Digital Marketing Definitions to Know

Understanding the myriad digital marketing definitions and concepts associated with modern customer database technologies and navigating the evolving (and sometimes confusing) martech landscape are two tall tasks for companies across industries today.

When selecting marketing technology for your martech stack — and, by default, your brand’s entire marketing strategy — you need to ask yourself countless questions:

  • “Do I and other members of my team have the requisite knowledge to own and operate a given data-driven marketing solution on a day-to-day basis?”
  • “Will we need assistance from marketing operations, data scientists, or IT specialists to set up and onboard certain martech (or even manage it over time)?”
  • “Can the customer database software ultimately go with ‘speak’ with other platforms in the martech stack, or do we need to create custom connections?”

The list goes on and on, of course.

And even when you have clear-cut answers to these Qs, there are still other to-do list items to handle: from researching tech vendors and their offerings to learning which is best for your company to make a business case for martech to leadership.

That’s why we created this guide.

20 digital marketing definitions every marketer should know

Below, we define 20 marketing terms to know today — ones that cover core martech features commonly incorporated in the top-tier technologies today and how to make the most of these solutions in your marketing efforts.

We could dedicate a whole section solely to martech buzzwords, of which there are many. Given they’re just that, though — buzzwords — chances are you’ve heard most of them (and know they aren’t the most important ones to learn more about).

So, for the sake of utility and time, we’ve listed the only most relevant digital marketing definitions that can actually help you make the most of your tech solutions today.

On This Page

#1: Data-Driven Marketing

#2: Customer Data Platform

#3: Data Unification & Activation

#4: Data Enrichment & Cleaning

#5: Data Management

#6: Customer Segmentation

#7: Personalized Marketing

#8: Individualized Marketing

#9: Single Customer View

#10: Single Source of Truth

#11: First-Party Data

#12: Second-Party Data

#13: Third-Party Data

#14: Real-Time & Batch Processing

#15: Customer Lifecycle Orchestration

#16: Progressive Profiling

#17: Identity & Identifiers

#18: Consent Management

#19: AI & Machine Learning

#20: Predictive Analytics & Models

Marketing Definition #1: Data-Driven Marketing

Data-Driven Marketing

Given every marketing definition, after this one falls under the data-driven marketing umbrella, we figured we’d get the biggest (albeit most obvious) term out of the way first.

Data-driven marketing refers to the process of using customer data and insights to make decisions on how they interact with their various prospects and customers. Given customer expectations today, data-driven marketing is needed to execute modern marketing.

This marketing involves all digital channels. It’s your key to building better brand awareness, getting more prospects into your funnel, improving your nurturing, converting more customers, and upselling and cross-sell other products or services to them over time.

Simply put, the most successful data-driven marketing approaches today are ones in which companies to take advantage of the sizable of first-party customer data sets.

The focus on the individual in modern marketing has reignited a need for marketing technology that can handle data in a specific way that enables marketers to:

  • Collect customer data through owned, consent-oriented tactics
  • Unify that first-party data into a centralized, single source of truth
  • Segment general contacts, top prospects, and customers into unique lists
  • Activate data via real-time messaging and long-term campaigns
  • Analyze customer behaviors and engagement to inform their strategy
  • Optimize their marketing messaging to gradually improve all metrics

One company’s data-driven program will look and function differently than others. But at the heart of all data-centric strategies are first-party data and customer identity.

Marketing Definition #2: Customer Data Platform

Customer Data Platform

You’re probably wondering, “What kind of martech do leading companies use in their marketing mix today and, in turn, helps them regularly improve marketing ROI?”

The simple answer? Lots of techs — both legacy (e.g., automation solutions to run short-term marketing campaigns) and emerging martech — like the customer data platform (CDP).

There are three flavors of CDP available to marketers today:

  • Relational database: This CDP type offers a highly structured architecture whereby the customer database enforces the relationship(s) between objects. Once that data schema is implemented, you’re forced to work within its structure. For instance, a relational CDP would need to pre-define a relationship between unidentified visitors to a website and a campaign in order to store that anonymous information. 
  • Event-stream database: In many respects, this kind of customer data platform is the polar opposite of a relational database because it collects massive amounts of raw data and stores it all for a limited amount of it because of the volume of data. It’s up to the marketer to sort through the raw data and determine what data should be mapped to the profile graph or be stuck with the event-to-graph schema the CDP ships with.
  • Profile database: This CDP offers greater flexibility and intention than the other two regarding data collection and consolidation. Why? No related tables or stores. This means values can be added easily and scale with no limitations. It also creates and stores truly unified profiles at the individual level, instead of just creating a chaotic graph with deconstructed events or enforcing an arbitrary data schema on the data set.

