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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

Kyle Gesuelli on October 31st 2018

This post is a guest submission by one of our customers at Frame.io for Segment’s Chain Letter blog series. The Chain Letter series profiles clever uses of Segment Connections partner tools that, when chained together, lead to some pretty advanced programmatic models, custom messaging strategies, and more. Thanks to Frame.io for sharing their story!

—Segment

Churn, it’s the secret killer of SaaS. Adding new customers and growing your top line means nothing if it’s all leaking out the bottom of your funnel in just a few short months.

At Frame.io, where I lead growth efforts, churn is always top of mind. For a little context before we jump in, Frame.io makes it really easy to upload large video files to the cloud, securely share those assets, manage multiple versions, gather timestamped feedback, and get approval  - all helping our customers deliver video up to 60% faster. Our clients range from video giants like Netflix and Turner on the high end, all the way to wedding videographers and small individual freelancers on the low end. While churn from larger customers like BuzzFeed and Vice who have hundreds of employees working on dozens of concurrent projects isn’t as much an issue for us, the story is very different in the long tail of the market.

For freelance editors and smaller 1-2 person production companies, projects are often inconsistent. Nobody wants to pay for a service they aren’t using, let alone during a period in which new revenue isn’t coming through the door. In fact, 60% of overall churn at Frame.io stems from the client’s project ending.

Fortunately, we see a 50% unbounded reactivation rate from this segment when they pick up their next project. So, the name of the game in combatting this particular type of churn is capturing revenue during that downtime.

Internally, we were averse to “optimizing” our cancellation flow. There are many companies who focus on creating barriers to canceling as a method of mitigating churn. Ever tried to quit the gym and they made you come back later because the manager was out that day? That’s one way to create a cancellation barrier, but this isn’t in our DNA. The real solution for these types of customers was creating a compelling use case for Frame.io between projects. Features in support of that idea could include low-cost archival storage, an embeddable video player, hosting for reels and portfolios, etc.

Like for many startups, bringing any of these ideas to life would be a big build, not an optimization. In fact, there hasn’t been a single time in my 6 years working in growth when I haven’t been strapped for engineering resources. It’s painful, but at the same time, it’s made me creative.

As a growth practitioner, I’ve been astounded over the past few years at how many interesting experiences we can craft with little to no engineering involvement. Using Segment helps us seamlessly collect, store, and transfer data, then chain together a variety of tools to personalize and enhance our customers’ experiences. Traditional email tools can enhance communications with push notifications and direct mail. Zapier connects APIs of disparate tools with a UI simple enough for a marketer. Drift lets you create chatbots, and parse the language of users’ responses to trigger an action. Clearbit helps you identify companies based on IP address. Optimizely lets you personalize your site experience, leveraging connections with Segment and Clearbit. The sky's the limit.

Frame.io’s churn mitigation workflow.

I decided to embark on an experiment to mitigate churn as fast as I could without much involvement from engineering - essentially crafting an experience where we send Drift critical account information via Segment at the time a user clicks to cancel, launch a chatbot via Drift, offer the user who wants to cancel a variety of solutions to prevent cancellation, and finally leverage Zapier to send instructions to the support team to handle the request to amend a subscription or cancel manually.

Sample .identify call from the churn mitigation workflow

Once this experiment proved out whether we could mitigate churn with a chained tool workflow, our plan was to either build a UI to replicate this experience, or keep the chatbot and send webhooks from Zapier to our API to handle the remediation with no human intervention.

Solutions for our customers who were cancelling because their project ended included transferring their account and billing to a colleague or client, pausing a subscription in-between projects for a lower monthly cost, or receiving a one-time discount for the next billing cycle to reduce the burden until more revenue came in the door. Below is what it looked like in practice.

Here’s a screenshot of how we use webhooks in Zapier to send the data back to Segment so it can be tracked and visualized/trended in Looker.

Frame.io uses webhooks in Zapier to send data back to Segment.

In the first month since launching this flow, we’ve saved 13% of potential churns. Although that doesn’t sound astronomical, the results for the business are tremendous.

Closing this leaky bucket even a little means the LTV of our average customer increases, allowing us to spend more to acquire new customers, of which I need fewer since I’m earning more from my existing paid base. Churn is one of the levers that can have the most significant impact on a business, and we’re happy when we make even a small dent.

Andrew Park on October 9th 2018

This post is a guest submission by one of our partners at Tray.io for Segment’s Chain Letter blog series. The Chain Letter series profiles clever uses of Segment Connections partner tools that, when chained together, lead to some pretty advanced programmatic models, custom messaging strategies, and more. Thanks to Tray.io for sharing DigitalOcean’s story!

—Segment

The marketing team at DigitalOcean knew it couldn’t usher in growth for its more than half a million global customers using traditional demand gen tactics. David Dorman, Director of Growth and  Demand Gen, and Andy Hattemer, Senior Growth Marketing Manager, needed a system that would deliver highly customized, context-heavy outbound and nurture messaging to individual users at different points in their customer journey. Creating and executing on an individualized messaging system at scale involved several hurdles. This is the story of how DigitalOcean evolved a reactive marketing approach, previously powered only with Salesforce and Marketo, into a highly personalized messaging strategy by layering in Tray.io, Clearbit, and Segment.  

A two-system state of the world

While DigitalOcean was using Salesforce for customer relationship management and Marketo as its way to integrate customer messaging into Salesforce, the growth marketing team quickly realized that these two solutions, while popular for certain marketing automation and CRM functions, weren’t going to scale without additional functionalities.

The marketing platforms lacked the customized multi-directional, multi-app syncs that DigitalOcean needed - the kind of syncs that would flow important customer data to and from Segment to both enrich user data in other applications, as well as to update Segment records afterward to ensure DigitalOcean’s campaigns were always properly targeted. The team also found itself up against Marketo’s low API call limits - typical for most marketing platforms, but a bottleneck for a team that needed to route huge amounts of mission-critical customer data. The solution was to enrich their customer data and craft messages that spoke to the distinct needs of each user while being able to deploy these messages multiple times per day, at scale.

