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Conventional TV advertising campaigns have historically relied on general audience metrics like impressions and ratings to measure outcomes. These metrics can help marketers understand how many people have seen an ad, but they don’t reveal its real-world impact, which leaves a gap between ad exposure and results.
Outcome-based TV measurement bridges this gap and helps marketers tie ad spending directly to their business goals. Instead of counting eyeballs alone, TV measurement zeroes in on what viewers do after seeing an ad — whether signing up for a service, visiting your website, or purchasing a product.
TV ad measurement helps marketers adjust campaigns based on clear, trackable outcomes rather than guesswork. Let’s talk about how marketers can get started with outcome-based TV measurement and start experiencing tangible results.
Why outcome-based TV measurement matters
Outcome-based measurement indicates a massive shift in how marketers evaluate TV advertising success. As a principal analyst at Forrester explained, the industry is about to “move into a whole different world” where multiple metrics are tailored to advertisers’ unique goals, such as sales, store traffic, or web engagement. This shift is driven by improved tools for tracking TV outcomes, which help justify spending and clarify ROI. With TV measurement, you can see how your campaigns impact aspects of your marketing like sales and engagement.
Aligning TV ad spend with business goals
Every business has distinct objectives. Outcome-based measurement ties your marketing efforts to business goals and enables smarter decisions, campaign optimization, and ROI improvements. Whether you’re a B2C brand wanting immediate sales or a B2B organization looking to drive website traffic, this method provides the insights needed for strategic decision-making.
Marketers can deliver the most value by adjusting TV ad spending to maximize desired results:
- Sales goals: Identify which ads and platforms directly influence purchases to ensure TV ad spend contributes to revenue growth.
- Customer engagement: Link actions like website visits or app downloads to TV campaigns and refine messaging to deepen audience connections.
- Desired outcomes: Align ad spend with goals like consumer awareness or repeat purchases to allocate resources effectively for measurable success.
Reducing wasted spend on ineffective channels
Outcome-based TV measurement allows you to pinpoint which networks, times, or programs drive the most engagement and conversion. When you know your underperforming channels, you can reallocate budgets to those with a higher ROI and avoid waste.
Core metrics in outcome-based TV measurement
The effective implementation of outcome-based measurement requires advanced TV advertising analytics and tracking metrics that shed light on TV ad performance.
Incremental lift
This metric measures the increase in desired actions and business results — like purchases or site visits — that can be attributed directly to a TV campaign. Incremental lift quantifies your campaign’s impact and separates organic activity from the results your ads have driven.
Let’s say a meal kit service experiences a 20% lift in subscriptions within a single week of running TV ads compared to a week without ads. They’d want to be able to isolate the impact of their ad from their organic growth so they can determine if the growth is actually a result of the TV ads or another effort.
Attribution and conversions
Attribution links TV ad exposure to specific customer actions, such as newsletter sign-ups and product purchases. Conversion data helps marketers understand the whole customer journey to optimize messaging, targeting, and channel mix to improve conversion rates. A retailer that knows 50% of TV ad viewers visit its e-commerce site within 36 hours of exposure could use that information to adjust the timing of its retargeting and align with site visit spikes.
Audience segmentation for targeted measurement
Outcome-based measurement breaks down performance across target demographics and allows for granular audience segmentation so TV ads resonate with the right audiences. For example, if a luxury brand saw better TV ad performance with high-earning Millennials, they’d want to refine their campaign messaging based on this group’s habits and preferences.
Customer journey tracking
Knowing how viewers move from awareness to conversion is critical. Outcome-based TV measurement helps you track the customer journey by pinpointing touchpoints where engagement happens and tying these to your TV campaigns. If a fitness brand found that TV campaigns drive app downloads, it could combine app analytics and TV exposure data to find out when most of their conversions happen after ad exposure and create follow-up messaging for that window of time.
Integrating these insights with other marketing channels allows you to fine-tune your messaging, channel mix, and audience targeting to drive better outcomes and deliver more personalized customer experiences.
Lifetime value (LTV)
Beyond immediate conversions, outcome-based TV ad measurement helps brands identify which TV campaigns attract high-value customers with long-term revenue potential. If a financial institution ran a TV ad campaign centered on its new credit card, for instance, it could use LTV to track new cardholders and determine whether ads occurring during financial news airtime produced customers with higher average annual spend compared to other segments.
How outcome-based TV measurement works
Outcome-based measurement is a data-driven process that involves collecting, analyzing, and applying insights to improve TV ad performance.
