanted to understand more about their customers, but it was too expensive. It was also a nightmare to collect user research to know which products to build and which features to improve.

The background for creating Wevo

Wanting to listen to their customers more, and knowing they were in an industry where costs could get reduced dramatically, were factors behind the creation of Wevo.

Reducing the time it takes

At Wevo, they take a process that usually takes about 200 hours to run, like a feasibility study or focus group, and reduce it to a half hour.

What Wevo does 

Wevo is an AI tool to listen to customers, hear their needs and challenges with their user or entire customer experience, and provide validated insights on the core problems, the core hurdles, and the things that are working very well. They answer why people are experiencing a challenge in their user experience. That enables a team to generate a better user experience. They also enable their users to validate their ideas.

The user

With Wevo, the user needs to define its target audience and identify the experience he is trying to optimize. That usually takes less than ten minutes. At the end of the process, the user gets a report with all the needed information.

Updates

The expectations of users are continuously evolving. Wevo believes in a continuous flow of updates around what people are feeling and how they are responding.

Partnership

Wevo has a partnership with multiple participant providers to help its customers find their exact target audience.

Three lenses

It is hard to evaluate the user experience, so through a process that began about four years ago, they have developed three primary lenses through which they look at each user experience:

What happens on the pages of a user experience

Areas that are roadblocks or hurdles to conversion

Areas that are helping the engagement

Diagnostic drivers 

The diagnostic driver, which examines areas to understand them and to benchmark progress. Areas examined include clarity, appeal, credibility, relevance, and overall experience.

Expectations versus reality

The technology asks people about their hopes and concerns when considering purchasing a product or service. Then, once they have experienced it, they ask them to answer whether their expectation got met or not. Human-augmented AI is vital in that process.

The Wevo Sentiment Map

The Wevo Sentiment Map shows the areas of the biggest problems in the experience and the areas that are the biggest helpers.

Quant and qual

Everything they do is quant and qual, so they also explain why there is a problem. (They call that qualt.) Nitzan points out that many systems today lack the partnership between quantitative and qualitative.

Those who gravitate to the Wevo solution

They are currently seeing much adoption with UX researchers, designers, marketers, and product managers. Engineering teams have also started using it.

The disruptive power of Wevo

Wevo is compressing the time it takes for people to get validated feedback. That means that organizations can move quickly because their learning starts to accelerate. Their ability to take risks also accelerates because they can test everything.

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Welcome to today’s podcast! In today’s special episode, Sima continues her conversation with Gary Laben, the CEO of Dynata in the second part of the exciting two-part interview.

In this episode, Gary and Sima continue with their discussion about the things that influence the size of opportunity within the industry and discuss whether doing things faster will denigrate the research process. They also talk about standardizing the data-collection process, acquisitions, integration, and culture.

The size of the opportunity for the entire Industry

Up until now, many people have defined the size of the market research industry at around 80 billion dollars worldwide. However, the advancement of technology is bringing more players and different buying audiences into the space. Gary believes that two things drive the number: the changes that are going on within traditional market research from a technological standpoint and the marketing services ecosystem, which Gary sees as being continuous.

The work that gets done in market research

Gary believes that market research is inextricably connected to lead and demand generation, CRM, and advertising. And the work that gets done in market research is ultimately to understand someone so that some behavior, purchase, or customer relationship can get driven. Gary believes the data that drives the decisions at the start should be the underlying or first-party data that drives the execution of those ideas. If you can keep that data consistent along that continuum, you will reduce the opportunities for getting it wrong.

An order of magnitude

Gary thinks there is an order of magnitude of size and opportunity in market research. And the rest of the marketing services spectrum is another order of magnitude on top of that. Gary estimates the total value to be a couple of trillion dollars, which is very exciting!

Agile research

The introduction of agile research into the industry has allowed Gary and his team to address the challenges in they could not reach previously.

Will faster denigrate the research process?