Adding in a customer marketing data stream

As a Tray.io and Segment customer, DigitalOcean realized that it could use Tray.io’s General Automation Platform and Segment’s Personas product to develop Audiences to send to their downstream tools like Marketo to tailor campaigns for each user. DigitalOcean created a system that chained together:

  • Segment Connections and Personas to inform the messaging customization and enrichment process. Once the enrichment and customer profiling process was complete, DigitalOcean used Segment Connections to build email metadata to properly identify which users would be appropriate for which messaging

  • Clearbit to enrich each customer with full lead information, including name, email, and location

  • Salesforce to confirm DigitalOcean customers against their account records in the CRM

  • Tray.io to integrate all of DigitalOcean’s cloud-based services, creating a sophisticated automated workflow that would enrich leads with initial webhook data, Segment Personas data, and Clearbit, compare against Salesforce account info, and prep Segment email metadata

Step 1: Enrich Messaging, Add Personas, Enrich Lead Data

DigitalOcean built a complex workflow on the Tray Platform that automatically fired off multiple times per day according to a scheduled trigger. The first part of the workflow, once triggered, enriches the current messaging queue via webhook for its many messaging parameters (_including _Attributes_Timestamp, SenderID, MessageAttributes_City, MessageAttributes_PostalCode), then sends a GET webhook request to pull Segment Personas details from a records.segment.com URL.

Next, the workflow combined this persona information with enrichment from Clearbit using a Clearbit Output function for specific lead details (including Person_FullName, Person_Employment -Domain/Name/Title/Role/Seniority__, and Person_EmailProvider__, Geo - City/State/Country Person_Company_Metrics - NumEmployees/MarketCap/AmountRaised/AnnualRevenue) to give the clearest possible picture of the target customer.

By knowing where each company is headquartered, DigitalOcean could ensure its messaging would be sent in the appropriate language, and the most relevant users could join their local Meetups. By connecting Crunchbase data, the team could ensure it was presenting appropriate messaging based on their users’ company size (for instance, marketing messaging for a small-to-medium-sized business won’t resonate appropriately with an enterprise customer).

As a result of this series of steps, DigitalOcean’s team would ensure it had enriched profiles for each of its thousands of customers in its messaging queue, profiled with Segment Personas and enriched with Clearbit data as well as with employee size details sourced from Crunchbase to ensure it was sending messaging appropriate to specific job titles and company sizes. For instance, a specific marketing message might hit home with an Associate IT Manager at a small-to-midsize company; but that same message would be unlikely to resonate with a VP of Engineering at an enterprise firm.

Step 2: Tray Helpers, Confirm CRM Account Details, Email Metadata

Once the automated workflow in the Tray Platform had enriched and profiled the company’s customers, DigitalOcean then directed it to use a variety of customized Tray Platform helpers, such as running an API query at api.crunchbase.com to confirm whether a customer’s company with missing data had a Crunchbase listing, then loading in a new profile if that company’s Crunchbase profile had not yet been added:

The workflow then made a call to Salesforce to Lookup Salesforce ID Output for Accounts and Account ID to confirm whether the customer’s company was already listed in the CRM. Finally, the workflow made a Segment Email Metadata Output call to identify users matching the enriched profile to prepare the appropriate customized messaging stream for those users.

Hundreds of thousands of custom messages sent monthly

As a result of using Segment’s Personas functionality and its ability to interact with email metadata, DigitalOcean quickly scaled from traditional marketing automation to enrich and prepare custom messaging for some 50,000 contacts in its first week of deployment. DigitalOcean is now sending out personalized messages at scale to their prospects and customers with the right message at the right time. This gives DigitalOcean the speed, visibility, and costs savings to be proactive with their marketing across all customer touch points.

The Segment Growth Team on August 6th 2018

This is a continuation of a post on analytics tools. If you’re just tuning in, check out part 1 here.

Analytics tools profiles

Adobe Analytics

Adobe Analytics is a comprehensive analytics suite that provides multi-channel web and marketing analytics. Often described as robust and highly customizable, Adobe Analytics is typically used by mid-market and enterprise companies with many data sources and large datasets; it’s less appropriate for bootstrapped startups who only need to conduct basic analyses. Recently, Adobe has focused considerable effort on mapping the user journey and understanding different audiences—it allows more sophisticated segment-building than many other providers and uses machine learning to help identify new audience groups. Mid-market and enterprise companies who need more advanced tools to transform their data into actionable insights should definitely consider Adobe Analytics.

Some key features include:

  • The ability to collect and process data from virtually any channel—online and offline, email, video, search, display and more.

  • Advanced analytics that go beyond basic reporting to provide a complete view of the customer journey such as fallout, flow, and pathing analysis.

  • Highly customizable and AI-driven segment definitions.

  • Powerful data science processes that can predict a customer’s likelihood to convert and churn.

  • Algorithmic attribution to help companies understand the impact of each user interaction.

  • Intelligent alerts that are sent when significant trends or anomalous events occur (e.g., KPI data, product performance issues, usage trends, etc.).

  • Highly customizable reporting and dashboards.

Adobe Analytics is great for:

  • Objectives: Identify new audiences, increase conversion rates, enhance user experiences, drive customer lifetime value through repeat engagement, optimize digital marketing effectiveness, improve site performance

  • Role: Marketers, product managers, developers

  • Customer company size: Mid-market and enterprise (100 – 10,000+ employees)

  • Industries: e-commerce, marketing and advertising, media, travel, financial services, technology

Amplitude

Amplitude provides real-time, cross-platform analytics with an emphasis on helping product managers understand the user journey, identify the best and worst-performing features and improve retention rates. Product teams can create custom events and tailored segmentations based on the actions users have taken. This provides insight into the common paths taken by users, identifies drop-off points and features correlated with increased retention, and helps to predict new user retention rates. By leveraging these user segments, marketers can also create targeted campaigns and A/B test results to identify winning variations. Moreover, Amplitude’s intuitive design, eye-catching visualizations, and customizable dashboards help teams gain insights faster.

Some key features include:

  • Web and mobile analytics that provide a clear picture of product health, including engagement, funnels, cohort retention, revenue, custom formulas and flexible segmentation.

  • The ability to funnel audiences by custom events and actions like checkout completed, item added to cart or payment entered.

  • Intelligent alerts that are sent when significant trends or anomalous events occur (e.g., KPI data, product performance issues, usage trends, etc.).

  • Account-level analytics and CRM-integration for B2B companies to help each team focus on the set of customers that can create the highest ROI through product changes.

