“AI will be huge in terms of its impact and helpfulness in social media analytics”

Tam Su
Tam Su

Interview with Tam Su, Senior Director of Product at Crimson Hexagon, a social media analytics company with headquarters in Boston, US.

Hi Tam, what is your background and what is included in your current role at Crimson Hexagon?

I have been in tech for about 18 years, having helped start a few. I am currently the senior director of product at Crimson Hexagon. My responsibilities include product design, strategy and user experience.

What differs Crimson Hexagon from other social media analytics platforms?

Great question. Our biggest strength over the other offerings is the strength of our tech platform and the vastness of our data store (the amount of data we store ourselves). Our CTO jokes that we have more public data than anyone on earth, except for the NSA. We have the best analysis platform, and a superior foundation, that is where we lead.

Traditionally, we are weaker on the user experience front, so we have been working hard for the last year and a half to overcome that. Look for exciting announcements from us in the coming months.

You recently received an investment of $20M in Crimson Hexagon; how will that affect your product in the near future?

As mentioned, expect exciting announcements in the next two to three months, as we launch our new offerings. These have less to do with our funding, and more with the fact that we’ve been focused on delivering a world-class user experience.

The funding will accelerate our efforts across the board – across product lines, technology, data sources, and our research pipeline as we get more into deep learning and related AI efforts.

What are your greatest challenges ahead at Crimson Hexagon when it comes to serving your customers analysis and develop your offer?

Great question. The greatest challenges for any company in an accelerated growth stage after a big funding event is the danger of not staying focused on the important stuff. In other words, a lot of companies have the tendency to say – we have the money now to spend on things that we didn’t have before, so let’s go after things that maybe we shouldn’t.

We need to stay laser focused on the most important set of needs for our customers. We continue to concentrate on social listening and analysis, with a special focus around insights derived from that data, as we build a road map to better predict customer behaviors using social data.

As we look to the future, we aim to become more prescriptive in what we can recommend to our clients.

About a year ago you introduced image analysis and logo detection. How well has that played out when it comes to quality and client adaptation?

A year ago we launched a rudimentary product; over the last year, we have sharpened those skills and continue to improve the quality of our product in terms of accuracy, volume, etc. It’s not as widely adapted as we hoped, which has to do with analysis maturity. Image analysis is in the early days; this has become an important source of data, but many of our clients are not at that point yet in terms of their journey in social media analysis.

What are the next steps when it comes to enhancing the use of image analysis?

We have an exciting pipeline, looking at scene, object and facial detection. We can tell if a person is in a restaurant or on a mountain top based on a scene. We can detect not only logos, but also items, allowing us to tell if a Starbucks logo is on a mug or on a can of coffee.

Facial detection now allows us to detect sentiment without having to analyze associated text, which may not even exist. If people smile, we know they’re happy, if they are frowning, we know they are not. Being able to discern sentiment straight from the image is huge.

How do you think the development in AI will affect the social media analytics business?

It will be huge in terms of its impact and helpfulness; it’s going to impact a number of areas, ranging from the analysis itself to certain recommendations that we may be able to offer our users. For example, this type of data is best paired with these types of charts. Another example is the idea of being able to recommend actions based on what we see. We can say that this tweet from an influencer is likely to be further amplified with certain promotional; if you promote it, it will likely be amplified by 10 times than what it is now. To be able to predict that and make recommendations is huge.

Another frontier is looking at data information and data ingestion, we are thinking about proprietary data and how AI can help us ingest proprietary corporate data, like chat logs, more efficiently, and be able to then sort through that data, make sense of it, and analyze it more effectively.

Which social platforms do you see having the most potential in the future?

We are very bullish on Tumblr, the third largest network by active users. We have a partnership with them, where we get the entire firehose. We are very excited to see their active user growth trends, and where the entire network will go.

Beyond that, Twitter and Instagram will continue to have bright futures. We are partners with Twitter, and have access to all their data. Instagram is challenging because of their limited API, but hopefully that will change.

Are there any social platforms that are closed today that you would be interested in tapping into for monitoring that would benefit your customers?

Instagram and Facebook are headaches for all of us. We all would like to know what is going on inside Facebook, with respect to the privacy of Facebook’s users. It would be great to get a greater peek in than what we are able to with current channels.

