Kelly Kirwan on July 15th 2021
Kelly Kirwan on August 3rd 2021
Customer journeys now span multiple devices and channels. From using smartphones to check emails, to researching an item on a laptop before buying in-store, it’s become increasingly difficult for brands to maintain a single view of the customer. Without an integrated tech stack, and central source of truth for customer data, teams are often working with only a snapshot of the customer journey – resulting in a disjointed, unpersonalized customer experience.
This can have real consequences when it comes to conversion and retention rates (not to mention overall customer satisfaction). In fact, 45% of consumers say they’re unlikely to become repeat purchasers with a brand after just one unpersonalized experience.
With an omnichannel approach, every channel a business operates on is connected. This means a consumer could switch between channels (whether it be email, in-app, live chat, etc.), without experiencing any friction.
But how do you actually put an omnichannel strategy into practice?
There’s a lot of confusion over how to implement an omnichannel strategy. In fact, 76% of businesses say they’re falling short.
To win when it comes to omnichannel, businesses need to have the right data infrastructure and the right technology. This includes:
A centralized data hub that easily integrates with new applications and platforms, and consolidates customer data from any source in real-time.
The ability to merge the complete history of a customer into a single profile.
The ability to orchestrate various actions across a set of customer engagement tools (like Twilio and Twilio SendGrid) to deliver personalized communications based on customer interactions and preferences.
In our latest guide with Twilio and SendGrid, we dive deeper into these “must-haves” for omnichannel engagement, while also covering:
How omnichannel differs from multichannel (and why these two strategies are often confused).
The data foundation and technology needed to implement omnichannel customer engagement at scale.
Real-life case studies of how different brands have used omnichannel tactics to increase engagement and conversion rates.
Download a copy of The Ultimate Guide to Omnichannel to learn more.
Sam Gehret on July 29th 2021
Kelly Kirwan on July 15th 2021
Jim Young on July 13th 2021
Geraint Davies on June 30th 2021
Analytics and BI teams are instrumental in shaping future business strategies.
Knowing who your customers are, and how they interact with your business, is paramount for any customer-centric business looking to improve their customer experience and drive valuable long-term relationships. Yet for many analytics teams, painting a complete picture of your customer is a challenge; data is often siloed, incomplete, or low quality.
In today’s digital world, not having access to customer data data will - sooner or later - result in a less desirable business performance: whether it’s missed opportunities for change, poor decision-making, or lack of accurate measurement. In 2016, IBM estimated the cost of bad data in the US alone exceeded $3 trillion dollars. It shouldn't come as a surprise then that data debt is a problem for 78% of organizations.
Good to know: Customer data is any piece of data that indicates who your customers are and how they are using your product or service. Some people call this "customer lifecycle data," "analytics data," "behavioral data," "digital marketing data," or "event data."
This article outlines the challenges of confronting customer data and how Segment can provide a solution.
In the last decade, analytics teams have become increasingly empowered with new tools to unlock business insights. The likes of Tableau, Looker, PowerBi and Thoughtspot make it easier than ever to answer business questions and democratize data. However, analytics teams know that it’s not as easy as simply implementing one of these tools. Cultural change is part of the challenge (which we’ll touch on later). But a more immediate limitation is the quality and completeness of data. Even with the best of breed analytics platform it still stands that bad data in = bad data, or limited insights, out.
There are three common blockers analytics teams struggle with when it comes to customer data:
1. The data is incomplete.
Customer data is often siloed, inaccessible or isn’t being captured at the right level of detail. Stitching together aggregated web traffic, transactions, CRM, and marketing data often requires a lot of manual effort or is simply not possible.
2. The data is not clean or in a consistent format.
ETL tools have proliferated alongside BI tools because in many cases, one cannot operate without the other. A report from Harvard Business Review found that analysts were still spending up to 80% of their time cleaning data before it was ready for analysis.
3. Lack of trust in the data.
With a significant amount of data wrangling needed to generate insights, it's no wonder that analytics teams struggle to instill trust in data. In a 2020 report from Experian, 40% of respondents said they don't trust data insights at their company.
The need to understand the relationship between a business and its customers is not something new. Take web analytics: when Google Analytics launched in 2005, they had to pause sign-ups for a year because the sheer demand caught them off guard. Collecting page views from websites is now just a small component of a customer journey. Engagement occurs across multiple platforms and channels, resulting in disparate data formats and silos:
Web events like page views and clicks might be captured by Google Analytics or Adobe.
Events in a mobile app might be captured by Amplitude.
Contextual information about the customer may be held in a CRM such as Salesforce or HubSpot.
Marketing data like emails may be in Braze or Intercom.
Chat or contact center data could be in a database.
Brick & mortar transactions might live in siloed POS systems.
Creating a true 360° view of the customer would require extracting and combining data from all of these locations. Without a CDP, data engineering and analytics teams spend too much time managing customer data sets instead of performing more valuable analysis.
It’s all too common to see organizations struggle to:
Validate and resolve data issues.
Reconcile customer identities across channels and platforms.
Ensure the refresh cadence meets the demands of decision makers.
As a result:
Insight into product adoption and usage is incomplete.
The user journey and experience is not fully understood.
Impact assessments of new features or initiatives are limited.
The bottom line is that conversions and revenues are not maximized.
Segment, the leading Customer Data Platform (CDP), consolidates and integrates customer data in a single location. It provides companies with the data foundation that they need to put their customers at the heart of every decision.
DigitalOcean, a New York City-based cloud infrastructure company, up-leveled its marketing efforts by turning insights into action with Segment. With a consolidated data set and visibility into a user’s actions across the entire funnel, DigitalOcean’s analytics team can access data and answer questions at a new macro level.