Amplitude is great for:

  • Objectives: Enhance user experiences, drive customer lifetime value through repeat engagement

  • Role: Product managers, marketers

  • Customer company size: Startup and mid-market (1 – 500 employees)

  • Industries: Technology (including B2B SaaS), media, e-commerce, financial services

Flurry

Flurry is a free Yahoo-owned mobile app analytics tool that is currently used by more than 1,000,000 apps. It monitors user events and usage trends to help product teams identify UI flow issues and enhance features to increase user retention. Designed to be easy-to-use and implement, Flurry is ideal for app companies who need fairly basic reporting with a little customization.

Some key features include:

Easy-to-implement, real-time mobile app analytics detailing user and session activity.

  • Funnel and user path analyses to compare conversion rates and other user actions across different dimensions such as age, device type or custom events.

  • Segmentation analyses based on standard attributes such as age, gender, location, acquisition channel and custom events.

  • The ability to track mobile in-app purchase revenue across iOS and Android and understand which products are driving revenue.

  • Real-time crash reporting that offers a clear description of the issue, which devices are impacted and when the issue was seen last.

  • Portfolio analytics that enable companies to manage their app portfolios, including data about overlap and cross-sell conversions.

  • Scheduled or behaviorally-triggered in-app notifications.

Flurry is great for:

  • Objectives: Enhance user experiences, drive customer lifetime value through repeat engagement and in-app purchases, improve app performance

  • Role: Product managers, mobile marketers, developers

  • Customer company size: Startup and mid-market (1 – 500 employees)

  • Industries: Technology, media, gaming

Google Analytics

Google Analytics is the most widely adopted web and mobile analytics provider. It takes a two-tiered approach, providing basic Google Analytics for free—targeted at small and medium-sized businesses—and Google Analytics 360, a paid offering for large enterprises. One of Google Analytics’ greatest differentiators is its integration with other Google services, such as Google Ads (formerly AdWords) and AdSense, which allows for a more holistic view of the user journey and improved targeting for paid media. For example, by linking Google Analytics and AdWords, companies can fill in gaps in their conversion tracking data and create remarketing lists based on Google Analytics data. With Google Analytics 360 comes higher data volume and more custom metrics, as well as an advanced, machine-learning upgrade to funnel reporting and attribution modeling. Larger companies that need significant customization and powerful reporting options, or those with substantial Google media spends, will want to consider Google Analytics 360.

Some key features include:

  • The ability to import interaction data from any internet-connected third-party system (e.g., CRM, desktop app, etc.) for Google Analytics 360.

  • Segmentation capabilities, funnel reporting and attribution modeling—basic for Google Analytics and sophisticated, AI-driven for Google Analytics 360.

  • Various tag management tools to streamline the configuration of complex tracking.

  • Seamless integrations across Google’s cloud offerings, including its digital advertising suite, machine learning libraries, and data warehousing solutions. (The latter only applies to Google Analytics 360.)

  • Customizable reporting and dashboards, as well as data access via mobile app, API, email notifications and more.

  • A recently released Analytics Intelligence feature for Google Analytics 360, which uses machine learning to return answers to users’ natural language queries (e.g., “Which channel had the highest goal conversion rate?”).

Google Analytics is great for:

  • Objectives: Increase conversion rates, enhance user experiences, drive customer lifetime value through repeat engagement, optimize digital marketing effectiveness

  • Role: Marketers and product managers

  • Customer company size: Startup and smaller mid-market for basic Google Analytics (1 – 500 employees); larger mid-market and enterprise for Google Analytics 360 (500 – 3,000+ employees)

  • Industries: Marketing and advertising, technology, media, commerce, travel

Heap

Heap enables companies to track first and build their funnels later by automatically capturing every user interaction on web, mobile and cloud services, not just the interactions that it’s pre-configured to track. This means that every department—from marketing to HR—can pull insights from Heap’s platform retroactively, without the intervention of a developer or analyst. This flexibility makes Heap ideal for companies with multiple departments who need user-friendly access to analytics, as well as those who don’t have the developer resources to support very rapid, data-driven decision-making.

Some key features include:

  • Automatic capture of every click, tap, swipe, form submission and more from a website or mobile app without the need to set up event tracking in advance.

  • Point-and-click web and mobile tag creation, allowing marketers to define custom events without coding.

  • Funnel analysis across different user segments, device types or attribution channels to identify friction points and improve conversion rates.

  • Complex segment definitions that combine user activity and user attributes (e.g., vertical, contract value and first touch attribution).

  • One-click integrations with third-party cloud apps (e.g., CRMs, marketing automation tools, payment processors, etc.) to enrich user-level and event data.

Heap is great for:

  • Objectives: Increase conversion rates, enhance user experiences, drive customer lifetime value through repeat engagement

  • Role: Marketers, product managers, salespeople

  • Customer company size: Startup and mid-market (20 – 1,000 employees)

  • Industries: e-commerce, technology, media, financial services

Kissmetrics

Kissmetrics is a web and mobile engagement platform that connects behavioral analytics with powerful email automation. It delivers simple yet valuable reporting of customer behavior across devices with precision segmentation and targeting options. A key differentiator for Kissmetrics is its inclusion of marketing automation tools such as behaviorally-triggered email campaigns, which can be sent to targeted user groups based on custom events or funnels. Kissmetrics provides excellent out-of-the-box reporting for small and medium data volumes and is a great option for companies who want to focus only on key metrics without being overwhelmed by data.

Some key features include:

  • Cross-platform behavior reports to help marketers identify and monitor custom audience growth segments, including up-sell and cross-sell opportunities.

  • Automated or manually-targeted emails based on completed custom actions, unique events or funnels. A/B testing for email campaigns is also supported.

  • Integration with third-party platforms like Shopify and Woo Commerce to import shopping history, segment customers, and follow them from their first visit to purchase and beyond—with no coding required.

  • Automated customer data reports, so marketers can review key metrics on active and churned populations.

Kissmetrics is great for:

  • Objectives: Increase conversion rates, drive customer lifetime value through repeat purchases

  • Role: Marketers

  • Customer company size: Startup and midmarket (1 – 1,000 employees)

  • Industries: e-commerce, marketing and advertising, media, travel

Mixpanel

Mixpanel provides web and in-app event analytics to help marketers improve conversion and retention rates. In addition to tracking user activity and funnels, Mixpanel identifies the user behaviors associated with higher engagement and retention and recommends actions to move the needle on these metrics. Additional marketing tools are included, such as the ability to A/B test campaigns and send in-app notifications to users based on specific actions they’ve taken. Mixpanel is relatively easy to implement and easy to use with an intuitive UI and polished reports.

