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Growth & Marketing

Jes Kirkwood on November 15th 2021

Shopify's VP, Growth Morgan Brown reveals how the company's growth team drives results in an exclusive interview.

All Growth & Marketing articles

Enzo Avigo on April 5th 2021

How do product managers get started with analytics? In this guest post, learn how to track the most important product metrics using Segment.

Sherry Huang, Caitlyn Sullivan on March 31st 2021

It’s time to say hello to the next era of digital advertising, powered by first-party data.

Shirley Javier on February 26th 2021

This a guest post from Shirley Javier, Product Manager at Taplytics, the mobile optimization platform. Try Taplytics out on the Segment platform today.

Building an effective experiment is one of the most powerful strategies you have at your disposal. So much of your company’s success depends on how you evolve alongside the market—being able to test out what works for your target audience is critical.

But creating effective A/B tests requires a deep understanding of both your product experience and your customer expectations. That understanding comes from a strong base of customer data and team insights. Without those two things, striking a balance between optimizing the customer experience and furthering your business goals is impossible.

Following A/B testing best practices helps you create experiments to improve conversion rates, connect with customers, and build out an engaging experience with your product, without negatively impacting customers or your business along the way. That makes it easy to learn more about what drives user engagement while adding value for your business.

1. Figure out what to test

Useful A/B tests are specific. Before you start building experiments, your team needs to determine what aspects of the product or user experience benefit most from experimentation. Defining the test parameters first helps you move forward, knowing you’ll make a real impact on customers.

Map out the customer journey to help your team get started. A customer journey map enables you to identify areas of improvement based on what aspects of the experience cause the most friction for users. Categorize what happens at each stage of the customer journey into:

  • Activities: what people do at various stages

  • Motivations: why people perform those actions

  • Emotions: how people feel as they perform those actions

  • Barriers: what stops them from taking action

Barriers will be the first thing you want to look at, because they are the most suitable for effective A/B testing. Removing these barriers makes it easier for users to accomplish their tasks, which leads to a better overall product experience. The easiest way to define these elements is to use your customer analytics. This data helps you pinpoint touchpoints along the journey, based on real-world customer experiences.

Understanding the typical paths customers take to learn more about your product, make a purchase, and engage with your team is the best way to identify what aspects of their experience need work.

2. Tie experiments to specific KPIs

Once you have a handle on the aspects of the product/user experience you want to test, define the key performance indicators (KPIs) you’ll use to track and analyze the impact of these experiments. A/B testing is only as powerful as the metrics you use to track your success. Without specific metrics, it’s impossible to categorize your results.

Choosing the right metrics helps set a baseline for your KPIs as well, which you’ll use to track the evolution of subsequent experiments over time. Outlining changes to these metrics make it easy to understand the impact a specific testing variant has on your hypothesis.

Analyzing these KPIs is one of the most high-leverage things you can do to refine the experimentation process because it helps you nail down exactly what works and what doesn’t. Especially when you pair that analysis with a well-defined A/B testing goal, these goals help you connect the impact of each experiment to your overarching business objectives.

The best way to do this is to formulate your testing hypothesis as a SMART goal. These goals are specific, measurable, attainable, relevant, and time-bound, so they provide your team all the context they need to understand results.

3. Leverage good data

Data integrity is a fundamental element of any successful experiment. Without good data, it’s impossible to track the real-world results of your A/B tests. Customer data platforms like Segment help your team build out their experiments with a solid basis of clean and actionable data.

With the Taplytics/Segment integration, you ensure parity between both tools and save your team a lot of time analyzing results. Information automatically syncs between the two platforms, so any data you’ve collected with either tool can be used to build out your experiments.

Example conversion rate experiment in Taplytics

Let’s say you’re running an experiment to track the conversion rate for your newsletter subscription landing page. If you have two plans set up, basic and premium, you can use that information to build out your test variants and manage these tests in Taplytics. That helps you narrow down specific metrics and KPIs to track on a per-experiment basis.

4. Target the right audience

A/B tests need to target a specific set of customers. Proper targeting not only makes it easier to define what changes to make your testing variants, it also gives you the ability to tailor the experience to specific sets of users and their needs.

