WHITEPAPER. Picking the Right CDP For Your Company

Transcription

1 WHITEPAPER Picking the Right CDP For Your Company

2 The Birth of the Customer Data Platform In order to compete in today s market, consumer brands need to build meaningful relationships with their customers on a personal level. They need to track implicit signals, like purchases, site interactions, and engagement, and use those signals to understand preferences and buying intent. They need to learn who their best customers are so they can go out and find more just like them. And they need to streamline every interaction with customers to compel them to come back and interact again and again. All of this requires data, which brands already know, and for years they ve been collecting it. The trouble is all that data exists in separate silos, languishing in inactionable systems that were never designed to integrate with one another. In valiant efforts to make that data actionable, IT teams have often toiled away at long and painful ETL projects, building and maintaining connectors that break as soon as a field in the source data changes. Without a reliable way to bring the data together, brands are left in a sea of data without a paddle. Enter the Customer Data Platform (CDP): it promises to bring together disparate data sources, finally making it possible to understand your customers no matter how they engage with your brand. At its core, a CDP should be able to bring in data from any source (online or offline), resolve duplicates within the records, allow for direct access and segmentation, and syndicate the data anywhere. CDPs have gotten a lot of hype in the past few years: the CDP Institute was founded in 2016 to educate and evangelize, and Gartner recently publishing their Market Guide to Customer Data Platforms, with a Magic Quadrant likely to come out sometime next year. Every tech journalist has published their own CDP manifesto, some claiming it s the new secret weapon that will save at-risk brands from annihilation. With all this excitement, it s no surprise that there are dozens of vendors suddenly donning the new moniker. However, many of the vendors who are now calling themselves CDPs were designed to do fundamentally different things from one another. This has resulted in a lot of confusion in the marketplace about what a CDP actually is and should do. For example, we receive RFPs that cover data creation, data collection, data unification, moving data from here to there, data analysis, segmentation, campaign management, and more. Some of those capabilities are core to CDP, but some are already performed by tools that brands have in place today. These RFPs also miss the nuance in how various CDPs ingest, unify, and orchestrate data, and their interoperability with other systems (especially if there isn t a pre-built connector for the systems you re using). And these nuances matter - a lot.

3 A true CDP should never require you to rip and replace an existing system; it should seamlessly integrate with the technologies you already have and the ones you want in the future, allowing you to build your bestin-breed technology stack. A CDP should also be able to handle raw data in any form from any system, not limiting you to a fixed (and therefore inflexible) schema for ingestion or orchestration. And finally, because these all these systems were never designed to talk to each other and don t have linking keys, the CDP needs to be able to unify all your data in ways beyond where two keys match; otherwise, most of your data will still be unreachable and inactionable. To help make sense of all of this, we ve analyzed the backgrounds of 10 CDP vendors (including ourselves) to understand their origins, what they re optimized for, what to watch out for, and recommendations for which brands should choose that type of CDP vendor. While the list we examined isn t exhaustive, we ve found that, generally speaking, CDP vendors fall into four categories: 1. Tag Managers or Data Pipes 2. Multichannel Campaign Management 3. Data Management Solutions for Analytics 4. Pure-Play CDPs Tag Managers or Data Pipes Example vendors: Segment, Tealium, mparticle, SessionM Tag managers first started around 10 years ago with a goal to manage all the tags and pixels brands were adding to their websites for a variety of external vendors. These tags started simply as pixels that loaded on each page that monitored and tracked traffic to a site. They evolved into more complex pieces of Javascript code that collected data beyond just traffic and visitors, like interactions with specific elements on your site. Because so many tools started to require a tracking pixel and performance on-site degraded with all the extraneous code, brands needed a way to add and remove tags and pixels without a developer and load them without slowing their site down. Tag managers were born to fill this need.

