Alliant’s Identity Graph Approach

Alliant’s Identity Graph Approach

Identity is the foundation for delivering amazing customer experiences — enabling data unification, insights, enrichment and the delivery of people-based marketing strategies. However, effectively connecting and managing identity has become an increasingly daunting task for brands as consumer buying behavior has fragmented across a myriad of channels.

These challenges are compounded by a daily stream of consumer privacy demands, tech provider announcements and regulatory updates. When Google first shared their plans for 3rd party cookie deprecation in 2020, there was hope that a single solution would address all concerns and become the new standard. That idea was quickly replaced with a more realistic mindset of uniting around a handful of interoperable IDs. With over 80 productized ID solutions in market there is still much debate taking place about how the post-cookie reality will look in 2023 and beyond, even with some clear leaders emerging from the pack.

As a data cooperative and audience data provider, Alliant is constantly improving its DataHub identity map with an agnostic and flexible approach. This journey includes constant evaluation of data hygiene rules, integrating with leading ID solutions and communicating throughout the industry. Our brand and agency partners know that they will have access to quality data when choosing the IDs, platforms and channels that work best for their needs.

Below is a summary of the key linkages within the DataHub:

Alliant Link Keys

Alliant ingests billions of data points from hundreds of sources, each of which might have a different representation of an individual (different variations of their name, addresses, various emails, etc.). We help marketers understand the purchase, demographic, geographic, behavioral, and lifestyle information about a consumer by resolving identity to an ID at three levels:

  • Person link key (PLK): the most granular level unique ID representing an individual
  • Household link key (HLK): A unique ID associated with all members of a household
  • Address link key (ALK): A unique ID associated with a specific postal address

These IDs also serve as the “bridge” between digital and PII-based data, creating powerful connectivity while preserving privacy.

Unified ID 2.0

Unified ID 2.0 is open-source software that doesn’t rely on third party cookies. Alliant integrated with UID2 in May 2021, joining a growing list of market leaders that have adopted the solution. Originating with The Trade Desk, the goal for UID2 is to improve and support an independent internet, providing a free solution to all parties in the supply chain, including SSPs, DSPs, DMPs and data providers. Ownership has since transferred to Prebid, a nonprofit organization working to standardize programmatic monetization, providing the industry with a trusted, neutral party to govern a scalable replacement for cookies.

RampID

LiveRamp’s RampID, formerly known as IdentityLink, is another leading solution due to its cross-device architecture, comparable to the likes of Facebook and Google. RampID incorporates information such as mobile device IDs, consumer histories, home addresses and other offline information. LiveRamp began developing the RampID long before Google’s plan and has been a part of Alliant’s solutions for several years.

Hashed Emails

The DataHub contains over 1.5B hashed emails and serve as a critical link key for matching and digital activation. While we continually ingest a massive amount of emails, the qualified pool after our rigorous hygiene processes is about 60% smaller. For individual projects or campaigns, different levers can be adjusted to control for quality and scale, resulting in a customized output designed specifically for a marketer.

IP v4 & v6

With the increased importance of connected and addressable TV, Alliant has increased resources to growing and improving upon IP addresses. Like hashed emails, dedicated hygiene rules eliminate low quality or public IPs. As a result, marketers have been able to recognize increased scale and accuracy with their TV campaigns.

The effective use of predictive data is predicated on the accurate linking of individuals and households. Both the data and ID layers must be accurately architected in tandem, then linked together with the same level of care. That is why the Alliant team is dedicated to building the best version of the DataHub ID graph to power 1st party data strategies and multichannel marketing activation.

Be sure to check out our recent webinar, featuring The Trade Desk and LiveRamp, on ways that brands and agencies can start building their own strategies around multiple ID solutions.