1. Collect data
When someone sees your TV ad, they might take action, like downloading your app or buying something. Outcome-based TV measurement begins by tracking these actions and gathering data from various sources, such as:
- TV viewership
- CRM
- Digital engagement
- Purchase behavior
- Cross-platform interactions
- And more
Data integration with digital platforms
Combining TV data with insights from platforms like social media or website analytics creates a more unified view of campaign performance. This integration powers easier retargeting and better alignment between digital and TV advertising strategies. Some marketers enhance this integration further using artificial intelligence (AI) to streamline data coordination and ensure campaigns are optimized for effectiveness and ROI.
2. Connect the dots
Next, marketers need to find out which actions were influenced by TV ads. It’s important to ask questions like these as you work to connect the dots:
- Did website traffic spike right after the ad aired?
- Did the ad viewers match the people who signed up for the service or made a purchase?
You can link TV exposure to real-world behaviors with tools and identifiers like hashed emails, device IDs, surveys, and privacy-safe data-matching techniques.
3. Analyze the data
Then, the data needs to be analyzed for patterns like these:
- Which TV ads or time slots drove the most engagement?
- Did certain customer groups respond better than others?
- Was there a noticeable lift in sales or signups after the ad campaign?
This step can help you uncover what’s working and what’s not.
Role of advanced analytics and machine learning
The data analysis required in this process can be overwhelming, time-consuming, and risky without the right tools. Fortunately, advanced analytics and fast, effective artificial intelligence tools can process large amounts of data from digital platforms, TV viewership, and customer interactions in less time to reveal accurate, actionable insights and patterns.
They can also predict which audiences, messages, and channels will be most profitable so campaigns can adapt in real time, whether by reallocating spend to higher-performing channels or refining audience targeting.
4. Turn insights into action
Once you have your data-derived insights, you can tweak your campaign in a number of ways, whether you decide to:
- Adjust your ads: If one message works better than another, lean into it.
- Refine your targeting: Focus on the audience segments most likely to act.
- Optimize your spend: Invest in channels or times that deliver the best return.
For example, if you see that ads during prime time lead to more purchases than morning slots, you can shift your budget accordingly.
This type of knowledge can be used to continuously improve your campaigns. Each time you run a new ad, you measure again, building on past insights to make your outcome-based TV advertising even smarter.
Applications of outcome-based TV measurement
Outcome-based TV measurement has wide-ranging applications across industries. Here’s how it’s helping businesses link TV ad exposure to real-world actions and optimize campaigns for better results.
- E-commerce and retail: Retailers can track how TV ads influence purchases and use those insights to refine their assets and target specific customer groups. A clothing retailer may track how well a TV ad boosts online traffic and in-store purchases. For instance, if a seasonal sale commercial correlates with a spike in website visits or mobile app downloads, the brand can refine its ad placement to focus on the most responsive demographics.
- Automotive: Automakers use outcome-based TV measurement insights to determine how ads drive dealership visits, test drives, or inquiries. A car manufacturer could analyze whether TV spots featuring a new vehicle increase traffic to its dealership locator or car configuration tool online.
- Healthcare: Pharmaceutical companies could assess whether TV spots lead to increased prescription fills, or a health provider could test how ads promoting flu shots result in appointment bookings through its website or app. If any messages resonate more with families, the provider can create similar campaigns for the future.
How Experian enhances outcome-based TV measurement
Experian has recently partnered with EDO, an outcomes-based measurement provider, to offer more granular TV measurement across platforms. Our identity resolution and matching capabilities enhance EDO’s IdentitySpine™ solution with rich consumer data, including age, gender, and household income, all in a privacy-centric way. Integrating these demographic attributes is helping advertisers achieve more precise audience insights and connect their first-party data to actionable outcomes.
As a result of this collaboration, brands, agencies, and networks can optimize their TV campaigns by identifying which ads drive the most decisive engagement among specific audience segments. We’re improving accuracy, targeting, and more so advertisers can maximize the performance of their CTV strategies.
Get in touch with Experian’s TV experts
If you’re ready to take your data-driven TV advertising strategies to the next level, connect with our team. We combine advanced data and identity solutions as well as strong industry collaborations to help brands optimize their TV campaigns. Whether you’re navigating traditional or advanced TV formats, our expertise ensures your efforts deliver maximum impact.
Connect with us today to drive engagement, connect with audiences, and achieve better ROI. Let’s transform the way you measure success on TV.