Meredith Corporation also opened up unprecedented opportunities for growth by using Segment to unify customer analytics across the enterprise. With more than half of Americans consuming Meredith content across 36 brands every month, they had to operationalize a data platform at massive scale. With a streamlined and unified data architecture built on a Segment foundation, Meredith eliminated weeks of manual effort previously required to merge disparate first-party data. Now, a central data hub powers real-time dashboards with standardized data for all teams to use.
LogMeIn is an access management, collaboration, and customer communication platform with over 2 million daily users. They used Segment to modernize their analytics infrastructure. Using Segment Warehouses, they piped all of their customer data into a database. With data in a structured format, their BI team now uses best of breed analytics tools to find new product insights for sales by looking into how specific cohorts of users behave based on what features they tried.
Below, we list the obstacles a CDP can help solve, and why these solutions are so vital to analytics teams.
Segment addresses the problem of incomplete customer data by capturing event data directly from the source. Event data could be page views, ads clicked, product added to cart, checkout completed, video started, email opened, and so on. Essentially, all interactions or touchpoints a customer might have with your business.
Product teams can implement Segment code on your website, application, or directly on a server to capture these events and send them to Segment. Segment also connects with many different cloud and SaaS applications, such as a CRM, email marketing tool, or ad platforms, further enriching the customer data available.
Then, there’s the problem of inconsistent customer data. Segment built a data governance layer, Protocols, that helps automate and scale data quality best practices. Using Protocols, you can ensure the events being collected meet your organization's data governance standards. This solves the problem at the source, before it ever hits downstream tools or your data warehouse.
Investing in data quality will improve trust in your data, reduce time spent by your engineering and business teams validating data, and ultimately allow your business to grow at a faster rate.
Resolving user identities across multiple systems presents significant hurdles to understanding your customers. Segment handles the problem of merging customer identities using Personas, which unifies the complete history of each customer into a single profile, no matter where they interact with your business. Identity Resolution allows you to understand a user’s interaction across web, mobile, server, and third party partner touch-points. No more extracting and joining data from multiple systems.
A data warehouse is often the single source of truth for an organization’s data. Segment automatically schematizes and pushes customer data into your warehouse, so it’s ready for analysis, machine learning, or the creation of other data products. The most important aspect of a schema is how easy the subsequent use of this data is.
The image below illustrates some of the tables Segment generates in a data warehouse.
For every source:
A table for each of the API call types received by Segment: page, track, identify etc.
Each event received also gets its own table (for easy analysis).
A table of your users so you can see the latest values of their custom traits, computed traits (think calculated fields), and audiences.
A table for each API call type sent by Personas: Identify, Track.
Check out our documentation to review the schema in more detail.
Data democratization means that everybody has access to data and that the sensitivity of the data does not prevent teams from making data-driven decisions. Therefore, removing sensitive information before it hits the warehouse is an important consideration. With Segment’s Privacy Portal and Selective Sync, you can prevent sensitive information reaching the warehouse – so rest assured that opening up access to the data will not expose customer PII.
Uncovering insights at speed is one thing, but acting on them is another. Analyzing your customers and their interactions might start with conditionally grouping them or quantifying their engagement. For example organizing users into groups by:
Their most frequently purchased product category.
The social channel they engage with most.
Their lifetime value.
The recent marketing campaign that they engaged with.
Personas provides a platform for calculating computed traits such as"LTV" or "most frequently viewed product category" and for segmenting users into audiences in real time. You can even query your data warehouse to enrich your customer profiles with SQL traits and pull in attributes such as propensity scores.
By bringing these computations into Segment, traits and audiences become available for all downstream tools. Marketing, CRM, support, sales and analytics tools all receive the same data, so everyone can sing from the same hymn sheet.
Developing a true data- driven culture requires organizations to enable cross team collaboration while using data to drive decision-making. Segment captures and sends customer data to tools across the organization, bringing together analytics, marketing, product and support teams to serve customers intelligently, from acquisition to conversion and beyond.
Here’s a hypothetical scenario for an e-commerce business using Segment:
Customer data is captured by Segment and automatically sent to a data warehouse, where the analytics team connects using their BI tool of choice to share insights with the business.
The analytics team identifies a group of customers that only ever purchase items when they’re discounted. They share the insight with marketing via a dashboard in the BI tool.
Marketing decides to run a campaign to target these users during the next sale.
Analytics and/or marketing, build the audience of these users in Segment and send it to all the relevant channels such as email, Facebook, Google Ad, Pinterest etc. to send the targeted messaging to the right users. It’s also sent to the warehouse, ready for analysis.
The audience updates in real time, so it can be reused in future campaigns.
All the event data is captured by Segment so analytics can easily monitor the performance of the campaign at all levels of the funnel:
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This is the digital feedback loop: use the insights, ideas, and innovation generated by the team as an accelerator for improving the product or service that you already have. When IBM Cloud implemented Segment, they increased revenue 70% by using reliable customer data powered to uncover expansion opportunities through nurtured communication.
When it comes to analytics, the end goal is to help make better decisions more often. Modern BI tools are great if you have access to the right data. They champion speed to insight, but only if clean data is available.
Segment empowers analytics teams with schematized data in the format they need to truly understand your customers. Analytics teams no longer need to worry about how to integrate disparate data. Instead, they can focus on what they do best: uncovering insights.
To be data driven, insights should be acted upon quickly. Segment removes barriers to agility by providing a platform to easily calculate realtime traits and audiences of customers and send identical data to downstream tools for activation or analysis. It’s the glue that binds analytics with product, marketing, and support.
Segment makes it simple to gather all of your customer data into one place, standardize it, and send it to where it needs to go (including the data warehouse). Having all this data in one place allows you to understand your customers on a deeper level to provide a great customer experience and grow your business.
To learn more about how to work with customer data, check out the related articles below: