Oct 02, 2018
Machine Learning (ML) has been heralded as a technology breakthrough that has the capacity to fundamentally change the way that we approach many tasks in our daily life, from communicating with friends to commuting to work, and much more in between.
For marketers, machine learning opens the door to predictive analytics, which provide a way to anticipate consumer trends before they actually happen. According to SAS, predictive analytics are defined as “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.”
Used in this way, Machine Learning can automatically parse consumer behavior that happened in the recent past in order to predict future buying habits. This type of “crystal ball” potential makes ML highly attractive to marketers. Imaging being able to target people precisely with personalized communications that match their preferences and buying history, in order to sell them exactly the right thing at the right time. That is the promise ML offers to marketers.
But in order to make accurate predictions, we need to know what we are predicting, and we need to be able to measure the efficacy of our predictions. In ML, there are three primary measurement parameters that can be applied: Accuracy, Precision, and Recall.
- “Accuracy” is defined as correct predictions divided by the total number of predictions (Correct Predictions/(Correct Predictions+Incorrect Predictions). Although accuracy is an understandable everyday metric, it’s actually a very poor measure of how well a machine learning algorithm is doing. For the sake of example, let’s say that we’d like to predict what percentage of the news articles mentioning a particular company are financial, in terms of topic. Using an old-fashioned rules-based approach, we may have programmed the computer to mark articles as “financial news” if they contained specific phrases like “share price”, “%,” and “$.” This would undoubtedly uncover a number of financial articles, but it would also likely miss some articles and surface a high number of false positives.
Now, let’s say that on the average, 10% of news articles mentioning Company X are financial. This means that 90% of news articles are NOT financial. We could create a machine learning model, known as a majority class classifier, that would always predict that an article is NOT financial and it would have 90% accuracy. But this model would be useless because it would never predict financial news articles and we might be “tricked” into thinking that they do not exist. This issue is known as “Class Imbalance” in machine learning and it is why accuracy is not necessarily the best metric for measuring ML models.
- Precision, defined as the number of True Positives divided by all Positives (both true and false), adds another layer of insight by illustrating how “precise” the algorithm is. So, using the same example, let’s say that for every 100 articles, we predict that we will have 10 true positives (correct financial predictions) and 40 false positives (incorrect financial predictions). Our precision would be 20%. This will provide us with some additional nuance to determine how well our model is performing.
- Recall is another lens through which to measure the performance of ML models. Defined as True Positives/ (True Positives + False Negatives), recall is essentially a measure of how many relevant articles were found, or recalled. Let’s say we know that yesterday, 10 financial articles came through our system, and we predicted 5 articles correctly (True Positive) and missed 5 articles (False Negative). Our recall would be 50%.
Precision and recall give us a better sense of how our algorithm is performing on a class-by class basis, for both financial and non-financial news. If we were to rely solely on accuracy, we would know how we are performing overall, but could also be led to some incorrect assumptions. And in some cases, those kinds of mistakes can be costly. For example, ML is used to predict credit card fraud. Let’s say that 1 in every 100,000 transactions is fraudulent. In this situation, it would be very important to measure precision and recall, because the goal is to predict fraud when it happens. If we rely only on accuracy, we might be tricked into thinking our algorithm is 99.999% accurate.
So, let’s create a Machine Learning model that evaluates and predicts financial articles about Company X. To do this, we will use a supervised learning approach, in which we provide a set of data for the computer to learn from, and we will look for patterns based on the evaluation of 350,000 features. The features could include whether the URL is from a financial news source like WSJ, Reuters, or Forbes; whether NASDAQ or DOW appear in the title; how many times the word “share” appears; and other text statistics.
If our ML model demonstrates 90% accuracy (meaning that 90/100 of our predictions are correct), 90% precision and 90% recall, it will not only result in better capture of financial news articles, it will also cut down on false positives and false negatives.
Once we have a model that yields sufficient results, we can engineer features that will help it to predict outcomes through data enhancement, which can ensure any data that is coming into the business is being filtered to maximize its value. The training data used in machine learning can often be enhanced by extracting features from the raw data collected. In our example, this would include marking articles as financial, predicting the sentiment of an article, extracting entities for customers, and more. This kind of data enhancement, or augmentation, increases the predictive power of learning algorithms by creating features from raw data that help facilitate the learning process.
