Customer Data Platform Archives

What is a Customer Data Platform?

Mar 11, 2020 | Customer Data Platform, Customer Experience

Read this article to better understand what a Customer Data Platform is an why your business might need one.

About

Customer data is anything that identifies a customer, or indeed any associated data that results from customer interactions including purchases, transactions, and customer service communications.

A Customer Data Platform is an enterprise computer processing platform used to harvest, aggregate, cleanse, manage, process, analyze and output customer associated data. Data is pulled from multiple sources, cleaned and combined to create a single customer profile. This structured data is then made available to other marketing systems. Unlike a Customer Database, a Customer Data Platform extends its functionality to all aspects of the customer lifecycle. Normally it will include campaign management and the provisioning of multi-channel communications. Advanced systems will manage customer offers and promotions.

The CDP Institute defines a Customer Data Platform as “packaged software that creates a persistent, unified customer database that is accessible to other systems.” Basically it’s a prebuilt system that centralizes customer data from all sources and then makes this data available to other systems for marketing campaigns, customer service and all customer experience initiatives. Gartner defines CDPs as – ‘integrated customer databases managed by marketers that unify a company’s customer data from marketing, sales and service channels to enable customer modeling and drive customer experience.’ An encanvas, we see this as a narrow definition, given that Customer Data Platforms normally serve up insights to strategic teams and all departmental functions to shape processes and priorities.

Why you need one

Encanvas CDP Model Architecture Illustration

Every business understands the impact of outstanding customer experience. Think of businesses like Amazon, whose singular purpose is obsessive Customer Fanaticism; placing the customer at the heart of everything it does. Even shareholders were taken aback when its founder Jeff Bezos explained that he was prepared to forego short-term profitability in exchange for an unbeatable customer experience. And he’s proven to be correct.

Like Amazon, if you want to delight customers with personalized offers then you will need to be world-class at capturing and making sense of lots of data about the customers you serve. Modern marketing methods rely on data to drive decision making. It eliminates the guesswork of content marketing because marketers know what motivates customers and the characteristics that profile the best-fit audience for their products and services.

Without a Customer Data Platform, the ability to appreciate the value and characteristics of customer relationships is rarely that straight-forward. In practice, data is stored in silos, whether organizational or technological, and this makes it challenging for companies to deliver consistent customer experiences.

Creating a complete single-version-of-the-truth

An important business driver for integration services comes from the desire of executives to harvest data from across their enterprise in order to make informed decisions. The drive towards a data-driven culture demands that systems connect to one another. The integrity of data executives review, is a major sticking point.

A survey of 442 business executives around the world by Harvard Business Review found that corporate decision makers have major concerns about access to, availability of, and the quality of internal and outside data. The result is reduced confidence in their decision-making ability.

Moreover, nearly half of the global respondents said their lack of confidence stems from a lack of information or easy access to data. The findings are puzzling given the emergence of big data techniques, the proliferation of global networks and the sheer processing power contained even in mobile devices.

One reason for the disconnect between big data and decision making, the Harvard researchers found, is that “silos of data, typically imprisoned in customer, financial, or production systems, are frequently inaccessible by individuals outside the functional group.”

In this regard:

43% of survey respondents said important external or internal data was missing
42% said data was inaccurate or obsolete, and;
33% said they “couldn’t process information fast enough.

Business impact

Your customer data has huge business value because it helps to improve your ability to engage customers in conversation, on topics they care about. Then, your marketing team can use customer insights to segment markets and develop tailored offers to specific groups of customers; to deliver personalized experiences, one customer, at a time. This attention to detail (and preference) has become a ‘killer-app’ in many consumer markets where buyers have so much choice.

“Customer Data Platforms help companies solve a huge and growing problem: the need for unified, accessible customer data. Like most packaged software, a CDP reduces risk, deploys faster, costs less, and delivers a more powerful solution than custom-built alternatives…With careful planning, a CDP will provide the foundation your company needs in the years ahead to meet customer expectations for exceptional personalized experiences.” — David Raab, Customer Data Platform Institute

Business benefits

A step-change in customer experience thanks to the ability of the enterprise to personalize offerings to satisfy customer needs.
Smarter evidence-based decision making thanks to a single view of the customer and an increase in the paucity of customer insights.
Operational effectiveness, particularly in areas of customer profiling, targeting, offer development, product design, customer service delivery, and customer communications.
Improved business agility, as the enterprise is better able to appreciate what motivates customers and to identify any changes in demand or requirements.
Expansion opportunities, by identifying product and market segments demanded by customers that are being poorly served.