Using Taplytics, you can easily define custom user attributes based on a variety of characteristics between Taplytics and Segment. As a bi-directional integration, it’s easy to pass these parameters between the two as well. Let’s say you have a subset of users who signed up for an account using the same landing page. By analyzing your customer data in Segment, it’s easy to identify what similarities these people share and how those characteristics impact their website experience.

Using that information, you can create a targeted testing variant for your landing page and serve it directly to website visitors that share these characteristics. A properly targeted A/B test has a better potential for positive results and makes it easy for you to see the impact of your experiments on a deeper level.

Targeting gives your team the context they need to validate certain assumptions about your target audience and what encourages them to take the desired actions on your site.

5. Create unique test variants

A/B testing takes time and resources to set up correctly, so it’s important that each test variant you make has the potential to make a significant impact on user behavior. While minute changes can help you refine experiences over time, more substantial changes make for more straightforward analysis and tracking at scale.

Unique test variants are also easier for users to differentiate. For example, changing the placement or size of a CTA on your site is potentially more impactful than merely changing the shade or color. Keep in mind that any A/B test should only change one aspect of the experience at a time. That’s not to say you can’t have multiple variants, just that it’s essential to keep variations to a single element. Otherwise, it’s difficult to nail down your results.

A/B test variant example via Taplytics

Keeping test variants unique also helps you keep track of which aspects of the experience have the most impact on your customers. Let’s say you’re testing out a transactional push notification. Changing the headline text in each variant is an A/B testing best practice because it gives you an easy method of tracking the impact of different languages on your target audience.

Refining this language over time helps you create a better overall notification. It helps you refine the language you can use on the rest of your site as well.

6. Schedule tests for the right time

A/B tests need time to gather enough data to either prove or disprove your hypothesis—two weeks is the recommended A/B testing best practice. As you build out experimentation processes at your company, include guidelines for both the scheduling and duration of tests. This standardizes workflows for your team and helps them build individual tests more efficiently.

Use customer data to understand the best timing for your target audience. If there’s an upcoming product release, sale, or promotion that will increase website traffic, scheduling your tests to coincide with those times is a great way to increase engagement with specific test variants.

As you schedule these tests, it’s also important to consider what aspects of the experience you plan to change. You don’t want to run into issues by changing primary product functionality during high usage times. And make sure you don’t run concurrent tests on individual aspects of the product or user experience either. This could skew your results and confuse your user base with too many changes in a short period.

7. Understand the statistical significance

Statistical significance tells your team that the outcome of each A/B test is actually a result of the experiment you created, and not just a random shift in customer behavior. These complex calculations look at the experiment’s result and measure how confident you can be that the data was a direct result of whatever you changed during the experiment itself.

Statistical significance in the Taplytics/Segment integration

Segment’s Taplytics integration calculates statistical significance automatically, to a confidence level of 95%. This takes a lot of the complex mathematical workload off your team and helps them gain a better understanding of the results. By proactively removing any uncertainty in the process, your team can act on the outcomes and make decisions faster.

An A/B testing dashboard keeps track of each experiment’s long-term impact in reference to other tests as well, which helps you refine the way your team builds tests with each iteration.

8. Share results with your team

Tracking the overall impact of various experiments is helpful only if you communicate testing results with your team. Highlighting successes and areas of improvement in this way boosts engagement with the A/B testing process and helps increase visibility into important aspects of the product and user experience.

When you increase visibility into these experiments, it holds the team accountable for the work they do to create and run your A/B tests. That helps everyone involved feel connected to the results and invested in the experiments they create. When you get multiple perspectives on the test results, it also builds a shared sense of commitment to the process and helps you brainstorm more diverse solutions to customer problems.

The bi-directional integration between Taplytics and Segment makes sharing those results easy by housing data on either platform. If you have a dashboard set up in Segment, you can share those results directly, or vice versa. This data parity ensures that, regardless of your team’s preferred tool, all A/B testing results are clear and easy to understand.

9. Apply changes through a phased rollout

Once you’ve run your A/B tests and determined which of the variants is more successful, it’s time to roll out those changes to your entire user base. A phased rollout mitigates the risk of implementing those changes by providing more control for your team.

Phased rollouts are especially important for changes that have a direct impact on the product experience. Let’s say your experiments were designed to make it easier for new customers to move through the onboarding process. Once you’ve determined what changes to make, pushing those updates live to the customer is the crucial next step

Using a phased rollout schedule helps you make these changes slowly for your target audience and decreases the potential strain on your infrastructure as well as your team. Whenever you make a change to core functionality, it’s important that you ensure the smoothest transition possible for your users. And bumps along the way can have a negative impact on your relationship with them.