Some key features include:

  • Sophisticated retention analyses that can answer questions like, “How many new mobile users from each of our mobile ad campaigns came back to use our product?”

  • Automatic segmentation to identify high and low-performing user groups, new audience targets, etc.

  • The ability to run A/B tests for specific user segments using an in-browser editor that can easily change the app’s UI—such as by removing features, changing colors, editing text, or uploading an image.

  • Predefined “Insight” questions that allow non-technical marketers to analyze data, discover how user engagement has changed, and pinpoint ways to optimize engagement.

  • Predictive analytics that identify users who are likely to perform an action based on past behavior, enabling more targeted and proactive marketing.

  • Intelligent alerts that are sent when significant trends or anomalous events occur (e.g., KPI data, product performance issues, usage trends, etc.).

Mixpanel is great for:

  • Objectives: Increase conversion rates, drive customer lifetime value through repeat engagement, optimize marketing campaigns

  • Role: Marketers, product managers, developers

  • Customer company size: Startup and mid-market (1 – 1,000 employees)

  • Industries: Technology, media, financial services

Woopra

Woopra is a real-time analytics platform that monitors how users behave across product, marketing, sales and support touchpoints. It’s designed to help answer questions ranging from, “Do users return after using our core product feature?” to “How does live chat impact conversions?” Woopra delivers both a high-level analysis of how different user groups move through the funnel, as well as detailed information on individual users’ journeys to assist customer success teams. Compared to other platforms, Woopra is intuitive and relatively easy to implement with dozens of one-click integrations.

Some key features include:

  • Cross-platform integration with CRM, mobile, email, marketing automation, social and support tools to provide a detailed picture of user interactions.

  • Customer journey analysis to reveal critical obstacles and opportunities at every point in the customer experience from marketing campaigns to product engagement.

  • The ability to create dynamic segments of users based on any combination of criteria—from opening an email, to signing up for a trial, to using a new product feature.

  • Trend reports to monitor product performance across multiple dimensions (e.g., location, subscription type, version, etc.) and to identify the features that drive long-term revenue.

  • Automated triggers to activate other engagement processes, such as updating the status of engaged leads in Salesforce or firing a Slack trigger to notify customer success when a user is ready to upgrade.

Woopra is great for:

  • Objectives: Increase conversion rates, enhance user experiences, drive customer lifetime value through repeat engagement, optimize marketing campaigns

  • Role: Marketers, product managers, customer success representatives

  • Customer company size: Startup and mid-market (1-1,000 employees)

  • Industries: e-commerce, technology, media, travel, financial services

Conclusion

Choosing an analytics provider can be a daunting task; there are dozens of options available with a wide range of features and sophistication levels. To start whittling down those options, consider your company’s size, data volume, and budget. Then make a list of your business’s most pressing questions and get input from the different teams who could benefit from enhanced customer analytics. If you already have some analytics in place, what answers aren’t you getting from them?

Instead of implementing each tool individually, you can use Segment to collect customer data with a single API. From there we automatically transform the data and send it out to any tools your team uses. Segment currently integrates with more than 200 tools—check out our full catalog or request a demo to learn more.

The Segment Growth Team on August 3rd 2018

This is our fifth post in a series about integration categories. Revisit our blog post “Choosing the right performance monitoring tool” here to read about our series objectives.

Understanding your customer data

The amount of customer data now available to companies has reached vast proportions, creating new opportunities to understand user behavior and anticipate their needs in increasingly sophisticated ways. Teams that make the most of this data have a significant competitive edge—and many would agree that enhancing their analytical capabilities is a top priority. However, every company has different analytics needs depending on its goals and life stage, so there’s no one-size-fits-all approach to designing the ideal analytics stack.

In the first part of this post, we’ll provide a framework for evaluating different analytics providers and highlight the top players in the market. From there, you’ll be able to consider different options in the context of your business’s specific needs and goals. Keep in mind that Segment supports more than 50 analytics tools, so we’ll only cover the tip of the iceberg here. To learn more about other analytics providers, check out the “Analytics” section of our catalog.

Basic requirements for digital analytics software

Before we dive in, let’s recap what digital analytics software does:

First, at the most basic level, it’s used for the measurement, collection, and analysis of user behavior within digital products. It works by installing a tracking library to record data from your website, mobile app or servers. Once installed, events such as a video view or an item added to a cart can be tracked. Common track events include visits, sessions, unique visitors, entry and exit points, and behavioral funnels.

Once tracked, product and analytics teams can use event tracking for insights on which features keep users engaged and identify potential friction or drop-off points in the user flow. Additionally, marketers can understand which channels drive the most (and the most valuable) visitors to optimize their marketing efforts.

Finally, digital analytics platforms help with user segmentation, customer lifecycle funnels, A/B testing, and automating messaging.

Developing your criteria

If you’re just getting started with analytics or looking to improve your current system, there are quite a few factors to consider. We suggest starting with these two key dimensions:

#1 - What volume of data do you expect to track?

The amount of data that your company generates is an important factor in determining which vendor (or level of service) to choose. Many analytics providers offer tiered plans where the lower tiers, which are designed for startups and smaller mid-market companies, limit the number of monthly total users, user sessions, user actions, or web properties that can be tracked. Analytics providers geared toward enterprises accommodate much larger volumes of data and guarantee that it will be updated in real-time. Additionally, these enterprise providers, such as Adobe Analytics and Google Analytics 360, have been developing their machine learning capabilities to mine large datasets more quickly and effectively. They allow for much deeper customization, but might require developer support to achieve their full potential.

As one might expect, data volume is also a key price driver. While an enterprise solution with all the bells and whistles might sound desirable for a startup, it’s often not very practical. Smaller companies with only a few thousand users likely wouldn’t have datasets large enough to yield statistically significant analyses from these more sophisticated providers. They might be better served by a more plug-and-play analytics tool with out-of-the-box functionality that could be utilized quickly by a business or non-technical user.