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Contextual ad targeting paves the way for new opportunities Advertisers and marketers are always looking for ways to remain competitive in the current digital landscape. The challenge of signal loss continues to prompt marketers to rethink their current and future strategies. With many major browsers phasing out support for third-party cookies due to privacy and data security concerns, marketers will need to find new ways to identify and reach their target audience. Contextual ad targeting offers an innovative solution; a way to combine contextual signals with machine learning to engage with your consumers more deeply through highly targeted accuracy. Contextual advertising can help you reach your desired audiences amidst signal loss – but what exactly is contextual advertising, and how can it help optimize digital ad success? In a Q&A with our experts, Jason Andersen, Senior Director of Strategic Initiatives and Partner Solutions with Experian, and Alex Johnston, Principal Product Manager with Yieldmo, they explore: The challenges causing marketers to rethink their current strategies How contextual advertising addresses signal loss Why addressability is more important than ever Why good creative is still integral in digital marketing Tips for digital ad success By understanding what contextual advertising can offer, you’ll be on the path toward creating powerful, effective campaigns that will engage your target audiences. Check out Jason and Alex’s full conversation from our webinar, “Making the Most of Your Digital Ad Budget With Contextual Advertising and Audience Insights” by reading below. Or watch the full webinar recording now! Watch now Macro impacts affecting marketers How important is it for digital marketers to stay informed about the changes coming to third-party cookies, and what challenges do you see signal loss creating? Jason: Marketers must stay informed to succeed as the digital marketing landscape continuously evolves. Third-party cookies have already been eliminated from Firefox, Safari, and other browsers, while Chrome has held out. It's just a matter of time before Chrome eliminates them too. Being proactive now by predicting potential impacts will be essential for maintaining growth when the third-party cookie finally disappears. Alex: Jason, I think you nailed it. Third-party cookie loss is already a reality. As regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) take effect, more than 50% of exchange traffic lacks associated identifiers. This means that marketers have to think differently about how they reach their audiences in an environment with fewer data points available for targeting purposes. It's no longer something to consider at some point down the line – it's here now! Also, as third-party cookies become more limited, reaching users online is becoming increasingly complex and competitive. Without access to as much data, the CPMs (cost per thousand impressions) that advertisers must pay are skyrocketing because everyone is trying to bid on those same valuable consumers. It's essential for businesses desiring success in digital advertising now more than ever before. Contextual ad targeting: A solution for signal loss How does contextual ad targeting help digital marketers find new ways to reach and engage with consumers? What can you share about some new strategies that have modernized marketing, such as machine learning and Artificial Intelligence (AI)? Jason: We're taking contextual marketing to the next level with advanced machine learning. We are unlocking new insights from data beyond what a single page can tell us about users. As third-party cookies go away, alternative identifiers are coming to market, like RampID and UID2. These are going to be particularly important for marketers to be able to utilize. As cookie syncing becomes outdated, marketers will have to look for alternative methods to reach their target audiences. It's essential to look beyond cookie-reliant solutions and use other options available regarding advertising. Alex: I think, as Jason alluded to, there's a renaissance in contextual advertising over the last couple of years. If I were to break this down, there are three core drivers: The loss of identity signals. It's forcing us to change, and we must look elsewhere and figure out how to reach our audiences differently. There have been considerable advances in our ability to store and operate across a set of contextual signals far more extensive than anything we've ever worked with in the past and in far more granular ways. That's a huge deal because when it comes to machine learning, the power and the impact of those machine learning models are entirely based on how extensive and granular the data set is that you can collect. Machine learning can pull together critical contextual signals and figure out which constellations, or which combinations of those signals, are most predictive and valuable to a given advertiser. We can tailor machine learning models to individual advertisers using all those signals and find patterns across those in ways that were previously impractical or unfeasible. The transformation is occurring because of our ability to capture much more granular data, operate across it, and then build models that work for advertisers. Addressability: Connect your campaigns to consumers How does advanced contextual targeting help marketers reach non-addressable audiences? Jason: Advanced contextual targeting allows us to take a set of known data (identity) and draw inferences from it with all the other signals we see across the bitstream. It's taking that small seed set of either, customers that transacted with you before that you have an identity for, or customers that match whom you're looking for. We can use that as a seed set to train these new contextual models. We can now look at making the unknown known or the unaddressable addressable. So, it's not addressable in an identity sense, it is addressable in a contextual or an advanced contextual sense that's made available to us, and we can derive great