Operationalizing Contextual AI in Advertising
What Industry Leaders Are Actually Doing at Scale Insights from discussion with leaders from Roku, DAZN, Philo, Intersection and Tavant at Streaming Media Connect on building real-time, AI-driven advertising systems: ▪ Why AI is now foundational to ad operations at scale ▪ How platforms align ads with tone, sentiment, and live context ▪ Where most ad tech stacks break and why ▪ The 4-layer framework powering contextual intelligence Download the Full article First Name * Last Name * Work Email * Phone Number Company * Job Title Download the Article Why This Matters Now Contextual advertising is no longer a targeting tactic, it’s becoming a must-have operational capability. Scale is challenging existing workflows Millions of creatives, real-time signals, and multi-platform delivery are overwhelming traditional systems. Ad operations are deeply fragmented Campaigns span CRM platforms, ad servers, DSPs, and reconciliation layers, creating friction at every step. Real-time decisioning is expected, but rarely operationalized Most teams still act on delayed insights instead of live signals Measurement gaps persist, but decisions can’t wait The gap between what advertisers want to know and what systems can prove remains unresolved “The question is no longer whether contextual advertising works, it’s whether your organization can execute on it and operationalize it at scale.” At scale, AI is not optional, it’s foundational The biggest constraint isn’t technology, it’s fragmented operations. AI-powered accelerators help unify workflows Context now means tone, sentiment, and real-time signals Leaders are optimizing in real time, even without perfect measurement Download the article to explore all The 4 Layers of Contextual Intelligence Learn how leading platforms structure their AI systems to process signals, enrich context, make decisions in real time, and activate across fragmented ecosystems. See how this framework works From the Experts Running AI in Production “AI isn’t giving you the answer. It’s giving you a confidence level.” Roku “The future isn’t about placing ads in a show — it’s about aligning with moments.” Philo See How Industry Leaders Are Scaling Contextual AI Download the Full Article
Web 3.0 – A Game Changer for Advertisers
Advertising has been evolving by leaps and bounds over the last few decades. As a result of technological advancements, we are now witnessing the shift of advertising from traditional forms to digital avenues. While advertisers seek to increase conversions, consumers demand data ownership and transparent information usage. Web 3.0 and the metaverse have provided a solution to this long-standing demand, and they have the potential to be game changers for both advertisers and consumers. Revolutionary Web3 The current system, web 2.0, has many major flaws, such as the big tech controlling the internet with an iron fist, a lack of transparency, and the involvement of a plethora of intermediaries. The transition to Web3, the most recent version of the internet, will provide enormous benefits, with advertisers playing a significant role. Blockchain, the underlying technology of web 3.0, helps advertisers improve data transparency, eliminate intermediaries, and directly connect brands to their consumers while saving millions of dollars. International brands have already begun to adopt web3 and complementary technologies such as the metaverse by hosting events, sponsoring, and creating unique user experiences. In web3, the focus will shift from improved visibility to enhanced user experience and relevant messaging by giving advertisers complete control over their data and providing meaningful value to their users Advertising – Changing Dimensions Role of interoperability – Interoperability is an important concept in Web 3.0. The initial prototype of Web 3.0 is based on shared platform experiences. Users can carry their avatars and digital profiles across multiple applications and websites while maintaining a unified experience. With interoperability, advertisers would have unprecedented freedom to engage with potential customers. Metaverse real estates – Since its inception, metaverse real estates have experienced rapid growth. Platforms such as Decentraland and Sandbox have grown exponentially in months. As this trend continues, businesses will need to consider metaverse real estates essential to their advertising strategy. Soon, advertisers’ primary metric of campaign success will be metaverse traffic. Cross-platform collaborations – The ownership of digital rights has changed how consumers interact with segments such as gaming, entertainment, etc. Data is the next stage in this transformation. Customers can finally take control of their data and decide how it will be shared and used on the internet with Web3. A shift in digital tools – With Web 3.0 and metaverse, the tools used for advertisements are expected to evolve. Advertisements in virtual reality – VR has primarily remained a secluded medium for advertising. However, as users move away from text and video-based interactions, businesses should increase their focus on VR advertisements. In-game advertisements – The play-to-earn economy is an important part of the metaverse. Companies should explore 3D rendered advertisements within games by determining how to work on in-game ads without interfering with the customer experience. User-driven advertising – Because of the transparency of blockchain, a significant shift toward ethical marketing is required by obtaining explicit consent from users before using their data. This will allow users to receive a portion of ad revenue. User-driven sharing will enable businesses to reach their target audience without relying heavily on previous data collection models. Looking Ahead Though Web3 is still in its infancy, advertisers have already begun to see the metaverse as a profitable channel for engaging with audiences and marketing. Decentralization is fast emerging as the internet’s future. With the aggressive growth expected in Web3, advertisers are expected to explore newer ways to engage with modern audiences and capitalize on the opportunities in the Web 3.0 era.