4 Over time, the tag manager became a tagger itself, adding their own pixels and tags to your site to create and collect behavioral data at the individual level. They also added mobile SDKs to collect and track behavior in apps; several of the vendors in this category started from the mobile SDK side and have since evolved the capability to collect data on-site too. Eventually, tag managers realized they could do more if they brought in data from other systems, so they added data pipes to their menu of offerings. It was at that point they started calling themselves CDPs because they were bringing together a variety of data types into a single platform. Given their background in data collection, vendors in this space are optimized for: First-party data collection from your site: these vendors have evolved fairly robust systems to collect first-party behavioral and event data from your touchpoints via tags or SDKs. Third-party data integrations on your site: their bread-and-butter, these vendors can manage thirdparty data integrations on your website (through tags) or in your app (through an SDK) with plug-andplay connectors and robust rule management. Anonymous to known tracking: because they provide a Javascript pixel or APIs, these CDPs, when setup correctly, can track when a user logs in or creates an account directly. This allows these vendors to directly track when an anonymous user becomes a known user (though they often can t associate two accounts from the same user). Because these systems were designed for digital data creation, here are some things to watch out for: Deterministic matching only: CDPs in this category only perform basic deterministic matching. Without something more robust -- that can handle data sources without linking keys -- much of your customer data remains unusable. There are also quality issues: a shared computer can end up blending profiles because someone definitively logs in on top of someone else s browser session. Pre-defined schema: because these vendors provide a specific set of tags or APIs you call on your website and in your mobile app, you re limited to collecting data that fits their schema. If you use the schema in an unsupported way, that data may not work as intended or be piped through properly (or at all) to another destination.

5 Pre-built connectors in and out: using a pre-built connector for ingestion only brings in part of your data, limiting it to only the data covered by the vendor s schema. If you use any of the systems in a special or customized way, you ll need to figure out if the connector can be customized or build one yourself. You ll also be restricted to the pre-built connectors available to send data out, limiting how you can act on this data for customer support, experience, and other engagement systems (unless you build and maintain a connector yourself). Lack support for offline and custom data: offline data sources (e.g., point-of-sale, customer surveys, reviews, etc) cannot be easily integrated into these CDPs unless a connector is provided. If there are no linking keys between your offline data source and this CDP, the data can t be used at all, even if you can map it to the vendor s set schema. Rip and replace: if you re already using a tag manager or another tool to collect data on your site, you often need to replace this with the CDP vendor s tool instead so they can properly communicate with all their pre-built connectors. They also require yet another tag on your website which may already have dozens of other tags for other vendors. WHO SHOULD CHOOSE THIS TYPE OF CDP? Tag Managers turned CDPs are great for internet-only brands with online-only use cases (like real-time site personalization and triggered ads) and data sources that are few, clean, and able to be connected via existing linking keys. If, however, you have a lot of offline data, if your data lacks linking keys, or if you want to drive a variety of use cases that require complete customer data in a platform that all your teams can access, this might not be a good fit on its own. In addition, if you already have a tag manager, using a tag-manager-turned -CDP will require ripping and replacing your existing tech to get started, which can be time-consuming and costly for limited benefit to your business.

6 Multichannel Campaign Managers Example vendors: Lytics, RedPoint Global, Blueshift Multichannel campaign management tools began appearing around the same time as tag managers, a decade or so ago. With the explosion of advertising on the internet in the mid-2000s, from Google to Facebook to display ads, it was challenging for brands to create and organize marketing campaigns across all these different channels in a cohesive and efficient way. These tools started as a way to organize all of your marketing campaigns in a single place and build out complex, multichannel journeys. Since then, they ve evolved to track the performance of campaigns, act in real-time on actions, and incorporate machine learning for personalization. Many of these vendors will, like a tag manager, have a Javascript pixel to integrate or API to call and will use that data to carry out marketing campaigns in real-time: event triggers, product and content recommendations, and offer personalization and management. Because they began by managing campaigns on many platforms, vendors in this space realized they needed to have a complete view of the customer to truly understand and report on campaign performance, so they began building out further CDP functionality. Given their background in orchestrating campaigns, vendors in this space are often optimized for: Campaign creation and management: these CDPs allow you to create, execute, and manage omnichannel campaigns, including message creation and testing, content and workflow/journey management, and campaign and message execution. Actionability in real-time: these CDPs execute your campaigns directly, so they also provide ways to act on events and behaviors in real time such as displaying a message to collect addresses for first-time website visitors. Personalization: because these tools are intended to be a one-stop shop for all your marketing campaign endeavors, they ve added the ability to create and manage personalization such as real-time content and product recommendations, message and content personalization, and offer management. Campaign reporting: these CDPs specialize in creating and managing campaigns, so they ve also built out robust reporting capabilities that can help you understand the effectiveness of your various marketing campaigns.

