Bringing Synergy to the Media Marketplace An Interview with KINESSO’s Jarrod Martin

The media industry’s increasing complexity demands solutions that more fully drive results. In some cases it is strictly tactical solution but in the case of IPG’s KINESSO, it’s a strategic combination of advanced technology and agency acumen. KINESSO’s Global CEO, Jarrod Martin, explained that his is a, “tech driven performance agency that gives clients the clarity and confidence to make decisions to drive results.” 

Essentially that means that advanced tech, using AI, works seamlessly with three agency divisions, crossing human expertise with data. “That's what we're trying to achieve; a simpler organization that is a combination of media and data and technology that helps to drive results for our clients. Think of us as an intel ship,” he affirmed.

According to Martin, “There's always a compromise between specialism and integration. We've created an agency that is more sustainable and future focused to have complexity where it's needed and simplicity where it's not.” KINESSO is an integration of three separate units that were operating in a coordinated but not in an integrated fashion. “We were building technology that wasn't necessarily being adopted at the scale needed to be to have the maximum impact. By bringing these groups together, there's less distance between the users of the technology and the people who develop the technology, between the people who have hands on keyboards and are running programmatic search and social campaigns and the client,” he added.

KINESSO, he explained, “offers a specific capability that exists within the agency arsenal like a collection of specialists.” This approach can serves clients of all types from a scaled in-house model to an embedded contractor to a traditional approach, depending on how the client’s business is structured. In this way the agency can either build the infrastructure for a client that has their own systems and protocols in-house, or embed as a contractor with certification access to function in a variety of services, or perform as a traditional agency working as a team on behalf of the client. It is in the flexibility and the expansive range of service offerings that gives KINESSO its competitive edge.

When implementing data solutions, privacy is paramount. “Just to be clear on this, we leverage first party data but Axiom handles all of that first party data on our behalf. Axiom has the credentials, the processes to ensure that we don't make mistakes and do things incorrectly,” Martin asserted. From there, “Axiom might merge that with other data to turn that into audiences where we can then access.” At this point it is anonymized and can be used in a variety of ways whether merging with other data sets or directly applying to processes like segmentations to better identify audiences. “And then,” he explained, “we activate those audiences in media. Axiom and Live Ramp get involved to convert that data into something that connects to partners. So we're really playing in a sand box that's privacy compliant to build those audiences to activate for our clients. We use our technologies to create better data that surrounds the client's first party data and give them a full view of what that consumer looks like using multiple onboarding providers.”

KINESSO has organized itself against four key product pillars – audiences, planning and optimization activation and finally, measurement. “The group or product family is used to build the audiences. Then the audience data flows into the planning and optimization module and then into the planning tool. That planning tool is able to calculate cross channel reach and frequency based on the optimized channel mix to deliver an outcome. At that point we go to the partner level where we figure out which partner will deliver on that channel mix and then we activate,” he stated and then admitted, “At the point of activation, things get a bit messy, because you've got all these walled gardens that won't allow you to combine data. But we have a sense during the planning phase of what our expected outcome will be, whether reach, consideration or some other performance outcome. Once it gets to the platforms, we optimize it within those platforms to get the best possible result.” KINESSO also experiments along the way to, “figure out how the results that occur within a platform reflect reality to find out what's the true impact of each platform in generating a sale.”

When it comes to the use of AI and how it will impact jobs in the future, Martin is introspective. “AI can take a lot of the drudgery out of what we do so that we can focus on value, add tasks and build better relationships with clients. What has happened in the last few years with the rise of digital is that we get obsessed with the plumbing, getting the plumbing right and making sure that we don't make mistakes. That's taken the focus away from bigger and better ideas and bigger and better client relationships. That's where the future is for humans inside this ecosystem with AI.”

It is clear that KINESSO has created a synergistic and open approach to today’s media world. “We've created a new positioning, mission, values, manifesto, visual identity, etc., etc., etc. Some people in our organization don't distinguish between the name and the brand. I would say the name is the same but the brand is very different. Where we're looking to move is in a different direction with more simplicity, better integration and being more agile in how we work,” he concluded.

