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Customer Data Platform: where audience data and sales strategy meet

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Are you having trouble improving conversion rates and connecting customer insights from different touchpoints? A Customer Data Platform helps you understand your audience in a granular way and enables you to craft better campaigns and product offers.

Understanding customers always required brands to look at their audience through different lenses – whether through other marketing channels, relationship platforms, or customer segments. On the verge of Big Data culture, however, just having a fragmented view of your audience is not enough anymore. 

What drives sales is the ability brands have to deliver a cohesive customer experience (CX) across different channels, which is only possible by fully understanding channel correlations and cause and effect connectors along the audience’s touchpoints. 

These days, people interact with brands more often than ever before, and so making sense of different interactions is a lot more complicated than it once was.

Recent research by Ascend2 and Research Partners consulted more than a thousand marketers and found that 43% see data integration across different platforms as one of their main goals. In contrast, 37% wish to enrich data quality and completeness.

No wonder executives are investing more and more in their technology stacks: one-third of industry professionals believe it’s essential to have the right technologies for data collection and analysis, according to a study by Digital Doughnut. Currently, 44% of marketers say they already have data management platforms.

But amongst all data platforms available, the Customer Data Platform is undoubtedly the best you can have if your goal is to understand your audience better and drive more sales. We’ll show you why!

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What is CDP? And how does it work?

A Customer Data Platform (CDP) is a software that unifies customer data from different data systems and customer-facing platforms. It combines customer’s demographic data, buying history, social media and content preferences, call centers, and customer navigation data. 

Once implemented, the CDP acts as a 360º data solution: it collects, filters integrates, and analyzes customers’ data in real-time. 

CDPs can ingest structured and unstructured data from Customer Relationship Management Systems (CRM), Data Management Platform (DMPs), customer support channels, eCommerce websites and apps, payment systems, social media, etc. They also track behavior across different devices.

By acting as a hub for many data sources, the Customer Data Platform allows marketers to build a holistic view of single customers and their pain points. 

But how exactly do they organize so much data? Well, CDPs rely mostly on first-party data so they can determine the so-called Unified Customer Profiles, which are profiles based on information from real customers and prospects.

That makes the data match consistent across different platforms, and hence the audience insights end up being much more reliable for marketers.

Check out some practical examples of data collected by customer data platforms:

  • Transactional data: order details, customer and product value, renewal dates, abandoned baskets, stage in the conversion funnel
  • Behavioral data from web and mobile: Products and categories browsed, clicks, store visits, interaction data, number of pages visited, etc
  • Profile data: Contact and opt-in data, psychographic data, details about channel and content preferences, lifestyle, etc
  • Brand Relationship Data: Email interactions with customer support, social listening insights, social media comments, etc

The end-to-end role of a Customer Data Platform (CDP)

In today’s competitive landscape, marketing executives are expected to keep track of all customer interactions and connect marketing efforts to other departments, such as sales and customer success, to provide customers with a satisfactory customer experience (CX).

The rush for data management optimization is seen clearly by the CDP industry’s growth in recent years. According to the Customer Data Platform Institute, the number of CDPs available in the market doubled from 2017 to 2018. Now, there are more than 50 CDPs in the industry worldwide.

The truth is that CDP can be an asset for every department within a company, working as an end-to-end solution to enrich customer experience. We’ll soon explore how brands can use CDPs to drive sales, but first, let’s explore CDPs’ overall benefits for companies. 

Breaking Data Silos

CDPs integrate data from multiple departments, which encourages different teams to collaborate and speeds operational routines. With a CDP, marketing, sales, customer experience, and support teams can be on the same page regarding customers’ needs.

Automating marketing workflow

Because they automate a lot of the data integration and analysis, CDPs make the lives of marketing professionals a lot easier, freeing them from repetitive work and allowing them to spend more time in strategic planning. 

Speeding up decision-making

As data processing happens in real-time in the CDP, it also makes it possible for companies to easily spot changes in customer behavior and act upon them while quickly sharing relevant insights with different teams.

The power of CDPs in driving sales

As we pointed out, CDPs are an excellent liaison point for different departments and can be at the heart of customer experience management. But to what extent can CDPs contribute to final sales? 

There are many ways CDPs can directly or indirectly improve conversion rates, drive customer loyalty, and decrease churn and bounce rates. In fact, a report from Forbes Insights highlighted that 44% of organization leaders believe the Customer Data Platform is helping them drive customer loyalty and increase ROI.