ABOUT THE AUTHOR

Chris Hemick, Sr. Director, Product Marketing & Content

Chris has a decade of media experience and drives product development for Alliant’s suite of audience and optimization solutions. Prior to joining Alliant, he ran the Media Strategy, East team at TubeMogul (acquired by Adobe) and negotiated national television buys at OptiMedia (now Blue 449). His mission is to bring together technical expertise and creativity to produce innovative product and campaign strategies. Chris is also an avid photographer & golfer, enjoying how both hobbies keep him outdoors.

 

3 Easy Ways to Apply Data Enrichment

3 Easy Ways to Apply Data Enrichment

Brands should be actively investing in the collection and activation of 1st party data. According to IAB’s State of Data 2021 report, which surveyed more than 200 data decision makers across brands, agencies, and publishers, 42% of respondents expect to increase spending on use of 1st party data due to changes to 3rd party cookies and identifiers. Yet, they also found that less than half of data users are collecting sophisticated media, shopping and behavioral 1st party data.

These stats begin to illuminate large gaps that will be present once the current data marketplace evolves. One data set, no matter how expansive, is only going to tell part of the story. Brands need to fill in these gaps, looking beyond cookies to gain additional insights about current and prospective customers and drive their businesses forward.

Finding quality data enrichment partners is a proven strategic investment to fill the gap. By appending, or combining data from other sources to 1st party data, brands will have a more holistic customer view, impacting all stages of the customer journey.

Even seasoned marketers may feel a bit overwhelmed at the thought of implementing enrichment while concurrently architecting an overall 1st party data strategy. But data enrichment doesn’t have to be hard, or even require new processes. While there are many high value, but more complex use cases, data enrichment can be as easy as uploading a 1st party data file and getting it back with more robust information. Here are 3 ways we’ve partnered with brands of all sizes to help them realize data enrichment ROI with some basic applications.

Improve Identity Resolution

Unique multichannel marketing and sales strategies help brands develop loyal customer bases, but they also come with challenges. Managing identity, understanding repeat buying behavior and measuring LTV across all touchpoints proves difficult with many different consumer purchase points. To maintain positive relationships and understand the impact of marketing efforts, accurate measurement of customer purchase history is critical.

Alliant recently evaluated 5 years’ worth of a DTC brand’s data across channels and touchpoints. Unique identifiers were added at both the individual and household levels, then integrated within the brand’s own CRM platform for increased insight.

This quick process resolved the identity of over 1.5MM customers, identifying 35% of individuals that were not properly identified as repeat buyers. With the addition of Alliant’s unique IDs, the brand was able to accurately calculate LTV and improve their cross-sell efforts by recommending products based on prior purchases.

Make More Informed Recommendations

Enrichment doesn’t always have to be used directly on 1st party data. Alliant partnered with sports marketing audience monetization platform, FanAI, to assess spending, lifestyle and demographic attributes for fans of specified sports teams. The goal was to identify opportunities for their client that would maximize exposure by aligning their budget with the right sports teams and their target audience.

TV viewership data across 11 professional sports teams in a designated metro area was matched to the Alliant DataHub, appending attributes such as age, income, estimated wealth, payment and lifestyle behaviors. By having this additional information, FanAI was able to rank the 11 teams across the 25 attributes, using the insights to make their final sponsorship recommendations to their client, proving that enriched data can lead to more effective planning, too.

Increase 1st Party Audience Match Rates

An increasing number of publishers and platforms are rolling out product updates that allow brands to upload their own audiences. Marketers may be familiar with this type of offering through Facebook’s Customer List Custom Audiences, or Google’s Customer Match, but publishers don’t have the vast logged in networks of the walled gardens. Marketers will need to be prepared for lower match rates for their audiences.

Alliant is in early stages of helping publishers enhance their audiences to improve match rates and audience size for advertisers, but the same can be done for brands looking to use these new platforms. To assure the highest match rates, audiences can be enriched with match key data before uploading to the publisher, including postal addresses, emails, or phone numbers.

Email is a leading link key, so ensuring you have not only the latest, but also the secondary addresses that people may use, will boost the size of your matched audiences. The impact of this effort will be immediately recognized with more scalable, targeted campaigns. Likewise, proving ROI can be easily managed by looking at before and after match rates, along with conversions. Even if conversion rates remain steady, the larger audience sizes will drive additional revenue.

