By Steve Offsey
Your company is now competing based on customer experience, but you’re only capturing feedback from a quarter of your customers and tracking maybe 1 out of 2500 interactions. You know you need the right customer data analytics tools to discover customer journeys, understand customer behavior and provide your customers with a better experience.
But standing in your way are existing customer data management challenges. These range from data quality problems, data governance issues, data ownership barriers, customer identity matching and data schema incompatibilities, as well as the duplication of data across sources.
Even if you’ve overcome these challenges, getting your customer data analytics questions answered often requires waiting in line to access data science specialists that have the proper training and tools—but are typically in short supply.
Today, the number of customer data analytics tools are overwhelming. To help you make sense of all your options, I’ll provide you with a detailed look at six popular categories of tools.
Six Types of Customer Data Analytics Tools
To get the customer insights you need to deliver software that can:
- Unify customer data and match customer identities across touchpoints
- Discover and visualize real customer journeys
- Segment customers based on behavior, demographics, and psychographics
- Orchestrate personalized, multi-channel customer experiences
- Rapidly generate insights to understand the factors impacting key KPIs, such as churn, revenue, acquisition, customer lifetime value, etc.
There are hundreds of software vendors that address some or all of these needs. In this post, I’ll review the pros and cons of six major categories: customer data platforms, business intelligence software, customer analytics tools, digital experience platforms, journey mapping tools, and customer journey analytics software.
Customer Data Platforms (CDPs)
The hype around Customer Data Platforms (CDPs) is at an all-time high. According to Gartner, client inquiries on CDPs doubled between the first half of 2017 and the first half of 2018. By some estimates, there are more than 80 vendors claiming to offer CDPs.
What are Customer Data Platforms?
“A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems” –CDP Institute.
Marketers are using a variety of systems today to design, manage and measure multichannel campaigns. Managing customer level data and audiences in separate systems for targeting and orchestrating multichannel campaigns is a challenge.
CDPs have promised to solve this challenge by centralizing data collection, unifying customer profiles from disparate sources, and creating and managing segments. Some CDPs can also orchestrate engagement with those segments across a variety of channels.
Customer Data Platform Pros and Cons
|Unifies first-party, individual-level customer data from multiple sources, to create “Golden Records”||Difficult to incorporate unstructured data|
|Consolidates profiles at the person level and connects attributes to identities||Immature analytics capabilities|
|Users can create and manage segments||Most don’t support time-series analyses (e.g. how many people did Y before Z but after X?)|
|Does not require the level of technical skill of a typical data warehouse project||Many lack advanced identity resolution capabilities such as probabilistic matching|
|Data can be used by other systems for analysis and to manage customer interactions||Can’t account for anonymous customers|
|More than 80 vendors each with their own definition of CDP|
- Unifies first-party, individual-level customer data from multiple sources, to create “Golden Records”
- Consolidates profiles at the person level and connects attributes to identities
- Users can create and manage segments
- Does not require the level of technical skill of a typical data warehouse project
- Data can be used by other systems for analysis and to manage customer interactions
- Difficult to incorporate unstructured data
- Immature analytics capabilities
- Most don’t support time-series analyses (e.g. how many people did Y before Z but after X?)
- Many lack advanced identity resolution capabilities such as probabilistic matching
- Can’t account for anonymous customers
- More than 80 vendors each with their own definition of CDP
Over 1/3rd of marketers say their inability to integrate data is the biggest impediment to the success of their analytics teams, according to Gartner’s recent marketing data and analytics survey. CDPs promise interconnectivity, which is highly valued by marketers whose stacks consist of a variety of technologies that don’t always work well together.
One of the core criteria for CDPs is that they’re a marketer-led technology. Marketers prefer tools that are purpose-built for their use cases rather than the scalable solutions preferred by IT that can suit multiple enterprise needs, and CDPs meet this preference.