We have made a list of 11 ways CDPs can help you drive sales while also better understanding your customer base. 

1) Know your customers across multiple devices or channels

The mandatory philosophy among marketers is that they should reach their customers on the right channel, at the right moment, and with the right messages and products. To do that, they need to let go of assumptions and understand exactly how users interact with them across different channels and devices.

With all such information concentrated in the CDP, marketers can tailor better experiences and advertising segmentation across devices, increasing campaign success chances

2) Accurately track shopping events

A CDP is a great tool for retailers and eCommerce as it tracks customers’ buying behaviors and relevant transactional data in significant volume. CDPs allow them to keep a consistent record of the products customers added to the cart, the duration of checkout and order completion, abandoned carts, and other information that is crucial for online operations.

3) Improve pricing 

Collecting data from many sources – from your eCommerce website, app, or even physical stores – CDPs help you clarify how much customers are spending and how much they are willing to pay for your products according to their stage in the customer journey, search, and navigation patterns. 

CDPs can also be connected to your supply chain systems to help you adjust costs and manage the relationship with suppliers, which are aspects that often impact pricing. With such information updated in real-time, you can be more assertive in your pricing strategy.

4) Offer personalized discounts and product recommendations

Having a holistic customer profile at hand also allows brands to offer clients personalized discounts and product recommendations that ultimately can turn them into loyal customers.

A study by Salesforce shows that 57% of customers are willing to share their data to exchange personalized offers or discounts. In comparison, 52% will share their data in exchange for product recommendations that meet their needs.

While knowing customers in detail, companies’ teams can offer precisely what users need to advance in the sales funnel – whether it is a discount, a free trial, reviews from peers, or a personal approach from the support team.

5) Connect physical and digital shopping experiences

For retailers that also operate offline, a CDP can connect insights from online and offline systems, which is often a challenge for companies looking forward to addressing omnichannel experiences. A survey from the CMO Council found only 7% of respondents said they are always able to deliver real-time, data-driven experiences across physical and digital touchpoints.

With a CDP, brands can offer better customer experience from the website to the physical store – and vice-versa – increasing sales opportunities.

6) Be quick to react to customers interactions

Being quick to answer customer’s signals is also crucial for both customer acquisition and retention strategies. Still, many marketers struggle with the amount of real-time insights they can access and act upon. 

Research published by MediaPost, commissioned by technology consultancy Vanson Bourne, shows that only a minority of marketers feel they can immediately react to online customer interactions. According to the study, only 43% act quickly over customer behavior in the pre-purchase stage, 38% during purchase, and just 35% in the post-purchase phase.

By providing CRM, sales, and marketing teams with a continuous data stream, CDPs can make customer data more actionable. Isn’t that the point of having so much customer data? 

7) Prevent churn and cart abandonment 

Retail managers and online marketers are often investigating why customers abandon carts or churn after a few purchases. A CDP can give you deep insights into what stands in the way between your customers and the checkout.  

It helps you spot gaps throughout the entire customer journey (and not just in specific channels) that might be leading customers to give up purchases. Are there problems with website usability? Is your customer support too slow? 

By figuring out what is wrong, your team can work on fixing these gaps and segmenting churn prevention campaigns to attract customers back. 

8) Optimize Customer Acquisition Costs (CAC) and Conversion Rates

The McKinsey Global Institute estimates that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times as likely to be profitable. 

With the Customer Data Platform’s assertiveness, companies can better streamline marketing segmentation and customer success efforts, thus optimizing results related to Conversion Rates (CR), Customer Acquisition Costs (CAC), and Customer Lifetime Value.

9) Qualify your leads

One of the best aspects of CDP for sales is that it allows you to qualify your leads better and nurture the relationship with customers across their entire lifecycle. Not only it supports marketers in optimizing strategies to attract qualified customers; it also gives you the necessary information to engage with customers who are ready to buy. 

A study by Forbes shows, for instance, that 53% of marketing executives are using CDPs to engage with existing customers’ needs, increasing the likelihood that they will become recurring clients and the chances of upselling them. 

10) Enhance predictive marketing

Predicting customer behavior and preferences are what helps giant retailers like Amazon to drive sales. This marketing technique, which determines the probability of success of different marketing strategies, is essentially fueled by high volumes of customer data, which only a CDP could support. 