Gary believes that market researchers have to be fast, ask the right questions, and have a sound methodology and diverse representation. They should also ensure the fidelity of a given project.

Value proposition

Dynata’s value proposition will always be to provide the highest quality first-party data to drive their outcomes. They will not drop that in the pursuit of speed or any other metric that adjusts to the changing market dynamics.

Standardizing data 

As an industry, we have not made the progress we need, so Gary thinks that standardizing the way data gets collected for market research is vital.

Acquisition

Dynata makes its acquisitions in one of two ways. If they want to expand their data asset or the capabilities surrounding it, they look for businesses in geographies where they might be under-represented, like the African continent. They look for companies and assets that would help them to build out their insights platform.

Insights platform

Dynata launched their insights platform a couple of years ago with the idea of having one place for their clients to come to do everything from accessing their data to performing the entire set of market research activities related to their data. They have been working toward arranging all the puzzle pieces to ensure the platform is a complete offering for their clients. Dynata hired third-parties to achieve integration with their acquired companies and to create value.

Culture

Gary points out that a shared purpose binds people together and wants to ensure the strategy and direction they have chosen at Dynata is the best it can possibly be.

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Dynata

In today’s episode, Sima is happy to welcome you to the podcast, which is the first part of an exciting two-part interview that she did with Gary Laben, the CEO of Dynata.

Today, in the first part of the series, Gary talks to Sima about his journey that led to where he is today. He also talks about his general leadership style and the impact Covid-19 has had on him as a leader and on his business. They close the episode with Gary giving a deeper understanding of how he views the landscape and where Dynata’s core area of focus lies within the market.

A perspective of Gary’s career and how he got to Dynata

Gary started his career by working his way up in the culinary world over the summers while in college. He was offered a position in a data marketing business, which he did for a summer before graduating. He enjoyed that so much that when he got offered a full-time role in the data marketing business, he put everything else aside to try out the world of data. From there, he moved on to various roles within different data companies.

Moving over to Dynata

Although he had never worked with first-party data accessibility before, it had always fascinated him. So, when an opportunity arose on that side five years ago, he moved over to the company now known as Dynata.

Gary’s experience at Dynata

Gary has had tremendous experiences at Dynata and even though the industry is rather tight right now, and people have been moving in and out of the organization, Gary has enjoyed seeing his relationships continue throughout the industry.

Learning the business

As he learns the business, Gary often jokes that even after five years, there is rarely a meeting that he attends where he knows anything more than anybody else about the subject at hand regarding tools, techniques, and capabilities in market research.

Gary’s favorite part of his job

Gary loves to meet with clients. The best part of his job is to spend time with the folks interested in working with Dynata, even though things are getting done virtually for the most part right now. The second best part for him is to do little talking and still come out way ahead because his clients will have learned a lot about what Dynata can do better and what they need to do in the future.

Getting back out

Now, the people at Dynata are talking about ways to make it possible for them to get back out to their offices around the world. It appears that Asia will be the first place to which Gary will be able to return.

Virtual engagement

At Dynata, they engage with their clients virtually. They also have town halls where they regularly engage at a regional level and an office level. Gary sometimes even gets asked to appear virtually and unannounced, at monthly team meetings.

Gary’s leadership style

At Dynata, the leadership style needs to be empowerment. They cover all geographies and have broad service offerings, so they have leaders of both geographies and functional areas within the business. Gary feels that he has less familiarity and experience in particular sectors than many of those leaders have. Because of that, he loves to give those people autonomy. They have an aggressive and methodical goal-setting and management process, so allowing people to operate very independently within the boundaries of execution, without having oversight, is the key.

Humor

The pandemic has shone a light on things far more important than people’s day jobs. Being able to approach leadership with consideration to priorities, and looking for places to inject some humor, is very important for Gary right now.

Covid

Covid affected the company’s goal-setting process in that they had to think differently. They set themselves up as a business to preserve liquidity and to ensure their employees were safe.

New goals

One vital new principle they set was to figure out how to thrive.

An aggressive expansion strategy

They set out an aggressive expansion strategies in parts of the business. That was so successful that they decided to continue using those strategies.

Acceleration of change

They also realized that the acceleration of change could happen at any given time and are currently happening a whole lot faster than they were before.

Resilience

Dynata decided to embrace the very thing they feared at the beginning of the pandemic and apply their newly-acquired resilience to their day-to-day operations going forward. So they started looking for ways to get to the best solutions as quickly as possible.

The business within which Dynata works

Dynata is in the provision of first-party data to drive insights and answers businesses and companies need to drive and execute their strategy and day-to-day success. They surround that with several products and services that facilitate their access and use to the data to drive those answers even better.

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On today’s show, Sima welcomes Mike Page, the Co-founder and Chief Executive of Phebi, as her guest on the podcast.

The founding story of Phebi

Phebi has been around for a little more than a year and their focus is on the insights industry. Their journey has been around the explosion in voice and voice tech when they saw the whole landscape of how people interact with technology was shifting to voice. That created a wonderful opportunity for insights specialists and researchers. It gave researchers the chance to offer their respondents a better experience while simultaneously creating a richer source of insight for themselves.

Talking versus typing

People talk more naturally than they type, so you tend to get longer, richer answers. The audio can get people used to listening to how people are speaking and not just responding to written word.

Webinars

Phebi started with some webinars with friendly companies, like Askia, which were very well received. Then, a London company wanted to try what Phebi was offering, so they presented at a healthcare research conference, and that was how Phebi really got its start.

They won the innovation award

In January, Phebi went to the IAX Conference and won the innovation award. That was very prestigious for a small company!

Phebi continued to grow

Soon after they won the award in 2020, the world shut down. Since then, things have continued to grow.

Things have taken off

Their message resonated. And the ability to capture non-conscious metrics to use alongside things like text analytics from an audio source that is easy to collect in a self-serve manner, has taken off.

Non-conscious measurement

With non-conscious measurement, you have the potential to pick up more nuanced sentiments and emotions that could not get spotted before. Doing that at scale is much of what Phebi does. Non-conscious measurements can help researchers get to insights quicker and more effectively. It adds the resonance that comes with audio so researchers can capture people’s first sentiments related to a question.

Emotional Resonance Score

The Emotional Resonance Score is one of the things that they provide for comparing things.

Non-conscious measurement is a good predictor

When you use a non-conscious measurement to compare things, it is very good at predicting what people are likely to feel the most passionate about or connected to.

A live setting

They even have some clients who are using Phebi in a live setting, where they are looking at people’s true sentiments and emotions while an interview is in process.

Non-conscious measurement combined with a 3-D transcription

When a non-conscious measurement gets combined with a 3-D transcription, it opens a lot of opportunity for understanding how people feel about something.

Getting better and quicker

At Phebi, they have gotten better and better and quicker and quicker at delivering self-serve tools that will help people extract insights from voice.

Going faster

2020 has allowed them to establish their technology and grow much faster. Now, everything gets done virtually, so their whole adoption process has accelerated.

People don’t always say what they mean

People don’t always say what they mean. So hearing how they say things can be very interesting.

The Phebi portal 

After gaining access to the Phebi portal, you can upload any audio into it. There, everything gets organized. You can then create groups and subgroups to get the emotional scores in all the different sections.

The Phebi plug-in

You will also get the Phebi plug-in to manage the technical aspect of things. It works with all the main survey platforms.

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In today’s episode, Sima is excited to welcome Gangesh Ganesan, the Founder and CEO of PeerNova, as her guest on the show. This podcast is typically about data and the data ecosystem. Today, however, Gangesh takes us into the area of the financial market. In this episode, he explains the importance of ensuring that data has good quality and is accurate.

