Customer data management (CDM) is defined as the process and framework of collecting, managing and analyzing customer data from diverse sources to form a unified view of each customer. In this article, we talk about why marketers need to prioritize CDM, its key components, best practices and popular technology platforms that enable robust customer data management.
Table of Content
- What is Customer Data Management (CDM)?
- 4 Reasons Why Marketers Need to Prioritize CDM
- 4 Key Elements of a Customer Data Management Strategy
- Popular Customer Data Management (CDM) technology platforms and software
- Customer Data Management (CDM) Best Practices for 2020 and Beyond
Customer data management (CDM) is the process and framework of collecting, managing and analyzing customer data from diverse sources to form a unified view of each customer. Using technology and software, the goal of CDM is to effectively deliver unique, personalized, real-time and channel-agnostic customer experiences.
Today, consumers can interact with a brand across multiple avenues. This gives brands a multitude of opportunities to collect more customer data and use it to enhance customer experiences. Unfortunately, this data gets stored in departmental and functional silos, and brands seldom utilize this data to their utmost potential.
Although we often read articles along the lines of â€˜we live in the data economy’ or â€˜data is the new oilOpens a new window ,’ it stands true only for organizations that manage their customer data effectively.
As the proliferation of data continues to rise, organizations need sophisticated mechanisms to collect, organize and analyze customer data. That’s where the discipline of customer data management comes into the picture.
CDM is a key link in the larger conversation around better leveraging organizational data â€“ including of course customer data â€“ to drive profitability and differentiated customer experiences. CDM is also the gatekeeper to the conversation on compliance with globally evolving customer privacy rights and regulations. The primary outcome of customer data management (CDM) is that marketing and other functions that access and manage customer touchpoints (for example, sales, accounts and service) all have a â€˜single source of truth’ when it comes to customer data, and are able to deliver a consistent, seamless and unfragmented customer experience based on that single source of truth.
In this feature, we will cover topics such as customer data platforms, identity resolution and identity graphs, data-driven marketing and customer analyticsOpens a new window platforms, customer data governance and so on.
Customer expectations are evolving and marketers are expected to be able to deliver meaningful personalization that can engage them through their customer journey. Customer data management enables marketers to address the new â€˜paradigm of personalization’ for real business outcomes.
1.The focus has shifted from anonymous persona-based personalization to Personally Identifiable Information (PII) driven â€˜segment of one’ personalization, especially with AIdriven technology that is able to customize on the fly.
2. From ad tech driven personalization to martech driven personalization: from the days
when ad targeting and retargeting based on Data Management Platform (DMP) persona segments was enough, today MarTech is expected to personalize everything from website content to email marketing â€˜in-the-moment’.
3. The focus is shifting from third party data to first party data, as CMOs realize that even rudimentary multi-channel customer interactions provide a wealth of customer and prospect data, even as the noose is tightening around the legal and compliant use of third-party data.
4. The omni-channel space has turned the spotlight back on data integration, versus data collection and storage. How data sources are connected, how freely the data can flow back and forth, and how it can be activated by martech solutions for conversion outcomes is the focus today.
Having a strong CDM strategy- the practices and processes necessary â€“ in place, lets organizations personalize the customer journey and deliver a seamless Customer Experience (CX).
4 Key Elements of a Customer Data Management Strategy
Marketing is no longer brand or channel centric â€“ it is customer-centric. This approach to marketing, which is based upon delivering personalized experiences to each customer along their unique buying journey, needs robust, reliable data in order to make the decisions at each touchpoint with the customer. A well-defined customer data management framework can help ensure that valuable customer data is not locked up in silos, but rather flows freely across the organization, not only providing a single source of truth to marketing and other customer-facing functions such as sales and service; but also enabling real-time, data-driven responses to customer actions.
Data collection and integration 4 Key elements of a customer data management strategy include:
- Data collection and integration
- Data management
- Data analysis
- Data activation
1. Data collection and integration
This is the first step in building a strategic and integrated customer data management strategy. According to a Forrester blog post, â€œOn average, between 60% and 73% of all data within an enterprise goes unused for analytics.â€ Today there is a data deluge in most organizations, where millions of data points are coming into the system from thousands of customer touchpoints across diverse channels, platforms and devices. Marketers first need to understand what data needs to be ingested into the system to effectively build a customer data management framework.
Understanding the various sources of critical data is followed by bringing it into a central system, in order to run it through a process called ETL or â€˜Extract, Transform, Load’ â€“ ingesting the critical data, â€˜transforming it’ in terms of basic duplication and formatting, and loading -which refers to bringing all of the critical data into the preferred central marketing data platform such as a data warehouse, a customer data platform (CDP), a data management platform (DMP), CRM, data lake or any other big data system. The outcome is â€“ all of the data you need, in one place, in one format.
2. Data management: refers to the phase of connecting the dots between the data points to start building complete, unified profiles of individual customers or segments. In the case of first party data for martech applications, this would involve processes such as probabilistic or deterministic identity resolution, building identity graphs, 360-profiles of customers and integrating consent into customer data, to ensure compliance. In case of adtech applications that need to be used through a DMP, the data needs to be anonymized in order to build â€˜look-alike’ segments.