Armed with a CDP, data scientists and marketing analysts can gather data from several sources and apply predictive models with a great accuracy level.

11) Improve attribution models

With so many touchpoints with the audience, it is often difficult for companies to determine accurate attribution models and discover which channels drive more sales. According to Google, almost 80% of all transaction value involves at least two marketing channel interactions – a number that can be much higher depending on your business’s complexity.

The Customer Data Platform can optimize the attribution framework since marketers can send attribution data to the CDP and have a more accurate view of campaign performance.

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Why CDPs are more complete than other data management platforms

So you have learned the many benefits that CDPs can bring to the table. Many leaders still ask themselves if they should ditch their existing data management tools for a CDP. What has to be clear for marketers and sales managers is that different data platforms don’t need to exclude each other. 

A Customer Data Platform can potentialize the outcomes of Customer Relationship Management (CRM) software and Data Management Platforms (DMPs).

In a survey by The Relevancy Group conducted in 2018 with US executive marketers, about 6 in 10 respondents said they were integrating CRM data into their CDP. 

From a digital advertising perspective, CDPs can make the work of Data Management Platforms a lot more precise as well – with at least 29% of marketers feeding CDPs with digital advertising response data.

Although CDPs, DMPs, and CRM systems share some similarities, they all have different purposes within a company, with CDP serving as a primary data hub to make your teams more confident in responding to customers’ needs. 

Want to become an expert in CDP?

If you plan to purchase a CDP for your company, the next step is to check out the platforms available in the market and consider which one is the best fit for your business goals.

If you feel like it is time to learn more about CDPs, we invite you to download our eBook Customer Data Platform: the future of marketing and sales.

The eBook will give you details about CDPs’ features, how they work, and how they can be incorporated into your marketing and sales strategies. We hope you enjoy it!

An essential guide to Customer Data

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Customer data is the key to understand customers beyond the confines of your own strategy, which makes you avoid dangerous assumptions to create relevant products and experiences.

Continuously data analysis will help you to come up with strategies that cover the market’s lacking points and elevate your marketing to a more meaningful approach. 

We are witnessing a new customer era. Every company’s engines work daily to think, develop, and incorporate business and marketing strategies that put customers at the center of everything they do. 

This new approach does not strike the market as a surprise. For the past years, with so much information in the palm of their hands, customers became more demanding and more willing to connect with brands that make proper use of their information to deliver more than just basic products and services.

If you have been paying attention, you already know customers value experiences more than anything else these days. This means true engagement will only be achieved by a remarkable customer experience that elevates your brand and creates a sense of connection with your audience. However, planning an exceptional customer experience takes a wide understanding and a sharp knowledge of how to solve consumers’ issues.  

That is why customer data is definitely here to stay.

What is customer data and why does it matter?

Customer data is all the information a company gets from consumers whenever they interact with it. Whether it is personal, psychological, or demographic, customer data help companies clarify facts and avoid assumptions when thinking and refining business strategies related to customer experience.

The importance of customer data is related to the incessant need to build a strong customer understanding. Many organizations have already noticed that, by using data as a pillar, their operations draw near customer satisfaction and proper marketing approaches that return investments and reduces waste. On the other hand, without concise information about their customer base, companies fail to engage their audience and make sense of the many market opportunities datasets provide. 

Customer experiences are tremendously affected by customer data, which means the right data extraction, validation and analysis are crucial to generate accurate outcomes that will enhance marketing and business plans. Customer data matters so many companies have embarked on customer data management (CDM) to correctly address data in their goals and daily work.

With trustworthy data at the palm of your hand, you will feel more confident in tactics to contact, acquire, and retain your customers, keeping their interest, and offering them exceptional engaging interactions. Customer data will also support your financial decisions, assisting you in where and when to allocate your budget.

How is customer data created?

As you read, a massive amount of data is being created. All around the world, people are navigating desktop and mobile devices. It doesn’t matter if they are shopping, replying to an online survey, or filling a lead form to get in touch with a software development company. Each of their digital interactions with brands creates data — which continuously provides the basis for algorithms to produce more data.

According to Deloitte in its Global Marketing Trends 2020 report, 90% of all global data were produced in the last two years, considering more than 26 billion smart devices circulating the globe.