Ultimately, the question of whether ML will live up to the hype for marketers depends on two conditions. The first is the validity of the data. ML models are really only as good as the data upon which they are based. If the data is old, tainted, or otherwise questionable, the model won’t work.
The second variable is the ability to visualize data in a way that makes sense. Regardless of which type of model marketers choose to work with, they will need to implement a data visualization strategy that helps easily digest and make sense of the information being tracked. Data visualization not only appeals to the eye, it can also be used to inform, inspire and guide actions based on customer behavior (and other business information). Machine learning-based data visualization tools help businesses to optimize operations and make important decisions. Without a good visualization strategy in place, all the data in the world may not help you make good decisions.
As an example of data visualization, Tickr’s solution implements a feature called MetaCloud, which uses machine learning to generate keywords in a news article, then visualizes them in a new and intuitive way. MetaCloud provides a deeper look inside a news article within the wider context of how it relates to other key phrases and topics being discussed in the news. Through a conversation flow analysis that illustrates the word connectivity between major themes, keywords, and topics of interest, MetaCloud allows people to easily grasp the meaning of the data.
We are just at the top of the iceberg of what ML is capable of, in terms of predictive analytics. But with 91% of top marketers saying that they are either fully committed to or already implementing predictive marketing, change is coming soon. Provided that the data is set up right and visualized in a digestible way, ML can be an invaluable asset, especially when applied to specific marketing challenges, such as qualifying and prioritizing leads or bringing the right product to market at the right time. Savvy marketers are not only embracing ML, but also learning more about how to measure its efficacy, in order to make sure that they get the most from their predictive models.
Image Credit: Enterprise Management 360
“The key to artificial intelligence has always been the representation.”
—Jeff Hawkins, Founder of Palm and Handspring
This blog post is authored by Sam Kahn, Data Scientist extraordinaire at Tickr. Prior to Tickr, Sam built machine learning applications for SalesForce.com, Market.Spae, NASA Ames Research Center, and Fullpower Technologies.
Marketers have been hearing a lot about Artificial Intelligence (AI) and Machine Learning (ML) for the past few years, but many are still wondering how they can use these new technologies to capture the hearts and minds of human beings.
This blog post will provide a few concrete tips for utilizing AI and ML to achieve specific marketing objectives. But before we do that, let’s take a step back and look at the bigger picture of the disciplines of AI and ML.
What is Machine Learning?
Artificial Intelligence is basically the science of making things smart. It is the study of agents that perceive the world around them, form plans and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience, and decision theory. Many fields fall under the umbrella of AI, such as computer vision, robotics, natural language processing and machine learning.
Machine Learning is a subfield of artificial intelligence. Quite simply, it uses algorithms to help computers find complex patterns in data, and to learn on their own. This view of machine learning can be traced back to Arthur Samuel’s definition from 1959:
“Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed.”
Arthur Samuel was one of the pioneers of machine learning. While at IBM, he developed a program that learned how to play checkers better than him. The Samuel Checkers-playing Program was among the world's first successful self-learning programs, and as such a very early demonstration of the fundamental concept of artificial intelligence (AI).
One might wonder why it’s important for machines to learn on their own without being programmed. One reason is that manual programming is slow. Another would be that humans often get things wrong. Traditionally used for making predictions, machine learning can be used to predict a wide range of things, from the outcome of a baseball game to the quality of wines.
Here are a few widely publicized examples of machine learning applications we encounter on a daily basis:
- The heavily hyped, self-driving Google car is the essence of machine learning.
- Online recommendation offers such as those from Amazon and Netflix apply machine learning in our everyday lives.
- Machine learning combined with linguistic rule creation enable us to follow what people are saying on Twitter and Facebook
- Machine learning is commonly used for fraud detection and other forms of online security
Machine learning is capable of analyzing enormous volumes of data – in fact, the more data a model has, the better it will perform. This enables it to do things that would have been cost-prohibitive even a decade ago. In a business setting, ML can be used to predict outcomes such as the likelihood of customer churn, what product features will be most successful, the best path to upsell new services, and more.