What kind of data, that you would need to do even better analysis, is the hardest to get hold of?

Facebook and Instagram; beyond that, we are curious about the various messaging apps, such as Snapchat, because our clients are becoming curious about them. We would love to get behind the curtains there, with respect to privacy restrictions, to understand what people are talking about and care about.

What kind of data or media that you do not have monitoring on today, can be interesting in the future?

The biggest thing in that regard is working on making our ability to work with proprietary corporate data more robust. We currently have an ingestion mechanism that works. Making it more open and robust so companies can gain greater value is a goal.

How do you think the media monitoring and social media analytics industry will change in the next five years?

The next five years will be very exciting for the whole industry as it grows and matures. A lot of unknowns will be shaken out of the system. Perhaps the biggest change is the ability for platforms leveraging AI to predict scenarios and outcomes in order to prescribe and recommend actions. So, in the next five years we should see that technology developing and maturing in a more visible way.

By Renata Ilitsky

“A social platform with voice recognition would be valuable, as long as it respected privacy”

Reza Sabernia - edit
Reza Sabernia

Interview with Reza Sabernia, founder and CEO of BrainMustard.

Hi Reza, please tell us which services BrainMustard provide?

BrainMustard offers a new way to scan and analyze Internet chatter and social media. We build comprehensive models of consumer behavior within the brand ecosystem. These models help our clients to find revealing, and often unexpected, insights about consumers, which in turn help companies enhance the customer experience and increase sales, sometimes by an order of magnitude.

Which type of companies benefit from your services?

The companies that benefit from our services are concerned about the consumer experience. Currently, our client roster includes a bank, a commuter railroad, a pharmaceutical company, manufacturers, retail stores (including the largest sports retailer in Canada, with 1200 stores and an apparel company in India), as well as world-famous drink brand Diageo, who sells drinks such as Captain Morgan, Smirnoff and Guinness.

What is your background and what made you start BrainMustard?

I studied computer science, specializing in natural language processing and artificial intelligence, and then received my bachelor’s of commerce degree from the University of Toronto. I received my MBA from Kellogg School of Management at Northwestern University.

When I graduated, I worked in an employee recognition startup for over a year; however, I was working on the idea of building BrainMustard throughout that time. Previously, I worked in a firm that did social media security, trying to find Internet criminals online. The technology for semantic algorithms for understanding the Internet chatter was not that different from what we use for BrainMustard, so I learned about information flows online and how to analyze the content to see what constitutes valuable signals and what is noise. That knowledge helped me in forming the technology that is used for BrainMustard.

I saw that the tools used for social media monitoring and sentiment analysis were limited to tactical purposes. They’re tactical because they helped social media specialists and members of marketing departments read verbatim sentiments and itemized information, as well as engage in a direct conversation with users online, But there were no tools in the market that offered a big picture solution that professionals with more strategic roles could utilize to make decisions.

I also noticed that social media monitoring solutions specialists knew that end-users can only process a couple of thousand messages at best in any given month, while they had access to tens of millions of relevant content volume. Because they wanted 100% accuracy and relevancy, they threw away a lot of content. Some of that content was irrelevant, but a substantial amount of the discards embodied interesting information. Because of this practice, brands didn’t get all of that information, and were losing important data.

Based on that need in the industry, I created BrainMustard. We don’t throw away any messages; instead, we allow them to get crystallized into thematic clusters and organically form modules that can be studied. These modules can offer new and fascinating insights from social media. We have a bottom up approach; if cluster is irrelevant, it will be deleted in the very end when we know what it means, but it will not be thrown out just because we want 100% accuracy. We don’t mind examining a lot of noise in order to capture all the information that’s out there.

What are your responsibilities in your current role at BrainMustard?

I am the Founder and CEO of BrainMustard. I supervise technology and business development, and meet with new clients to see what their needs are and if we can offer value to them.

A big focus of mine is on innovation. For example, we are building technology to track customer behavior inside stores. We are working on identifying new ways to flag and tabulate information, which is a never-ending process. We work closely with our clients to see how we can offer them meaningful information. A big portion of our innovation comes from solving the problems for our clients.

What is the current focus for BrainMustard in the near future?

Our current goal is to offer generalized industry reports. We have been working with brands directly, but are now trying to create syndicates that would be useful for all the brands in a particular industry. It’s a challenge so far, but we hope to have it ready in the next few months. Currently, our focus is on customized customer experience maps and social influence maps, which I would say is the core of our business and an area where we outperform our competitors.

Can you give specific examples where one or more of your clients have made changes in their communication, products or similar, based on the information or analysis you provided?

Bauer Hockey came to us because they are dealing with a changing marketplace. To increase hockey popularity among millennials and immigrants, Bauer Hockey collaborated with NHL to build Wal-Mart-size flagship stores in the original six cities where the NHL started. Those six cities are really the hockey mother-ship, the places where hockey is the most popular. The goal of the stores is to offer a memorable hockey experience that resonates with the parents and the kids. Bauer wanted to know what matters most to parents whose kids play hockey. They believed that a major concern for parents was safety, and they wanted to make the focus of the stores safety.

However, after BrainMustard provided an analysis of the hockey eco system, it became evident that while safety was an issue for parents in general, it was not the main issue for parents of children who actually played hockey; instead, what was concerning to them was the ice time their kids would get during weekly practices and games. When the first store opened in Boston, its main focus was on the improvement of performance in order to allow children getting more ice time. Because of the innovative information that BrainMustard provided, the store is a huge success.

Another example is of a startup that became a very successful franchise and was actually acquired by a major coffee and beverage company. The founder wanted to offer exceptional experience for tea drinkers, but the problem was that he was trying to do it in San Francisco, which is a saturated market. BrainMustard looked at the social spectrum and identified segments, such as ritualistic drinkers and the health conscious.

We also focused on segments that were overlooked, such as stay-at-home moms. These are women who are career oriented and successful, but who choose to stay home after having children. The problem is these women can feel disheartened when seeing their peers climbing the work ladder, while they continue to be just mothers. These moms not only feel like they are missing something, but they also don’t have much to share at social gatherings. They can only talk about kids, while other women talk about their jobs.

To tap into this market, the founder and BrainMustard came up with idea of creating gears for the tea drinking experience that is exotic and has a story behind it. The gears consisted of pots and saucers and other items. The items encourage ritualistic behavior and a certain level of preparation. The founder would sell gears and offer workshops to prepare exotic teas, which had a story behind them. This strategy was a huge success because moms could invite friends over and have a story to share about that experience.

What information from external sources do you use today to make your analysis, and which are the most important?

We use information from many companies; one of the better ones is actually Twingly. We use Twingly for blog sources; their service is great. When we have questions or requests, they responded quickly. We also get information about Facebook users from another source, and are provided with forum content, as well as having our own in house solutions. And there are many others.

Is there any consumer data that is difficult to retrieve today that would help you provide an even better service?

It’s all about privacy today; for instance, Facebook data is great, but most providers provide it as an aggregate form. While that is good for dashboards, it is not very beneficial for insight to know what words people use and what are the main associations between brands.

Another source that would help us provide better service would be comments from Amazon, which can offer value because they’re words from exact customers who have used the product and are sharing their experience. Pretty much the rest is accessible, which is good news.

Are there any social platforms that are closed today that you would be interested in tapping into for monitoring that would benefit your customers?

Facebook information would be very interesting. As well, millennials are active in teleconferencing and voice software, so if there was a social platform with voice recognition, that would be valuable, as long as it respected privacy.

What are your greatest challenges ahead at BrainMustard?

Our greatest challenges are in developing ways to communicate findings effectively. Our findings are numerous and we work hard to make the narratives behind them understood. We are developing new technologies that will correlate what people do either consciously or subconsciously in the stores with what they say on social media. We’re working on being able to segment the market based on people’s actual store behavior so our insights won’t be based simply on what people say on social media.

How do you think the business you are in will change in the next 5-10 years?

We are not in a business that is as fast moving as some pure technology players in the market. In terms of insights, that will not change much because societies don’t change as fast as technology does. The output of our solutions wouldn’t change as much as the backbone of the system, which will. We have to keep up with platforms to offer solid coverage; while the output will not change that much; the technology used for the sake of presentation will change. For example, augmented reality and 3D printers are new innovative ways to offer a more tangible and interesting output with additional dimensions to clients.

By Renata Ilitsky