Aware of data potential, the market has amended digital initiatives to maximize data collection, extraction, and validation methods. Big data analytics have been embedded to extract useful information from huge datasets, such as CRMs, that can’t be manually validated. Simultaneously, data scientists have been growing as popular as the need to adopt a data-driven culture. 

These market signs alone are an extremely important indication that data is everywhere, and companies that don’t welcome it proactively will be in a tight spot.

Types of Customer Data

Customer data is separated into four main categories. In their own way, these categories will help you enhance the customer experience in different and empowering perspectives. 

Personal data

Also known as identity and basic data, this type of information allows customers to be recognized by individual details and is divided into linked and linkable information.

Every information that can be used to identify a person without extra details is linked personal data — full names and emails, for example. Date of birth, physical address, and phone numbers are linked personal data too.

Now, linkable information doesn’t identify on its own. Still, when combined with other pieces of information, it is useful to draw a bigger picture. ZIP codes, age, gender, job titles, education level, marital status, and number of children are examples of linkable personal data.

Interaction data 

If you ever wondered how your customers behave on your website, or how they interact with your emails and your social media accounts, interaction data will answer all of your questions.

Sometimes known as engagement data, interaction data brings a meaningful and solid viewpoint of how customers interact with your brand’s touchpoints. 

Examples of interaction data are: Time spent on your website, page views, social media engagement, traffic sources, customer service feedback, and paid ad conversions.

Behavioral data

If you want to know how customers respond to experiences with your products and services, pay attention to behavioral data. 

This type of data assists you to have a deeper understanding of behavior patterns your customers have throughout the purchase journey. This means interaction data may or not be considered behavioral data — it depends on the big picture and the goals you wish to achieve.

Some examples of behavioral information you can track are: Previous purchases, website heat-maps, customer loyalty program usage, repeated actions related to your products, and CTAs clicked. 

Attitudinal data

The fourth and last type of customer data is related to how consumers perceive your brand. Unlike metrics you can easily measure, such as product purchases, click rates, website visits, and social network interactions, attitudinal data refers to emotions and individual opinions. It embraces feelings, which makes them highly subjective — that explains why this type of data is also referred to as qualitative data. 

Continuously mining through attitudinal data will get you closer to proactively responding to consumers’ issues and anticipating trends they might be interested in. This is the perfect chance to get to know your consumers’ individual preferences and their point of view towards topics that interest you.

Attitudinal data examples are: Customer lifestyle, motivations, pain points, sentiments, and desirability. Customer reviews are excellent to gauge this sort of information.

Collecting customer data

As we have already emphasized, digital transformation has made every channel a powerful source of customer data. If your customers are interacting with your marketing and shopping channels, you can easily extract customer data from them using Customer Data Platforms (CDP).

There are several ways to collect customer data from distinct data points, and they depend on your goals.

That being said, before jumping to conclusions on which channel is the best to collect data from, make sure you address your data goals first. Is it to accelerate revenue? Develop new products? Get a more precise understanding if you ought to invest in a new marketing campaign? Start with the why, and then move forward.

More than predicting upcoming trends, recall that customer data should be highly attached to things that happened in the past. Past customer sales, buying decision patterns, abandonment rates (and much more) can be extracted from customer data to develop better strategies that respond directly to what your consumers did and said months, weeks, and days ago.

We have compiled some collecting data options ahead.

1. Website Analytics

Web analytics reports are excellent to understand what is resonating with your audience and how they are talking back to you. 

When investigating this type of report, remember behavioral data insights can be extracted from heat-maps, bounce rate, page views, and even the devices your target market uses the most.

2. Social media engagement

Social media-based data can tell you a lot of things. Shares, likes, and comments on social media are basic engaging metrics you can measure to understand what customers think about your brand and what type of message they enjoy. You will likely get a good amount of data from social media analytics to make sense of customers’ sentiment towards you.

We highly recommend you to go even further and run social listening while analyzing social media insights. This will guarantee you interpret your customers’ interactions more accurately.

3. Customer interviews and feedback surveys

Feedback is crucial. Whether customers love what you’re doing or criticize it, you need to be available to take their considerations and opinions into account.

When done right, customer interviews, and feedback surveys will gather your audience’s interests, opinions, preferences, and how you can improve your products and services to serve them better.

Another data collection suggestion is to investigate customer churn. This explains the reason why some customers buy from you for a while and then leave. What affected their experience so they felt like not coming back? Is there anything you can do to improve this experience and avoid other customers from turning their backs on you?