Unsupervised Machine Learning
In one type of machine learning, the machine is “unsupervised,” or asked to learn from data that is unlabeled. For example, if unlabeled data about clients is fed into an unsupervised machine learning algorithm, it will naturally group similar clients together into clusters based on their features. These clusters of clients often exhibit similar purchasing habits, upsell potential, and other features. This is one way to segment a business into peer groups.
Tickr’s MetaCloud algorithm is another great example of unsupervised machine learning. We feed it text and it uses complex statistical patterns to extract keywords that are mentioned in the text.
Similarly, our Top Stories algorithm uses unsupervised machine learning to look at news articles to determine how “similar’ they are, and then decides whether they should be clustered together.
Supervised Machine Learning
The other way to approach machine learning happens when the machine is “supervised,” or supplied with training data to learn from. In order to find the correct answer, machines are able to develop algorithms that can start to identify patterns and predict outcomes. But in order for supervised ML to work properly, the data needs to be labeled accurately. This is essential, because the accuracy of the predictions will correspond directly to the accuracy of the labeling.
How can PR practitioners use Machine Learning?
There are a number of ways that machine learning can be used by PR practitioners, as well as other members of the marketing team. Here are three concrete ways:
- Generate Actionable Insights. A machine learning model that uses statistical patterns to extract keywords from text can help to surface insights about customer preferences and behavior. It can also help to illustrate some of the major themes and topics in the traditional and social media activity about a particular industry, company, product, or person. For example, if we use a machine learning model to find news article about a product, it can also tell us what kinds of related sub-topics and keywords are being associated with that product. This can provide valuable insight into the wider context around these stories, and how they relate to the other key phrases and topics in the news. Because machine learning is capable of accommodating huge volumes of data quickly and easily, the insights can be surfaced in real-time, and prescriptive action can be taken immediately. It allows you to contextualize existing coverage, and readjust messaging and outreach strategy on the fly.
- Improve the Signal:Noise Ratio. If you are like most PR practitioners, you spend your day drinking from the firehose when it comes to incoming news and social activity around your brand. Machine Learning can reduce the noise in your news feed by grouping together articles that are similar, and by extracting mentions of specific companies, people, products, or other keywords in those articles. This can provide highly tailored, at-a glance comparisons of trending stories that are most relevant to your brand, along with immediate metrics on reach, engagement and velocity.
- Track customer sentiment. There are many tools out there that help to monitor the quantity of news and social activity, but discerning the quality of that activity can be more difficult. A machine learning model can be used to determine whether a news article is positive or negative, so that brand marketers can keep their finger on the pulse and react appropriately. It can also help provide additional insight about what’s trending, in order to help dynamically shape new content in real time.
—Ginni Rometty, Chairman President and CEO of IBM
Jan 30, 2018
Let’s face it -- marketing is no longer a Monday through Friday, 8-5 pursuit. In today’s always-on environment, brands need to be there with their audiences 24/7, listening and identifying opportunities to engage. When a brand is present and responsive, it has boundless opportunities to build relationships and loyalty with its customers. But in order to do so, it needs to go beyond siloed teams, disconnected data, disparate tools and technology and a lack of collaboration between departments.
The most progressive brands are addressing this challenge by building dedicated Command Centers that display data-rich visualizations from multiple sources and allow team members from different departments to visually monitor activity, engage with customers and capture business insights. The Brand Command Center provides a central place for people to see important metrics on one cohesive dashboard that illustrates performance across multiple departments and functions in real-time. It also serves as the nerve center for the brand by utilizing technology to transform data into human relationships.
Are you interested in learning more about how a Brand Command Center can lead to enhanced loyalty and smarter decision making across the board? Get your free copy of the new whitepaper from David Beebe, Tickr's Storyteller-in-Residence. This whitepaper provides an overview of the how, why and ROI of brand command centers, along with 5 ways your brand can implement a command center in the coming year.
Oct 31, 2017
On Wednesday, November 8, 1:00 p.m. ET/10:00 a.m. PT, Sysomos' Chief Strategy Officer David Berkowitz will be joined by David Beebe, Emmy-Winning Branded Content Producer, and former VP, Global Creative and Content Marketing at Marriott International for a live discussion. The topic will be “Put Your Data to Work”: Unlock the power of metrics with a Command Center Suite.”