PeerNova

PeerNova is a Silicon Valley-based data company founded by Gangesh Ganesan about five years ago. They have offices in Silicon Valley, San Jose, London, and New York. Although their themes mostly get distributed worldwide, the core is in Silicon Valley. Some of their development happens in Eastern Europe and they also have a theme in Brazil.

A global company

PeerNova is a global company. They are venture-funded and raised a substantial amount of capital to develop their software platform.

Software platform

The PeerNova software platform is called Cuneiform and offers data quality for financial institutions.

Data quality

Data quality means ensuring the correctness, completeness, consistency, and timeliness of data that might get distributed across different applications, databases, data stores, and data warehouses in a global financial institution.

Making the correct decisions

Currently, data quality is vital for making the correct decisions in the financial industry. It has been a big problem in that industry for the last two or three decades.

Financial Institutions

Financial institutions typically solved their data quality problem by employing many people to ensure that data was correct, complete, and consistent. In some cases, it could take tens of thousands of people to solve the problem for large financial institutions.

The Cuneiform platform saves time and money

The Cuneiform platform is about applying technology to automate as many of the workflows as possible. It can do that at a substantially lower cost, and more importantly, it does it near real-time.

Inspiration

Early on, Gangesh was inspired by some ideas that came about in blockchains. A crucial notion in blockchains is that of lineage, with which you can find the origin of any transaction. And you can track it either forward or backward to find the seed or genesis event. That idea inspired Gangesh to build a generic software platform with which every piece of data could get tracked in that way.

Data quality

They soon realized that maintaining the lineage and ‘audit trails’ of the data quality of financial institutions at scale and solving for correctness, completeness, and consistency problems was a vital challenge. It took them four or five years to build their Cuneiform platform. Financial institutions have been undergoing many changes

Financial institutions have been undergoing many changes over the last twenty years. And their pain points have become very acute.

The current focus

The entire focus of Gangesh’s business is currently in capital markets and financial institutions.

The Zero Code

Using what is known as Zero Code, Gangesh has ensured business users, who are often tech-savvy but are not developers, can have the four attributes of data quality-correctness, consistency, completeness, and timeliness for all their data.

Accuracy

In the financial industry, they cannot afford to make any mistakes, so their data quality needs to be accurate one hundred percent of the time.

Enterprise selling

Enterprise selling of software at very high levels can be a problem, so Gangesh offers his clients the chance to do a free pilot to see if their model works for that specific use case. Reference-able clients are a big deal at PeerNova, so they also try to identify easy-to-replicate use cases to have a step-and-repeat concept that is easy to demonstrate.

The problem of data quality

The problem of data quality is something fundamental. The financial industry has always lacked a comprehensive end-to-end solution for that problem. Gangesh believes that data quality is a category-making idea for which PeerNova is well-positioned. He believes they can build a large, successful business within it.

The future

PeerNova has built a platform with many features and integrations. Over the next five years, they will continue building and adding more capabilities and features to the Cuneiform platform. They are bringing more and more clients onto the platform and they are growing their top line and footprint within the industry.

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On today's show, Sima is excited to host a panel of three powerful and well-respected women as her guests. Tiffany Hays is the CEO of FUEL Global. She is and also CEO of The Focus Room. Aryn O’Donnell is the Vice President of Corporate Services of Fieldwork. And Kelli Hammock is Senior Client Solutions Manager at L&E Research.

Tiffany, Aryn, and Kelli are all in the qualitative space in which Covid-19 greatly impacted.

The impact of Covid-19 on Fieldwork

Fieldwork has in-person qualitative research spaces that got impacted by Covid-19. At Fieldwork, they have always been solving their clients’ problems, and right now, they are solving new problems. They got forced to think differently, ask questions, and find new ways to connect with their industry colleagues.

The impact of Covid on L&E Research

L&E Research had to make a big pivot to learn how to get proactive around the changes that happened within the industry and the company. They had to come up with solutions to apply to the new problems that their clients were having. The biggest thing for them, professionally, was getting out of the reactive mode and figuring out how to make things work.