3. Data analysis: at this stage, the data is ready for use. Marketers can build targeted segments of look-alike customer profiles, run predictive analysis to model potential campaign outcomes etc. Intelligent systems can also start recommending next best steps and customized interactions and experiences based on the data, for each customer.
4. Data activation:
Having all the data in one place, even if its organized into unified customer profiles, is necessary but not sufficient to ensure that the data can then be activated to run well-orchestrated marketing campaigns across channels. The last mile of a complete customer data management strategy also accounts for how the data will be actually moved into marketing technology systems and used to run data-driven marketing campaigns. For this, the marketing systems need to be integrated not just with the data and each other, but also with the performance tracking and analytics systems to optimize campaigns in real-time.
Customer Data Management is a strategy, but it requires the right software platforms and technology tools to execute. The volume, velocity and variety of customer data in the digital world is such that even local or regional marketers need a complex technology stack to manage and activate the data at scale.
The most pervasive customer data management software platforms today include Customer Data Platforms (CDPs), DMPs, CRMs and DXPs. Without these technologies, working with the complexity of customer data integration, analytics, and activation would be impossible.
- Customer data platform (CDP) is a marketer managed tool that helps create a unified, persistent view of the customer and makes that data available to other marketing systems to activate campaigns and other marketing efforts. CDPs usually manage first party customer data and consists of PII (personally identifiable information).Opens a new window
- Data management platforms (DMP) on the other hand, are software platforms that store and manage campaign and audience data, primarily for running digital advertising campaigns. The data is usually anonymized and consists of third-party anonymous data such as cookies, device IDs and IP addresses. AS GDPR and other data privacy and compliance regulationsOpens a new window have set, organizations are refocusing on CDPs and first-party data given the inherent advantages of working with owned data.
- A Customer Relationship Management (CRM) system maintains your customer, service provider and supplier data. Although CRM systems and CDPs share some similarities, they are different when it comes to collecting data. CRM systems store data only if the user has communicated with the brand. On the contrary, CDPs use multiple online and offline channels to capture and store customer data.
- Digital Experience Platforms A Digital Experience PlatformOpens a new window (DXP) is a collection of technologies used to create and manage websites, mobile apps, forums, and other digital properties. CDPs provide unified customer data, customer segments, etc. to DXPs to improve customer experience.
For a detailed, feature-by-feature tabulated comparison of Data warehouses, CDPs, DMPs and CRM platforms, read: What is the difference between CDPs, DMPs and other data management platforms?Opens a new window
Aside from the more mainstream customer data platform (CDP) and data management platform (DMP) that tend to do most of the end-to-end tasks needed for first and third party data management respectively, you may need a few additional layers depending on your business context and priorities, use-cases, and complexity. These could include some or all of these:
- Data Enrichment tools: data gathering is not a one-time activity. Customer data â€“ whether first- or third-party, changes frequently, and therefore you need to have a process in place that ensures your records are up to date, or enriched, as regularly as possible. The data enrichment process typically begins with cleansing existing customer records; this includes verifying the quality, accuracy, and validity of the data. It is not restricted to the firmographics and contact details of customers. Data enrichment encompasses technology and devices used, user activities and any data point that might help sales and marketing teams better their efforts. Today there are several SaaS based tools as well as specialist data enrichment vendors available to help with this ongoing requirement.
- Customer Data Infrastructure Platforms (CDI)Opens a new window :Opens a new window CDIs bring everything into the same view for business users and address the problem of siloed data. They capture data robustly with well-modeled APIs and SDKs from different systems, cleanse and normalize the data (a process also referred to as ETL or â€œExtract, Transform, Loadâ€), and execute tag management and data governance. By solving the data silo problem, CDIs act as â€˜reliable data orchestrators’ since the outcome is a single source of raw but clean, reliable, event-driven data that can be routed to different systems â€“ from analytics and automation to audience management for ad tech â€“ and their respective business users.
- Customer Intelligence Platform: is a software that enables you to collect and analyze customer insights, activities, ideas, opinions, and feedback, and distill this information into actionable takeaways. This helps organizations create individual experiences that appeal to different customer segments. A CIP shares some similarities with a CDP â€” they both collect crucial customer information and empower organizations to deliver superior customer experience.
- Customer Data Activation Platform (CDAP): CDI’s can solve the challenge of unified data. Unfortunately, unifying the data alone cannot solve the problem of fragmented customer experiences. Marketers also need to activate that unified data in an intelligent, orchestrated way, across all channels. While CDI ensures we capture each event of the customer journey, the people building the Customer Experience need to understand it. By â€˜activating’, we mean moving from a series of connected customer events to a holistic, seamless customer experience. That is where the CDAPs come in. They consume data from the CDI to build real-time customer profiles, and start making automated, dynamic decisions by correlating the unified data with what content (creative and offer) to serve each customer, on what channel, at what time. It works with existing marketing automaton and channel apps to not just execute but orchestrate campaigns across channels with real-time decisions enabling ever stronger campaign outcomes.