Make your first-party data work for you in advertising – Implementing identity solutions using cloud
The recent debates on digital privacy and several decisions by government and influential corporations have brought to focus how customer data is collected and used by companies. The broad trend is towards more transparency for users as to how their data is utilized and shared. Generally, users have shown more willingness to share data with the platforms they use. The direct use of that data for personalization and better engagement is a win-win situation for both parties. Such a scenario shifts the onus to the first-party platforms to use their consumer data judiciously, and any enrichment or extension of that data is only allowed with proper consent in place. This implies that the publishers have control over providing a meaningful and personalized advertising experience for their customers. Identity solutions built custom or otherwise are vital tools for publishers in such scenarios. What are identity solutions? Consumers can have multiple touchpoints with media companies. The service is accessed through a variety of devices, customer service interactions, social media interactions, content consumption, etc. Identity solutions assist in tying these various interactions together around a single user. Sometimes the connections between these interactions are obvious, such as a consumer’s ID and identity are easily established in this case. It isn’t always the case. Identity solutions are built around well-established databases, such as graph databases, which make indexing and searching easier. Moreover, it necessitates the execution of specialized algorithms, such as the connected component algorithm, which generates a consistent virtual ID for a user. End-to-end identity solutions include data collection from various sources and the creation of an identity database. This identity database serves as a downstream reference for developing multi-tiered use cases that provide end-user personalization. Identity Graph Use Cases in Advertising Identity serves as the foundation for the development of numerous use cases. It is extremely useful in maintaining a low latency profiling database that can be used to feed downstream solutions. Audience segments: Instead of third parties, publishers can create customer segments themselves using an identity solution. Business rules set up for different segments help in the classification of audiences. These audience segments get auto updated as they tend to change over a period. Personalization Engine: The identity graph captures the actions of users, with respect to interests and preferences not only explicitly but also implicitly. Since all actions are in one place, giving a 360-view, the personalization engine can feed off this information. Creative optimization: Not everybody gets the same advertisement as the information available about the history of the user enables advertisers to show personalized creatives. Brand safety: If the content being consumed does not match the ad shown, the reputation of the brand can be impacted. An identity graph can provide supplementary information regarding user preferences that can protect the brand. Campaign Analytics: The performance of campaigns can be measured against audience segments. These are key metrics on which advertising is bought and sold. Why cloud for Identity Graph implementations? Identity solutions map and unify billions of relationships and query customer data with millisecond latency. There are many purpose-built cloud databases made for identity solutions, for example: AWS Neptune, Neo4j, etc. Cloud solutions reduce the total cost of ownership by storing and querying billions of nodes and edges, with lower latency and lower costs for storage compared to other models. Usually, these solutions are quick to deploy and can be up and running without taking much time. Conclusion It has become imperative for media publishers to develop identity management solutions of their own as they ensure that first-party data is fully consolidated. Identity solutions can not only provide personalization for the publisher’s users, but also serve as data rooms for their advertisers. These solutions help publishers meet the privacy rules and also provide their users all the relevant content they require, including advertising.
The Power of Digital Out of Home (DOOH) Explained
What is DOOH? DOOH (Digital Out-Of-Home) media is the term that refers to any digitized display advertising that appears in a public environment. This includes digital billboards, outdoor signage, and networked screens found in even businesses-oriented gatherings areas such as stadiums, malls, and hospitals. DOOH has been gaining popularity for several reasons. But primarily offer tremendous reach and control to the advertiser while catching the audience’s attention more effectively than static billboards. In fact, a 2015 study by Nielsen found that 75% of respondents recalled seeing a digital billboard in the month prior, and 82% of those recalled seeing advertising specifically. At a time when traditional advertising is often seen as a nuisance, DOOH could be the novelty that marketers are looking for in the advertising world. How is DOOH better than OOH The critical difference between DOOH and OOH is one word. Digital. OOH or Out Of Home is advertising that also reaches people outside of their homes in public places. But these are either static billboards (with fixed images) or electronic. In contrast, Digital Out Of Home advertising is dynamic. This means that the content can be changed anytime to any ad or information from a networked computer. Additionally, DOOH allows for personalized advertising based on individuals viewing the displays. Real-time Messaging DOOH offers advertisers the ability to update their messages in near real-time. This means a far greater capability of testing messages in various locations. OOH communication, however, cannot be updated as easily as DOOH ads. Vendors can also offer