However, much of the functionality core to the CDP is not new. Data integration, identity management, segmentation, and activation are familiar features. Other drawbacks of CDPs are that they typically have immature analytics and identity resolution capabilities, nor do they track anonymous customers which is so important to acquisition marketers. If you’re considering a Customer Data Platform, keep in mind that data unification should be viewed not as your end goal, but as simply a means to an end.
Business Intelligence Solutions
Since they were first introduced in the late 1970s, Business Intelligence (BI) tools have played a critical role in helping businesses turn raw data into insights. BI platforms are used by analysts and business users alike to turn raw data into meaningful insights and actionable information. Over the past 30 years, BI tools have become the standard for enterprise decision making.
What are Business Intelligence Tools?
“A set of technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision making that contribute to improving overall enterprise performance.” –Forrester
Business Intelligence Solution Pros and Cons
|Mature, scalable technology||Requires technical expertise to set up and maintain data warehouse/lake|
|Supports an IT-enabled workflow, from data to centrally delivered and managed analytic content||Difficult to do time-based series analyses (e.g. how many people did Y before Z, but after X?)|
|Enables interactive dashboards with visual exploration and embedded advanced and geospatial analytics||No ability to visualize customer journeys|
|Large pool of analysts trained in BI analysis and report generation||Complex queries may require hours to run|
|Applicable to non-customer data & analyses||High cost per seat may limit the scope of deployment|
- Mature, scalable technology
- Supports an IT-enabled workflow, from data to centrally delivered and managed analytic content
- Enables interactive dashboards with visual exploration and embedded advanced and geospatial analytics
- Large pool of analysts trained in BI analysis and report generation
- Applicable to non-customer data & analyses
- Requires technical expertise to set up and maintain data warehouse/lake
- Difficult to do time-based series analyses (e.g. how many people did Y before Z, but after X?)
- No ability to visualize customer journeys
- Complex queries may require hours to run
- High cost per seat may limit the scope of deployment
BI platforms are no longer just about reporting, analysis, and visualization of structured data. Business users want to analyze diverse, often large and more complex combinations of data sources. BI technologies now integrate terabytes and petabytes of data from Hadoop, Spark, and NoSQL DBMS platforms, as well as traditional relational databases.
Today, BI tools perform batch and streaming analytics and the lines between structured and unstructured data analysis are blurring. And since they are so prevalent, finding analysts familiar with the leading BI tools is typically not an issue.
The main barriers to the widespread adoption of Business Intelligence tools are cost and complexity. Most BI platforms still require IT groups, to set up and maintain the data infrastructure that BI tools rely on.
Although modern BI platforms can now handle structured and unstructured data, it is still difficult to use them for time-series analysis, which is essential for understanding customer behavior. And despite the fact that data visualization is a strength of BI platforms, they cannot visualize customer journeys.
Customer Analytics Tools
Modern Customer Analytics platforms support the full analytic workflow from data preparation and ingestion to visual exploration and insight generation. Unlike traditional BI platforms, they typically don’t require significant involvement from IT staff to pre-define the data model or store the data in a traditional data warehouse.
Customer Analytics tools are typically used by analysts and data scientists to analyze customer data and optimize customer decisions. Insights obtained from Customer Analytics tools are used to address a variety of use cases, including acquisition, retention and cross-sell/upsell.
What are Customer Analytics Tools?
Customer analytics tools “are used to segment buyers into groupings based on behavior, to determine general trends, or to develop targeted marketing and sales activities.” –Gartner.
Customer Analytics Tools Pros and Cons
|Contain extensive libraries of statistical analysis techniques, e.g. analysis of variance, regression, clustering, etc.||Requires advanced data science skills to create, train and run models|
|Include prebuilt models for common use cases, e.g. forecasting, propensity, lookalike targeting, and churn/attrition||Difficult to do time-series analyses or visualize customer journeys|
|Extendable with your data scientists’ custom models built in R, Python or SAS||Solutions focused on business users lack transparency by only exposing model output to users|
|Most include or integrate with text analytics modules||For automated data ingestion, integration, to work with machine learning algorithms, your data must be mapped to the solution’s data model|
- Contain extensive libraries of statistical analysis techniques, e.g. analysis of variance, regression, clustering, etc.