Obstacles – Data volume isn’t the issue, quality and value is

In most businesses today, marketing teams are awash with data. The problem is, much of the data is spread across operating systems and platforms that have no means to present a single view of the customer. Only recently have senior management teams come to recognize the strategic importance of customer data and its role in delivering a competitive advantage.

As data volumes grow, and is spread across your organization and its partner ecosystem, you lose the ability to leverage it and gain a competitive advantage. Organizations come unable to assemble unified customer data.

Data quality is a huge problem. When the currency or completeness of is poor, the usefulness of insights is tested.

It’s common for operational systems not to populate all of the tables and rows that exist in back-office systems when they don’t directly benefit the specific process they serve. This can result in many of the harvested data-sets to be incomplete, or of poor quality. In consequence, organizations are unable to apply unified customer data in delivery systems.

To create high-quality data requires data to be cleansed and graded when sourced from operational systems. This requires specialist computer tooling, often driven by artificial intelligence technologies, used to vote on the best quality sources and graduate data as it is being harvested. According to the CDP Institute, something like 43% of business-to-business companies lack the ability to extract data from source systems.

Departments all benefit from a single view of the customer, but rarely can they agree who owns it and are prepared to share data to achieve it. Projects can easily be derailed as the result of inadequate budgets, poor cooperation across organizations, and a general lack of time in marketing and technology departments to commit effort into what is essentially a change management project.

How Customer Data Platforms Help

Implementing a Customer Data Platform will gather data to create a unified data-mart that presents holistic landscape views of your customers and their interactions with your business – irrespective of where the data is held. The result is that all teams across the enterprise benefit from an improved understanding of what customers care about to segment more effectively, shape customer offers in more refined ways and fine-tune processes to deliver above and beyond (and personalized) customer experiences.

According to the most recent study by the CDP Institute, the top reasons that organizations buy CDP solutions are to:

Create a unified customer view by collecting data from all sources with powerful identity matching and management, and to deliver customer profiles to other systems in real-time (86%)
Improve predictive modeling and recommendations with full detailed access to data collected (59%)
Improve the orchestration of customer treatments across all channels (49%) Improve message selection and personalization (49%)
Reduce reliance on IT resources (40%)
Access otherwise-unavailable data (40%)
Improve data analysis and segmentation (39%)
Spend less time on data management (29%)
Improve message delivery (27%)
Faster response to changing data management needs (25%)

The ultimate litmus test of quality

For most organizations, the ultimate test of whether a Customer Data Platform is delivering optimal outcomes lies in its ability to do three things exceedingly well:

To deliver fine-grained and detailed observations on the make-up of customer segments and communities; to determine ‘what makes a customer?’
To articulate customer lifecycles and conversational paths.
To accurately account for the most profitable customers and prospects.

Putting ‘the customer’ at the heart of your data story

Most organizations today structure their data within the operating departments that manage processes. This only serves to fragment customer data. A Customer Data Platform requires a rethink in the way data is managed, to ensure that all facets of the customer world are incorporated into the customer data model not limited to:

What makes a customer?
What jobs do they do?
What do they care about?
Where do they go to seek advice?
What voices/sources do they trust?
Who (role or persona) has the problem?
What solution preferences do they have/are they likely to have?
What conscious and unconscious undesirables do they seek to resolve?
How do they prefer to be contacted?
How do we interact with them?
What is the revenue opportunity the customer represents?
What does a typical deal look like?
How much business do we do with them?
What have they bought in the past?
When are they most likely to review their requirements?
What else are they likely to want to buy?
What are the alternatives to what we can offer them?
How much are they prepared to pay for a solution?
What’s the next best alternative?

The enterprise roles with an interest in customer data insights

One of the challenges in putting together a Customer Data Strategy is the broad cross-section of stakeholders with a voice on what the end-game should look like. The key stakeholders will tend to be any of the following:

Chief Marketing/ Data/ Innovation/ Digital/Brand/ Customer Officer
Customer Communications Manager
Digital Marketing Manager
Global Marketing Manager
Head Of Brand
Head of Customer Experience Supply Chain
Head of Data-driven Marketing
Head of Marketing & Communications
Head of Research & Marketing Platforms
Head of Retail & Commerce
Manager Customer Experience
Marketing Communications Manager

Technology anatomy

There are typically five main components of a Customer Data Platform architecture. Note, the terminology used here is formed around the Encanvas CDP but alternative solutions follow a similar path:

Customer Data Integration (CDI) platform – Information Flow Designer is a data movement. transformation and integration toolset that gathers data from the source operational systems and third-party data sources.
Clearing House (Database) – A clearinghouse records data uploads and checks for errors. It also acts as a cached memory of data assets that may not have arrived into the data warehouse yet but is being referenced by other tables (for example, perhaps rich asset or product data is recorded in a proposal but not on financial systems).
Data Warehouse – This is a repository of data that re-models data assets into a more consistent, coherent and usable form (adding relationships between data assets that may not previously have existed and presenting new views of data).
Analytical and Predictive Engine – This component is used to make suggestions on how to leverage data and turn it into actions. This component particularly needs to be very customization and may be tailored to each industry or purpose.
Customer Treatment and Conversational Engine – The final stage is to create empathetic and fine-grained targeted campaigns utilizing personalized messages.

The processes powered by a Customer Data Platform

Create marketing campaigns – enabling the formation of fine-grained marketing campaigns that benefit from relative date, geo-referencing, profile segmenting and other advanced query and search tools.
Publish financial and franchise reports – equipping accounting teams to rapidly formalize new reports for financial applications such as management accounts and shareholder updates, and for franchises using reporting groups and codes that are completely customizable.
Produce ad-hoc marketing reports – Providing the means for marketers to generate ad-hoc reports for marketing suppliers and partners.
Generate actionable insights for operational managers
Bring access to rich insights – including dashboards, charts, map-based views, and pivot-table style reports – to fully develop understanding of operational performance and business challenges.
Measure and managing customer loyalty – by profiling customers by levels of revenue, repetitive ordering, average order value – and a comprehensive story of their engagement and interactions throughout their relationship history is provided.
Manage customer complaints and suggestions – an invaluable resource for managers to learn from mistakes and improve overall customer service experience across the business.
Securely share data with third party suppliers and systems – whilst satisfying data protection regulations

Obstacles to creating a Customer Data Platform

Here are some of the common obstacles we’ve encountered through previous projects implementing Encanvas as a Customer Data Platform:

No reliable mechanism is thought to exist that can be employed to identify sources of data and harvest useful content
No data engine exists to organize and manage data
Concerns exist over data governance and threats to data loss when data is exported from systems brought together in a single place
It’s not known what data exists, or where it is
Data quality issues compromize the delivery or RoI of projects
No data relationships exist between important data sources (such as financial records, service records, transaction history records, etc.) because the enterprise operates fragmented IT systems that create silos of data.

Completeness of data insights matter

A single view of the customer is only as effective as it is comprehensive and actionable. In the first place, the number of data sources available to a CDP is critical to the richness of insight it offers. You will want to have a comprehensive and current data set, along with the broadest platform to make use of the insights. If a Customer Data Platform has access to website data alone, then gaps will exist in the knowledge of customer interactions, transactions, etc. – and it will be impossible to accurately judge profitability. When solutions only use data to personalize a customer’s website experience, there are crucial gaps in customer experience delivery that extends beyond it – such as customer service and support.

Defining and sizing the Market

Market analysts offer conflicting data on the size and potential of the CDP market.

Early-stage reporting from researchers at Raab Associates – attributed to having coined the category term in 2014 – made the prediction in 2017 that, after analyzing 27 CDP vendors that collectively generated more than $300 million in revenue – Customer Data Platforms (CDP) would reach $1 billion by 2019. According to a Marketsandmarkets.com survey, the customer data platform market is predicted to grow from $639.9 million in 2017 to reach $3,265.4 million by 2023 at a Compound Annual Growth Rate (CAGR) of 29.3% during the forecast period. The base year for the study is 2017 and the forecast period is 2018–2023. The customer data platform (CDP) is said to have a potential scope for growth in the years to come due to increasing adoption of the customer data platform for omni-channel customer experience and demand for real-time data availability. Researchandmarkets.com suggest major growth drivers for the market include an increasing demand for omni-channel experience and actionable insights by marketers and effective tracking of customers to understand their behavior for target marketing activities, and increasing pressure on CMOs to deliver personalized content spurring the demand for real-time data availability. Gartner, one of the generally most trusted enterprise IT analysts, have incorporated Customer Data Platforms into a broader category called Customer Experience and Relationship Management Software. They say Worldwide spending on customer experience and relationship management (CRM) software grew 15.6% to reach $48.2 billion in 2018, according to research from Gartner, Inc. CRM remains both the largest and the fastest growing enterprise application software category.

The cross-over between Customer Data Platforms and Data Science

Gartner sees much of the data science aspects of Customer Data Platforms integrated within its data science and machine learning (DSML) platform category. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Players in this space include Alteryx, Anaconda, Databricks, Dataiku, DataRobot, Domino, Google, H20.ai. IBM, KNIME, MathWorks, Microsoft, RapidMiner, SAS, TIBCO and Altair.

To define the DSML market and score vendors, Gartner has determined fifteen critical capabilities.

Data access: How well does the product support data access across many types of data?
Data preparation: Does the product have a significant array of noncoding or coding data preparation features?
Data exploration and visualization: Does the product allow for a range of exploratory steps, including interactive visualization?
Automation and augmentation: Does the product facilitate the automation of feature generation, algorithm selection, hyperparameter tuning, and other key data science tasks?
User interface (UI): Does the product have a coherent “look and feel” and have an intuitive interface?
Machine learning (ML): How broad are the ML approaches that are easily accessible and shipped?
Flexibility, extensibility, and openness: How can various open-source libraries be integrated into the platform?
Performance and scalability: How can desktop, server and cloud deployments be controlled?
Delivery: How well does the platform support the ability to create APIs, or containers?
Model management: What capabilities does the platform provide to monitor and recalibrate hundreds or thousands of models?
Precanned solutions: Does the platform offer “precanned” solutions ?Collaboration: How can users of various skills work together on the same workflows and projects?
Coherence: How intuitive, consistent and well-integrated is the platform to support an entire data analytics pipeline? Based on these criteria, Encanvas would also sit within this enterprise software classification were we not so fanatical about customer experience!

Players in the CDP software market

In addition to Encanvas, the following vendors are active in the CDP market include Oracle, SAP, Salesforce, Adobe, Nice, SAS Institute, Tealium, Segment, Zaius, AgilOne, ActionIQ, BlueConic, Ascent360, Evergage, Lytics, mParticle, NGDATA, IgnitionOne, Signal, Usermind, Amperity, Reltio, Ensighten, Fospha, and SessionM.

About Encanvas

Encanvas is an enterprise software company that specializes in helping businesses to create above and beyond customer experiences.

From Low Code to Codeless

Better than code-lite and low-code, we created the first no-code (codeless) enterprise application platform to release creative minds from the torture of having to code or script applications.

Live Wireframe

Use Encanvas in your software development lifecycle to remove the barrier between IT and the business. Coding and scripting is the biggest reason why software development has been traditionally unpredictable, costly and unable to produce best-fit software results. Encanvas uniquely automates coding and scripting. Our live wireframing approach means that business analysts can create the apps you need in workshops, working across the desk with users and stakeholders.

AppFabric

When it comes to creating apps to create a data culture and orchestrate your business model, there’s no simpler way to install and operate your enterprise software platform than AppFabric. Every application you create on AppFabric adds yet more data to your single-version-of-the-truth data insights. That’s because, we’ve designed AppFabric to create awesome enterprise apps that use a common data management substrate, so you can architect and implement an enterprise master data management plan.

Customer Data Platform

Encanvas supplies a private-cloud Customer Data Platform that equips businesses with the means to harvest their customer and commercial data from all sources, cleanse and organize it, and provide tooling to leverage its fullest value in a secure, regulated way. We provide a retrofittable solution that bridges across existing data repositories and cleanses and organizes data to present a useful data source. Then it goes on to make data available 24×7 in a regulated way to authorized internal stakeholders and third parties to ensure adherence to data protection and FCA regulatory standards.

Encanvas Secure&Live

Encanvas Secure and Live (‘Secure&Live’) is a High-Productivity application Platform-as-a-Service. It’s an enterprise applications software platform that equips businesses with the tools they need to design, deploy applications at low cost. It achieves this by removing coding and scripting tasks and the overheads of programming applications. Unlike its rivals, Encanvas Secure&Live is completely codeless (not just Low-Code), so it removes the barriers between IT and the business. Today, you just need to know that it’s the fastest (and safest) way to design, deploy and operate enterprise applications.

Learn more by visiting www.encanvas.com.

The Author

Mason Alexander is a senior consultant specializing in helping organizational leadership teams to grow by implementing enterprise software platforms that improve data visibility, process agility; and organizational learning – creating an enterprise that learns and adapts faster. He writes on subjects of change management, organizational design, rapid development applications software, and data science. He can be contacted via his LinkedIn profile.

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