A/B testing best practices help you build experiments at scale

Designing, implementing, and analyzing your A/B tests is a complex process involving a number of moving parts. As your company grows and your team expands, your approach to experimentation needs to mature as well. Following A/B testing best practices helps you build experiments more efficiently as your company needs evolve and helps you create and analyze experiments that have a real impact on your users as well as your business goals.

Kelly Kirwan on February 24th 2021

How StackCommerce used Segment to improve attribution accuracy

Geoffrey Keating on February 23rd 2021

Marketing automation software is quickly becoming a priority for competitive companies—businesses that are looking to raise customer satisfaction, boost ROI, and work more efficiently. 

But not all marketing automation software is created equally. Before you choose a tool, assess your software needs and the goals you want to accomplish. Then, evaluate the marketing automation software landscape so you can choose the best tool to help you reach your marketing goals.

For your marketing automation efforts to be successful, your data needs to be well-managed. Bad data can lead to misguided engagement efforts and frustrated customers. A customer data platform like Segment will power your marketing automation software with clean, organized data.

Table of Contents:

Familiarize yourself with the basics of marketing automation software

Before choosing marketing automation software, you’ll need to understand the basics of these tools and why you might want to use one. Then you can decide if it’s time for your team to invest in a marketing automation tool.

What is marketing automation software?

Marketing automation software is a tool that streamlines your digital marketing activities and delivers targeted messages to your customers and prospects based on their behavior and preferences. It helps marketers acquire new customers, nurture existing relationships, and analyze campaign performance across channels.

Marketing automation software functions by tracking user actions on your website, app, digital product, email campaigns, and other events. Then, this behavioral data triggers targeted content via email, SMS, push notification, in-app messaging, social, web, and more.

Using customer behavioral data, marketing automation software can help you create curated experiences—displaying a different landing page to a returning customer, delivering ads based on recent browsing history, or sending an email introducing products related to the customer’s interests, to name just a few.

Personalizing the customer experience can get complicated with so many customer interactions across channels and devices. But, with the right behavioral marketing automation software, you can track customer behavior and then deliver targeted customer experiences at scale while reducing the amount of time you spend on marketing processes.

Why use marketing automation software?

Customers want personalized experiences. According to Epsilon, “80% of consumers are more likely to make a purchase when brands offer personalized experiences.”

But, it’s not as simple as sending a follow-up email after a purchase to advertise products that are similar to the purchased product. You need to be able to anticipate what a customer would like to buy, even if they haven’t shown a direct interest in that product.

In a consumer survey by McKinsey, one shopper noted that after buying a puffy jacket, she received an email recommending similar jackets. “‘Regarding this product, you only need one. Why send an email for other similar coats?’ she asked.”

This company would be better off recommending winter boots or a scarf that reflects the customer’s style based on their first purchase. To deliver this level of personalization, companies need a tool that uses complex algorithms to make smarter campaigns and foster relationships by providing value to customers.

Through marketing automation, you can give a personalized and relevant experience to your customers while saving time by automating the process. You can put the time gained back into your marketing strategy development and leave repetitive tasks to the robots.

Assess your marketing automation software needs

Marketing automation platforms are not a cheap investment, so you want to make sure you pick one that meets your company’s needs. Consider these three questions to develop your criteria to find the best marketing automation software for your business.

What are your goals?

By knowing your goals, you can make sure the software you choose has the marketing automation features, functionality, and reporting you need to succeed. Some goals that marketing automation software can help with are to:

  • Boost customer LTV

  • Acquire new customers via inbound marketing

  • Increase upsells and cross-sells

  • Raise customer satisfaction

  • Conversion rate optimization

  • Elevate ROI

Mention your goals to marketing automation software sales reps when you take a product demo. They can share relevant success stories and show you features that will help you reach your objectives.

Look for reporting features in tools, so you can find one that provides the insights you need to track your progress toward your goals and improve your marketing workflows.

What other marketing tools do you need to integrate with?

While many marketing automation platforms will cover similar channels, each will likely integrate with different marketing tools. Find a marketing automation tool that connects with your existing marketing and engagement tools, such as your:

When you take a demo, ask to see these integrations in action, so you can envision how they will work for your use cases.