With pricing as a factor, more sophisticated solutions start at approximately $30,000 per year, and they can stretch to well over $100,000. They require significant investment, both in the technology itself as well as in the in-house developer resources required to implement and maintain these more powerful tools.

#2 - From a data standpoint, how complex are the business questions you need to answer?

Next, you’ll want to think about your business’s specific tracking needs. Do you work for a lean startup that needs to focus on key engagement and retention metrics without being overwhelmed by extraneous data? Or, do you work for a mature company that has multiple teams with different needs and more nitty-gritty, complex questions like, “Which content is most successful at converting users who discovered our website by entering these specific keywords?”

While all analytics providers offer certain basic functionality, many have different specialty areas and are better at answering certain types of questions than others. For example, Adobe Analytics and Mixpanel offer advanced segmentation capabilities, including predictive analytics to identify users who are most likely to convert. Amplitude focuses on helping product teams enhance user experiences and increase retention. Woopra maps user journeys at the individual level and, with its live chat integrations, can answer questions like, “How does live chat impact conversion rates?” Think about which questions are most important to your business and at what level of detail you need them answered.

You may also want to consider these questions:

  • Do you want the ability to run any analysis retroactively, without having to put the right set-up in place beforehand?

  • Do you have significant technical support available, or do you need a tool that’s relatively easy to implement and integrate with other services? Once the tool is up and running, will non-technical users need the ability to access data and run reports?

  • Would marketing automation features, such as behaviorally-triggered emails and in-app notifications, be an asset?

In part two of this post, we’ll profile these providers in more detail.

Considering an analytics tool? You may want to look into Segment — instead of implementing each tool individually, you can use Segment to collect customer data with a single API. From there we automatically transform the data and send it out to any tools your team uses. Segment currently integrates with more than 200 tools—check out our full catalog or request a demo to learn more.

Tianyou Gu on July 26th 2018

This is our second post in a series about integration categories. Revisit our post on choosing the right performance monitoring tool to read about our series objectives.

Unlocking your business data

Business Intelligence (BI) software enables access to and analysis of information used to inform business decisions. Through automation and visualization, these tools should provide a better understanding of your organization’s key metrics and drivers of those metrics. 

In this post, we’ll provide a framework for evaluating BI tools, profile a few of the key players in the market and discuss which tools might be best for you based on your primary objective.

What matters to you?

BI tools are geared primarily toward either business users or analytics users. Some tools, first and foremost, enable business users to explore data, while others enable real analytical powerlifting but require more technical prowess. (Of course, any tool can and should be used and maintained by both groups—this distinction simply refers to the primary audience.)

Business-user-focused tools

Business-user-focused tools (like Tableau and Looker) require some initial technical setup via an intermediate semantic layer (i.e., logic that maps complex data to familiar business terms, like customer or revenue) to define dimensions and measures. After set-up, fewer technical chops are needed on an ongoing basis. The tools allow a business stakeholder—say, the head of marketing—to build their own analyses without structured query language (SQL). More robust end-user functionality—like drag-and-drop, filtering, drill-downs and computed fields—enable this. 

However, companies generally require dedicated resources to maintain and translate the underlying data, so there can be a time lag when business users require new metrics or fields to be defined in the modeling layer. For instance, if your company were to shift focus from Gross Revenue Retention to Net Revenue Retention, an analyst would need to revisit the semantic layer to map complex underlying data to the new business metric. These tools really shine for dashboards, tracking high level KPIs and showing customer specific usage but are less fitting for ad hoc, one-off questions that require deep analysis. 

Here’s just one example of how you could use a business-user-focused tool:

  • Build a report of weekly sales by region, including a map visualization. Show various cuts of the data within each region, e.g., product, product category, industry, etc.

  • Automatically refresh the report each week and then send to sales managers

  • Allow each sales manager to explore trends in the data themselves, e.g., drag and drop additional fields to see what could have driven a change in sales.

  • When something changes on the business side—we might, for example, want to change the categorization of our headphone product from “Accessory” to “Audio Add-On”—an analyst will need to be looped in to update the semantic layer. 

Analyst-focused tools 

Analyst-focused tools (like Periscope Data and Mode), on the other hand, are “code-first,” powerful data exploration layers that sit on top of databases. These tools are generally used by analytics teams; although business users can refresh existing reports, code is needed to create and customize them. With these tools, analytics teams are empowered to be data explorers instead of reactive, database-maintainers. In those cases when business users may not know the right questions to ask, data analysts can step in and use these powerful tools to investigate more deeply. Over time we expect these tools to add more native visualization features, but for the moment they’re best suited for SQL pros. 

Here’s just one example of how you could use an analyst-user-focused tool:

  • The VP of Customer Support notices a big uptick in tickets from small and mid-sized business (SMB) customers and asks the analytics team to investigate further. 

  • An analyst creates a SQL query, joining data from a user database, product usage database, support database, etc.

  • The analyst then creates basic visualizations within the platform to understand what’s going on in the data and where the analysis should be fleshed out.

  • The analyst can share the results back with the VP to show her that the tickets correspond to a new feature that was rolled out without enough explanation to SMB customers.

  • The VP can refresh the analysis in two weeks to ensure that the problem was resolved.

Tool profiles

Tableau

Tableau is one of the most mature BI tools available with robust analyses features. Tableau is known for best-of-breed visualizations that are accessible to all types of business end-users once analytics has performed the data modeling. Its drag-and-drop interface allows users to create dashboards and in-depth analyses, but first-time users will need a good deal of initial training. 

Top features:

  • Connect, query and visualize your data without writing code

  • Use a drag-and-drop interface to create powerful, in-depth analyses or simple dashboards that can be optimized for desktop, tablet or phone

  • Set up alerts and subscriptions for reports and share across your organization

Great for:

  • Company size: Mid-market (MM) and Enterprise companies 

  • Role: business teams across the organization with a data analytics team to support

Looker

Looker is also geared toward giving business users the ability to do self-service analytics, but it’s even more sophisticated and transparent under the hood. Looker uses an intermediate data modeling layer to define business metrics once for consistent use across the entire organization. (It’s coded in a SQL-like language called LookML that makes version control and debugging easy.) Once an analytics team has set up the environment via data modeling, business users can generate their own drag-and-drop reports and use visualization features to explore data without much Looker training. Each analysis generated through drag-and-drop also creates a SQL query that explains what’s going on behind the scenes, so it’s not a black box. 