7 Because these systems were designed specifically for online marketing campaign management, here are some things to watch out for: Deterministic matching only: similar to tag managers/data pipes, CDPs in this category only perform basic deterministic matching. Without something more robust -- that can handle data sources without linking keys -- much of your customer data remains unusable. There are also quality issues: a shared computer can end up blending profiles because someone definitively logs in on top of someone else s browser session. Pre-defined schema: because these vendors provide a specific set of tags or APIs you call on your website and in your mobile app, you re limited to collecting data that fits their schema. If you use the schema in an unsupported way, that data may not work as intended or be piped through properly (or at all) to another destination. Pre-built connectors in and out: similar to tag managers/data pipes, using a pre-built connector only brings in part of your data, limiting yourself only to the data covered by the vendor s schema. If you use any of the systems in a special or customized way, you ll need to figure out if the connector can be customized or build one yourself. You ll also be restricted to the pre-built connectors available to send data out, limiting how you can act on this data for customer support, experience, and other engagement systems (unless you build and maintain a connector yourself). Lack of scale: these tools were built with ways to collect data themselves and may not be able to scale to your all of your data sources, including current and future online and offline systems. Rip and replace current campaign or personalization: if you re already using campaign management or personalization tools, you likely need to replace them with the vendor s tool(s) for full functionality and tracking. WHO SHOULD CHOOSE THIS TYPE OF CDP? Similar to a Tag Manager, Campaign Managers turned CDPs are great for internet-only brands with online-only use cases (like real-time site personalization and triggered campaigns) and if you only have a few, clean data sources with linking keys between them. Campaign Managers turned CDPs are also good for brands who want

8 an all-in-one tool and are willing to sacrifice best-in-breed for convenience of having everything in one tool or who haven t already invested in a full-fledged marketing cloud yet. If you have offline data sources, data sources that cannot be connected via a linking key, or have already invested in campaign management tools you d like to retain, this CDP might not be a good fit on its own. Data Management Solutions for Analytics Example vendors: Treasure Data, AgilOne About a decade ago, right around the same time as tag managers and multichannel campaign tools were born, brands began to realize that all the data they were collecting wasn t optimized for analysis. With data in a variety of silos, and tools that often looked at a single channel or function (marketing, data science, etc), data management solutions for analytics emerged. These vendors promised to give brands a way to bring all their data together for better analytics like understanding customer behavior and predicting intent. They focused heavily on mapping in your data (via APIs and/or ETL tools), customer-based analytics and models, and some advanced MDM capabilities. As they built out more functionality to bring in and manage customer data, vendors in this space realized they needed to facilitate action on the insights they were providing. It wasn t enough that they provided the tools to analyze customer data - they needed to help brands bring it to life. They began building out ways to send data and insights to other sources via APIs and connectors, giving them orchestration capabilities and furthering some of their CDP offering. Given their background in an enterprise data warehouse-like space, vendors in this category are optimized for: Data from most offline or online sources: vendors in this space often didn t start collecting the data themselves, so they have tools to bring in data from many sources. Advanced analytics: CDPs in this space started as solutions to optimize your data for analytics, so they often include out-of-the-box predictive models and direct integration with data tools such as R and Python. Out-of-the-box dashboards and reporting: because they optimized their data storage for customer insights, they ve built out robust capabilities in self-serve dashboards and reporting.

9 Because these vendors original purpose was to aggregate data for analysis, here are some things to watch out for: Deterministic or fuzzy matching only: similar to tag managers/data pipes and campaign managers, CDPs in this category only perform basic deterministic matching or fuzzy matching. Without something more robust -- that can handle data sources without linking keys and uses advanced data science techniques -- much of your customer data remains unusable. There are also quality issues with deterministic matching: a shared computer can end up blending profiles because someone definitively logs in on top of someone else s browser session. Range of approaches to data management: because these CDPs are several years old, they may not be optimized for the cloud and have a range of data management abilities. On one end, some of these CDPs take a more data lake approach - they simply bring all the data together with little unification, requiring you to manage and store data in a more usable, relational way. On the other end, some vendors rely on fixed schemas and a limited set of pre-built connectors to bring data together. Time to value: because of the huge range in the approaches these CDPs take to data management, it can take serious technical expertise and many months before you get real value out of these platforms. WHO SHOULD CHOOSE THIS TYPE OF CDP? Similar to the CDPs above, if you are an online-only brand or all your data has linking keys (so you don t need more advanced identity resolution to bring your data together), this might be the right CDP for you. Additionally, depending on the CDP s approach to data management, for some of these vendors, if you have a very technical team who just needs the infrastructure tools to bring your data together and can manage this process longterm, this CDP may be the right choice for you. Pure-Play CDP Example vendors: Amperity A few years ago, despite all the tools we ve profiled, it became clear that there was still a need for something else. Brands continued to have siloed data across a variety of existing tools that they weren t able to use because it couldn t be deterministically matched. They already had a tag manager, a campaign management

10 tool, and an enterprise data warehouse, and they wanted to build on those investments while taking advantage of all the rich data they had in every other system. To meet this need, a new category of CDPs emerged. These were built from the ground up to solve for disparate customer data: collecting it, unifying it, and sending it where it needed to go. These CDPs were designed to solve the core customer data problem, and focus on stitching the data together to create a unified view of the customer in a way that coexists and thrives with a brand s current and future technology investments. Often, they use machine learning techniques and other more advanced matching algorithms and data science to resolve identities and duplicates within and across data sources. Rather than specify a pre-defined schema, they organically adapt based on the unique data and systems in a company. Given these vendors have only been focused on the CDP capabilities, they re often optimized for: Advanced identity resolution: because these systems were designed with identity resolution and completely disparate data sources at their core, these vendors can use techniques like fuzzy matching and machine learning to resolve identity in a variety of ways. Raw data in: these vendors have started well after the many thousands of technology vendors in the MarTech space, allowing them to design to take data in any form and maintain the integrity of that data throughout their system. Scale: these vendors started well after many cloud and big data milestones, allowing them to take advantage of these cutting edge breakthroughs and tackle trillion entry scale. Flexibility: these systems don t use a single fixed schema to house your data, making them flexible as data sources and destinations change. Interoperability: these systems are designed to fit in with all of your existing investments, including tag management, campaign management, and data management systems - allowing you to get the best-of-breed capabilities where you need them with full identity resolution and flexibility.

11 Because these vendors are brand new to an evolving space, here are some things to watch out for: Collecting data: vendors in this space assume your brand has all the systems in place to capture and collect data at various points of engagement with your customers. If you don t already have these, you ll need to invest in these systems. Acting on the data: these vendors have honed in on bringing all your data together but don t provide actionability beyond sending the data to another tool. If you need a tool that can also do full-fledge campaign management/journey building, web/mobile/offer personalization, or reporting/dashboards, these CDPs are not the right choice. WHO SHOULD CHOOSE THIS TYPE OF CDP? If you ve made investments in a variety of MarTech tools like a tag manager and campaign management tool, or have a lot of offline and legacy data, these CDP vendors could be the right choice for you. Additionally, if you have a very large amount of data or a variety of teams and use cases you d like to fulfill, these CDPs could be the right choice because of their scale and flexibility. Often, a pure-play CDP can interact well with another type of CDP since a pure-play CDP offers robust identity resolution but likely doesn t collect data itself or fully manage marketing campaigns. About Amperity Amperity is helping some of the world s most loved brands transform their marketing, analytics, and operations by unlocking their customer data. Using advanced machine learning and a large-scale, distributed data infrastructure, Amperity rapidly delivers complete and actionable customer data from all of a brand s disparate data sources. By accelerating, streamlining, and maximizing customer data unification and usability, Amperity powers improved marketing performance and new customer-centric initiatives to drive top line growth Amperity contact@amperity.com