How much control do you want over your marketing automation flows?

If you already have marketing flows in mind that you want to automate, make sure the tool you choose will be able to react to specific user actions or a series of actions in the way you want it to.

You’ll likely want to develop multiple series of complex triggers based on different customer journeys and actions. Bring these flows up in the demos as well.

Most marketing automation software can handle complex marketing flows for SMBs. Enterprise businesses will benefit from the greater flexibility provided by more comprehensive marketing automation tools that help companies fine-tune their marketing orchestration.

Review the best marketing automation software in 2021

Now that you know what you need from marketing automation software, consider these top marketing automation tools. Bonus: They all integrate with Segment, so you can power your marketing efforts with clean, standardized data.

1. HubSpot

HubSpot is an all-in-one SaaS marketing automation tool that will help you attract new leads and turn them into customers.


Features include email marketing automation, landing page creation, analytics, lead scoring, and a built-in customer relationship management (CRM) system. HubSpot also has plenty of educational materials and strong customer support.

Best for: CRM and lead generation

2. Autopilot

Autopilot is a marketing automation tool focused on helping small businesses create engaging customer journeys.


Build lifecycle marketing campaigns with an easy drag-and-drop interface. Autopilot includes A/B testing, customizable CTAs, landing pages, and lead scoring. Autopilot even offers annotation and collaboration features right on the platform, so you can add context to your customer journeys and review with your marketing team. Dozens of integrations mean you can create a powerful marketing tech stack.

Best for: Lifecycle marketing

3. Iterable

Iterable is a marketing platform that prides itself on integrating your customer data and then using it to create targeted campaigns.


A/B test campaigns with up to 50 variations and automatically implement the most successful variant. Iterable also helps you easily test messages on different email clients, which can be tricky to get right. Plus, they offer a clean visual interface for creating campaigns.

Best for: Lead nurturing and customer retention

4. Intercom

Intercom allows you to analyze customer behavior on your site and communicate with customers 1-to-1 in real time or via automated campaigns.


Use Intercom to guide new users through an onboarding process and streamline customer support. In addition to robust customer service features, Intercom also has powerful prospecting tools for lead generation and nurturing, A/B testing, and the ability to customize CTAs.

Best for: Customer service, lead generation, and acquisition

5. Pardot

Pardot by Salesforce is a comprehensive marketing automation platform ideal for helping B2B enterprise businesses identify and engage high-quality leads.


Track pageviews, form fill out, downloads, and social interactions. Create dynamic emails right from the platform. More features include the ability to deploy landing pages, manage social media marketing, and integrate with Google Ads to track ROI and keyword performance.

Best for: Lead generation and engagement

6. Taplytics

Taplytics lets you target users across platforms to ensure a consistent experience.


Design complex user journey campaigns to engage customers with the intuitive drag-and-drop interface. Perfect your campaigns with A/B testing and roll out new features and digital products with feature flags. Taplytics prides itself on working with businesses 1-on-1 to make sure you get the most out of the platform.

Best for: Lifecycle marketing for digital products

7. Extole

Extole is a referral marketing tool to turn your customers into brand evangelists.


Encourage your current fans or subscribers to bring in new customers by rewarding them with Extole’s wide range of advocacy products. Enterprise businesses will benefit from advanced features like fraud protection, A/B testing, APIs and webhooks, and the sophisticated reward engine.

Best for: Referral and loyalty programs

8. Customer.io

Customer.io is designed to help companies reach customer activation and retention goals with a focus on automated messaging.


With Customer.io, you can create multiple branches in a single campaign to customize messages based on conditional statements, user attributes, or segments. Enhance your efforts with comprehensive reporting and A/B testing for more relevant and personalized customer interactions.

Best for: Customer activation and retention

9. Drip

Drip is primarily a B2C ecommerce marketing automation platform that allows you to send personalized messages to customers and leads.


Segment customers by tag, event, and even custom fields, so you can understand how different customers are interacting with your company. Create messages with dynamic content to personalize the customer experience with things like product recommendations and unique discount codes.

Best for: Customer acquisition and retention

10. Marketo

Marketo is another all-in-one solution ideal for B2B marketing that helps align marketing and sales teams.