These are just a few quick examples to feature how other brands and their partners are leveraging data enrichment today. One important takeaway is that each of these solutions fit within their existing tech stack and processes, allowing them to recognize quick wins. If you are a brand marketer trying to figure out what to do next, data enrichment might just be the flexible, easy to implement solution you’ve been looking for.

Ready to get started with your own data enrichment journey? Our consultative team will help guide you through the process to make it as seamless as possible, including strategic recommendations on the data that will make an impact on your business! We look forward to hearing from you.

ABOUT THE AUTHOR

Chris Hemick, Director, Product Marketing & Content

Chris has a decade of media experience and drives product development for Alliant’s suite of audience and optimization solutions. Prior to joining Alliant, he ran the Media Strategy, East team at TubeMogul (acquired by Adobe) and negotiated national television buys at OptiMedia (now Blue 449). His mission is to bring together technical expertise and creativity to produce innovative product and campaign strategies. Chris is also an avid photographer & golfer, enjoying how both hobbies keep him outdoors.

 

Think You Know 2nd Party Data? You Might be Surprised.

Think You Know 2nd Party Data? You Might be Surprised.

Commonly accepted definitions of 1st, 2nd and 3rd party data have been around for a while, taking hold in the early 2010’s and becoming more prevalent in the last 5 years. Unlike its siblings though, the concept of 2nd party data has never quite solidified itself in the lexicon of marketers. That is all going to change in the face of an evolving marketplace and 2nd party data isn’t likely to suffer from middle child syndrome much longer.

Several related but distinct factors are reshaping digital marketing and advertising in the U.S.: the deprecation of the 3rd party cookie, Apple restrictions on IDFA, regulations such as CCPA and consumer demand for data transparency. These collective changes will increasingly limit the availability of 3rd party data and place greater emphasis on a brand’s 1st party data. There is acknowledgement however, that to maintain a competitive advantage and continue to grow, brands will need to go beyond their 1st party data.

These seismic shifts, especially over a relatively short period of time, prompted the Winterberry Group to explore how the marketplace is responding in their latest research paper, Collaborative Data Solutions: The Evolution of Identity in a Privacy-First, Post-Cookie World. Haven’t had a chance to check out the paper? Check out the link below for easy access.

Winterberry’s research, which consisted of hours of interviews and surveys with leaders from brands and data providers across the marketing industry, uncovered the growing adoption of solutions that enable data collaboration between multiple sides of the ecosystem, including brands, data owners, publishers and tech providers.
Collaboration between organizations is predicated on the sharing of data resources —  which calls for a clear definition or classification of what the resulting data sets would be. This need was evidenced by the responses from brand marketers when asked “What does second-party data mean to you?” The two most common answers align with the previously accepted basic definition: “2nd party data is simply someone else’s 1st party data.” While not entirely incorrect, that definition does not fully capture the possibilities of 2nd party data.

Focusing on the collaboration use case, Winterberry set out to update the definition of 2nd party data to reflect the range of participants and required permissions as the data flows from the primary owner to a shared environment.

2nd Party Data as Defined by the Winterberry Group
Second (2nd) party data is data that is shared in a dedicated environment with a clearly defined set of permissions and rights set between each of the parties and, most often, the 3rd party provider managing the environment.

The updated definition and supporting details establish a more sophisticated framework for a common understanding of 2nd party data. Additionally, Winterberry provides helpful distinctions that 2nd party data can involve 1st party data from more than two parties and that it can change state to 3rd party data once it is commercialized (licensed). While it will have an impact on permissions and rights, the definition notably leaves out the type of data being shared, such as PII and anonymous linkages or IDs. This intentionally creates coverage for more use cases.

Alliant’s perspective, as a data cooperative at its core – is that this basic definition is a foundational component that will reduce semantics and pave the way for more strategic conversations among partners. Our Members benefit from a secure and fully permissioned collaborative marketing environment and we are excited for this concept to be more understandable and accessible to a wider group of marketers.

