An identity graph is defined as a database that creates linkages between all identifiers that are associated with an individual customer.
In this article, we explain what an identity graph is, why marketers need it, with 2 brand examples of implementation.
Table of Contents
- What Is Identity Graph?
- 5 Key Reasons Why Marketers Need Identity Graphs
- Examples of Identity Graph in Action: Amazon and Netflix
An identity graph is defined as a database that creates linkages between all identifiers that are associated with an individual customer. These identifiers include PIIs and other digital and device identities such as email, username, phone number, IP address, cookies, physical address, etc. ID graphs essentially provide you with the demographic, geographic, behavioral, purchase and other crucial data about a customer and make that customer â€˜addressable’, so you can deliver an enhanced customer experience.
In today’s complex customer-first environment, if you want to be a customer-centric marketer, you will need the ability to map each of your customers to their data and devices.
But it’s complicated. On average, a user accesses the internet from four different devices throughout the day. Your analytics tool might show each device as a different visitor even though it was the same user. To complicate matters further, companies also use different software such as marketing automation, CRM, social media management tools and so on to collect customer data. The data is scattered across multiple applications, and it is difficult to get a composite view of your individual customers. Identity graphs bring all these scattered identifiers together to create or â€˜stich together a unified customer view.
So, even though you might have the same customer in different software, an ID graph will collect data from the various applications and collate them into a single view will all the available information on each user. This process of gathering and merging identities from disparate data sources is known as identity resolution.
Successful identity resolution using identity graphs have helped successful companies like Netflix drive exponential growth.
While there are several external services such as data on boarders and DSPs that claim to give you unified customer data that can be immediately activated, there is merit in considering your own identity graph solution in-house or with a single technology partner because it ensures consistency in the way rules are defined for linking individuals to devices. This drives up the accuracy of the linkages and delivers better marketing campaign outcomes.
Here are 5 key reasons you need identity graph solutions:
1. Flexibility, security, and control over your data: with an owned identity graph, you are assured of one consistent set of rules to define linkages between identifiers. This also means you can activate the data on any platform and are not stuck with one walled garden or DSP for targeting and campaign performance measurement. It is also safer as you cannot be sure that these third parties aren’t selling or sharing your â€˜custom audiences’ to other entities that may misuse the data. All you need to do is share the anonymous identifiers to DSPs in order to deliver the ad to the target audience via their platform.
2. Cross-device attribution: a user interacts with a brand through different channels via multiple devices during their customer journey. An identity graph maintains all this information, helping companies analyze the effectiveness of each channel. They can further delve into individual campaigns to measure their impact and alter them effectively. Having this data at hand can help organizations make better decisions when it comes to allocating ad budgets to different platforms â€“ once publishers and DSP’s share the digital ID’s they targeted with ads during a campaign, you can link them back to â€˜person ID’s’ and complete a single measurement study based on all the impressions delivered during the campaign for cross-screen, cross-publisher, closed-loop measurement. With the help of an ID graph, marketers can accurately identify customers in real time and measure campaign results through cross-device attribution.
3. Consistently improving â€˜single customer view’: identity resolution creates a unique identity of each customer. This allows marketers to accurately identify each customer regardless of the device, channel, IP address, etc. As the customer continues to engage with the brand, their identities automatically get updated allowing companies to serve their customers better.
4. Online and offline customer engagement: identity graphs can capture offline customer data such as subscription details, contact information, loyalty card details, in-store purchase history, etc. and match it with existing digital identifiers within the identity graph to get a holistic customer view- i.e. connect online and offline identifiers. This opens up a whole new set of opportunities to build personalized experiences.
5. Personalized Customer Experience: Amazon, Spotify, Netflix, Hulu, and other top streaming services have managed to nail down personalization. They have been able to do so because they utilize identity graphs to understand their users’ preferences and make recommendations accordingly. The clarity and direction provided by an ID graph and ID resolution enables marketers to get a holistic view of data and justify the return on investment in various marketing activities.
Of course, it is important to bear in mind, with identity graphs that you can never have a completely perfect map â€“ so do not expect to map 100% of the IDs or every single interaction on every single device. It is quite a realistic scenario for some customers to have items shipped to their office or a friend’s house, or to have used a relative’s credit card account to complete a transaction, and these may lead to some blips. However, a good identity graph platform should be able to cover most of the use case scenarios and give you as strong and robust set of data that is realistically possible. A persistent identity graph of each customer means that the data is not just collected real time, but in a persistent manner â€“ as it happens, when it happens, on an ongoing basis. This only serves to make the graphs stronger and more intelligent the more the customer uses the services.
How Amazon drives exponential value with identity graphs
Amazon’s market value is over $50 billion larger than the market value of Walmart, Target, Macys, Best Buy, Kohl’s, Nordstrom, JC Penney and Sears combined. The company’s focus on customer personalization is legendary. Behind the legend, however, it is hard-at-work identity graphs that enable the kind of insight into customers that is needed to deliver the kind and level of personalization that converts at scale. Imagine for a moment, the mind-boggling data that Amazon has access to â€“ data about customer interactions and transactions from a range of channels and touchpoints. To spell it out, it is everything you do on their website and app, your interactions with Alexa â€“ what music you listen to and what OTT you access on the Fire Stick, the books you read on Kindle, and what you search for, review, share and more. This identity graph has access to this unparalleled trove of information that is informed by millions of data points and sources, for each of its 300 million users. Imagine the possibilities for content and experience personalization, product recommendations and in-the-moment offers based on preferences and patterns.
How Netflix changed TV viewing with its use of customer intelligence
Netflix has set the bar pretty high with its personalized content recommendations. The personalized profiles you can set on the home screen is not just for customer convenience alone of course- it provides Netflix with a treasure trove of data and content consumption habits for various members of the same family at the individual level as well as patterns of more generic usage across similar profile individuals. The personalized profile adjusts content to the individual’s interests, preferences and viewing history. Every element of the viewing experience is personalized â€“ from the banner to the text and the search â€“ even the artwork of the shows. The famous â€˜recommended for you’ is a vital component of Netflix’s customer experience and business strategy and lets them serve up a great experience irrespective of when and where you choose to watch Netflix. With its 2018 show Black Mirror though, Netflix took personalization to the next level, by letting viewers choose the ending they wants to see. This move gives Netflix so much more intelligence on preferences and cultural patterns that it can significantly impact the way it leverages content for advertising â€“ including in-show product placements.
Learn More: Identity Mapping Traps and How to Avoid Them