- Include prebuilt models for common use cases, e.g. forecasting, propensity, lookalike targeting, and churn/attrition
- Extendable with your data scientists’ custom models built in R, Python or SAS
- Most include or integrate with text analytics modules
- Requires advanced data science skills to create, train and run models
- Difficult to do time-series analyses or visualize customer journeys
- Solutions focused on business users lack transparency by only exposing model output to users
- For automated data ingestion, integration, to work with machine learning algorithms, your data must be mapped to the solution’s data model
One key benefit of Customer Analytics solutions is that they ingest and integrate structured and unstructured data from multiple sources for analysis. Most customer analytics solutions perform identity resolution, de-duplication, and other nasty data transformation processes. Customer analytics solutions provide out of the box capabilities for common tasks such as behavioral segmentation, churn modeling, and customer lifetime value analysis.
In addition, they still enable organizations with more advanced data science capability to develop, test and deploy their own custom models.
Customer Analytics software has traditionally been used exclusively by data scientists and analysts, who are among the most coveted workers today. As a response to the scarcity of data science talent that plagues most organizations, a new breed of customer analytics solutions has emerged that is more focused on business users. But these solutions, although they allow non-data scientists to apply common data analysis techniques (e.g. behavioral segmentation, churn modeling, and customer lifetime analysis), expose only the output of these models to business users rather than the models themselves.
While most business users lack the technical knowledge to deeply understand the nuances of machine learning algorithms, this lack of transparency makes it more difficult for them to trust the results.
As with BI tools, it’s also difficult to do time-series analyses using Customer Analytics solutions, nor do they provide a way to visualize customer journeys.
Finally, while they ingest and integrate data from multiple sources for analysis, Customer Analytics tools still ask for your data to be mapped to their internal data model, which may require the assistance of vendor or third party service providers.
Customer Journey Mapping Solutions
Customer journey mapping has been growing in popularity over the past few years, not only with customer experience professionals, but also within marketing, customer service, user experience, and product management groups.
Some companies continue to use flowcharting or drawing tools to create their journey maps, but many are moving to purpose-built software.
Customer journey mapping tools are used by CX professionals and marketers to visualize their customer’s experience from the customer’s point of view across touchpoints, as the customer seeks to achieve a specific goal. Journey maps communicate key information about your customer’s interactions with your business in a succinct and visually compelling format.
What is Journey Mapping?
Journey mapping tools are software that “illustrates customers’ processes, needs, and perceptions throughout their relationships with a company.” –Forrester
Journey Mapping Software Pros and Cons
|Visually communicates your customer’s current experience across touchpoints||Fails to reflect the millions of real-world paths customers actually take since they’re typically based on a small sample of qualitative data|
|Captures customers’ emotions and feelings in addition to their actions||Hard for employees to avoid bias using their own experiences, desires, and goals|
|Powerful for aligning the organization around a customer-centric model||Can’t analyze and influence customer behavior in real time|
|Can serve as change management and governance tool||Difficult to directly measure the impact of customer behavior on KPIs|
|Motivates discussions to identify and prioritize opportunities to improve CX|
- Visually communicates your customer’s current experience across touchpoints
- Captures customers’ emotions and feelings in addition to their actions
- Powerful for aligning the organization around a customer-centric model
- Can serve as change management and governance tool
- Motivates discussions to identify and prioritize opportunities to improve CX
- Fails to reflect the millions of real-world paths customers actually take since they’re typically based on a small sample of qualitative data
- Hard for employees to avoid bias using their own experiences, desires, and goals
- Can’t analyze and influence customer behavior in real time
- Difficult to directly measure the impact of customer behavior on KPIs
Customer journey mapping tools can help you identify the most important moments in your customer’s journeys, understand how your firm is delivering on those key needs and expectations, and prioritize investment in customer experience improvement projects to drive growth.