It’s no secret that we are currently operating in a business climate marked by constant change and volatility. In an unpredictable environment where things are constantly shifting, marketers can no longer rely exclusively on gut instinct in order to make good decisions. Real-time data is the key to keeping on top of what’s happening in the business, and it’s essential for making informed decisions and aligning teams.
Business leaders who take an active role in capturing and analyzing data and metrics have a more realistic view of their businesses. These types of performance indicators clearly illustrate the critical barriers to success, and help to provide a better understanding of how to overcome them. They also offer a more accurate picture of the organization’s performance as a whole.
But with information coming in from so many sources of, it can often be difficult to digest and synthesize everything. Many companies use a multitude of analytics solutions, some of which require expensive implementation, but often provide fundamentally confusing and often contradictory data -- and consequently, no actionable insight.
The answer is the Command Center – a central dashboard that showcases all social media and metrics into one view. The Command Center pulls together metrics from a variety of 3rd party sources, combined with social media feeds and news in order to provide a complete sense of context of how the brand is being perceived across the web. As just one example, imagine being able to see in real-time how a news announcement is impacting your e-commerce sales, media coverage and traffic on your social properties – all in one view.
Command Centers provide marketers with a 360° view of metrics and other key indicators as they happen, and empower them to create new content on the fly to engage with customers on topics they are already discussing. They enable brands to insert themselves into the existing dialogue, rather than trying to pull the audience out of those conversations in order to view branded content elsewhere.
The Command Center approach also offers an opportunity for tighter alignment across departments and teams in an always-on capacity. The brand command center is manned by a cross-disciplinary team that can apprehend data in real-time, and translate it into rapid response action.
As explained by David Beebe, “The command center approach doesn’t have to be expensive, and can actually be more economical than buying an array of one-off solutions to track data and metrics. But the benefits go beyond simply having an assortment of software – pulling everything together into one unified view enables a whole new level of alignment and collaboration between departments and teams.”
Join us for the live discussion on Wednesday, November 8th at 11:00 a.m. ET and find out how to use a Command Center to build your brand, engage your audience, and align your business – register now, as space is limited!
This round of funding will enable us to further productize our platform and unify new data streams from a multitude of business-critical applications. As explained by Tuan Palmer, a Founding Partner at Angels 3.0, “This will enable Tickr to do for enterprise data what Netflix is doing for video programming, by providing a single unified interface for a very wide range of sources, enhanced by filtering, post-processing, API support, and smart search.”
We will also use the funds to build out and accelerate Tickr’s partnership program. Many different types of businesses can benefit from reselling our platform to their existing customer base. Tickr offers a white-labeled integration that can boost revenue by expanding deal size, with zero development overhead on the reseller’s part. Reselling Tickr creates upsell opportunities as new data integrations are added. It also allows resellers to attract new customers and increase their market share by integrating new information and insight in to their existing product platforms
We’re excited about the potential we are unlocking here, and look forward to working with you to realize our vision over coming months.
Mar 13, 2017
This morning, Tickr announced its acquisition of Market.Space, a company that provides visualizations of competitive data in real time, across a wide variety of online sources, including news, blogs, social media, video, apps, and more.
We are very excited about this acquisition because it will provide a turbo boost to our overall capabilities while simultaneously accelerating our momentum in productizing our platform. The addition of Market.Space’s world class technology, in combination with the unique talents of their team members, will enable us to scale faster without compromising the high quality and reliability that you’ve come to expect from Tickr.
Market.Space helps companies follow competitors, clients, prospects, and portfolios with a full range of real-time competitive business data, in the form of visualizations and scheduled charts. Their platform uses curation and machine learning to gather, filter, and visualize social media activity, and delivers customized visual results directly into team messaging platforms like Slack, Hipchat and Office 365. As part of the acquisition, Jason Beatty, CTO and Co-founder, Market.Space, will now lead the engineering team at Tickr, supported by other members of the core development team, including Andrew Lyon, Josh Cottrell, and Sam Kahn – giving us amazing bench-depth in engineering talent.
We hope that you will join us in welcoming the Market.Space crew to our team. With this acquisition, Tickr offers the most complete solution available for tracking a brand’s presence in the cloud, with a holistic view of real-time data from thousands of sources, all enhanced by filtering, post-processing, API support, and smart search.
We’re honored to be working with some of the most well-respected brands in the world, and we appreciate your support.