Be sure to download the Winterberry Group’s Collaborative Data Solutions: The Evolution of Identity in a Privacy-First, Post-Cookie World for a deep look into the future of data-driven marketing. Interested in learning more about how Alliant can jumpstart your collaboration initiatives? Reach out to us to set up time to connect!

ABOUT THE AUTHOR

Chris Hemick, Director, Product Marketing & Content

Chris has a decade of media experience and drives product development for Alliant’s suite of audience and optimization solutions. Prior to joining Alliant, he ran the Media Strategy, East team at TubeMogul (acquired by Adobe) and negotiated national television buys at OptiMedia (now Blue 449). His mission is to bring together technical expertise and creativity to produce innovative product and campaign strategies. Chris is also an avid photographer & golfer, enjoying how both hobbies keep him outdoors.

Getting Started with Data Enrichment

Getting Started with Data Enrichment

Brands that have continued to grow and evolve in the face of challenging times have harnessed data across their organization, particularly in their marketing efforts. Rapid change to how consumer data is acquired and activated is forcing brands to rethink data strategy. Marketers and their partners are focused on the pending deprecation of 3rd party cookies, along with the likely shuttering of device IDs for ad tracking. The sticking point – this is often interpreted to mean they can only use first- party data from here on out. While understandable, especially as the industry slowly explores a cookie-alternative, it is not the case. Brands can continue extending the value of their first-party data through data enrichment.

What is Data Enrichment?

Data enrichment is the process of appending, or combining, data from other sources to first-party data. This can include data from siloed systems within a brand’s own organization, or from external sources.

Providing real-time, relevant customer experiences that lead to high customer loyalty is the best and most obvious reason to invest in data enrichment.

External data sources fall into one of two categories based on the type of data assets being leveraged. When consenting parties are sharing and leveraging their first-party data it is referred to as second-party data. This can exist as a 1:1 partnership, or in a cooperative environment, where multiple first parties can build a collaborative data set. Third-party data is typically sourced from many different public and proprietary sources, including cookie-based web behavior data. Brands use third-party data in digital platforms such as DMPs to augment their own cross-channel digital data and build a more robust customer profile. This use case doesn’t enrich offline data and will be limited in the near future due to the changes with third-party cookies. Customer data platforms (CDPs) promise cross-channel first-party data management (offline + digital), but the options for enriching with other sources are currently limited.

Data enrichment provides more flexibility and customization than these platforms. It can deliver a more holistic customer view that will impact all stages of the customer journey, from the top to the bottom of the funnel.

Why Invest in Data Enrichment?

First-party data is a brand’s most valuable asset, but it has limitations. Understanding more about customers results in more effective engagements. Consider a first-time t-shirt buyer – where the retailer captures a small set of data on this new customer: name, address, email, t-shirt size, and a Star Wars affinity based on SKU. With predictive models, the brand might be able to forecast common behaviors for this consumer based on other first-time t-shirt buyers; when or what they might purchase next or how much they might spend. But their limited view of t-shirt buyers might not be enough to move these consumers through a high-impact customer journey that results in repeat purchases and high customer loyalty.

Imagine a scenario where at the time of purchase, a real-time API provided by a data partner returns a handful of other helpful attributes, such as the customer’s propensity to buy specific brands or products, their estimated household income and a suite of demographic and lifestyle information. The retailer is now empowered to customize their follow-up confirmation e-mail, future communications and product offers that will resonate with the t-shirt buyer in a way that wouldn’t have been possible otherwise.

Providing real-time, relevant customer experiences that lead to high customer loyalty is the best and most obvious reason to invest in data enrichment. However, there are other excellent, albeit less obvious, benefits worth considering.

Leveraging Prior Investments

Many brands have significant resources surrounding data organization and processing. It’s common to have in-house data scientists who build customized machine learning models, or expensive tech platforms powered by advanced ML running behind the scenes, or both. These solutions look at immense data to make accurate predictions. With a large portion of that becoming less accessible in the near future, those systems will be hungry for more, and data enrichment can keep them satiated.