The Impact of Covid on Tiffany, as a leader

The biggest thing for Tiffany, as a leader, was making sure that her employees had even more flexibility than she was used to giving them. There were new challenges that she never expected to face as a leader. She had to set boundaries for her teams to ensure that everyone gave each other space and checked up on one another’s mental health within their organization of about 25 employees. They had to become aware of the new challenges with which people were dealing.

The impact of Covid on FUEL Global 

In the business itself, Tiffany’s clients preferred to be in-person. So there was some push-back to them having to use an online qualitative platform. Working on a global scale, the company had to learn to manage clients across the globe. One of their biggest challenges was ensuring that the clients could get what they needed in the online world.

A breaking point

Tiffany believes that this will be the breaking point in our industry, where the clients realize that they can do both in-person and virtual. And we will see a permanent reduction in in-person research.

Clients are thinking critically

Aryn believes that clients are now thinking critically about what the goals of their research are. And they are deciding what they need because all research cannot get done remotely.

Producing revenue

Understanding client’s objectives clearly, and helping them figure out how to get what they need out of the research is vital right now. Tiffany is helping her clients figure out how to get things done because that is not going away. And that, in itself, produces revenue.

Safety within the facility

From a facility perspective, Aryn feels that it is her company’s role to educate the clients. And work with them to find the right solution in terms of safety protocols.

From where the hesitation to get back to in-person research is coming

Kelli does not think that any hesitation to get back to in-person research is coming from the respondents. She feels that it is coming from a corporate legal perspective.

Where we are most likely to see in-person research get back to normal

We are most likely to see in-person research getting back to normal in the markets with many local clients who need not travel too far to get to the facility.

Flexibility and support

Some challenges that research companies have to manage currently around their workforce are offering flexibility and support. And setting boundaries. They also have to address the mental health repercussions of the pandemic.

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Today, Sima welcomes Andy Davidson from the Engine Group as her guest. Andy is the Head of Analytics and Data Strategy at Engine.

The Engine Group

The Engine Group is a full-service marketing services firm specializing in a range of things from insights and analytics to planning and content and distribution.

Background

Andy has been with the Engine Group for about two years. Before that, he spent twenty-five years in various assignments across research analytics and data science. He worked on the client-side as well as on the agency side. His focus has been on helping advertisers and their agencies in addressing business problems with data analytics. He spent a lot of time helping his clients develop marketing strategies. And then personalizing the customer experience and measuring those efforts in the returns they are getting.

Engine specializes in three areas

Insights and analytics: This function supports both advertisers and agencies in making better decisions using data and analytics.

Planning and content: These are about creative and media strategy and media planning.

Distribution: Engine owns a proprietary ad exchange. They have relationships with a lot of the publisher networks. Through that capability, they can place media programmatically for much of the market. That includes both the agencies and the advertisers directly.

A combination of services

The Engine Group has a combination of services, from a data and analytics perspective and a distribution perspective, that many other firms in their arena don’t normally have. That creates a seamless experience for their customers. Their clients are increasingly becoming multiple threads. They also have plenty of more single-threaded situations.

There are some key challenges in the ad space

When it comes to audience targeting, media campaigns don’t always reach where they are supposed to. That can happen as often as fifty percent of the time. At Engine, they recognize that challenge, and they have invested a lot in helping their clients overcome it.

Data

The targeting challenge, at its core, is a data and analytics problem. Data is behind every decision that an advertiser makes around bidding on an opportunity, whether it is a video ad, a display ad, or a CTV-type impression.

Accurate data

That data is usually pretty accurate when it comes to straight-forward behaviors, like past purchases and demographics.

Where things tend to fall apart

Things tend to fall apart, and problems become apparent in the area where advertisers spend a lot of money. That area includes attitudes, intentions, and sentiments in the softer type of targeting requirements.

