Jes Kirkwood on November 15th 2021
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.
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.
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?
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
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.
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.
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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.
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.
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.
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.
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.
New to Segment? Sign up for a demo to learn how Segment can help you better understand your customers and engage with them effectively.
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