CDM, as an organizational practice, requires human and tech investments, data process reorientations and prioritizations. To have an effective customer data management program with minimal opportunities for errors, here are the top 5 best practices for customer data management in 2020 and beyond:
1. Design with data governance in mind:
From ensuring consent is integrated into all data collection effort and respected in all marketing campaigns; to ensuring that data is safe and protected, data governance is central in the data-driven economy. Customer privacy and preferences need to be kept in mind when delivering any brand interaction, especially, personalized communication and marketing. For this, data governance and data security need to be designed into the process right from the data collection stage to ensure privacy, compliance and safety at every step of the engagement.
In an exclusive article on MTA, Mathias Lanni, Executive VP, Marketing, Velocidi lists five tips that all Brand Managers should understand about their data management.Opens a new window Two of these typical pitfall include:
- Over-collecting data: there’s no better way to get overwhelmed by your data collection, and have trouble pulling key insights out of it, than to simply be accumulating data for its own sake like a hoarder whose apartment is full of old newspapers. There truly is too much of a good thing where data is concerned. Know why you’re collecting the data: What questions you’re trying to answer, and problems you’re trying to solve. Focus your collection efforts in areas where there will be practical gains. Resist the urge to collect data â€œjust in caseâ€ you need it in the future, without specific initiatives in mind that it will be applied towards.
- Not having hard rules in place for data entry, tagging, and other categorization, which can help ensure easy access and activation in future. For example:
- Using descriptive, human-readable long file names.
- Standardizing all tags.
- Picking a handful of file formats and sticking with them
- Tracking all data changes so that timestamps can be compared
The more you do to standardize data entry at the collection stage, the better outcomes you will have from CDM in the future. Trying to sort through gigabytes of poorly organized data is a nightmare that even the best CDM technology will have a hard time fixing.
2. Customer-centricity: The basic purpose of customer data management is to deliver better customer experiences. For this, the strategy should be designed from the outside in- in other words, from the customer’s perspective and following their buying journey, versus the other way around, i.e. leveraging existing marketing channels and tools based on marketer’s convenience.
3. Invest in customer journey mapping: A customer journey map is a visual description of every experience a customer has with your brand. Although it is not meant to be a 100% accurate representation, it gives marketers crucial insights into how customers interact with the brand at each touch point, enabling them to deliver the most relevant customer experience. However, customer journeys are intricate and unique. No two buyers follow the exact same purchase path. It is important to carefully observe customer behavior to identify drop-off points along the journey that let us know possible hindrances in the experience. Using technology tools to build a visual representation of customer journeys across multiple touchpoints can help come up with new ideas, triggers, and interactions to optimize the experience.
4. Build integrations to activate the data across the journey: Having unified customer data in one place is no guarantee that you can successfully activate that data to run cross-channel campaigns. Delivering customer centric marketing also means removal of silos â€“ both data silos and experience silosOpens a new window . Data silos are addressed by platforms such as CDPs which deliver a single, unified view of the customer to all functions within the organization. Experience silos are addressed by orchestrating marketing campaigns across channels in a way that customer has a seamless â€“ and not fragmented â€“ experience. All the marketing technology platforms that will be used to activate the data and run the campaigns too need to be integrated so they can all use the same source of data to deliver those seamless experiences.
5. Invest in the right technology: In an article on MTA featuring 20 expert industry insights on the importance and future of CDMOpens a new window , Chris Jones, SVP Product, Amperity predicted a shift from DIY data management to focus on insight and action. He said, â€œ3 years ago, most brands were investing a lot of internal resources in customer data management â€” building their own Customer 360 data capabilities from scratch, in-house. In 2019, smart brands will pivot to using trusted vendors to manage their customer data, shifting their focus to analytics, insights, and activation to drive better customer experiences.â€ Whether you are doing it in-house or finding the right technology partners to help you execute your CDM strategy, having the right customer data management technology stack is crucial to winning with customer data. The right platform(s) help better manage and access data, eliminate silos, and provide real-time insights.
David Raab, Founder, CDP Institute and MarTech Advisor Category Expert for Customer Data Management listed 3 themes marketers need to think about this piece that featured 20 expert views on the future of CDMOpens a new window :
- New data types, such as voice, location, IoT, and augmented/virtual reality, will require new processing methods and marketing techniques
- Increased concern about privacy by consumers and governments, will significantly limit what data is available and how it’s used, especially what can be shared as third-party data
- As the â€˜single customer view’ â€“ until recently restricted to the top 15% of organizations â€“ gets mainstream, will come the realization that just building the customer view doesn’t create value. You have to find ways to use it that meet business needs and customer needs (which are not necessarily the same)
In summary, marketers are starting to see that customers don’t really want personalization for personalization’s sake, and instead want personalization that provides concrete benefits, at the right time, on the right channel. Having a sound customer data management strategy in place can help deliver relevant, timely personalized customer experiences by de-siloing the customer data, bringing it together under a single customer view, and enabling seamless activation of that data across ad tech and martech to deliver those concrete benefits wrapped in delightful experiences to their best customers.