If you’re looking for ways to extract behavioral and attitudinal data, consider searching for customer feedback and combine this information with other data to get a bigger picture.

4. Contact information 

Contact information is vital to customer data. If you wish to communicate with your consumers, you should know where to find them according to the stage of their journey.

From phones to physical addresses and social media, contact information is needed to build a good amount of personal data you can rely on.

5. Customer service

Customer service is related to feedback but contains high potential itself. It is critical to allow customers to reach out to customer service software enabled to bring useful data your way. 

As a consequence, customers can quickly seek help for big and small issues and solve their problems more easily all while providing you with more information.

Validating customer data

As vital as it is, customer data is useful when you are ready to properly extract and validate it. This means you need to find actual helpful information from your data sources, and pull them out to understand how valuable they are. This is what we call data extraction — and it will prevent you from swimming in a random sea of data.

Data extraction uses the right tools to streamline data from customer experience to marketing, and makes sure the information provided is useful for your teams to make better decisions.

To avoid wasting all the money, time, and effort you put into collecting data, there are some essential considerations about customer data validation you should pay attention to:

  1. Customer data must be a source of truth and facts about your audience: Above all things, remember customer data is supposed to assist you in using factual information to come up with fitting solutions. For this reason, the data you extract must be reliable and revolve around your clients. If the data you’re extracting isn’t customer-centric, you better reconsider why it is important.
  1. Customer data must be goal-oriented: Exploring multi-channels to extract data from without a clear goal in mind might lead you to conclusions that don’t make much sense. Make sure you know what your goals are from the beginning and set milestones to measure your progress. This will help you visualize how much data is affecting your operations and what else can be done.
  1. Customer data must be integrated: Thanks to technology, data can now be transferred from one channel to another in a matter of seconds — and that is essential to every company that needs fresh information to anticipate opportunities and maximize their potential. By using customer data integration (CDI) tools, you ensure information gets to the right people and set a pattern to collect, organize, unify, and visualize customer data wholesomely. Best of all, CDI automates these processes and cuts down routines that take time. This means your human resources will have more time to focus on what they’re good at: Finding perfect responses to all the data software and algorithms have extracted and assembled for you.
  1. Customer data must be contextualized: In-house data, also known as first-party data, should be combined with external data to give the information you’re putting your hands in a more precise context. There are two types of external data you can blend with your in-house insights to ensure a broader understanding: second party data and third party data. The second party data is the information provided by another company towards the audience that interests you — this type of data is usually shared between partners. In the meantime, third party data is collected by companies that don’t have a link to customers and sell information to other organizations. These data types can enrich your first-party data and elevate your insights.

Customer Data Analysis

Customer data analysis is crucial to any customer data strategy. Wrong data analysis might cause disconnected and poor responses from brands to erupt and bother customers with interactions they don’t need nor asked for.

As complex as it might sound to gather validated data to be analyzed, some special technologies can help the process be smoother and more efficient. Data mining is one of them. By mixing machine learning, statistics, and artificial intelligence (AI), data mining can analyze loads of data using sharp techniques —and the greatest thing about it is that its analysis is automatic.

Analyzing quantitative data

When it comes to quantitative data, you might come across the need to categorize it according to some classifications and segmentations. Or, perhaps, you will notice it is necessary to relate different data points and comprehend how specific characteristics affect the customer experience. Luckily, data mining provides many programmatic settings that can be adjusted to return the insights you are looking for. 

If you’re willing to fragment customers to create more dynamic and creative ads, customer data analysis will help you find segmentation opportunities. If your team needs to associate behavioral patterns to develop a new campaign, customer data analysis will gather helpful information to predict how people will respond to your strategy. The opportunities are countless.

Analyzing qualitative data

On the other side, when we talk about qualitative data, many companies face the challenge of making sense of subjective information, such as sentiment. People’s emotions and feelings vary individually, and being stuck in the middle of so many variations is an uncertain place for your company to be at. If you’re wondering how to absorb valuable insights from this context, we have good news for you: there are ways technology can track important keywords to translate qualitative data into actionable decisions.

When analyzing qualitative data, pay attention to patterns that might make the situation clearer. Are your customers using the same keywords when they give feedback? Are the stories they tell somehow similar? Are there common elements in the ideas they communicate that can help you create a further sense of how they feel about your brand?