Mar 09, 2017
We (politely) disagree:
1. Data is much more valuable than oil
2. Data is an unlimited commodity; it doesn't have limitations of a fossil fuel. There will be no "peak data."
3. A unit of oil occupies a given space, data can be copied at marginal cost with zero degradation. Much tougher to use as a bargaining chip if it can be everywhere, freely
Interesting article, though:
At Tickr we are heavy Slack users, so this story definitely got our attention:
Employers are creepily analyzing your emails and Slack chats to see if you’re happy
Here's a very interesting analysis of Trump tweet content (text, links and images) by David Robinson, Data Scientist at Stack Overflow: "When Trump wishes the Olympic team good luck, he’s tweeting from his iPhone. When he’s insulting a rival, he’s usually tweeting from an Android. Is this an artifact showing which tweets are Trump’s own and which are by some handler?"
Jun 18, 2016
TICKR AND GLOBAL MARKETING AGENCY OMD ARE JOINING FORCES TO BRING DATA TO LIFE FOR A WEEK PACKED FULL OF INSPIRATIONAL TALKS AND INTERACTIVE VISUALIZATIONS AT THE OMD OASIS AT CANNES.
Tickr will be on hand to deliver a range of engaging and powerful insights via real-time visualisations on trending topics using #OMDOasis, #CannesLions – and not forgetting the football tournament taking place at the same time – relevant UEFA European Championship hashtags.
Feb 17, 2016
DITTO LABS AND TICKR PARTNER TO PROVIDE INTEGRATED VIEW OF SOCIAL PHOTOS AND TEXT ANALYTICS
Most marketers utilize a variety of analytics platforms to track campaign success and measure brand performance, but up until this point, have been unable to analyze the content of related photos. Today, Tickr announced a partnership with Ditto Labs,the leading provider of photo analytics for social media, to afford marketers real-time performance metrics that include social media photos.
Jan 26, 2016
Tickr has joined forces with Oracle Social Cloud to make real-time social intelligence accessible and actionable across the entire organization. Together, Tickr and Oracle Social Cloud will deliver powerful data alongside other key marketing metrics, as interactive visualizations, live in the browser. This enhanced data visualization provides the entire marketing organization with a holistic view and better understanding of business metrics and performance.
With new cloud data platforms like Oracle, marketers can access the most up-to-date information available about customers, competition and the market. Until now, the ability to share this information at scale and in business context was limited to static and out-dated reports distributed by email. Tickr provides real-time data, live in the browser, accessible to everyone on the marketing or agency team. Our relationship with Oracle Social Cloud helps connect every employee in the organization directly to their customers’ behaviors and attitudes, in real time.
You can read more about the partnership here: http://www.destinationcrm.com/Articles/CRM-News/CRM-Across-the-Wire/Tickr-Pairs-Up-with-Oracle-Social-Cloud-108718.aspx
Aug 13, 2015
Here's a look at the newest Tickr visualizations for your brand. Award-winning information designers at Bureau Oberhaeuser have worked with us bring your information to life.
Aug 05, 2015
Cannes Lions Announces Official Social Tech Partners:
Crimson Hexagon, Hootsuite & Tickr selected
Cannes Lions Announces Official Social Tech Partners
Crimson Hexagon, leader in social media analytics software; Hootsuite, the most widely used platform for managing social media; and Tickr, the real-time business and marketing performance platform, today announced that they have been selected as the official social technology partners for the Cannes Lions Festival of Creativity from June 21-27, 2015.
Just outside the Palais, Cabana No. 5 will become the “Social Tech Cabana,” where Crimson Hexagon, Hootsuite, and Tickr will be showcasing the very best technology for leveraging social data to inform creative thinking and strategy. United by the common conviction that creativity is at its best when it is informed and driven by data, the Social Tech Partners will illustrate how the left brain and right brain can work together to greatly improve the return on creative campaigns through real case studies from major consumer brands.