advertisers and network digital display network owners ad insertion capabilities by implementing client-side or server-side ad integration with third-party or in-house ad servers. Programmatic Content Programmatic DOOH advertising works similar to online advertising but for public ad spaces. The advertiser uses a platform and creates their campaign, providing targeting, scheduling, placement details. Ads are then run on public digital boards that match the advertisers’ requirements, saving tremendous time and effort. This was never possible with OOH advertising and is one of the reasons Programmatic DOOH has quickly become a leading revenue driver for overall advertising. In fact, programmatic buying accounts for 40% of all revenue, netting an estimated $4 billion in 2018. Dynamic Advertising DOOH advertisers are only just beginning to explore the range of capabilities they can achieve by combining DOOH content with technological capabilities. For example, by analyzing weather data, DOOH can be programmed to change content depending on whether it’s sunny or raining. They can also be dynamically changed based on unforeseen events. For example, if there are flight delays, restaurants can create offers. Additionally, using image recognition allows ads to switch based on sensing the demographics of the viewers! Reporting & Analytics One of the critical benefits of DOOH over OOH is that media buyers pay only for impressions and received detailed reports on the campaigns that they are running. DOOH campaigns can also generate viewership analytics, similar to online ads. This is very useful to both marketers and network operators. It offers data such as proof-of-play, report scheduled, and any incidents, which allows the advertiser to stay on top of their ad campaign. Additionally, advanced analytics technology vendors can use this data to help advertisers see real-time operational metrics through model building. In Conclusion Given the flexibility over messaging and greater control over targeting and reports, DOOH is becoming central to digital marketing campaigns. And with platforms offering easy purchase of DOOH ads, it’s not surprising to know that Upbeat predicts the DOOH market will see $8.5 billion by 2023. SOURCES:
The Forensic Goldmine of Smart Television Viewing
In the United States, the total smart TV household penetration has been increasing rapidly from 2012 onwards, from around about 9% in 2012 to 60% in January 2020. This growth comes from shifting consumer preference towards online content. The wide availability of high-speed internet and the smart features of connected TVs have also contributed to the fast growth of the smart TV market both in the United States and the world. And, added to that, the pandemic has further pushed the consumption of content up even further. Americans spend an average of 3½ hours in front of a TV each day, according to eMarketer, the market research company. With more and more consumers opting for Smart TVs, marketers and publishers today have access to a wealth of consumer information. Content Recognition & Targeting Automatic content recognition (ACR) technology has the potential to capture all types of TV viewing: linear, OTT, video on demand, commercials, and video games. When tracking is active, Smart TVs can record and send out everything that comes up on the screen regardless of whether the source is cable, an app, the DVD player, or a set-top box, but without personally identifiable information. Once collected, media analytics companies consume the ACR data, then clean, compare and combine it with other data sets to make it more usable and accurate. TV advertisers, therefore, no longer need to rely on Gross Rating Points (GRPs) and have greater capabilities of showing their ads to the right person at the right time. Analytics & Advertising While the world of advertising is moving to digital, TV advertising still accounted for $84 billion in 2018 in the US alone. But data-driven methods are enabling these dollars to be spent more efficiently using automated systems over programmatic TV. Advanced advertising technology enables advertisers to have more control with end-to-end inventory visibility, audience, and demand. Using specially developed software solutions, marketers can integrate and streamline omnichannel advertising and marketing activities. Advanced analytics technology for advertising can help businesses also use ACR data to connect ad spend to business goals, like driving in-store traffic and make intelligent media advertising plans. Data-Driven Media Subscription Management Subscription rates after March 2020 grew between 3 times for digital news and up to 7 times for streaming services as published in the Covid19 Subscription Impact Report conducted by Zuora. By analyzing television viewership data in conjunction with product subscription information, publishers can manage subscription features specific to OTT platforms, such as auto-renewing and churn management. Netflix has claimed that its media recommendation solution could be saving up to $1 Billion a year by decreasing churn. AI-based Viewership Recommendations Content metadata in smart televisions are often only applicable to an on-demand video where there is time to generate it before distribution. Advance recommendations based on prior knowledge are irrelevant in cases like live sports, where viewership is based on expectations instead of prior information. For this reason, operators need to leverage AI/ ML to generate effective recommendations, even for VoD content. Recommendation engines help uncover video content for users that they would not be likely to find themselves. As a result, video and TV services can increase their content reach without having to constantly acquire new content. Tavant specializes in advanced advertising technology and media analytics to help companies gain the most from smart tv data. For more information on how we can help you write to us at [email protected]; or click here. SOURCES: https://www.washingtonpost.com/technology/2019/09/18/you-watch-tv-your-tv-watches-back/ https://dl.acm.org/doi/10.1145/2843948