However, the unfortunate reality is that many companies sink a lot of time into creating beautiful-looking journey maps that are released with great fanfare, only to gather dust as employees go back to the real work that they’re measured by.
Why is this so?
The most common complaint of the journey mapping process is that it is designed in a meeting room by employees with an inward focus. When most customer-facing employees put themselves in their customer’s shoes and try to imagine their experiences, it typically results in an imaginary journey that fails to reflect the variety of real-world paths their customers actually take.
Even if you conduct focus groups or interview a few dozen customers, you’re not even close to discovering the millions of real, unique journeys taken by your customers. Finally, journey mapping tools aren’t analytics platforms, so don’t rely on them to help you quantify the impact of customer behavior on KPIs such as revenue, sharing, or customer lifetime value.
Digital Experience Platforms
The market for Digital Experience Platforms (DXPs) has been consolidating, as software vendors either extend, create, or acquire the technologies necessary to create and deliver a great digital experience.
What are Digital Experience Platforms?
“An integrated set of core technologies that support the composition, management, delivery, and optimization of contextualized digital experiences”–Gartner.
Digital experience platforms have a broad set of capabilities that encompasses content, marketing, e-commerce, services, analytics, customer data, and personalization. For companies that sell or distribute their products digitally, product and operation staff will be heavy users of the DXP.
Digital Experience Platforms Pros and Cons
|Provides a broad set of tools for digital experience management, including content management, personalization, some analytics||Complex suites requiring multiple products to be successful|
|Support B2B, B2C, and B2E use cases||Limited connectivity to other data sources (e.g. call center, billing)|
|Some include AI and ML for the next-best offer and next-best-action||Some organizations may be challenged by complex integrations, high costs of ownership and steep learning curves|
|Most vendors have large partner ecosystems||Analytics capabilities are typically light|
- Provides a broad set of tools for digital experience management, including content management, personalization, some analytics
- Support B2B, B2C, and B2E use cases
- Some include AI and ML for the next-best offer and next-best-action
- Most vendors have large partner ecosystems
- Complex suites requiring multiple products to be successful
- Limited connectivity to other data sources (e.g. call center, billing)
- Some organizations may be challenged by complex integrations, high costs of ownership and steep learning curves
- Analytics capabilities are typically light
DXP capabilities are typically complemented by analytics and customer data management. Many DXP vendors also incorporate artificial intelligence and machine learning into their products, usually to provide the option to automatically determine next best action or offer.
However, most DXPs are offered as a suite of products, either because the extreme breadth of features offered are not applicable to everyone, or because they were created through acquisition or merger of several vendors.
As a result, they may be more complex and less integrated than they appear, and the learning curve and cost of ownership are often higher than expected.
Finally, DXPs are not really true analytics platforms, so the analytics capabilities may be light, and they may be unable to get you answers to the questions that you’re looking for.
Customer Journey Analytics Platforms
CX professionals, marketers, and analysts all use customer journey analytics software to discover, measure, and improve the actual paths their customers take as they engage with your company across touchpoints and over time. More and more companies are now using customer journey analytics software to understand and engage with individual customers at a personal level, at scale.
What are Customer Journey Analytics platforms?