May 20, 2015
Sysomos and Tickr Power #AppYourService
Promotion to Launch Marriott Hotel's Mobile Request Chat Feature
Companies Combine Social Intelligence With Key Enterprise Metrics to Help Marriott Delight New Yorkers and Bring Their Brand of Exemplary Customer Service Into the Mobile Era
TORONTO, ON and SAN FRANCISCO, CA--(Marketwired - May 20, 2015) - Sysomos, the largest independent social intelligence company, and Tickr, the real-time marketing performance platform, joined forces May 14 to power enormously successful buzz marketing stunt of Marriott Hotels' Mobile Request chat feature, which allows travelers to ask for anything, anytime, anywhere. The promotion took place in various locations around Manhattan, with dozens of Marriott ambassadors in Red Coats providing surprise perks and gifts to New Yorkers.
Marriott's new Mobile Requests chat feature, available on the Marriott Mobile App, enables members of Marriott Rewards to immediately connect with 500 Marriott Hotels worldwide before, during and after their stays to request special services and amenities. The new feature has already been introduced at 46 hotels and will be rolled out this summer to the entire global portfolio of Marriott Hotels via the Marriott Mobile App.
Marriott celebrated the launch of Mobile Request on Thursday, May 14 with dozens of Marriott ambassadors in Red Coats engaging in small acts of kindness like hailing a cab or purchasing a cup of coffee. Surprise gifts were given away at key locations such as Grand Central, Central Park, Madison Square Park, Wall Street and Union Square. In addition, some lucky consumers who used the hashtag #AppYourService were met by Red Coat representatives who fulfilled their individual requests.
From a command center in Times Square, Marriott dispatched the Red Coat representatives throughout the day. Marriott tapped Tickr and Sysomos to analyze the millions of conversations that transpire on a given day in New York City to quickly learn and gain understanding about what their customers were saying. With this real-time intelligence, Marriott was able to quickly send out numerous Red Coat responses to #AppYourService requests. Throughout the day, Marriott deployed dozens of Red Coats who fulfilled thousands of requests, garnering tens of millions of social media impressions.
"The #AppYourService Command Center is a perfect example of how Tickr and Sysomos have partnered to provide marketing intelligence for some of the biggest name brands in the world," said Tyler Peppel, CEO and founder, Tickr. "The combination of Tickr's real-time integration and visualization of campaign data with Sysomos' monitoring and analytics tools creates a powerful performance platform. As businesses turn to social intelligence to get a better picture of how their company is perceived and how effective their brand marketing campaigns can be, we are seeing a growing demand for this type of real-time visualization."
"Together, Tickr and Sysomos deliver social intelligence displayed in a highly visual and customizable user interface, making it possible for premier brands like Marriott to immediately understand how their customers and fans are engaging with them," said Lauren Vaccarello, senior vice president of marketing at Sysomos. "Tickr enriches Sysomos' social intelligence platform by connecting disparate enterprise data sources -- such as sales and web metrics, as well as online and broadcast media -- to give marketers and business owners an unmatched real-time view of their brand and business."
Tickr provides a real-time, unified marketing performance platform, empowering organizations with insight into how their brand, products, and services are being perceived and discussed across the web, side-by-side with key performance metrics on advertising marketing, sales and operations. Tickr's cloud platform unifies enterprise data streams from internal business-critical applications like Marketo, Salesforce.com, and Oracle, as well as external sources from social networks like Facebook, Twitter, Pinterest and more. Tickr allows CMOs, CFOs, and other executives to easily create custom real-time dashboards to visualize, evaluate and manage the performance of their programs, from individual marketing campaigns to business-wide efforts. Top brands and agencies like Marriott, Lego, Gatorade, Nike and Edelman rely on Tickr to gain a real-time view of marketing performance, across multiple brands, geographies, and products. To learn more about Tickr, please visit www.tickr.com.
Sysomos provides the world's most valuable brands immediate context to the hundreds of millions of online conversations happening every day. Marketers rely on the Sysomos social intelligence platform to learn what their customers are saying and to understand the impact on their business. Founded in 2007, Sysomos has offices worldwide, including San Francisco, New York, London and Toronto. Learn more at www.sysomos.com.
Apr 30, 2015
Great to be a part of this with our friends at LEGO! #kronkiwongi
Apr 14, 2015
"Hire Math Men, not Mad Men." This is a must-read for anyone in the marketing ecosystem. The shift "from art to science" is gathering momentum in ways that will profoundly affect how brands, agencies, and audiences interact. At Tickr we see these forces at work every day with our big brand customers and agency partners.