insight from it. One of the terms I like to use is contextual indexing. This is where we take a set of users we know something about. So, I may know the identity of a particular group of households, and I can look at how those households index against any of the rich data sets available to us in any data marketplace, for example, the data Yieldmo has. We can look at how that data indexes to those known users to find patterns in that data and then extrapolate from that. Now we can go out and find users surfing on any of the other sites that traditionally don't have that identifier for that user or don't at that moment in time and start to be able to advertise to them based on the contextually indexed data. Historically, we've done some contextual ad targeting based on geo-contextual, and this is when people wanted to do one to one marketing, and geo-contextual outperformed the one to one. But marketers weren't ready for alternatives to one to one yet. We want marketers to start testing these solutions. Advertisers must start trying them, learning how they work, and learn how to optimize them because they are based on a feedback loop, and they're only going to get better with feedback. Alex: Jason, you described that perfectly. I think the exciting opportunity for many people in the industry is figuring out how to reach your known audience in a non-addressable space, that is based on environmental and non-identity based signals, that helps your campaign perform. Your known audience are people that are already converting – those who like your products and services and are engaged with your ads. Machine learning advancements allow you to take your small sample audience and uncover those patterns in the non-addressable space. It's also worth noting that in this world in which we are using seed audiences, or you are using your performing audiences to build non-addressable counterpart targeting campaigns, having high-quality, privacy-resilient data sets becomes incredibly important. In many cases, companies like Experian, who have high quality, deep rich training data, are well positioned to support advertisers in building those extension audiences. As we see the industry evolve, we're going to see some significant changes in terms of the types of, and ways in which, companies offer data, and make that available to advertisers for training their models or supporting validation and measurement of those models. Jason: Addressable users, the new identity-based users, are critical to marketers' performance initiatives. They're essential to training the models we're building with contextual advertising. Together, addressable users and contextual advertising are a powerful combination. It's not just one in isolation. It's not just using advanced contextual, and it's not just using the new identifiers. It's using a combination to meet your performance needs. It's imperative to start thinking about how you can begin building your seed audiences. What can you start learning from, and how do you put contextual into play today? You are looking to build off a known set and build a more advanced model. These can be specialized models based on your data. You can hone in and create a customized model for your customer type, their profile, and how they transact. It's a greenfield opportunity, and we're super excited about the future of advanced contextual targeting. Turn great creative into measurable data points Why does good creative still play an integral part in digital advertising success? Jason: Good creative has always been meaningful. It's vital in getting people to click on your ad and transact. But it's becoming increasingly important in this new world that we're talking about, this advanced contextual world. The more signal that we can get coming into these models, the better. Good creative in the proper ad format that you can test and learn from is paramount. It comes back to that feedback loop. We can use that as another signal in this equation to develop and refine the right set of audiences for your targeting needs. Alex: If you imagine within the broader context of identity and signal loss, creative and ad format becomes incredibly powerful signals in understanding how different audiences interact with and engage with different creative. In the case of the formats that serve on the Yieldmo exchange, we're collecting data every 200 milliseconds around how individual users are engaging with those ads. Interaction data like the user scrolling back or the number of pixel seconds they stay on the screen, fills this critical gap between video completes and clicks. Clicks are sparse and down the funnel, and views and completes are up the funnel. All those attention and creative engagement type metrics occupy the sweet spot where they're super prevalent, and you can collect them and understand how different audiences engage with your ads. That data lets you build powerful models because they predict all kinds of other downstream actions. Throughout my career, I learned that designing or tailoring your creative to different audience groups is one of the best ways to improve performance. We ran many lift studies with analysis to understand how you can tailor creative customized for individual audiences. That capability and the ability to do that on an identity basis is starting to deteriorate. The ability to do that using a sample of data or using a smaller set of users, either where you're inferring characteristics or you're looking at the identity that does exist in a smaller group, becomes powerful for being able to customize your creative to tell the right story to the right audience. When you layer together all the interaction data collected at the creative level on top of all the contextual and environmental signals, you can build powerful models. Whether those are driving proxy metrics, or downstream outcomes, puts us in a powerful position to respond to the broader loss of identity that we've relied on for so many years. Our recommendations for marketers for 2023 and beyond Do you have recommendations for marketers building out their yearly strategies