Top features:

  • Access, explore and operationalize data using Looker’s library of visualizations or by creating your own

  • Create a consistent set of data definitions in Looker using LookML

  • Connect Looker to other best-of-breed solutions via the Looker R SDK

Great for:

  • Company size: MM and Enterprise companies 

  • Role: business teams across the organization with a data analytics team to support

Periscope Data

Because it bundles hosting, data connectors and visualization layers all in one, Periscope Data is great if your organization is data driven but unable to dedicate a lot of resources to spinning up an Extract, Transform and Load (ETL) pipeline and warehouse. Periscope Data also offers embedded functionality and allows a high degree of control and customization for visualization. Periscope Data’s data cacheing (loading customer data into multi-tenant Redshift clusters) enables much faster querying. 

Top features:

  • Plugs directly into your databases and lets you run, save and share analyses over billions of data rows in seconds

  • Easily explore data and perform basic calculations and aggregations

  • Create rich visualizations and share dashboards using a drag-and-drop interface

Great for:

  • Company size: Small and midsized business (SMB) and MM companies

  • Role: analysts 

Mode

Mode is the most technical and flexible tool for analytics powerlifting. Because it is very easy to get up and running, it’s a particularly good choice for both smaller companies that want to move quickly and for analytics or data engineering teams that prefer coding. With its HTML editor, Mode offers users full access to a custom environment and the option to embed a white-label version in your site.

Top features:

  • Query data using SQL and Python

  • Visualize data with interactive reports and share across the organization

  • Integrate custom, interactive analytics into your application using White-Label Embeds

Great for:

  • Company size: SMB and MM companies

  • Role: SQL analysts and data scientists 

Making your decision

As you evaluate which BI tool will work best for your company’s data needs, we encourage you to consider who will be responsible for initially setting up the tool (along with a data warehouse and ETL process, if you don’t already have one). Whoever maintains the tool, will also need to be proficient in SQL for any of these options. You’ll also need to decide whether you want to enable business users to create custom analyses within the confines of a pre-defined environment or whether you want to give your analytics team the horsepower to answer any sophisticated, ad hoc business questions that may arise. Keep in mind, some companies choose to set up a mix of these tools so they can pair a business-user-focused one with an analytics-first platform. 

If you’re interested in setting up a BI tool for your customer data, we’re here to help. Segment makes it easier for you to collect data from a range of sources and send it safely to your data repository or any of our 200+ integrations

We currently offer native integrations with each of the BI tools described here, plus more. With our partnership with Looker, you can even enrich Segment data with customer cohorts defined in Looker. 

To get started with Segment, you can create a free account here or sign up for a premium, business account demo here.

Prateek Srivastava on July 24th 2018

Over the next few weeks on the blog, we will profile a series of integration categories, starting today with Performance Monitoring tools. Our goal is to provide an initial overview of the range of tools available on the market and to suggest a few key criteria that might be useful to your organization as you set out to select a new tool. These are complex ecosystems and we did not attempt to exhaustively cover every player in the space, but rather share an initial overview. The tools themselves are continually evolving, and we’ve done our best to reflect the facts and latest features. Stay tuned for more posts like this in the coming days.

The need for performance monitoring

Having visibility into your apps is critical both for a positive customer experience and for protecting your business. Without the right visibility, your customers could be, unbeknownst to you, running into issues that cost you revenue. This problem compounds on mobile where the cost of customer acquisition is very high, and a negative customer experience not only risks churn but also creates the risk of negative reviews which can deter new customers. Ultimately, you can only fix what you can see, and you need the right tools to get visibility at scale.

Traditionally, developers have relied on analytics data as a proxy for realtime visibility, but as the app landscape becomes more complex, developers are recognizing the need for specialized tools for monitoring.

There are a number of performance monitoring solutions, so it can be easy to get overwhelmed by choosing the right one. This piece will cover an introduction to performance monitoring tools with a focus on the ones best suited for mobile. We’ll also explore the following: 

  • Which types of monitoring are better for various team sizes and required level of reporting customization. 

  • Why some monitoring tools with built-in integrations are popular (out-of-the-box). Cost is a big factor, so we will weigh in on pricing. 

  • Whether the monitoring solution is open source. 

  • Level of functionality: such as if it wraps into a larger dashboard that gives a big picture view of any SDKs. 

  • Whether certain tools are better for certain types of code bases? 

We'll help you understand the two broad categories of these tools and how to decide which tools are best suited for your requirements. But before you embark on your journey to find an application monitoring tool using the above detailed criteria, you first have to ask yourself two simple questions. 

The first question to consider is whether you need performance monitoring or error tracking

An error tracking tool allows you to both automatically capture any user-facing errors (and sometimes internal errors) that make your app unusable and report these errors for later inspection. This should be your first line of defense; a rule of thumb in customer success is that for every single user that writes in about an error, there are typically twenty-six more users who don't write in. Error tracking tools will help you see which errors are happening and how often specific errors are happening by aggregating them. This makes it easier to understand why these errors are happening by de-obfuscating stack traces and surfacing trends so you can fix that bug that only affects iOS 7 users on cellular networks on T-Mobile in the USA.

However, not all issues escalate to the level of an error. For instance, you may ship some code that slows down page load times on cellular networks. It doesn't make your app unusable but makes it annoyingly slow enough that users might be silently frustrated and stop using your app. Performance monitoring allows you keep an eye on more fine-grained information so that your developers continue to improve your user's experience.

The second question we recommend asking is whether you prefer a tool that does it all or a focused tool that is best-in-class

For instance, for our purposes, we prefer Bugsnag and Sentry for error reporting because that's the primary focus of these tools, and by fully investing in one area, they deliver a great experience.

However, using multiple tools comes with the SDK bloat problem. All in one tools may make more sense for your business when you are truly using all of their various features. They will not only make your app smaller, but the integration between their various offerings will deliver even more value.

Typically, we recommend choosing the best tool for the job. However, some tools may offer both, which can be great for reducing SDK bloat. However, you should understand that this comes with the tradeoff that you may be selecting a tool that is not best in class.

To make sense of where various tools on the market stack up against these dimension, here’s a comparison chart: 

As you can see from our chart, many of the performance monitoring tools are being acquired by larger software solutions and are being integrated with other functionality into a single solution. This consolidation trend may very well continue.