Save, and Make, Money

Data storage and management is expensive. The part that really stings? Studies often find that value is extracted only from a small portion of the massive amounts of data businesses manage. With data enrichment, additional data is only appended when needed. This saves money on storage and provides confidence the added cost is being applied to deliver value. The savings can be reinvested in other revenue-driving initiatives.

Keep Up with the Speed of Change

Other than the moment it is created, a data point’s default state is decay. If using data to inform customer experience, marketers must be confident the data is accurate and up-to-date. Consumers evolve, change jobs, move, develop new interests and so on. Brands would benefit from enriching even the most tenured customers. When enriching first-party data within their own systems, rather than only in a DMP or other platform, brands have the flexibility to understand and reach their evolving customer-base across both traditional and digital channels.

Quick Tips for Getting Started with Data Enrichment

Convinced data enrichment should be part of your strategy? Now comes the fun part – getting up and running. Below are a few high-level steps on how to approach data enrichment.

Identify Gaps and Set your Strategy

Understand what data you have, what you’re missing and what your objectives are. This requires cross-functional teaming within your organization. Involve stakeholders from different teams and identify which use cases and data sets will add the most value. Then make a determination on what type of financial and time investment you are willing to make.

Find the Right Partner

Once you know which type of data you need, it’s time to start searching. This can be easier said than done as you will be balancing multiple priorities like scale, price and accessibility. For example, you may find the perfect data set, but it may be way out of budget. Partner evaluation will take time, but quality partners will do everything they can to make the data evaluation process seamless and secure.

Activate & Measure

Once the data starts coming in you will need to measure the impact on the business. Similar to marketing campaigns, it’s important to set the right KPIs to measure upfront, ensuring they are aligned with the use cases and goals. Once proven out, data enrichment should become an integral and ongoing component of any data-driven brand’s workflow.

It might feel overwhelming to start thinking about a new data strategy. However, the flexibility and scalability of data enrichment will open up new possibilities that extend the value of quality first-party data. Don’t forget, there are great resources out there that help brand marketers with the evaluation of potential partners.

Interested in learning more about data enrichment? Thousands of customizable data points are available via Alliant’s real-time API, with sub-100ms response times. Have more analytic use cases in mind? Our batch processing appends data to millions of records in single enrichment request. Contact our team to discuss flexible data packages that are tailored to meet your specific needs.

ABOUT THE AUTHOR

Chris Hemick, Director, Product Marketing & Content

Chris has over a decade of marketing & advertising experience, driving product marketing and content development for Alliant’s suite of audience solutions. Prior to joining Alliant, he ran the Media Strategy team at a leading independent DSP and negotiated national television buys at a media agency. His mission is to bring together technical expertise and creativity to deliver engaging content that empowers marketers. Chris is also an avid photographer & golfer, enjoying how both hobbies keep him outdoors.

4 Quick Tips to Guarantee Digital Audience Testing Success

4 Quick Tips to Guarantee Digital Audience Testing Success

Digital marketers and ad buyers invest copious amounts of time to plan, optimize and measure all stages of programmatic campaigns. In planning stages, strategists pay particular attention to defining their target audiences. Yet despite the initial investment in developing an audience-first strategy, all too often segments come and go from the campaign without the same level of consideration that was given upfront, and suffer because of it.

Buyer personas are foundational to an audience strategy, but developing them can be a laborious process – evaluating current customers, historical campaign performance and real-time sales and site data. A combination of 1st and 3rd party data then helps reach that cohort or inform custom audience development. Once live, the beauty of programmatic – speed of results and simplicity of change – tempts media buyers to start optimizing audiences. 

Define what success looks like over a realistic time period and understand how performance should be trending at various stages.