As the CDP Institute’s David Raab noted, interest in CDPs continues to grow among companies — from media and publishing to financial services — and it’s easy to see why.

Marketing Definition #3: Data Unification and Activation

Data Unification and Activation

With fragmented data compiled in a variety of martech and not easily sorted and provided to marketers in need of quick data access to refine advertising and email lists and develop other targeted messaging for target markets, companies are turning to CDPs to unify customer data and activate it in core campaigns and channels.

  • To unify customer data is to collect and reconcile data from across systems (CRM, ESP, adtech) and into a single profile for every individual that includes (but is certainly not limited to) their unique behaviors, demographics, events, transactions, campaign history, and lead scoring.
  • To activate customer data is to leverage it in your marketing communications. Every marketing action (on-site personalization, triggered personalized emails following cart abandonment, targeted ads across the web, and social networks, to name just a few common examples) is based on unique attributes about and actions related to a particular customer. Since all your marketing technology is connected in a CDP, you can interact with customers based on their intent and in the moment.

Without unified customer data and integrations across your martech stack, your ability to activate data in a timely, efficient, streamlined manner is greatly hindered.

Marketers that have traditionally had to rely on their IT  and data science or analytics teams to sort through their mountains of data to create and hand off accurate sets that can be used for activation have access to unified, democratized data in the platform.

The customer data platform creates efficiencies in processes and makes it far easier for marketers to access, activate, and analyze data (particularly their niche segments) according to their teams’ and customers’ needs.

Marketing Definition #4: Data Enrichment and Cleaning

Data Enrichment and Cleaning

Data scientists spend an inordinate amount of their time on data cleaning today.

In other words, their hours and skills are spent pulling manual lists of customer data from martech systems and making it usable – transforming the data, correcting records, etc. — to later (turnaround times vary widely) be able to use it to inform marketing tactics.

Think about that for a second: The majority of high-skilled data scientists’ time boils down to combing through martech systems and eventually handing off requested data sets to their marketing teams for activation.

Data scientists can focus their energy and attention on other critical business tasks, like discovering new patterns in customer data and solving business challenges with deep analysis of customer behavior, while marketers can activate data as they see for their digital marketing strategy without the need for assistance.

Once data is clean, marketers can enrich their customer profiles with accurate data. In other words, they can enhance, refine, or otherwise improve their data.

Having a progressive customer profile (more on that later), allows you to enrich your data with attributes from other martech systems as the customer continues to interact with you.

Given that 46% of marketers cite data hygiene and quality as a barrier to success today, this remains a top task for data scientists (or whoever handles this task at your company).

Marketing Definition #5: Data Management

Data Management

DAMA International said it best with its detailed data management marketing definition:

  • “The development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data … throughout their lifecycles.”

In other words? Data management is about more than just organizing data wherever it’s housed. It’s also about ensuring that data is of the highest quality.

“That’s what my data management platform is for — right?”

This is an FAQ many-a-marketer ask when evaluating the marketing technology landscape today.

It’s certainly not a bad question. After all, “data management” is right in the name of the platform. But there are limitations to DMPs, and other solutions, like CDPs, manage your data differently.

If you’re fine with only having access to anonymized data, data latency (waiting a day or longer for processing), and probabilistic identity matching instead of deterministic matching, you can stick with a DMP.

If you want unified data from all databases in your martech stack to sync in real-time and dynamically updated customer profiles as those contacts’ attributes change over time, a CDP is your best bet.

Marketing Definition #6: Customer Segmentation

Once you’ve implemented your platform of choice to help with your data management and unify your customer data, it’s time to learn who the individuals in your once-disconnected databases are and how you can segment them based on their attributes, behaviors, and engagement, among other factors.

At its core, customer segmentation is the process of grouping current and potential customers by geographic area, buying behavior, research patterns, shared brand perceptions, purchase history, lead scores, and other known data points.

Across industries and verticals (retailers, publishers, DTC companies, etc.), it can be beneficial to individualize their marketing to a single person (more on that momentarily).

But first, it’s worthwhile to understand how you can build segments to drive the outcomes and determine your touch points and communication strategy to these segments across channels.

Customer segmentation can be used to optimize your marketing by:

  • 1) More efficiently targeting high-volume and high-intent buyers to learn which prospects and customers are and aren’t worth targeting (after testing messaging and campaigns)
  • 2) Refining your marketing to those segments after testing various messaging until you find the ideal approach: the one that best converts them into new and returning customers
  • 3) Making it easier for your and others on your team (email marketers, demand gen specialists, etc.) to ultimately connect with your audience in a repeatable, scalable fashion

And with dynamic segmentation, you have the added benefit of the most accurate segments that you can send across your marketing tech stack. Customers are:

  • A) Added to the segment in real time if they meet the criteria
  • B) Removed from the segment if they no longer meet the criteria

With the right technology in place, testing paths of lifecycle marketing and segmentation strategies can help improve your relationships with customers over time.

As marketing experts Ann Lewnes and Kevin Lane Keller stated in a piece for MIT Sloan Review, testing is the “new lifeblood” of modern marketing, and “it is important to build a culture of testing” — and that’s especially true when it comes to segmenting your contacts in your database.

Marketing Definition #7: Personalization

Whether you create 10 customer segments or 1,000, the endgame is to deliver the same thing: personalized messaging to each group of customers to get to your desired outcome.

Regardless of your specific goal — targeting high-value customers (i.e., those who purchase high-priced products, buy a sizable volume of items, or have shown interest in purchasing) or increasing customer retention through engagement — personalization can help.

Personalized marketing has become more nuanced (and, due to data privacy measures, arguably difficult) in recent years. But it’s still highly effective

As Kantar Consulting’s J. Walker Smith wrote for the American Marketing Association, advances in martech have allowed traditional marketers to provide more personalized experiences to customers:

  • “Data, digital, algorithms, and AI have unlocked the ability to study and engage with individual consumers in personalized and predictive ways. … Predictive personalization is the power unleashed by precision.”

Personalization functionality, for instance, provides marketers the opportunity to customize on-site and in-app messages with specific messaging based on a customers’ lifecycle stage or a segment they belong to.

Using product recommendations, for example, you can tailor the homepage experience to customers by displaying products they buy regularly or have checked out recently and sending emails that only show complimentary products to ones they’ve already bought.

Marketing Definition #8: Individualization


Marketers who invest in individualized marketing “know that their consumers deserve to be treated singularly, however, wherever, and whenever they engage.”

Personalization is a fantastic method to incorporate into your marketing strategy, but traditionally, marketers have been limited to it as it allows you to target groups of people with some or many unique commonalities.

Individualization, meanwhile, is a much more meticulous, hyper-focused digital marketing approach.

Those same machine learning algorithms that help to target buckets of prospects and buyers provide distinct product and content suggestions and can help marketers also deliver real-time messaging as individuals’ characteristics and behaviors change dynamically in their customer profiles.

With CDP, for instance, you can tailor messages (e.g., a 15% discount offer or a complimentary product for a discount) after a certain amount of time on your site or in a post-visit email to enhance your odds of getting them to convert.

Whereas personalization testing can be accomplished and fine-tuned over a lengthy period of time, individualization is more about meeting the customer “in the moment” to win their business based on a unified view of the customer.

The ability to orchestrate customer lifecycles with martech like a customer data platform to provide the most applicable product recommendations in real-time is invaluable.

Marketing Definition #9: Single Customer View

This is the Holy Grail for many digital marketers today: the ability to have all customers’ personal information, shopping and browsing histories, and other crucial customer data all in one location instead of siloed in separate databases that don’t sync with one another (or easily, at the very least).

We think of the single customer view as a data discipline rather than a simple concept — and one you can master with a customer data platform as the centerpiece of your martech stack.

“A CDP is able to connect all types and sources of customer data, whether internal or external, structured or unstructured, batch or streaming,” guest contributor Jordie van Rijn wrote for Econsultancy. “This allows you to form a much more comprehensive view and to better understand your customers, and act on it even in real-time.”

A unified data infrastructure connects your disparate data sources by using consistent IDs and naming conventions and, in turn, makes it much simpler to analyze and activate customer data in real-time.

As customers interests and behaviors change, their attributes within their profile will update.

This helps your business better understand the ways in which prospects and customers interact — and create more cohesive, bespoke experiences for these individuals.

For instance, with a single view of the customer, you can optimize advertising dollars by targeting individuals with a product they’ve expressed interest in but haven’t bought.

Alternatively, you can suppress specific ads from individuals who have already purchased a product to ensure you don’t waste ad spending — a major problem for many brands today.

Our CDP is purpose-built to give marketers the tools to build a single view- something that’s critical to improving acquisition, building bespoke customer experiences, and beating the competition.

Marketing Definition #10: Single Source of Truth

Just as important as it is to achieve a single view of the customer, it’s equally imperative to implement a marketing database that can act as your single source of truth — or a “single flow of truth” that connects databases.

Simply put, if marketing professionals don’t have access to accurate and holistic customer data, how can they be expected to optimize cross-channel messaging and campaigns?

That’s where a single source of truth database comes into play. Having a unified database means marketers can rely on a single data for the most pertinent and up-to-date customer data:

  • A single source of truth that brings together once-disconnected data points from data management platforms (DMPs), customer relationship management (CRM) software, and other similar marketing databases.

Often, marketers will get asked how many customers their company had last year and year-to-date. Depending on who you ask — an email marketer, a CRM manager, an ad specialist — you’ll get a different number.

Why? Because that customer count is dependent on data available in their respective marketing system.

With marketing teams using an average of 12 solutions to manage data, it only makes sense they desire a platform that brings together all of their customer data in one, single digital locale where everyone who needs access to it can gain it and near-instantaneously activate it as needed.

Marketing Definition #11: First-Party Data

First-party data

The number of customer data types available to you and your team can certainly be a bit daunting, especially with the recent introduction of “zero-party” data.

The most pertinent place to start is first-party data.

We think it’s the most important element of every brand’s marketing program today, whether you’re B2B or B2C, a startup or enterprise, or another kind of organization altogether.

Simply put, first-party data is the information you collect from and about your audience.

As it relates to display advertising, for instance, first-party data has typically been cookie-based.

But first-party data includes info from analytics platforms, CRMs, consent preferences, purchases, and synthetic properties you derive from these sources (e.g., CLV scores).

All in all, we firmly believe first-party data is the cornerstone of any legitimate marketing strategy today — especially more so than third-party data (which we’ll dive into in a few).

Marketing Definition #12: Second-Party Data

Second-party data

Second-party data, on the other hand, is essentially someone else’s first-party data.

Oftentimes, brands will engage similar, non-competing brands — ones in adjacent verticals with similar audiences — to exchange data and, in turn, target new, seemingly interested audiences.

Another way to put it? Second-party data can be commercialized via arrangements with trusted partners who are willing to share their customer data (usually segmented) with you and vice versa.

Let’s say you run a shop that sells customizable baseball hats but wants to expand your marketing beyond those in your existing customer databases.

By securing reputable second-party data from, say, a successful baseball equipment store (with whom you’ll obviously have to develop a relationship first) and perhaps offering them yours in return (or simply paying them), you could identify similar prospects who’ve yet to buy from your brand but fit the ideal customer type or persona, based on their demographic, contextual, and behavioral data.

This modest Marketing Week graphic visualizes the second-party data-sharing process.

The trick with second-party data is ensuring you get data for customers to whom you can actually market to those who have provided consent and whose consent remains valid (meaning the individual in question hasn’t “opted out” of the second party’s marketing communications).

Since GDPR, consent management has been the name of the game, so you need to be 110% certain you’re able to market to individuals whose second-party data you’ve obtained.

As long as this is the case, the possibilities for taking advantage of second-party data are essentially endless. The key is to foster and maintain mutually beneficial partnerships with like brands.

Marketing Definition #13: Third-Party Data

Third-party data

And last (and definitely least) is third-party data — the kind of customer data you (and many other businesses) have leveraged since the dawn of the internet.