At the customer level, the biggest trend we see is that more and customers are wanting to treat this data just like any other source of data in Segment. They want to join their application monitoring data with their other sources of customer data to derive additional insights on how errors impact the customer experience.

Some common use cases we see with Segment customers:

  • Join performance reports with Stripe data to measure revenue impact

  • Join crash reports with Zendesk data to understand support cost of different bugs

  • Use crash data to create an audience to target with an email campaign

We hope this deep dive gives you a better idea which performance monitoring tool might be best for your needs! If you’re interested in exploring these performance monitoring tools, you might want to look into Segment. We make it easier for you to try performance monitoring, analytics, and optimization services in unison. Instead of integrating each SDK one by one, you can collect customer interaction data with our API, integrate one SDK, and then flip a switch to integrate new tools. (No submitting to the app store!)

At Segment, we use our own platform to track the Segment customer experience (CX) and to flag if the application experiences latency. We use analytics.js to match Sentry data with other integrations, like FullStory and Warehouses, giving us a much richer context on our customers. Understanding how, when, and for whom our app crashes has enabled Segment to iterate quicker and fix bugs that would be very difficult to reproduce otherwise. In this way, tasks that would be typically engineering-driven can be accomplished easily by CX and product teams. 

We currently offer native integrations for each of these monitoring tools on our platform. You can create a free account here or sign up for a premium, business account demo here.

Erin Franz on December 20th 2016

We welcome Erin Franz, data analyst at Looker, to the Segment blog! Looker is a Segment integration which allows you to explore and visualize the data you collect with Segment, in Looker. Last week, Looker launched Data Actions—yet another way for customers to interact with their Segment data on the Looker platform. Erin will share how you can take advantage of Segment Sources and Looker Data Actions together to operationalize your data.

Leveraging Segment Sources in your centralized data warehouse provides a complete view of each customer. Combining customer event data from mobile and web with data from cloud sources like Salesforce, Zendesk, and Sendgrid gives you actionable insights for sales, customer success, product, and other areas of your business. Using Looker and Looker Blocks on top of the data Segment collects, you can quickly build a centralized data model in Looker with visualization and exploration capabilities, powering everyone’s decision-making with data.

Looker recently launched Data Actions, which is yet another way you can interact with Segment data on the Looker platform. This feature lets you use your external tools to take action without ever leaving Looker. Segment customers can take advantage of Data Actions in the Sources they’re already using today, such as updating a record in Salesforce, triggering an email in SendGrid, or assigning a ticket in Zendesk – all directly from Looker. Let’s walk through a real life example, where we’ll tackle modeling and joining data from Segment’s Salesforce source and Segment customer event data in Looker, and then create a Data Action to trigger an email in SendGrid directly from the Look we’ve created.

Modeling Segment Sources in Looker

Looker’s LookML modeling layer allows you to model data and expose it to your organization without having to move the data from its source. Looker supports all Segment Warehouses endpoints: Postgres, Amazon Redshift, and, most recently, Google BigQuery. Let’s assume we’ve synced Salesforce as a Segment Source in addition to collecting customer event data with Segment’s browser library. We’ll also consider pageviews as an indicator of engagement on our application. We can join our pages table to our Salesforce accounts table by creating the following explore in Looker. We’ll also bring in some contact information to use in the SendGrid Action later.

By joining our pageview data to our Salesforce data, we can get engagement insights at the account level that could indicate propensity to churn. For pages, we’ll define measures in our LookML pages view file to help us calculate week over week change in pageview count:

Assuming we’ve already defined a dimension for account names in the Account view file, we’ll select Account Name, Pages Count Last Week and Pages Week Over Week change in Looker’s Explore section. (We also added some conditional formatting to Week over Week change to easily identify at risk accounts!)

We now have a list of accounts that have shown a recent decrease in activity. We’d point our account management team to this Look so they can take action on better engaging those accounts.

Closing the Loop: Adding Data Actions

Our account management team could take this list of at-risk customers and take action in external applications like Salesforce or email. But wouldn’t it be easier if they could take action directly from Looker? Let’s add a Data Action to email in the view file for our contacts table. Using the Looker documentation for Data Actions and SendGrid’s Web API docs, we can construct the action in LookML to send an email to the Contact displayed in Looker.

Suppose we want to reach out to Bubble Guru, an account that showed a 74% decrease in usage. We can filter the Account Name on Bubble Guru and add in Contact Email to get a list of emails associated with the account. Now when we click the menu next to each email, we see the option to send Check-in Email.

When we click this option we’ll see a modal window where we can directly compose email from Looker using SendGrid.

Solely using Looker and Segment, we’ve been able to attribute usage data to our accounts, model that data for actionable insights, and take action directly from Looker. We talk to companies all the time about eliminating “data breadlines” — how we can help companies break down data silos and empower business users to get insights from their data. Data Actions is the next step in building a data-driven culture. We can enable teams across an organization to simplify their workflows in Looker with data from Segment, no context switching required.

If you’re not already exploring your data with Looker, we’d love to hear from you!

Julie Jennifer Nguyen on December 19th 2016

According to Comscore’s 2016 Mobile App Report, mobile users spend 9 out of 10 minutes using only their top five favorite apps. Companies are fighting for a coveted spot on that short list, and these days, a highly engaging app isn’t a nice-to-have — it’s a necessity.

In this high-stakes climate, the companies that come out on top aren’t just the ones who have built performant apps, but the ones who constantly iterate and improve their users’ mobile experience and drive ongoing engagement and retention.

Here’s how to make sure you’re one of them.

1. Don’t trade app performance for analytics.

Successful companies know that using different tools to analyze what their users are doing and optimize their product can sometimes lead to unnecessary SDK bloat. The more bloat, the slower an app is to load, the more it crashes, the more battery it drains — and the higher the risk of an uninstall.

Sometimes, though, adding multiple packaged SDKs in your app makes sense. You need one for analytics, one to send to your own systems, maybe something for email and attribution. But if you could preserve functionality in end tools AND reduce the weight of your mobile app, why wouldn’t you?