Data Driven Ad Attribution Models
Marketing today relies on a variety of metrics to gain insight into its efficacy. Given the variety of online and offline channels available to marketers, understanding the impact and interaction of individual channels has become an onerous task, to say the least. Marketers rely heavily on two methods to obtain data-driven insights into the marketing process, Media Mix Modeling (MMM) and Data-Driven Attribution. MMM provides a “top -down” view into the marketing process in order to generate high-level insights into the efficacy of different marketing channels. For example, by looking at data over months or years, MMM can give marketers insight into consumers’ interaction with different marketing media. Attribution models, on the other hand, take a more “bottom-up” approach to the marketing process. These models look at an individual user’s interaction with different media. Since each user is exposed to a combination of marketing channels, the problem lies in ascertaining how much credit to give each marketing channel towards influencing a user’s choice about making a purchasing decision. Historically, marketers have used common attribution models such as last touch (first touch) attribution. Last touch attribution models assign all credit to the last channel (first channel) a user has been exposed to prior to conversion. The flaw in the last touch (first touch) attribution lies in the fact that channels further from (closer to) the conversion funnel are systematically undervalued. To allocate credit more fairly, algorithm-based methodologies have received significant traction in the past decade. In a series of three blogs will introduce three papers that discuss algorithm-based models for media mix modeling and attribution modeling. The dominance analysis approach for comparing predictors in multiple regression (Budescu, 1993) Regression models have become a common way to explore the interaction between revenue and advertising efforts. Budescu introduces a general framework known as dominance analysis that aims to decompose the coefficient of determination (R2). For the sake of simplicity, we will only deal with linear models in this post. Budescu’s work can be extended to any area of research that tries to deal with variable importance. Review of Legacy Methods Various methods have been developed over time to measure the importance of variables. These methods mostly rely on using the coefficients of independent variables from standard linear models to explain variable importance. Let’s look at a standard linear model defined as the following: y=β1 x1+⋯+βi xi+⋯+βp xp+ϵ Let’s denote the coefficient of determination of this model as R2y,X. The vector β= (β1, β2,…..βx) represents the change in the dependent variable y, associated with a unit change in each independent variable, given the other independent variables are left unchanged. Under these constraints, it is reasonable to conclude that the squared coefficients perfectly partition the coefficient of determination, as described in the equation below: R2y,x = ∑pj=1 p2y,xj = ∑pj=1 β2j While this method of using variable coefficients as importance measures is intuitive and appropriate in the case of no intercorrelations between dependent variables, in most real-world applications, dependent variables (advertising channels in this case) have some level of correlation, making this method inappropriate. Dominance Analysis Dominance Analysis compares coefficients of determination of all nested submodels composed of subsets of independent variables with that of the full model. Too much jargon? Let’s take a look at an example. Let’s say we have a total of ‘p’ independent variables in our linear model. We will build 2p-1 models, since these are the total number of subset models that can be created. We will then compute the incremental R2 contribution of each independent variable to the subset model of all other independent variables. Let’s take a scenario where we have 4 independent variables X1 , X2 , X3 and X4. We will build 24-1 models ie. 15 models. These will be 4 models with only one independent variable, 6 models with 2 independent variables each, 4 models with 3 independent variables each, and finally 1 model with all the independent variables. Thus, the incremental R2 contribution for variable X1 for example, is the increase in R2 value when X1 is added to each subset of the remaining independent variables (i.e., the null subset { . } , { X2 } , { X3 } , { X4 } , { X2 , X3 } , { X2 , X4 } , { X3 , X4 } and { X2 , X3 , X4 } ). Similarly, the incremental R2 contribution for variable X2 is the increase in value when is added to each subset of the remaining independent variables (i.e., the null subset { . } , { X1 } , { X3 } , { X4 } , { X1 , X3 } , { X1 , X4 } , { X3 , X4 } and { X1 , X3 , X4 } ). The beauty behind dominance analysis lies in the fact that the sum of the overall average incremental R2 of all independent variables is equal to the R2 of the model with all independent variables (the complete model). This allows easy partitioning of the total coefficient of determination amongst independent variables. An inherent problem with dominance analysis is the lack of computational efficiency. The need to train 2p – 1 models means that the number of models that would have to be trained increases exponentially as the number of independent variables increases. Relative Weights Analysis Another paper, which can be found here, builds on the concept of relative weights analysis as an alternative to dominance analysis. However, relative weights analysis is a fundamentally flawed method of determining attribution and has been debunked, most famously in this paper. The reason I even bring this up, is to forewarn a reader that the theoretical underpinnings of relative weights analysis is dubious, and to recommend dominance analysis as the superior R2 decomposition method.
Increased Data Privacy for Advertisers and Publishers
Where do we go from here? Privacy complaints made in November 2020 from Europe over the use of IDFA tracking code on iPhones, have pushed the industry to take privacy more seriously. In response, Apple said it would enable privacy control for its iOS users, by allowing them to opt-in to ad tracking. In 2021, consumers are more aware than ever about sharing their data. As regulators continue to step up privacy requirements, many businesses are exploring ways to use data to their advantage without violating industry regulations. In a webinar sponsored by Tavant, Strategies to Enable Advertising, Targeting and Measurement in a Privacy-Regulated World,experts from DISH Media, Integer Group (an Omnicom Group company), Sequent Partners, and Tavant got together to discuss the impact of these regulations on the media, publishing, and advertising industry. Here’s what the Panelists had to say: THE ADVENT OF PRIVACY REGULATIONS Privacy standards (GDPR, CCPA) are separating those that own data and those that do not. Third-party cookies and ad IDs are going away, but multinational companies in Europe (who worked with GDPR) are ready for it. Advertisers will need to offer opt-ins. Publishers need to educate users to outline the opt-in message with information on what data is being collected and how it’s beneficial. The consensus is that we will get smarter about ways to protect data, be compliant, and protect user privacy. Yet, the current state of data quality is messy. Data used for targeting or attribution may have come with different levels of quality, which can introduce bias in the data processing and impact the efficacy in targeting or attribution. Will the increased privacy force the advertisers to put more emphasis on media mix and innovation? WHAT WILL BE THE IMPACT OF STRINGENT PRIVACY REGULATIONS? Targeted advertising may be dampened a bit due to ID loss. Consumers may begin to experience slightly more irrelevant ad content than earlier. Companies that license their data from third parties may be in a tough position as they don’t have a direct relationship with their customers. Advertisers across the globe are still struggling to measure the reach and frequency of their campaigns, particularly across platforms. Will these privacy changes put more pressure on measurement tactics? Who will gain from these changes in data privacy? WINNERS AND LOSERS Many believe that consumers, media companies, and advertisers will all be impacted negatively. Companies that have first-party consumer data will come out the least impacted. Additionally, companies like Verizon and ATT, which have a huge reservoir of valuable first-party data, will be able to leverage it in different ways. The experts noted that everything now done on our smart TVs results in rich digital data for advertisers and publishers. As we begin to see a lot more contextual advertising, there is likely to be more investment by publishers in NLP and video image processing. What other innovations can be expected thanks to increased privacy regulations? FUTURE EXPECTATIONS New players may come into the market with the workaround innovation to capture ID but maintain privacy. Consumers may be offered incentives to opt-in. Loss of IDs will not impact work in deep learning models for attribution and mixed media modeling. Ad companies may hire specialists whose job will be to develop ID graphs which their brands can use. Ultimately, the feeling is positive as change that creates contention often triggers market forces to innovate. As data privacy begins to fall into place, the new issue is data security and effective measurement.
How to Decipher Customer Journey with Relevant Advertisement Attribution
Every advertiser has a unique context. Are you enabling the right choices? A large multinational company is launching a new lifestyle product and is looking to gain the attention of high-income, mid-career women across tier-one cities. Their advertising campaign has unique requirements, and they want the best slots suited to their product promotion. How would you guide them to make the best choices and drive premium returns for your platform? You need deep insights on advertising performance across segments that demonstrate the optimal ROI to win the trust of the advertisers. You can promote specific segments, drive higher revenue, and make better pricing decisions based on quantifiable metrics unearthed with actionable analytics. Accuracy in advertising attribution is your key for precise audience targeting, campaign optimization and improved performance that boosts the bottom line while bolstering top-line growth. Campaigns leave important cues. Are you listening? The new product line you launched last week has caught the imagination of young shoppers. Your e-commerce site has an upsurge in traffic. You used different media to advertise and promote the product line, and it has worked. The ad spots on prime-time TV and jingles on FM radio are on for a week now. You placed inserts in newspapers with QR code for discount coupons. The redemption of those coupons is doing well too. Your social media campaign is running in parallel, and you are ready for another round of emails to roll out referral offers. You are convinced that your ad spend has delivered the desired results. It is important to trace your customer journey through all the different touchpoints up to the conversion or buying stage. Your ad spend needs to be rationalized and focused on the media mix that delivers optimum results. In short, you need to analyse ad attribution, to zero in on your campaign effectiveness. Ad attribution unlocks the significance of every touchpoint to conversion. Personalized campaigns demand an intimate understanding of customer behavior, as well as customers’ channel and platform preferences. Your campaign ROI depends on your knowledge of the customers. Advertising attribution processes allow you to trace your customers’ actions across multiple touchpoints to reveal the levels of interaction that brought them to the point of sale. This data is crucial for evaluating past campaign performance and intelligently planning the next ones for better outcomes. Advertising attribution is a quantitative measure of each touchpoint in nudging the customer journey towards conversion. Single-touch ad attribution – for example, measuring the first click or last click for a given promotion on any platform – can deliver straightforward analysis if the action is definitive, such as a discount offer for the first 50 customers within a day, advertised on Facebook. The marketer can assign success to the specific promotion. Multi-touch journeys are deciphered through various ad attribution models. A multi-touch customer journey is more difficult to attribute. For instance, a customer may have seen a newspaper insert ad, noticed a similar advertisement on a social platform, received a promotional offer through a friend, checked out the company website, and then received remarketing enforcement before making a purchase. Now, every touchpoint is a nudge forward and must be accordingly scored. This multi-touch attribution is a flexible scoring model for marketers to assign due credit to each interaction for a comprehensive performance insight. Linear models assign equal value to each touchpoint. Shapely values, such as U and W-shaped scoring models, give more credit to the first and last touchpoints and first, middle, and previous touchpoints, respectively. Some marketers prefer a time decay model that treats the touchpoints closer to purchase as more important than touchpoints at the beginning of the journey. Advanced algorithmic and statistical models leverage AI and ML for ad attribution. The complexity of advertising data requires advanced custom models to assign performance metrics to every touchpoint adequately. Data-driven and statistically evolved, these attribution models leverage AI-based algorithms to score the customer purchase decision milestones appropriately. Machine learning algorithms guide the marketers in deciphering conversion probability to plan promotions accurately. The right choice of ad attribution model drives campaign performance. The pressure on marketing budgets has created a greater need for campaign precision. Marketers must choose the right attribution model to improve campaign performance and sales lift. However, there are no absolutes in this game. No statistical model, no matter how evolved and data-rich can guarantee 100% accuracy. Marketers must consider models that align with their customer journey and campaign intricacies. An advanced AI and ML-powered analytics platform can algorithmically design attribution models to deliver timely and accurate metrics. Making the right choice for ad attribution is intrinsic to campaign success. Marketers can leverage these attributes to design just-in-time campaigns with higher confidence. Which ad attribution model would you bet on for your campaign analysis? Please share your thoughts with us at [email protected]; or to learn more about Tavant’s media solutions, click here.
How to Create a Targeted Ad Experience with Addressable Advertising?
The Changing Television Landscape Technology now exists to seamlessly integrate identified audiences (who have voluntarily declared intent to known data sources) with the content that these audiences consume. However, there are many challenges to be overcome both on the data as well the business side. The data available on households which consume TV (whether linear or through streaming options) is patchy at best. There are different techniques which data marketers deploy to make the data closer to reality. They deploy probabilistic and stochastic methods to project data audiences. However, with these techniques and such fragmented, time-shifted audiences, advertisers do not always reach the right viewers with the right message. Addressable Advertising is the Need of the Hour! One of the ‘buzzwords’ in recent days in the media world has been addressable advertising. Addressable advertising is a name given to personalized advertising or messaging sent to an identifiable audience segment referred to as addressable. Addressable advertising allows marketers to reach more specific audiences with greater creative flexibility, deep insights, and reliable ROI data. Moreover, with much more granular TV attribution and measurement, advertisers can understand the real performance of their ad – including engagement, brand lift, and conversions. The addressable segment is identified by specific characteristics that could be demographic or behavior oriented. It is coupled with the ability to deliver campaigns tailored to the desired demographic and behavior intent. Among all advertising’s holy grails, addressable TV is often held out as the holiest of them. The difference now as compared to some two years ago is that the inventory that was made available for addressable is hugely expanding. Earlier, there were only two minutes out of an hour’s programming was available for the MSO’s for addressable targeting. Now with the significant MSO’s owning much of the content as well courtesy their big-ticket acquisitions, they are continuously working to expand their addressable inventories. Not only this, all VOD available on STB or streaming box options is also identified as addressable. Final Thoughts: The business of selling inventory needs to change as well, to offer scale to the buyers as traditional media space selling does. Advertisers need to shift towards buying audiences than being media space. Addressable is an excellent option in this space as it offers exceptional value regarding money spent on buying audiences as almost no leakage takes place. The delivery is 100% to the desired audience as long as the data is correct. The hit rate in traditional advertising is supposed to be not more than 10%. This means that there is ten times more leeway that the broadcaster or owner of the inventory gets regarding addressable. Addressable advertising is the future of TV advertising. It might take a while more to become completely addressable, but the road is becoming more evident every day. How Tavant helps organizations deliver intelligent interactions with their customers? Tavant has been very actively involved in media measurement technologies for more than 12 years. Tavant has been working actively on addressable TV advertising with its clients who are in the data and broadcasting network space. Tavant also has a solution accelerator around media planning, execution, and measurement, which enables buying of the media space for the addressable audience. Ready to start talking one to one? Connect with us today at [email protected] to strategize your next addressable TV campaign.
5 Tips to Survive Dreamforce 2016
It is that time of the year again. In less than a week more than 150000 people will take the city of San Francisco with a storm to attend one of their favorite software conferences, Dreamforce 2016. So what are you to do if you are one of those enthusiasts? Here are some tips that might help you sail through dreamforce without missing the important activities. 1) Read and study the FAQs diligently before you enter the conference arena. The FAQs on dreamforce website cover everything from badge collection to the shuttle timings. This will save you a lot of time and avoid last-minute confusion. The last thing you want is to end at the wrong location for badge collection. 2) Plan the sessions that you are interested in attending much in advance. With over 2000 sessions in this year’s dreamforce, it is very important that you make optimum use of the agenda builder and mark the sessions that you would like to attend. 3) Preschedule your meetings with key vendors that you would like to meet during the conference. The expo hall is huge and spans across multiple floors. With all the different activities and sessions happening around the event, it helps to have at least 5-6 confirmed vendor meetings on your agenda. 4) Study the trail map before getting to the conference and follow the trail while stopping at the signposts. By doing this, you will not only do a huge favor to your feet but also make sure that you don’t miss the important salesforce solutions. 5) Make sure you download the Uber app. If your hotel is not connected by Shuttles to Moscone Center, it can get quite challenging to get around. To avoid the frustration, Uber app on your phone can come in very handy. Among all the dreamforce enthusiasts, Tavant Technologies will be showing its salesforce solutions in the Meeting room- MR124B. We will also host an Industry Session on ‘Streamlined Warranty Management: What your customers want’ on October 4, 3.00 p.m.-3:35 p.m., Moscone, South, Industry partner Theater. Drop an email on [email protected] or call +1866-9-828268 to pre-schedule a meeting.
Data Management Platforms: Enabling Better Business
Data is at the core of all marketing and advertising operations today. Without it, companies would have no direction, let alone any edge over competition. They all know it, but only a few understand. For others, there are data management platforms (DMP) to help marketers and publishers make sense of it all. A data management platform is like a data warehouse software that houses and manages information (for example, cookie IDs) to assist in tasks such as generating audience segments and targeting clusters. Advertisers today communicate to a number pf publishers and buy media across a huge range of sites. DMPs collate information on all those activities in a centralized location and use them to optimize future media buys and ad requirements. Essentially, using a DMP is all about better segmenting, profiling and targeting customers. An enterprise DMP can scale millions of data points and offer marketers insights on a host of market and campaign variables. What do DMPs offer? The benefits: Prospecting: DMPs seamlessly integrate with third-party customer data. This way, they help in achieving higher accuracy and scaling better with targeted campaigns. Re-targeting: Businesses can analyze buying records, browsing behavior and customer profile, among others, using DMPs. Using online and offline variables, they help in implementing customized re-targeting campaigns. Audience segmentation: DMPs allow marketers to create several granular as well as broad segments. This way, marketers are able to reach out to audiences with the right message at different stages of the purchase cycle. Optimized site content: By using third-party data, DMPs gauge customer profiles and offer personalized content to users when they visit the brand’s site. Analytics: With DMPs, companies need not maintain cumbersome excel sheets. DMPs have inbuilt dashboards that provide measures and compare campaign performance across channels, give insight into audience interaction, etc. These reports help marketers optimize and channelize marketing efforts in the best possible way. Of brands, customers and ROIs New brands are growing by the day, and making the marketplace even more cluttered. So much so that consumers are now developing a ‘blind-spot’ for advertisements. Hence, it is of supreme importance that marketers channelize their efforts in a way that the right message reaches the right people; people who have a high possibility to earn value by investing in the brand. DMPs help marketers effectively analyze all their disparate audience and campaign data, allowing them to make better media buying decisions, target prospects, and offer personalized content leading to higher brand awareness and conversion. With DMP capabilities, brands can judge the effectiveness of marketing efforts and optimally alter and implement them. Such platforms also help brands to better connect with consumers by setting in place a platform to reach out for quick customer service. All in all, it is a win- win situation for marketers; with access to valuable business insights, they can reduce wastage of resources, scale operations and ultimate earn a higher ROI.