This article first appeared in www.MediaVillage.com

Artwork by Charlene Weisler

 

TVOT 2023 with Tracy Swedlow

May 24-25 marks the next TV of Tomorrow conference in San Fransisco and a time of vast changes in the media landscape. Tracy Swedlow, CEO TMRW Corp. and Co-Executive Producer of TV of Tomorrow (TVOT) among other notable achievements, gives us an overview of what has been happening, where we are going and what to expect at the San Francisco conference.

Charlene Weisler: Can you give me a short recap of the media landscape for the past three years and any impact good or bad from the pandemic?

Tracy Swedlow: Subscription-based streaming services grew rapidly during the pandemic, but there’s been quite a slowdown in subscription growth rates in recent months (in part, because people are no longer sitting around the house with nothing to do, as they were during the lockdown; and in part simply because subscriptions are increasingly expensive) and churn rates, which have always been an issue for SVOD, are increasing significantly (40% according to a new Deloitte study). FAST/AVOD has also been growing rapidly, partly as a result of subscription-inflation. The fact that it’s accessed primarily on CTV platforms presents significant opportunities for gathering granular audience data and for enabling FAST’s monetization through increasingly personalized, targeted and interactive advertising—so, for example, one thing we’ve been seeing recently is a swell of interest in new, interactive advertising formats.

In addition to fueling the growth of streaming, it’s arguable that the pandemic also accelerated the rise of shoppable TV by increasing consumer usage of/familiarity with ecommerce: again, the wide distribution of CTV platforms has also been a catalyst here.

Another significant development over the past three years—even though it’s one whose full potential hasn’t yet been realized—is the ongoing roll-out of broadcast TV’s ATSC 3.0 standard (which has the consumer-facing brand, NEXTGEN TV) and the increasing availability of TV sets that support it. This will allow major improvements in audience measurement, advanced advertising and interactive services and promises a renaissance for local broadcasting.

Last but not least, the past three years have seen significant attempts to improve audience-measurement and the data-driven advertising it enables, not only through competition and the innovation that competition encourages, but through major industry coalitions and collaborations.

Weisler: What is this year’s TVOT about? And why?

Swedlow: We don't really impose an overarching theme on each event since we cover multiple, often quite disparate areas of and developments in the TV industry (everything from JICs to Web3, from AI to local CTV), and trying to fit everything into a unified concept would probably come across as somewhat forced. However, one thing I think I can say that TVOT is about this year—and every year—is community; a chance for leaders who’ve been working in this industry (and attending the show) for many years and who’ve built up a huge store of knowledge and experience not only to connect with one another but to meet and exchange ideas with emerging talent and new entrants into the TV business. The event is a little like a salon, where disparate ideas, strategies and industry sectors cross-pollinate.

Weisler: What are the top issues for media companies coming up?

Swedlow: Improving how viewership is measured in an increasingly multiplatform environment; improving how the data thus generated is interpreted and made use of while, and this is an issue that seems to be increasingly on everyone’s mind, maintaining privacy and promoting transparency; and in general, figuring out how to survive and thrive as some areas of the industry undergo rapid disruption (for example, by AI) while other areas (such as SVOD) that saw disruption just a few years ago reach maturity and possibly even start to retrench.

Weisler: What will the impact of AI be?

Swedlow: Honestly, I think the question is really, “What will the impact of AI not be?” I’m convinced it will affect most if not all areas of our industry—and, for that matter, of our daily life. Content creation, distribution and discovery, creative versioning for addressable advertising and data collaboration are just a few areas that immediately spring to mind.

Weisler: Give me three predictions for the next 3 years.

Swedlow: First, NEXTGEN TV will lead to a renaissance in local broadcasting, enabling improvements in audience measurement/data and advanced advertising, as well as more interactivity and multiple new consumer-facing services. Second, every press release announcing a new product will have “AI” in the headline, but (joking aside) this won’t be the usual marketing hype; AI will have huge consequences for television, advertising and pretty much every other area of human endeavor. And third, TV advertisers will know a lot more about audiences than they ever imagined they could, and those audiences will find advertising way less intrusive—in fact they might actually enjoy watching it, because it will be finely tailored to their interests and needs.

This article first appeared in Mediapost.