For clarity’s sake (though we suspect you’re familiar), third-party data is basically:

  • Acquired from data-sales houses or other large website and system operators
  • Not typically secured from a single website, but rather many across the web
  • Often licensed to (you guessed it) third parties for use in data and ad targeting

Third-party data has long been commoditized. While it has its time and place to augment your existing data, leveraging third-party data won’t give you a competitive advantage or much information about howwhen, and why you should interact with your customers.

Furthermore, data privacy laws like GDPR, the CCPA (California’s regulation coming in 2020), and similar measures enforced globally have made it much more difficult to capture third-party data and, therefore, have all but rendered third-party data obsolete.

Our take? Good riddance.

Why? Because first-party data has proven to be far more valuable for marketers and their organizations today, given it’s secured straight from the customer with their consent and is bespoke to your company.

At the end of the day, it’s more reliable and trustworthy for your specific marketing needs to give you and your team obtained said data via your own channels, activities, and campaigns rather than relying on third parties who may not give you the most accurate or quality data you truly need to succeed.

Marketing Definition #14: Real-Time and Batch Processing

Real-time and batch processing

Of course, your first-party data needs to be accurate and available for you to use if you want to interact with customers at the right time with the right message.

Real-time processing refers to a system in which input data is processed in milliseconds, so it’s available virtually immediately as feedback for your marketing.

With audience data flowing between these systems constantly updated, you and your team get the most precise picture of your customer at any given moment.

Batch processing, on the other hand, requires a bit more patience on your part (or that of your analytics or data science team). Data is previously collected in various jobs, then uploaded in a single batch (i.e., at a given time interval).

Since this is a scheduled update as opposed to a dynamic one, it (typically) means you don’t get instant, real-time access to data changes for existing contacts — a delay that can potentially hurt your bottom line if you’re unable to activate the data in urgent messaging and campaigns.

While there is a time and place for batch processing— a retailer doesn’t need to update a customer’s marital status in real-time for example — it’s important that marketing technology is holding marketers back from real-time processing.

“Making the shift from batch [processing] to algorithmic is like going from the age of propeller flight to jet engines,” McKinsey noted in recent research. “And the implications are just as momentous.”

Marketing Definition #15: Customer Lifecycle Orchestration

Customer Lifecycle Orchestration

The customer lifecycle and customer journey: Two entirely separate marketing concepts that are too often interchangeably used by too many professionals today — including marketers.

Customer lifecycle orchestration is the next frontier of marketing, whereby all interactions in all touch points are a direct result of where each individual customer is on her unique journey.

Gone are the days of solely planning and executing on workflow-based, static, outbound campaigns.

Taking your individual customer’s preferences and journeys into account, you can build meaningful experiences for them based on said journey by setting up lifecycle marketing.

Pinpointing the perfect place and time to meet your customers with that compelling message for a product for which buying intent is substantial is the heart and soul of customer lifecycle orchestration.

Overall, it’s an approach that exhibits the value of “small” data for your marketing.

Marketing Definition #16: Progressive Profiling

Persistent customer profiles create a comprehensive view of each customer by capturing data from multiple systems, linking information related to the same customer, and storing the information to track behavior over time.

That is to say, this data doesn’t arbitrarily delete after a certain amount of time.

Progressive profiling is to build on the same persistent profiles over time with order data, browsing behaviors, and more, making the information on your customers richer progressively over time. Profile merging from your various databases into your single source of truth — ideally, a CDP — helps in keeping info in one central profile precise.

With progressive profiling functionality, a retailer can track customer purchases over time. Say a customer purchases a baby crib this year. In four years, they can email or target this customer on social with toddler bed offers.

The value of a progressive, persistent profile is to be able to store an incredible amount of data at an individual level so that you can build out customer profiles over time.

Marketing Definition #17: Identity and Identifiers

This is yet another subject discussed at length on the BlueConic blog.

With that in mind, here’s how BlueConic SVP Strategy Cory Munchbach differentiates these customer data terms:

  • Identity refers to an individual person, known or unknown, whose attributes change.
  • An identifier is an info that guides your quest to recognize individuals in your database.

The former marketing term is fairly basic. The latter is where some marketers trip up.

“An identifier might be an anonymous cookie ID, a device ID, an email address, or a customer record,” Munchbach notes. “Each of these can be useful in their way, but they are decidedly not all created equal in terms of marketing utility, and certainly not when it comes to privacy.”