Video Ads are a Huge Hit with Millennials
In a study identifying major celebrities popular among the youngsters (aged between 18 and 30) YouTube superstars topped the list. Millennials have been found flocking mostly to videos recently. These video sites are reaching more than any other networking media across people within the age group 18-35 (Source: Sprinklr.com). Every year the time spent on watching videos is growing by 60%, average video watching on mobile being higher than 40 minutes. This provides a huge opportunity for advertisers to specifically design ads for videos targeting millennials. The advertisers on video channels like YouTube have grown higher than 40% and the big companies are trying to capture millennials on other video sites as well (Defy-sponsored survey in 2014). It is the time spent by millennials on YouTube and Google Video that has resulted on top brands (rankings by Interbrand) spending almost 60% more on these channels than they did a decade ago (around 2005). Although site owners are not explicitly coming out with revenue figures, the available data indicates there is an explosion of demand for video advertisements. Advertisers have found millennials spending substantial time on these sites. Also, Google has said there is a huge growth in revenue from YouTube recently. The influx of advertisements has made many video sites turn their platforms more appealing for advertisers. They created a computing systems and dashboards where marketers can compare effectiveness of video ads with television advertising. A recent survey on millennial women found the main influencing factors for their shopping decisions to be websites, social media and word of mouth. See figure below. Source: AdWeek Guided by the huge popularity of video networks, many renowned brands on television and the internet are coming up with new video channels. They are trying to cover topics that interest the age group of 18-49 mainly, some specifically catering to the audience looking for answers to questions continuously. For brands trying to capture this vibrant and media-proactive group of millennials, video advertisements offer immense potential. Using a comprehensive campaign management solution, you can design your content and run it across the video networks to find maximum conversions happening within a short time. Make sure your products and your ads match the intellectual and emotional worlds of these millennials, and your job is almost accomplished.
What’s So Cool about OOH Advertising?
It consists of public display ads, both digital and print, but the plus side is you can choose your OOH ads to be displayed at particular locations according to how demographics travel run errands. Digital displays can be easily controlled in real-time, according to the movements of people with specific tastes and needs. OOH has evolved, thanks to big data! Big data allows the discovery of detailed information on specific groups of people. Based on that, OOH campaigns can be displayed to prospects from particular cultures, age groups, and professions at specific locations, and thousands of them can be targeted once you are aware of their movements. A greater-than-life ad is something no one misses. Outdoor media platforms may include posters, billboards, public vehicles, kiosks, etc. OOH is cost-effective OOH advertising can be cost-effective to promote a brand or service. An OOH advertisement is a one-time investment. You can ideally keep an ad running for both, short and long terms, depending on context and objective. The price you pay for OOH ad spaces largely depends on popularity, footfall and other related factors. However, once you mine data, a location relevant to your demographics may be available at a surprisingly affordable cost. And that happens quite often. OOH has better reach It is true that personalized ads have a conversion rate of 80% on average. However, viewability and fraud have been major challenges in the recent past. Although many OOH “impressions” may be irrelevant, a whacky ad gets people’s attention. That gets the word around. Moreover, with big data, the right prospects can be reached at strategic locations quite easily these days. Outdoor ads reach a wider range of viewers. Compelling viewership is one of the highest advantages of OOH advertising. Programmatic advertising has been a remarkable development in the last few years, but affordability, scope of creativity, non-stop exposure to audience, and potential reach to large audiences are some undeniable advantages of OOH.
User Identification in Programmatic Advertising and Other Challenges
Without a ‘proxy’ to accurately identify individuals, programmatic marketing is simply unmanageable. While cookies have been the proxy for identification, it’s time to ask about the future. Programmatic experts are yet to figure out a way, but if cookies are dying out, programmatic will surely face a threat. The cookie seems to have been doing a great job. Reports from Internet Advertising Bureau (IAB) and PricewaterhouseCoopers (PWC) found total digital revenue reaching $12.4 billion in the last quarter of 2014. Programmatic is basically a combination of different kinds of technologies that buy, place, and optimize advertising automatically, and hence enables highly profitable ad campaigns. As the job of programmatic is to identify the right viewer and make that viewer see the ad, the first thing required is a real viewer. Unfortunately, some rogues in the web world are capable of misleading even the smartest software programs. Challenges like viewability and fraud are constantly troubling the world of programmatic. Most advertisers shy away from programmatic buying because of the looming presence of reputation concerns and fraud. As of May 2015, the leading hurdles in using programmatic ad buying in UK and USA were statistically evaluated from a survey: Leading Challenges in Programmatic Buying, 2015 (UK and USA) – % of survey participants More than half the traffic on websites is essentially bot traffic. It has been estimated that more than $6.3 billion damage will be experienced because of bots. Hence, out of an estimated $43.8 billion, if more than $6 billion is fraudulent activity (fake clicks through a set of automated software programs), it is a huge loss for advertisers. Huge organizations with complete set ups operate with botnet malwares to extract millions of dollars. For folks in programmatic, ad impression viewability continues to remain a huge challenge around online display ads. In order for an ad to qualify as viewable, a minimum 50% of its pixels should appear on desktop screens for at least one second. For video ads, 50% pixels should be viewable for at least 2 seconds. These challenges may be confronted by micromanaging customized platforms. Advertisers need to use technology that maximizes outcomes. It’s not enough to seek impressions or aim at a single behavior pattern for producing the desired results. It is necessary to depend on technology that executes the numerous conditions correctly for ad-space selection. By using such a platform, advertisers with different needs can improve their ROIs in clear-cut ways.