 

Using AI to Translate Human Language into Data. An Interview with Data Scientist Dr. Melodie Du

The rush to develop AI is leading to some fascinating applications. Take, for example, Natural Language Processing (NLP) which is a subfield of AI that according to Inuvo’s Data Scientist, Dr. Melodie Du, deals with the interaction between computers and human language to enable computers to interpret human language and then generate data from that interpretation.

Her company is expanding on the concept of NLP by the creation of a language-model-based, generative AI capable of identifying the words associated with the audience for any product, service, or brand. “The result is the ability to reach those audiences utilizing our AI systems without requiring any client or third-party data,” she explained.

Charlene Weisler: How does Inuvos’s NLP facilitate consumer intent measurement?

Dr. Melodie Du: We convert the entire open web into an interconnected language model of what we call intent signals. Then we assign categories, sentiment, and deduced demographic info to these signals based on a series of interconnected AI systems. This means our AI breaks down, in real time, any piece of information to its core signals and then aggregates them to determine if it matches against the custom intent models our AI builds for each client’s product, service or brand. This approach allows for a meaningful analysis on all ad impressions even when there isn’t a cookie or an ID-based profile available, including impressions from Safari.

Weisler: You speak of vectorization. What is that?

Du: In NLP, vectorization refers to the process of converting text data into numerical vectors or arrays that can be processed by machine-learning algorithms. Vectorization is a critical step in many NLP tasks, such as text classification, sentiment analysis, and language modeling.

Vectorization is essential in NLP because it enables machine-learning algorithms to process and analyze textual data typically unstructured and difficult for computers to understand. By converting text data into numerical vectors, machine-learning algorithms can more readily identify data patterns and relationships and make predictions or classifications based on that.

Weisler: How does it meet or improve measurement in a privacy-compliant manner?

Du: Without cookies, attribution on a per-source basis is difficult for anyone, especially in channels like display and CTV where the majority of conversions are not associated with clicks, and thus cannot append click IDs. We’ve approached this using different machine learning with an approach we call our Inuvo Media Mix Models. By analyzing the constant variations in spend between traffic and spend sources, our AI is able to probabilistically determine what channels / tactics are driving conversions – totally agnostic of cookies and identifiers.

Weisler: Can you give me an example of how this all works?

Du: When conducting a contextual analysis of a web page, hundreds or thousands of signals may be returned, but most of them are usually not related to the main topic. We use a combination of methods—including frequency-based probabilities, concept-graph weights, taxonomy and vectorization of the concepts—to filter out irrelevant signals and suggest relevant ones that may not be explicitly mentioned in the text. This approach can be especially valuable in a consumer-privacy-first world devoid of cookies, where inferring user intent with a single page visit is crucial. By using these methods and our language model, our AI can more accurately infer user intent and suggest relevant content, even if it is not explicitly mentioned on the page.

Weisler: What are the challenges with these methods (NLP and vectorization)?

Du: One of the main challenges of NLP is the ambiguity and complexity of natural language. Natural language is full of nuances, idioms, expressions, and sarcasm, which are difficult for machines to understand. The meaning of a sentence can vary depending on the context, and sometimes even subtle changes in wording can alter its meaning. The challenge of vectorization is to represent complex data in a way that can be efficiently processed by algorithms. The challenge here is scale. When you’re dealing with high-dimensional data, such as text, the number of features can be in the millions or billions. Based on Inuvo's domain expertise, we devolved our own model to make this procedure less time-consuming while maintaining compactness and precision.

Weisler: How can these methods be best implemented?

Du: Our team has implemented custom pipelines for scaling our AI algorithms to handle big data, using technologies like Spark, Hadoop, and MapReduce, as well as implementing our models in both Java and Python—and we hold several key patents for these components.

Weisler: Is anyone else doing this?

Du: No. We are not aware of any company that has created a similar AI system. Inuvo has an end-to-end advertising solution that leverages AI language models at its core to generate audience insights, deliver media and determine attribution across channels. Others might have pieces or parts, but no one is able to generate audience models purely from an AI.

Weisler: How can this be rolled out in scale?

Du: Inuvo has invested a significant number of resources and capital to build out a world-class infrastructure that allows us to process the entire daily open web traffic signals in North America—and have our AI process all that data to regenerate its learning at a back-breaking speed of every 5 minutes. Our AI is then able to create custom client intent models in real time to ensure the client’s message reaches its intended audience at the right time and before others. 