Given identifiers can vary from one solution to another, it’s vital for tech users to have a system that consolidates naming conventions for them to a more uniform set.

This, in turn, is the only real way to get a complete, single customer view.

One of many problems you could run into without this organized data collection is having multiple profiles for the same individual. You have different identifiers from different software that don’t speak the same language. Therefore, you have a flaw in your overall database marketing.

Marketing Definition #18: Consent Management

General Data Protection Regulation defines consent as, “Any freely given, specific, informed and unambiguous indication of the data subject’s wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her.”

Consent management platforms, therefore, are marketing tech tools whose sole purpose is collecting and updating that consent over time.

Our CDP gives marketers capabilities to manage privacy consent for their channels and for individuals, with tools for marketers to let their customers manage their own privacy. This includes tools to implement privacy management for GDPR (and CCPA, and countless other consumer protection laws).

Having a system in place to control which prospects and customers see which messaging (form fields, page copy, on-site dialogues, etc.) based on the consent provided (or removed) is how you avoid those pesky fines the EU is handing out to GPDR violators.

Marketing Definition #19: AI and Machine Learning

There’s still a fair amount of confusion surrounding these digital marketing terms, so let’s dive right in:

  • Artificial intelligence (AI) refers to a computer’s ability to process info, find patterns, make decisions, and even predict future outcomes — to function like a human brain. AI embedded in machines enables robots to sort and carry items, autonomous cars and trucks to drive, and industrial devices to react and decide how to move. Marketers like you use AI to process enormous amounts of customer data to identify customers and predict customer needs, behaviors, and reactions.
  • Machine learning (ML) is a branch of AI in which algorithms and models trained on thousands or millions of data samples help brands make better decisions or predictions. Machine learning can be used to determine optimal product and content recommendations for customers’ audiences. Ecommerce companies, major media brands, and professional sports teams use machine learning functionality to serve up relevant, timely messaging to customers.

The use of AI in marketing may seem daunting to some digital professionals today, but you are probably using AI within some of your marketing technologies without even realizing it. For instance, social listening tools use deep learning to garner insights.

Marketing Definition #20: Predictive Analytics and Models

Once you adopt the AI marketing mindset, you can work with your data science team to develop and deploy machine learning models of your own to improve conversion efforts.

Predictive machine learning models use customer data as input to predict what customers will do in the future.

But, often, with data siloed in various marketing technology, it’s impossible or extremely time-consuming to get all relevant customer data in one place before even beginning to build models (see data cleaning definition).

For instance, data scientists and marketing teams in place can build and run predictive models and gain predictive analytics against their unified profile database that is continually being updated with data from systems it’s connected to.

Since segments are updated in real-time, you can use any combination of profile attributes and predictive scores to market to specific individuals at the right time:

  • If you want to predict customer churn, import your propensity model to the AI workbench to start attaching propensity scores to profiles. Then, you might offer segments of high-risk for churn customers special discounts through email, on-site, or through ads.
  • If you want to forecast customer lifetime value (CLV) for an individual to determine their long-term profitability, there’s an out-of-the-box model you can run. As a marketer, you have the ability to adjust parameters on the model (for instance, the time frame in which you want to use data to calculate the CLV).
  • If you want to conduct an RFM analysis to see which customers are your most valuable at a given moment in time — that’s right — we’ve got a pre-set model for that as well. You might create premium experiences for your best customer with high RFM and high CLV.

Incorporating the output of machine learning into your business can help you improve ROI.

What next step after studying these marketing definitions? Modifying your martech stack

Some of these modern marketing definitions and concepts are likely ones you were already well aware of (you’re a data-driven marketer, after all).

But some of these digital marketing terms may be marketing features you didn’t know existed today — ones possibly missing from your current martech stack.

If this is the case, your next steps (after mastering these marketing definitions) are clear:

  • Pinpoint the specific gaps and problem areas in your current marketing tech
  • Research customer marketing software that could resolve those pain points
  • Make the case for your preferred martech to your brand’s leadership team

If your existing martech stack doesn’t allow you to unify your first-party data across systems and activate that data in cross-channel lifecycle activities with ease, your next database software choice is already made: It’s time for a customer data platform.

Ruben Harutyunyan

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