Journey Analytics platforms enable you to “combine quantitative and qualitative data to analyze customer behaviors and motivations across touchpoints and over time to optimize customer interactions and predict future behavior.” – Forrester
Customer Journey Analytics Pros and Cons
|Easily unify data across all systems tools and sources||Not all solutions cover all CJA capabilities with the same depth or, in some cases, at all|
|Discover and visualize your customers’ experiences||Some solutions have extensive features for orchestration but are not capable of discovering journeys|
|Analytics platforms that are built to perform longitudinal, time-series analysis across billions of customer interactions||The set of data types and sources that can be integrated may be limited|
|AI and machine learning capabilities rapidly discover insights in minutes that would have taken data science teams days or weeks||Analytics capabilities in some solutions may be constrained to data from interactions orchestrated by the platform|
|Enable business users and analysts alike to understand factors impacting KPIs such as revenue, churn, cost-to-serve, CLTV, etc.|
|Turn insights into action by orchestrating engagement with your customers at optimal points along their journey, in real time and through the most effective channels|
- Easily unify data across all systems tools and sources
- Discover and visualize your customers’ experiences
- Analytics platforms that are built to perform longitudinal, time-series analysis across billions of customer interactions
- AI and machine learning capabilities rapidly discover insights in minutes that would have taken data science teams days or weeks
- Enable business users and analysts alike to understand factors impacting KPIs such as revenue, churn, cost-to-serve, CLTV, etc.
- Turn insights into action by orchestrating engagement with your customers at optimal points along their journey, in real time and through the most effective channels
- Not all solutions cover all CJA capabilities with the same depth or, in some cases, at all
- Some solutions have extensive features for orchestration but are not capable of discovering journeys
- The set of data types and sources that can be integrated may be limited
- Analytics capabilities in some solutions may be constrained to data from interactions orchestrated by the platform
Customer journey analytics software helps you identify the costliest failure points in your customers’ journeys so that you can allocate company resources based on what matters most to your customers and your organization.
Customer journey analytics platforms allow you to quantify customer experience with journey-based metrics and KPIs, so you can measure CX metrics like NPS®, CSAT, and FCR at various points in the customer journey, and understand the kinds of behavior that drive changes in each metric.
You can also pinpoint the drivers of customer satisfaction by understanding the journeys that influence your most and least satisfied customer. And you can determine the impact of a poor experience on business objectives like revenue, churn, or repeat purchase rate as well as the effectiveness of remediation that you may make.
Customer journey analytics platforms can also help you design personalized experiences by allowing you to create hypotheses and test them with new journeys and visualize their impact on the current experience.
Unlike other technologies I’ve described above, customer journey analytics platforms are not a category consisting of identical products. Since there are a variety of problems that customer journey analytics can be used to solve, these platforms do not include identical capabilities, nor do they prioritize the capabilities they do include in the same way.
So how do you choose which customer journey analytics platform is right for you?
To avoid wasting your time comparing apples to oranges, you should start by clearly understanding and prioritizing your business needs, and then use them as a basis for determining the best customer journey analytics platform for you.
Prioritize your needs and make sure that the solutions you’re evaluating have deep capabilities in your highest priority areas. For example, if one of your high priorities is orchestration, consider how you will discover what you should personalize first, or who to personalize it for, or when to personalize it and how.
Make a list of your touchpoints and determine where interaction data is captured for each of them. Then determine which journey analytics platforms work best with your data types and sources.
Some journey analytics solutions use the term ‘analytics’ as a fancy way to say you can create charts. If you’re looking to truly understand your customers’ experiences, how they impact your business and find high-impact opportunities to improve them, then don’t forget to do a thorough evaluation of each solution’s analytics capabilities.
How to Use this Framework to Evaluate Different Customer Data Analytics Tools?
If you’ve reached this point, I hope that I’ve provided you with useful information to understand each of these technology categories better. Below is a framework you can use to evaluate which one will work best to:
Get your customer data in one place and unify it to provide a single view of your customers, so you can discover and visualize actual customer journeys and segment your customers based on their behavior, as well as demographics and psychographics.
The technologies I have described above and the framework used to differentiate amongst them will help you to finally answer questions about how specific customer behaviors impact hard metrics you’re being measured by, like revenue, churn, cost to serve, and customer lifetime value. And by choosing the right platform, you’ll also be able to optimize your customers’ journeys in real time by orchestrating personalized experiences across all your channels—all without having to wait in line for access to scarce data scientists.
Choose your customer data analytics solution wisely!
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