Segment provides a single, lightweight SDK that allows companies to use hundreds of mobile growth vendors without having to add each one natively to their app. By leveraging, the latest in mobile technologies to optimize for app size and data deliverability, we help companies reap the benefits, not the risks, of using the right tools for analyzing and reengaging their users.

2. Know what you’re putting into your app.

Savvy mobile teams think very carefully before adding vendor SDKs and “black box” code into their app. Best case scenario: an extra vendor SDK adds weight to your app. Worst case scenario: it causes crashes and errors that result in uninstalls. In a world where 80% of users remove an app after 3 months, it’s important to know what’s going into your app and to reduce uncertainty and risk wherever you can.

Segment’s libraries are all open-source, so teams can see exactly what our code is doing under the hood. Through our extensive catalog of server-side integrations, we minimize the need for companies to load partner SDKs into their apps, which means less code to troubleshoot, less uncertainty to worry about, and more time spent building an app that delivers and delights.

3. Eliminate redundant tracking.

Winning mobile teams know better than to track the same event, like an app install, over and over as they add new vendors. Implementing all of that tracking isn’t just repetitive and mundane, it also means more work to maintain those codebases as vendors make updates to their API or as a company expands or changes their tracking needs. And, sometimes your numbers and event names don’t match up in all of your tools.

Segment’s Native Mobile Spec standardizes events like “Application Installed” and collects them automatically through our SDK. So instead of having to write and rewrite new events to track them in downstream tools, teams can write them once, and we’ll transform them in the end tools.

4. Analyze the entire customer journey before zeroing in on mobile.

Smart companies focus on the mobile experience. Smarter companies focus on the customer experience and use what they know about how users interact across every platform to drive more engagement and retention on mobile.

They know, for instance, that their most engaged users tend to download their app after seeing a paid search ad on their mobile website. They also know that those users re-engage when they receive a push notification after 6PM on Sundays and Tuesdays. They even know that users who write in to support have a 20% higher CLTV than users who don’t. They know this because they’ve analyzed it.

Segment Sources combines user behavioral data with data from Google Adwords, Salesforce, Zendesk, SendGrid and more to help companies build better apps and more personalized customer experiences.

Segment’s customer data platform powers the analytics stack for 3,000 mobile apps that, collectively, have over 500 million downloads. Companies like HotelTonight, VSCO, and DraftKings, use Segment to track the entire customer journey and level up their analytics.

If you’d like to learn more about our mobile offering, feel free to shoot us a note, or check out this detailed doc on how we help mobile teams.

Jessica Kim on December 15th 2016

At Segment, we’re constantly looking for new ways to help our customers. Our core mission is to simplify how you collect, unify, and act on customer data – by adding brand-new integrations like Google BigQuery, for instance, or providing expert insights on building your marketing stack for 2017. But we also do plenty of helping in our day to day, when our thousands of customers reach out to us looking for help and advice.

We know that there’s nothing more frustrating than questions gone unanswered, so we hosted our first Brainiac Bar with the sole focus of creating a space for customers to come ask our brilliant Success team their toughest questions. We added a full bar, onsite massages, and a DIY burrito station to help make the process as fun as possible (and also because burritos, am I right?).

Our Brainiacs whiteboarded, troubleshooted, and otherwise applied their collective genius to the tasks at hand. We also took the opportunity to chat with our customers for their take on some common questions about Segment. Here are the top themes addressed at our event – some by our Success team, others straight from our customers themselves:

On how product managers and engineers solve different problems with Segment

One of the top questions we get at Segment is about how our platform helps solve different problems for different teams. While chatting with Blake Barrett, CTO of fundraising video startup Pitch.ly and software engineer at a popular music streaming service – both clients of Segment – he hit the nail on the head:

“We have our own analytics-type pipeline where we report events, log them locally, and save them to a data warehouse [where] we run queries on the data. Because we have actual data scientists work on that, the turnaround between asking a question and getting data out of it is a long tale. So the PM who suggested Segment wants to be able to see stuff right away and discover as much information as possible.”

As a CTO and engineer, however, his priorities are different: the value lies in being able to skip the building of integrations for the different tools that PMs and other departments are looking for.

“I don’t really know or care what the PM is doing – he’s going to make dashboards and [explore tools] that are really not my concern. What I really care about is an easy way to feed data in [so the PM can do what he needs to]. That was why Segment seemed appealing.”

On moving data from tools like Apache Spark into Segment

Others wondered how to move data from other tools (in this case, Apache Spark) into our platform. If you have historical data you want to import into your Segment warehouse, you can always use one of our server-side libraries:

  1. Export your data from whatever repository it’s stored in

  2. Format it so that it’s ingestible by one of our server-side libraries (Analytics-node.js, Ruby, Python, etc. – see a full list of Segment’s sources here)

  3. Pass the data to Segment in the form of track, identify, page, screen, or group calls

As long as you’ve connected a data warehouse to your Segment account, your historical data will start to populate in the form of nicely schematized Segment events.

On how startups use Segment to maximize resources

One of the best parts of the Segment community is discovering the shared values between our clients and our own company. MonkeyLearn, a Machine Learning API for developers building text analysis applications, is both a vendor and client of ours: they help us optimize our email content to provide better experiences, and we help keep their engineering team lean and nimble as they scale.

MonkeyLearn Co-Founder and COO Federico Pascual put it this way:

“As a small startup, anything that can make our team more efficient is a good thing. The less time our engineers use to implement [different] integrations, the better, so they can focus on the important things like building new features within our product. Segment empowers us to do more with the same amount of resources.”

On preventing inconsistencies with .track() call properties

Over 62% of customers told us they had questions about tracking plans prior to this event, and it’s easy to see why. Tracking plans differ based on each company’s business priorities and the kinds of questions they’re hoping to answer through their data; this leads to a lot of different questions. (Which is also why we give out a thorough, battle-tested tracking plan you can use as your baseline. Just sayin’.)

Though the properties for each business use case may vary, some things are universal. One such tip: to stay consistent across tools, send flat properties instead of nested ones.

Nested properties:

Flat properties:

Some tools will flatten for you, but others will keep the data hierarchy. Sending everything flat from the start will keep things consistent and save you a potential headache later.

All in all, it was a night of great conversation and problem-solving, two of our favorite things at Segment. Have any more tricky questions? Tweet us @segment– and stay tuned for news of the next Brainiac Bar to come chat with us in person. There will probably be burritos.

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