Take advantage of what customers say to you in feedback interviews, surveys, and methods of the sort, to gather enough data and take action. 

Benefits of Customer Data Analysis

Relying on customer data will benefit you in countless ways. To help you understand how, we’ve listed some benefits in-detail right ahead:

1. Segmentation

Segmenting your customers is a smart way to get a broader view of what their issues are and how you can reach out to them more effectively. Separating them by age, demographics, gender, job title, and more makes it easier to plan specific marketing campaigns that will straightforwardly attract them, whether your goal is to attract them, engage them or make them buy more.

2. Personalization

People no longer wish to be contacted with general messages that lack a personal touch. Customer experience is a synonym of personalization and, if you don’t use data-based strategies to customize interactions with your consumers, it is highly likely that they will get frustrated by irrelevant content and mass communication. In contrast, personalization increases service quality and customer satisfaction.

3. Deeper audience understanding

Data is becoming the centerpiece of companies that desire is to remain relevant in customers’ minds. This happens because, without a detailed and precise understanding of their audience, companies will hardly add value to their customer base. 

Customer data is the key to understand customers beyond the confines of your own strategy, which makes you avoid dangerous assumptions to create relevant products and experiences. Continuously data analysis will help you to come up with solutions around creative, data, and media — the right combination to empower your marketing and business approaches.

4. Revenue

When used correctly, customer data helps you understand how to increase your consumers’ loyalty and lifetime value, reducing churn at the same time. It also gives you a better understanding of where to invest in valuable campaigns and trends that will bring you more ROI.

5. Humanization

Human connection isn’t just another trend. The fact customers need to embrace companies with a purpose is changing their relationships with brands. People don’t want to be treated as a transaction. On the contrary, they expect companies to act authentically and be transparent in what they believe in, treating them individually. Pulling the right customer data contributes to humanize your brand so it corresponds to these expectations. It also frees your team to have more time to focus on how to genuinely engage customers. 

Data-base your decisions

Now that you’ve come to the end of this article, you know there isn’t space for assumptions to guide your brand’s decisions anymore. More than ever, new ideas and improvements need to connect with customers’ expectations. In this context, embracing the right technologies to collect, extract, validate, and analyze data is crucial. 

To find out more advantages that the use of Customer Data can provide for your company, contact one of our consultants, and resolve any doubts on the subject.

What is and how to do Customer Data Management CDM

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This guide will teach you how to use data frameworks to offer differentiated customer experiences and optimize marketing ROI.

It has been a few years since the buzz around “Big Data” started. Working with marketing in the media space, you probably hear your peers bragging about their data-driven strategies a lot. Do you consider yourself a data-driven marketer too?

Beyond marketing, modern CMOs have had to assimilate abilities in information technology and customer data management.  In 2020, you should expect most marketing teams to effectively use customer data to drive growth and customer satisfaction.

Getting there can be quite a journey, though. Research from the Dentsu Aegis Network from 2018, made with 1,000 CMOs, shows that to two-thirds agree that while there is increasingly more consumer data available, it’s harder to extract insight from it. 

Another report from Harvard Business Review shows that less than half of an organization’s structured data is actively used in making decisions, while less than 1% of its unstructured data is analyzed or used at all. 

Historically, companies have relied on excel sheets and on manually storing and analyzing customer data through different software, with little to no integration. No offense to excel and isolated systems, but things have changed.

The amount of customer data flowing to companies’ databases continues to rise through new channels and platforms, and that’s where customer data management comes to play. More than ever, organizations need a complete set of practices and automation tools to help them manage customer information.

In this article, we will talk about the importance of having customer data management on top of the marketing agenda. We’ll also explore the types of data, best practices for data management, and the role of different data software in data management.

What is customer data management?

Customer Data Management, shortly known as CDM, is the framework in which companies collect, track, organize, analyze and share customer data throughout the organization. 

The term “Customer Data Management” was coined in the 1990s, initially as a way to describe software that replaced disc-based or paper-based data storage. Such software was often used independently by departments within companies.

The concept of CDM evolved along with the Software as a Service (SaaS) industry and nowadays embraces a wide array of cloud computing applications that centralize access to customer data. It also embraces a set of methodologies that help marketers to locate, cross-analyze, and act on customer data.

Why marketers should invest in customer data management?