A huge opportunity

All the requirements around attitudes are a huge opportunity. At Engine, the focus is on investing in solutions that build a better cross-walk between the people who the advertiser wants to reach and the people to whom the advertising gets sold to reach that goal.

Programmatic media

Programmatic media is all about understanding who you are looking at and what their attitudes and behaviors are.

No validation

There is no validation for many of the data solutions out there, particularly those related to attitudes.

Improving precision

At Engine, they are working in the opportunity area of data solutions to improve precision. They do that by building from the ground up using survey research, starting with the voice of the customer.

The measure of success

Their success can get measured by comparing the legacy model with the new model to see how many more people they have reached in a particular segment. Their best measure of success is whether or not they got to the person they wanted to get to. Their success can also be measured by whether or not the campaign ultimately had the expected effect.

The future of ad measurement

The future of ad measurement looks very exciting for Andy. We will soon reach the point where most things are connected. All consumers will get connected. Therefore all advertising delivery will be digital and connected in some way, shape, or form. The best thing from a data standpoint is to have connected data sets across the entire consumer journey.

Protocols

Andy feels that it is important for us to have the right protocols in place to protect people and their wishes and desires.

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Sima is excited to welcome Erinn Taylor as her guest for today’s podcast. Erinn is the Executive Vice President of Product and Platform at Canvs AI. He has had a breadth of experience within the field of market research.

Some of Erinn’s background

Erinn grew up in market research. He worked his way up at several different organizations. About a year ago, he met the founder of Canvs and was intrigued with what he was doing related to text analytics, so he joined Canvs.

A great year

Canvs had a great year last year. They have done a lot technology-wise, and they have grown the platform.

Canvs

Canvs is a text analytics platform, and they think it is the best solution in marketing. They call it an evolution of sentiment analysis because they use more nuanced emotions. They take data and analyze it and provide themes and insights directly from the consumer.

The organization is about six or seven years old. They work with companies like Disney and Netflix.

Speaking unnaturally

With the technology they were using, they could expose what consumers and viewers were seeing. And what they were saying about programs. They utilized that information to learn how people speak unnaturally. They found that in a modern era with a lot of digital text, we tend to talk differently than we might otherwise because we use shorthand and emojis.

Learning new things 

Things change over time, so we have to keep learning. Many other applications have tried to do text analytics, but it requires a lot of training and data to get and understand a small sample, and Canvs has already done that work.

An ontology

Canvs has an ontology that they have cultivated through AI and NLP. They also have some social listening tools to see people on YouTube, Facebook, or Instagram.

The best bring-your-own-get-a-platform 

Erinn thinks that Canvs is the best bring-your-own-data-platform in the industry to identify what people are saying. Also, to identify how they are feeling about what they are talking about.

Any survey platform

The Canvs platform is intelligent and easy to use. It works with any survey platform.

It’s quick and easy

It only takes a few clicks of the mouse, and most data sets get processed in one to two minutes.

Sentiment analysis vs emotions

Traditional sentiment analysis is positive/negative/neutral. At Canvs, they have their Canvs proprietary emotional mapping, with which they can identify forty-two core emotions. And they have a data science team who have spent many years getting to understand groupings around the core emotions.

Things change over time

People and language change over time, so they continue to upgrade.

The way people speak

Through the way people speak, they can identify and expose how they are feeling. And also the context in which they are feeling it. And they can work out what people are talking about.

Use cases

Canvs started in the media space. Currently, they work with companies like Disney to find out how well people are speaking about their airings, and to see how their movies are trending. They also do ad testings to see what people are saying about the ads.

The goal for the future

They would like Canvs to be the core platform for analyzing unstructured data for organizations, brands, research partners, and communities.

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Sima Vasa is excited to welcome Hunter Thurman, the President and Founder of Alpha-Diver, as her guest today. Hunter has been in the insights and strategy world for the last twenty years.