or a campaign strategy? Jason: Be proactive and start testing and learning these new solutions. I mentioned addressability and being in the right place at the right time. That's easier in today's third-party cookie world. But as traditional identity is further constricted, you will have these first-party solutions that will not be at scale, so you're less likely to find your user at the scale you want. It would be best if you thought about how to reach that user at the right place at the right time. They may not be seen from an identity basis. They might not be at the right place at the right time when you were delivering or trying to deliver an ad. But you increase your chance of reaching them by building these advanced contextual targeting audiences using this privacy-safe seed 'opted-in' user set; this is a way to cast that wider net and achieve targeted scale. Alex: Build your seed lists, test your formats with different audiences, and understand what's resonating with whom. Take advantage of some of the pretty remarkable advances in machine learning that are allowing us, really, for the first time to fully uncork the potential and the opportunity with contextual in a way that we've never done before. Jason: At the end of the day, it's making the unaddressable addressable. So, it's a complementary strategy; having that addressable piece will feed the models. But also, that addressable piece still needs to be identity-based, addressable still needs to be part of your overall marketing strategy, and you need to complement it with other strategies like advanced contextual targeting. The two of them together are super complimentary. They learn from each other, and it's a cyclical loop. Now is the time to take advantage and start testing and understanding how these solutions work. We can help you get started with contextual ad targeting Contextual advertising can help you stay ahead of the curve, identify your target audience, and continue to drive conversions despite signal loss. We've partnered with Yieldmo to help make sure that your marketing campaigns are reaching the right target audiences on the platforms that are most relevant. To get started with contextual ad targeting to reach the right audience at the right time and drive conversions, contact our marketing professionals. Let's get to work, together. Contact us today Find the right marketing mix in 2023 Check out our webinar, "Find the right marketing mix with rising consumer expectations." Guest speaker, Nikhil Lai, Senior Analyst from Forrester Research, joins Experian experts Erin Haselkorn, and Eden Wilbur. We discuss: New data on the complexity and uncertainty facing marketers Consumer trends for 2023 Recommendations on finding the right channel mix and the right consumers Watch now Get in touch About our experts Jason Andersen, Senior Director, Strategic Initiatives and Partner Solutions, ExperianJason Andersen heads Strategic Initiatives and Partner Enablement for Experian Marketing Services. He focuses on addressability and activation in digital marketing and working with partners to solve signal loss. Jason has worked in digital advertising for 15+ years, spanning roles from operations and product to strategy and partnerships. Alex Johnston, Principal Product Manager, YieldmoAlex Johnston is the Principal Product Manager at Yieldmo, overseeing the Machine Learning and Optimization products. Before joining Yieldmo, Alex spent 13 years at Google, where he led the Reach & Audience Planning and Measurement products, overseeing a 10X increase in revenue. During his time, he launched numerous ad products, including YouTube's Google Preferred offering. To learn more about Yieldmo, visit www.Yieldmo.com. Latest posts

In 2022, Google began changing the availability of the information available in User-Agent strings across their Chromium browsers. The change is to use the set of HTTP request header fields called Client Hints. Through this process, a server can request, and if approved by the client, receive information that would have been previously freely available in the User-Agent string. This change is likely to have an impact on publishers across the open web that may use User-Agent information today. To explain what this change means, how it will impact the AdTech industry, and what you can do to prepare, we spoke with Nate West, our Director of Product. What is the difference between User-Agents and Client Hints? A User-Agent (UA) is a string, or line of text, that identifies information about a web server’s browser and operating system. For example, it can indicate if a device is on Safari on a Mac or Chrome on Windows. Here is an example UA string from a Mac laptop running Chrome: To limit the passive fingerprinting of users, Google is reducing components of the UA strings in their Chromium browsers and introducing Client Hints. When there is a trusted relationship between first-party domain owners and third-party servers, Client Hints can be used to share the same data. This transition began in early 2022 with bigger expected changes beginning in February 2023. You can see in the above example, Chrome/109.0.0.0, where browser version information is already no longer available from the UA string on this desktop Chrome browser. How can you use User-Agent device attributes today? UA string information can be used for a variety of reasons. It is a component in web servers that has been available for decades. In the AdTech space, it can be used in various ad targeting use cases. It can be used by publishers to better understand their audience. The shift to limit access and information shared is to prevent nefarious usage of the data. What are the benefits of Client Hints? By using Client Hints, a domain owner, or publisher, can manage access to data activity that occurs on their web properties. Having that control may be advantageous. The format of the information shared is also cleaner than parsing a string from User-Agents. Although, given that Client Hints are not the norm across all browsers, a long-term solution may be needed to manage UA strings and Client Hints. An advantage of capturing and sharing Client Hint information is to be prepared and understand if there is any impact to your systems and processes. This will help with the currently planned transition by Google, but also should the full UA string become further restricted. Who will be impacted by this change? Publishers across the open web should lean in to understand this change and any potential impact to them. The programmatic ecosystem supporting real-time bidding (RTB) needs to continue pushing for adoption of OpenRTB 2.6, which supports the passing of client hint information in place of data from UA strings. What is Google’s timeline for implementing Client Hints? Source: Google Do businesses have to implement Client Hints? What happens if they don’t? Not capturing and sharing with trusted partners can impact capabilities in place today. Given Chromium browsers account for a sizable portion of web traffic, the impact will vary for each publisher and tech company in the ecosystem. I would assess how UA strings are in use today, where you may have security concerns or not, and look to get more information on how to maintain data sharing with trusted partners. We can help you adopt Client Hints Reach out to our Customer Success team at tapadcustomersuccess@experian.com to explore the best options to handle the User-Agent changes and implement Client Hints. As leaders in the AdTech space, we’re here to help you successfully make this transition. Together we can review the options available to put you and your team on the best path forward. Get in touch About our expert Nate West, Director of Product Nate West joined Experian in 2022 as the Director of Product for our identity graph. Nate focuses on making sure our partners maintain and grow identity resolution solutions today in an ever-changing future state. He has over a decade of experience working for media organizations and AdTech platforms. Latest posts

Up next in our Ask the Expert series, Ben Rothke, Senior Information Security Manager, reviews two certifications that should be part of your information security strategy: Service Organization Control (SOC) 2 Type 2 and International Organization for Standardization (ISO) 27001. Tapad, a part of Experian, is 27001 and SOC 2 Type 2 compliant. Two information security certifications you can trust Seals from Good Housekeeping and Underwriters Laboratories give consumers confidence that they can trust the product that they’re buying. For IT solutions or service providers, what, or who can you turn to for that seal of approval? There are many equivalent third-party attestations you can use. But which should you trust? The International Organization for Standardization (ISO) 27001 The American Institute of Certified Public Accountants (AICPA) System and Organization Controls (SOC) International Organization for Standardization (ISO) 27001 is an international standard for information security from the ISO. ISO 27001 is globally acknowledged and sets requirements for controls, maintenance, and certification of an information security management system (ISMS). This international standard provides organizations with a framework to identify, manage and reduce risks related to the security of information System and Organization Controls (SOC) The SOC, as defined by the AICPA, is a set of audit reports. SOC reports, like 27001 certificates, are used by service organizations to give their customers the confidence they have adequate information security controls in place to protect the data that they handle. SOC 2 is an assessment of controls at a service organization regarding security, availability, processing integrity, confidentiality, and privacy. The purpose of the report is to provide extensive information and assurance to a broad range of users about the controls at a service organization that are relevant to the security, availability, and processing integrity of the systems that process user data, as well as the confidentiality and privacy of the information processed by these systems. Why ISO 27001 and SOC 2 are important The value of these third-party attestations is two-fold: Organizations can show they have passed an independent external audit Third-party attestations save organizations the time of having to do their own audits In addition to 27001 and SOC 2 Type 2 compliance, we are also certified with ISO 27017 and 27018, which are add-ons to 27001 that are specific to cloud computing. We take the security and privacy of our customers’ data as seriously as they do. Every cloud service provider (CSP) has a responsibility matrix that details what security and privacy tasks they are responsible for and which ones the customer is responsible for. Any cloud customer that needs to be made aware of what their security tasks are is putting themselves at risk. So, when you want to engage a CSP, ask them for their attestations. They worked hard for them and will be proud to share their compliance. We’re powered by decades of setting standards in marketing services At Experian, we’re a privacy-first business. We’re highly focused on respecting people, their data, and their privacy. We continue to show our dedication to information security by completing these security audits every year. The constant changes to data compliance regulations can be challenging to navigate, but you don’t have to do it alone. Contact us today. We will be your guide so you can ethically and confidently reach your customers. Contact us today Contact us today About our expert Ben Rothke, Senior Information Security Manager Ben Rothke, CISSP, CISA, is a Senior Information Security Manager at Tapad, a part of Experian. He has over 25 years of industry experience in information systems security and privacy. His areas of expertise are in risk management and mitigation, security and privacy regulatory issues, cryptography, and security policy development. Ben is the author of Computer Security – 20 Things Every Employee Should Know (McGraw-Hill), and writes security and privacy book reviews for the RSA Conference Blog and Security Management magazine. Latest posts