Use Marketo for lead management, email marketing, and predictive web content. Marketo organizes leads for sales reps and provides context for them to understand how a lead has interacted with your website and marketing campaigns. Offering a variety of pricing plans, Marketo is a viable option for nearly any B2B company.

Best for: Lead generation and engagement

11. WebEngage

WebEngage is a multichannel B2C engagement platform.


WebEngage supports real-time segments, so you can engage customers based on their behavior. They offer pre-built templates for easy campaign creation. Use funnel and behavior analysis to better understand the effectiveness of cross-channel marketing campaigns throughout the customer lifecycle.

Best for: Lifecycle marketing

12. Blueshift

Blueshift automates behavior-based messaging across many marketing channels, including email, push notifications, Facebook, and display ads.


Use Blueshift’s behavioral segmentation to identify users who are more likely to perform actions like a repeat purchase, activation, or churn. Then, create automation workflows based on those segments to get more customers on a successful conversion or retention path. Campaigns in Blueshift are self-optimizing, so you’re always getting the best results from your efforts.

Best for: Segmenting and multichannel messaging

Supercharge your marketing automation with a customer data platform

A marketing automation tool will provide synergy between your marketing efforts and your customers’ actions and preferences. Amplify your marketing automation platform’s capabilities even further with a customer data platform like Segment.

Segment gathers, cleans, and standardizes data from all your data sources and sends it to your marketing automation platform to help you create hyper-personalized campaigns.

Segment even has pre-built infrastructure, so you can test different marketing automation tools with just a few clicks before choosing one. See how Halp used Segment to test and select a marketing automation solution and then improved their customer onboarding flow and increased activation by 4x.

Jim Young on February 17th 2021

What is the most important ingredient for a successful customer experience program? 

According to hundreds of business leaders across the globe, the answer is good data. 

With the explosion of digital adoption last year, data quality became a top priority for companies looking to adapt. Unfortunately, many businesses lacked the right technology to manage the increased volume and complexity of customer data.

At the same time, a number of firms found that managing customer data effectively was a challenge that paid off generously. Savvy business leaders turned to customer data platforms as a result, ensuring that all teams across the org could operate with access to clean, reliable data.

Because a customer data platform can empower multiple different teams across the business, we are often asked: “What’s the exact ROI from investing in a CDP?”

To address this question, we recently conducted a survey with Aberdeen, the leading industry research firm, to investigate in precise terms the numerous benefits a CDP can have on your bottom line.

Let’s dive in.

ROI benchmarks to justify your customer data platform investment

One of the simplest ways to illustrate the cumulative ROI of a customer data platform is to measure the performance of businesses that are using a CDP against those that are not.  Across several KPI categories, businesses that use a CDP are knocking it out of the park.

Notably, 9.1x greater annual growth in customer satisfaction and 2.9x greater YoY revenue growth represent impressive returns on investment for customer-first businesses. 

It should then come as no surprise that, by 2022, close to 90% of enterprise firms will have implemented a CDP across their organizations. 

With customer data platforms in place, these companies are better able to connect and unify first-party data across channels, ensure that data is accurate, and personalize every customer interaction to each individual’s preferences.

Source: Aberdeen, September 2020

It’s important to remember these benchmarks only represent the average impact of a CDP on business outcomes. Some businesses will achieve greater returns, some less. 

That’s why you must emulate businesses that have successfully deployed a CDP. To help you maximize the ROI of your CDP, follow these three steps.

How to maximize the ROI from your CDP investment

1) Use data to understand buyer behavior

The ability to seamlessly integrate data from all relevant sources is critical for businesses looking to build a comprehensive understanding of customer behavior. 

Put simply, a CDP helps standardize your data across the organization. This allows your customer-facing teams to better deliver relevant, personalized experiences by segmenting customers based on various criteria such as previous spend, loyalty, or demographics. 

In turn, you can uncover trends and correlations influencing customer behavior that would be otherwise hidden to non–CDP-users.  

Standardized data can also help your business reduce churn by identifying common elements in the journey of lost customers, and drive revenue by targeting high-profit clients or those with the best product fit. 

SpotHero, a popular parking reservation service, is a great example of a company using Segment to standardize their customer data and drive conversions by unlocking insights into user purchase behavior. 

Source: Aberdeen, September 2020

So you have already established a single view of customer data — now what? 

2) Use data to hyper-personalize customer interactions

The next step is going beyond integrating data across enterprise systems and toward activating customer insights to provide hyper-personalized experiences. 

The research shows that companies using a CDP are better equipped to deliver consistent messages to their customers through multiple channels (89% vs. 82%). In addition to consistency, you can use real-time insights to tailor the content and timing of your interactions to the unique needs of each buyer. 

This capability can substantially improve customer satisfaction, retention, and LTV.  In other words, using consistent data to hyper-personalize your customer interactions will result in a direct impact on your bottom line. 

For companies looking to maximize the ROI of a CDP, personalization is the name of the game. 

Source: Aberdeen, September 2020

3) Use data to continuously improve marketing performance

The last insight we can apply from Aberdeen’s research involves the effect of good data on employee performance.

Consistent, reliable data drives better performance by providing business leaders a frame of reference to evaluate employee activity and determine areas of inefficiency or training needs.  

Although a CDP can empower every team across the organization, the most impactful benefits are often enjoyed by the marketing department. More specifically, the ability to accurately map each step of your customer’s journey or target the most profitable customers can vastly improve the ROI of your marketing campaigns. 

Segment Personas, for instance, is a powerful toolkit for orchestrating the customer journey that allows marketers using Segment’s customer data platform to: 

  • Build custom audiences 

  • Sync those audiences to advertising, email, a/b testing, chat, and other tools in real-time

  • Get a single view of the customer across all digital properties and the tools they engage with

Source: Aberdeen, September 2020

The velocity of customer data last year left many businesses scrambling to accelerate their digital transformation roadmaps. Prior to COVID, personalizing the customer experience was an aspirational project for many businesses. Now, it is imperative.

However, the fact of the matter is that nearly 80% of enterprise companies struggle with the challenge of using data in their customer experience efforts. Poor data quality, fragmented customer insights, and outdated technology each present considerable obstacles for non–digitally-native firms. 

Fortunately, the rapid expansion of customer data platforms helped many businesses overcome these challenges by improving their ability to harness and activate customer data. 

On this point, Aberdeen’s research is clear — CDP investments are paying dividends. Companies with a CDP are outperforming non-users in annual revenue growth, customer satisfaction, and employee engagement, among other KPIs. 

However, the implementation of new technology is not enough. It’s essential to follow best practices as well. 

Using good data to understand buyer behavior, improve marketing performance, and deliver hyper-personalized customer interactions are three steps you can take today to ensure you are getting the maximum ROI from your CDP investment. 

Geoffrey Keating on February 16th 2021

A data lake is a key component of a modern data management strategy. Data lakes gather and store raw data in its original form.

Segment Data Lakes helps you unlock the full potential of your data by providing ready-to-use data architecture. Unlike traditional data lake solutions, Segment takes care of designing, building, and maintaining the data lake architecture, so you don’t have to. Segment Data Lakes loads data automatically and reduces the amount of processing required to derive insights, while providing low-cost data storage costs and saving you valuable engineering hours.

Table of contents

What are data lakes?

Data lakes are central data repositories used to store any and all raw data. A data lake has no predefined schema, so it retains all original attributes of the data collected, making it best suited for storing data that doesn’t have an intended use case yet.

James Dixon, founder at Pentaho, who coined the term “data lake,” explains the concept like this: “If you think of a datamart as a store of bottled water — cleansed and packaged and structured for easy consumption — the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”

A data lake allows for easy, flexible storage of different types of data because it doesn’t have to be processed on the way in. It’s important, however, to have good data-quality and data-governance practices in place. Otherwise, you can end up with a data swamp, making it hard to access data and get real value out of it.

What are the differences between data lakes and data warehouses?

While a data lake stores unfiltered and unprocessed data in its native format, a traditional data warehouse stores data that has already been filtered and processed. The data in a data warehouse is stripped of any excess attributes except those needed to run predefined queries against your data sets.

Data warehouses are best suited to structured and semi-structured data and metadata. Data lakes, on the other hand, can hold any data type, including unstructured data (think images, audio files, PDFs, etc.), at a low cost.

While a data lake is optimal for storing archival data, a data warehouse aggregates and organizes all stored data to make it easy to analyze. A data warehouse’s organizational schema allows you to efficiently run queries and visualize your data to aid in decision-making.

This makes for quick analysis, but because the data in a data warehouse has already been processed for a specific use case, you can’t get answers to questions that the data hasn’t been prepared for. A data lake provides considerable business value because it retains data attributes for questions that may come up in the future.

Why do companies use data lakes?

Data lakes are able to store a large amount of data at a relatively low cost, making them an ideal solution to house all of your company’s historical data. A data lake offers companies more cost-effective storage options than other systems because of the simplicity and scalability of its function. For companies storing vast amounts—sometimes petabytes—of data, using a data lake results in significant cost savings for data storage.

Because data lakes keep all data in its native form, you can send the data through ETL (extract, transform, load) pipelines later, when you know what queries you want to run, without prematurely stripping away vital information.

A data lake gives you a central repository for your data, making data available across the organization. When you store data in individual databases, you create data silos. Data lakes remove those silos and give access to historical data analysis so every department can understand customers more deeply with the same data.

By combining all your data into a data lake, you can power a wide range of functions, including business intelligence, big-data analytics, data archiving, machine learning, and data science.

Why Segment Data Lakes is better than a traditional data lake

Traditional data lakes, like Hadoop, require engineers to build and maintain the data lake and its pipelines and can take anywhere from three months to a year to deploy. But the demand for relevant and personalized customer experiences, which require well-governed data, won’t wait. Companies need a data lakes solution that can be implemented right now to attain deeper insights on their customers with their historical data

Segment Data Lakes is a turnkey customer data lake solution built on top of AWS services that provides companies with a data-engineering foundation for data science and advanced analytics use cases. It automatically fills your data lake with all your customer data without additional engineering effort on your part. It’s optimized for speed, performance, and efficiency. Unlike traditional data lakes, with Segment Data Lakes, companies can unlock scaled analytics, machine learning, and AI insights with a well-architected data lake that can be deployed in just minutes.

Additionally, Segment Data Lakes makes data discovery easy. Data scientists and analysts can use engines, like Amazon Athena, or load it directly into their Jupyter notebook with no additional set up for easy data querying. And Segment Data Lakes converts raw data from JSON into compressed Apache Parquet for quicker and cheaper queries.

When Rokfin implemented Segment Data Lakes, the company was able to decrease data storage costs by 60%. Furthermore, Rokfin unlocked richer customer insights by leveraging the complete dataset without extra engineering effort. These richer insights provided content creators at Rokfin with valuable information about factors that led to higher acquisition and retention rates and helped them increase dashboard engagement by 20%.

Segment Data Lakes provides foundational data architecture to enable companies to create cutting-edge customer experiences using raw customer data.

Discover the untapped power of your data lake with a customer data platform

While data lakes are essential for storing archival data, you also need to be able to put that data to use. By pairing your data lake with a customer data platform (CDP), like Segment’s, you can combine your historical data with real-time data to power and optimize your marketing and product teams with actionable customer insights based on a complete customer profile.

Segment’s CDP improves data accessibility across the business. Segment’s CDP automatically cleans and standardizes your data before sending it on to third-party systems such as your analytics, marketing customer service tools, customer engagement platforms, and more. So IT and engineering teams can use the data for broader data insights to form a long-term strategy. At the same time, nontechnical users, such as marketing and product teams, will be able to draw actionable insights and supercharge personalized engagement strategies with historical and real-time data.

With a customer data platform, you can make even more informed decisions with a comprehensive, single customer view. Through identity resolution, Segment’s CDP gathers data points from your data lake and other data sources and merges each customer's history into a single profile. With identity resolution, you can glean actionable insights, power your customer interactions, and create relevant, personalized experiences with data.

Segment Data Lakes and Segment’s CDP activate all the historical data you have on a customer, with new data collected more recently for accurate insights and meaningful customer interactions.

Segment Data Lakes is available to all Segment business-tier customers as part of the current plan. Get started today by checking out our technical documentation and setup guide.

New to Segment? Sign up for a demo to learn how Segment can help you better understand your customers and engage with them effectively.

Jim Young on January 21st 2021

Scott Brinker, VP of Platform Ecosystem at Hubspot, and Katrina Wong, VP of Product Marketing & Demand Generation at Segment discuss what rapid digital transformation means for the martech landscape moving forward.

Chipper Nicodemus on December 16th 2020

How to choose the best attribution tool for your business, based on your role, company size, and objective.

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