Some background for Alpha-Diver

In 2011, Hunter got to a point where he felt dissatisfied with how he was doing insights and research and translating it into a strategy. (Subsequently, he learned that his approach had been model-free. And the industry had also been operating in a model-free way at the time.)

Around that time, Hunter started exploring, stepping out of the industry, and hanging around with neuroscientists, psychologists, and people from the world of academia. He forged a copasetic relationship with the academics, which became the basis for the company, Alpha-Diver. They use mostly neuroscience. But they also use a lot of psychology, which they translate for business applications to answer the questions that help companies serve their consumers.

A framework

Hunter later realized that when you have a framework, a place to start, a foundation, and a model, it’s much more rewarding. And it can lead to much richer discoveries.

The pain point

With the model-free approach, it was hard to explain how they were doing what they were doing. Although Hunter was in a leadership role, he did not feel that he could teach very well because he did not have a model from which to work.

In a model-based space

In a model-based space, you are going out, and using an established model instead of collecting data broadly and bringing it back to an existing model.

Some surprises when talking to people in academia

Hunter was surprised at how applicable the academic knowledge that he received was. He was also surprised at how relevant those insights could be, how little connectivity there is from the academic world to the rest of the world, and how dissatisfied many academics are. He had always assumed that neuroscience would be far more technical, far more physiological, and much less applied than he discovered it truly is.

Insights challenges

Going out, and looking at insights challenges, whether you are looking at the segmentation, the journey mapping, or the drivers and barriers of your brand or category, is very chaotic, noisy, and difficult to organize. That caused researchers to overlook what is going on between the ears of a consumer. Yet we have quite a simple explanation of human cognition and the lenses we use to react to the world around us.

What a model-based approach means

A model-based approach can be likened to us using all five of our senses.

Neuroscience

What neuroscience has discovered is that there are durable and predictable drivers and barriers, which are pretty simple, and happen mostly subconsciously in people’s brains.

The 9 Whys

Hunter and his partners developed a framework that they call The 9 Whys. It tells us that there are four drivers and five barriers that can simplify the world.

The drivers

The drivers range from very rational and practical, to social and tribal, sensory and exploratory, to impulsive and instinctive. In a given context, one of those four drivers would become the primary lens that a person uses to make decisions. And in a different context, a different driver could apply to the same person.

Context

We have gained from neuroscience the recognition that context has an enormous impact on the driver that we are using. Currently, occasions are the incarnation of how the industry is looking at context.

The five barriers

The five barriers are:

Price (Price is the least punitive)

Time

Effort or Physical barriers

Social

Emotional

The questionnaire

The questionnaire that Hunter and his team are using is simple in that it can get administered in a regular survey. The data from it provides them with certain tells of an individual's psychology, and their cognitive processes, in the context they are studying. And the algorithms on the back end let the computer know what those responses mean and help the strategy team understand them.

Consumer’s decisions

From one of his studies, Hunter discovered from the subconscious data provided that the consumer’s decisions were based far more on their personal preferences than they realized. It revealed that the consumers were more self-reflective than socially reflective. The study was wildly successful, and it drove a double-digit increase.

The future

Hunter thinks that the merging of qualitative and quantitative will continue in the future.

 

Hunter’s quotes:

“I had the assumption that neuroscience was going to be far more technical and far more physiological and much less applied than I discovered it is.”

“In a model-based space, you are going out and using an established model instead of just collecting data broadly and bringing that data back to an existing model.”

“When you have a framework, a place to start, a foundation, a model, it’s much more rewarding, and it can lead to much richer discoveries.”

“We bought in that world of mostly neuroscience, but there is a lot of psychology that we use as well, translating that for business applications, to answer the questions that help companies serve their consumers.”

“What I was experiencing in the model-free was that it was hard to explain how we were doing it.”

 

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Hunter Thurman on LinkedIn

 

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