In a scenario where customers interact with brands through dozens of channels, there is almost no room for guessing and gut-feeling in marketing. Having a good hunch about what will engage audiences is not enough, and so the role of customer data management is to provide companies with accurate and actionable insights.

It reduces your chances of making mistakes, since mismanaging your customer data can lead to actions that will ultimately reduce engagement and profitability. Additionally, using customers’ data in a biased, inaccurate way can lead to poor customer experience (CX) and harm your brand. 

Good customer data management is key to building a data-driven culture and bolstering customer-centricity in marketing. Isn’t it everything you wish for? 

Data Management strategies can bring marketers a holistic view of customers’ journeys, connecting the dots between different channels, and offering cues to enhance their experience.

Customer Data Management is important for: 

  • Customer acquisition
  • Increasing retention and engagement rates
  • Knowing customers in detail and in real-time, from individuals to clusters
  • Increasing data quality by breaking data silos
  • Simplifying customer relationship management (CRM) 
  • Drive higher revenue

How customer data management is connected to customer lifetime value (CLV)

Having well-structured customer data management practices is what allows marketing teams to follow up on important indicators, like the Customer Lifetime Value (CLV).

Amidst a seemingly chaotic user journey – with different channels, devices, and purposes – customer data management can help marketers understand customers and guide them through the conversion funnel. 

Collecting and organizing relevant customer data will allow you to better segment your audience, find out behavior and buying trends, and drive personalized campaigns. As a result, marketers can ultimately attract more qualified leads and reduce customer acquisition costs (CAC) – improving overall marketing ROI.

But customer data management is useful only for marketing. It can help sales, IT, and customer success manages customer touchpoints. The great news is that every department can have access to the same data and deliver a consistent, unfragmented user experience.

Four Types of Data to pay attention to

Before trying to set up a data management framework, your team should have a roadmap of data types and specific information that can enrich your strategy, according to your business goals. 

We will now explore four data types and a few examples for each.

1) Identity Data

Identity data is collected through micro-transactions and interactions in the company’s channels – when a customer signs up for a newsletter or enters their payment information on the checkout page.

By collecting customers’ identity data, marketers have the minimum amount of information to start a conversation (and hopefully a long relationship) with the customer. Such information is also helpful to help companies build brand personas. 

Examples of identity data: Name; Personal data (date of birth, region, gender, etc); Address; Contacts; Social media profiles; Account data.

2) Quantitative Data

Quantitative data is mostly related to the customer’s decision making process as they interact with your brand. Such data covers different channels throughout the customer lifecycle, from emails and customer support channels to purchase transactions and social media. 

The idea is to understand the specifics of how customers are interacting with your brand through important operational data. You could use quantitative data to find out details about channel interactions and steps that led customers to convert.

Examples of quantitative data: Transactional data, such as the number of purchases, time of purchase and subscription value; Order dates; Cart abandonment and Bounce info; Click-through-Rates; Website visits; Product views; Number of Interactions.

3) Descriptive Data 

Descriptive data comprehend additional lifestyle information that complements customer personas. Collecting this type of data typically requires doing deeper research and interviews with customers in order to dive into individual buying behavior. Such data is pretty helpful if you want to use predictive analytics in your marketing strategy.

Examples of descriptive data: Family Data such as marital status and number of children; Lifestyle data, like hobbies and interests; Education and career data.

4) Qualitative Data 

Qualitative data should describe the motivations behind the customer’s actions. Gathering such insights might be more time-consuming and expensive than simply collecting quantitative data, but it is worth it. After all, tackling into customers’ deepest motivations is how you’ll captivate them.

This type of data is better collected on a one-to-one basis, mainly through the marketing teams’ interpretations of customers’ opinions throughout their journey – through analyzing CRM notes or reviews in websites, social listening tools, feedback questions, and Net Promoter Score (NPS) systems. 

Best practices in customer data management

An effective customer data management framework requires marketers to make human and tech investments, have well-defined processes and priorities. We have picked a few key practices involved CDM:

Data collection 

A lot of the data within enterprises go unused, and so data collection is the first step in building an integrated customer data management strategy. There are millions of data streams coming into companies’ systems from many touchpoints, and so marketers need to make sure relevant data doesn’t go to waste. 

It’s important to understand what data needs to be ingested. Ask yourself: What goals do I want to achieve with my marketing strategy? Which data points are directly or indirectly related to my Key Performance Indicators (KPIs)? From there, you can start filtering your sources of data and the indicators you will track.

Data Integration

Centralizing all company’s data into a central system is also vital for customer data management. That enables the “ETL Process”, which stands for “Extracting, Transforming and Loading” data. This stage is where you will check your data integrity, filter it, and validate it. 

A good data system will ingest relevant data, convert it in necessary formats and load it into different tools such as a data warehouse, a customer data platform (CDP), a data management platform (DMP), a customer relationship management (CRM) or any other system. The result? You will have a single hub for all the data you need.

Data management

This is where you connect the dots between data points to build robust, unified profiles of individual customers or segments. This could mean using statistic models to create identity graphs, applying data governance to make sure you integrate consent to customer data, or anonymizing data to be used through a data management platform (DMP).

Data analysis and activation

Data management tools: the difference between CRM, DMP, and CDP

Although customer Data Management can be described as a framework, it requires companies to have the right technologies. 

Your data software stack could be more or less complex depending on the size of your business and the number of touchpoints with the customer, but, essentially, your CDM strategy will require a combination these platforms: a Customer Relationship Platform (CRM), Data management Platform (DMP) and Customer Data Platform (CDP). 

Each one of them plays a role in your strategy. But what is the difference between them? 

The basis of data management starts with customer relationship management systems (CRM), which are built to engage with customers by tracking their relationship with your company. They only store data if the customer has interacted with the brand in some way, and they are based on historical and general information such as contact, demographics, and notes made by CRM teams.

Data management platforms (DMPs), on the other hand, have been widely used by marketers to serve ads and lead digital campaigns. These platforms focus on third-party anonymized data collected through cookies (that typically expire after 90 days), device IDs, and IP addresses.

In a different model, a Customer Data Platform (CDP) is a software capable of unifying customer data from various sources, internal or external, gathering quantitative and qualitative information from multiple touchpoints between a company and its customer base. It allows you to build a holistic view of customers and their pain points in a granular way. 

Why CDPs are the ultimate trend in customer data management

Although CRM systems, DMPs and CDPs share similarities, they are different when it comes to managing data. Customer data platforms, specifically, have increasingly been used as an integration hub for data systems because they are built to ingest large volumes of data from multiple sources – unlike CRM systems and DMPs.

There were days when marketing segmentation based on DMP persona segments and CRM was enough, but today, brands are expected to personalize every step in the customer journey – which is only possible through CDPs.

A study by Forbes shows that 53% of marketing executives are using CDPs to engage with existing customer’s needs, increasing the likelihood that they will become recurring clients.

The focus of CMOs is also shifting from third-party data and anonymous data to first-party, single customer data, which also underlines CDPs’ importance. As data privacy and compliance regulations arise, organizations also seek to work with their own, integrated data.

CDPs are capable of providing marketers with a historical record of identified customers that can be used not only for advertising but for other purposes as well.  By centralizing information in a single platform, companies can optimize resources and avoid having to rework their data over and over through different systems. 

Bonus tips for successful data management

Make data widely available to different teams: The Harvard Business Review study we mentioned before reveals that 80% of a data analyst’s time is spent on just discovering and preparing data. Customer data can be an important asset across departments, so it’s important to centralize access to it instead of storing it in separate departments and warehouses. Let the data flow!

Always keep data governance in mind: Understand the privacy policies of your data tools and ensure consent is integrated into all of your data collection, while also respected in marketing campaigns. 

Don’t over-collect data: Understand exactly why you’re collecting the data your collecting, and which questions your company is trying to answer with them. Resist the impulse to gather too much data “just in case” you need it, without a proper purpose.

Create rules for data categorization: Set up file formats you’ll be using, standards for tags, file-naming, and timestamps. Such standards will make it easier for your team to navigate through the data.

Beware of new data sources: Pay attention to emerging data types, such as those from voice activation devices, geo-localization in smart devices, Internet of Things, Augmented and virtual reality platforms, etc. New data points will eventually require new processing and marketing frameworks.

Still want help defining your customer data management strategy?

Now that you have learned a bit more about customer data management, maybe your next step will be to study data management solutions.

If that is the case, we recommend you check out Arena’s customer data platform blog section to dive deeper into the subject. You can also get in touch with one of Arena’s consultants and learn the specifics about our CDP.