This article first appeared in Mediapost.

 

Encouraging Girls in Technology. Q&A with Sonpreet Bhatia.

Sonpreet Bhatia, CTO and co-founder of MobileROI with deep and extensive data science experience. She started at Watson Labs at IBM and then moved to Wall Street where she got firsthand experience in monitoring how products move through the consumer funnel. From there she created her own company, My-City-Way, where she created a mobile platform to engage with consumers before co-founding MobileROI, a mobile marketing automation company. Bhatia is one of those rare technologists whose profound sense of mission lends not only to for-profit enterprises but also to those initiatives designed to help a common cause – in her case, poor women in India.     

In this fascinating interview, Bhatia talks about her mobile engagement and marketing company MobileROI and how it captures data to enable brands to uniquely interact with customers. She also talks about what it is like to be a female CTO, the opportunities for women to excel in the STEM (Science, Technology, Engineering, Math) curriculum and how the media landscape will change with the advancement of artificial intelligence.

There are five videos in the interview that can be viewed at www.weislermedia.blogspot.com:

Subject                                                                 length (minutes)

Background                                                         4:33

MobileROI                                                          5:57

Second Screen                                                     4:15

Women in Technology                                        5:52

Predictions and AI                                               3:52

Charlene Weisler interviews MobileROI CTO Sonpreet Bhatia who talks about her background and how she got into techology in this 4:33 minute video:

CW: Tell me about MobileROI.

SB: I am the CTO of MobileROI, which is a mobile automation platform that connects brands to the customer real-time through highly personalized content, offers and experiences across the entire customer journey. With smartphones, we now have a very powerful computer in our pocket and it is on for 24 hours. How can brands take advantage of that and how can customers get the most relevant experience? We capture the intent and customer context, including the time of day, location and weather, among other things, and we help the brand connect with the customer using all of the external and structural signals around them. For example, is it raining outside? Is there an event going on in that area? What is going on around the customer and how can a brand organically participate in a person’s life by providing the right information or experience at the right time? We also use external sensors such as beacons so that if a customer walks into a store that is beacon enabled, the brand can connect to the history of that customer so that she can get the best, most personal service and relevant shopping experience.

Sonpreet Bhatia talks to Charlene Weisler about her company MobileROI in this 5:57 minute video:

 

CW: What advice can you give a woman who is considering a career in technology?

SB: Since I come from a family of engineers the idea of STEM was instilled in me early on. It was the atmosphere in which I was brought up. So I think the whole idea of STEM needs to be instilled early in little girls. We see companies like Goldieblox, for example, which is trying to create toys that are not standard types of girl-oriented toys in that they have a science basis to it. There is a lot of information, resources and mentorship networks, such as Sheryl Sandburg’s Lean In Mentorship network, which can be very beneficial for a woman considering a career in technology. There are women who are making good progress in the industry. So start networking. Start reaching out. Connect with other women. Find female mentors.

CW: Tell me about the platform you are building to bring more women into STEM.

SB: This technology platform would give an opportunity for young girls in developing countries to start thinking about the area of STEM. This platform will actually help them become entrepreneurs as the students will be thinking of new ideas that can actually change the world.  They can form groups. They create ideas and build plans. They will have a platform where they can actually pitch to VCs. Remember this is in developing countries where these women might have never had the opportunity to understand STEM, nonetheless get their ideas in front of investors who can help them bring their ideas to life. The ultimate goal is to solve for poverty in these developing nations by empowering women.

How can more women enter careers in technology? MobileROI CTO Sonpreet Bhatia talks about STEM and her new initiative in this 5:52 minute video:

CW: Where do you see the technology landscape going in the next few years?

SB: You have probably heard that software is eating the world. That is where I think it is going. Technology is going to rule in every sector. Because of technology there will be a lot of innovation and a lot of changes. One area that we once saw a while ago, then it faded away and now it is coming back is artificial intelligence. I think that a lot of jobs that are repetitive may be taken over by artificial intelligence systems.

In this final video, Charlene Weisler asks Sonpreet Bhatia about how the video and digital landscape may look in the next three to five years and the impact of Artificial Intelligence. This video is 3:52 minutes:

This article was first published in www.MediaBizBloggers.com