Often this results in testing audiences that were not included in the initial recommendation. But testing audiences is unlike testing creative, or even channels, and requires a few extra steps. If done correctly, data testing will refine and improve your audience-driven strategy, if not, it can unwind the hard work done in the early stages and send you down the wrong path. To avoid the common pitfalls, we’ve compiled four important considerations for testing new audiences on a live campaign.

Know Your Audience Data Quality

As the data marketplace ballooned buyers struggled to identify the best sources, and data providers fought to break through the noise. Fortunately, data quality and transparency are easier to identify today —  with initiatives like the IAB Tech Lab’s Data Label, and companies like Neutronian, empowering the marketplace with accessible insights. Need to feel empowered? We’ve compiled this 8 question guide to help you ask your data partners the right questions.

Armed with this info, buyers can think about how each piece fits into their audience strategy. Likely this is the approach being taken by strategists and planners pre-launch, but once a campaign is live it can be easy to fall into the trap of doing a quick search and turning on any segment that has a name and description close to what you’re looking for.

Try to give all new audiences the same level of scrutiny as the initial recommendations. Investing in the review process will let you go beyond branded segment names and determine which audiences can be additive, fill gaps, or full-on replace existing segments. Understanding audience quality and composition will allow you to design better tests. To this end, many brands and agencies maintain a shortlist of go-to data partners to help their teams make these decisions quicker.

Control the Variables

When experimenting with new tactics on of any portion of a campaign, it’s important to test only one hypothesis at a time. Introducing multiple changes will prevent you from effectively knowing which change impacted the performance.

Say you plan to run a head-to-head test between data partners. To accurately compare these two different audiences, you should keep all other factors, such as bids, bid strategy, creative, dayparting, etc., the same. To test multiple factors – how the audiences will perform at multiple bid points for example ­– be sure to create multiple, mirrored placements for each segment. Be mindful, this may cause some campaign bloat as you generate multiple placements for each.

Balance Your Budget

Most marketers and buyers don’t need reminding to keep their eye on spend, but in testing, carefully watch the daily budget and don’t underspend. It’s not recommended to come out of the gate blowing budget on an unproven audience but be sure to keep your foot on the gas when testing new audiences. You don’t necessarily need to maintain the same spend between your test and control audiences, but be sure that you’re not unintentionally throttling performance in the name of caution. The new segments need enough volume to perform and generate statistically significant results. Commit to the test and see it through.

Give New Audiences Time

When adding new audiences to a campaign, have specific timelines and goals in mind. It would be amazing if adding a new audience would double campaign performance overnight, but they are not magic wands. Define what success looks like over a realistic time period — and understand how performance should be trending at various stages. As you compare new and existing segments on your campaign, look back at how those audiences performed at launch. This will allow you to establish benchmarks for comparison and factor in that existing segments have had the benefit of weeks, if not months, of optimization by your platform’s algorithms.

Our team has seen advertisers invest in new audiences for a campaign only to turn them off soon after launch. Typically, this happens because the advertiser did not define success criteria upfront, and when the new test audiences did not immediately perform at the same level as control audiences, they hastily abandoned them. A deeper analysis would have shown them that the new audiences were trending well above the controls at comparable early stages of activation. Abrupt moves such as these do a disservice to the time and effort put into identifying the audiences and updating campaigns, and squanders any financial investment.

When all of your teams are aligned around an audience strategy, testing new segments will become a powerful component of campaign optimization. And with more accessible details on the data available in market, it is easier than ever to execute with thoughtfulness and care.

Want to talk strategy or build a custom audience? Contact the Alliant team to learn how powerful transactional data can improve your campaign performance

ABOUT THE AUTHOR

Chris Hemick, Director, Product Marketing & Content

Chris has over a decade of marketing & advertising experience, driving product marketing and content development for Alliant’s suite of audience solutions. Prior to joining Alliant, he ran the Media Strategy team at a leading independent DSP and negotiated national television buys at a media agency. His mission is to bring together technical expertise and creativity to deliver engaging content that empowers marketers. Chris is also an avid photographer & golfer, enjoying how both hobbies keep him outdoors.