What Your CX Stack Should Look Like

Most customer experience (CX) professionals know that data is essential to providing great customer experiences. What's not as well known is which specific data is needed and where to find it.

In order to build a world-class customer experience, you need a world-class data stack. What is a data stack? It's a collection of technologies used to manage and process data. The components of a data stack vary depending on the company, but typically include data sources, data integration tools, analytics tools, and AI.

The importance of a good data stack was highlighted in a recent report from Fivetran. The report found that companies that had a well-integrated data stack were able to reduce data engineering cost by over 90%, improve data team productivity, reduce reporting time by times, boost performance metrics, and more.

What Exactly is a CX Data Stack?

The idea of a CX data stack is to unify data across a wide range of sources, including internal systems, social media, the web, and customer surveys. This data can then be used to power customer-focused analytics and to drive customer engagement.

A good data stack is essential for a couple of reasons. First, it enables you to collect and process data from a wide range of sources. This is important because it gives you a 360-degree view of the customer. It also helps you to identify customer trends and preferences. 

Second, a good data stack makes it easy to analyze data and to understand how it impacts the customer experience. This allows you to make data-driven decisions about how to improve the customer experience.

Without a data stack, you're limited to data that is stored in your internal systems, which is often siloed. This data is valuable, but it's only a small slice of the customer data that's available. With a good data stack, you can access data from a variety of sources, including social media, the web, and customer surveys. This data gives you a much broader view of the customer and helps you to understand how they interact with your brand.

Traditionally, building a data stack has been a difficult and expensive proposition. But that's changing as more companies are offering data integration and analytics tools as cloud-based services. This makes it easier and more affordable to build a data stack.

Important Components of a CX Data Stack

Now that we've talked about what a data stack is, let's take a deeper dive into the components of a CX data stack.

A typical CX data stack includes the following components:

• A customer data platform (CDP)
• A customer experience management (CXM) platform
• Data integration tools
• Analytics tools
• AI tools

Let's take a closer look at each of these components.

Customer Data Platform (CDP)

A customer data platform is a central repository for customer data. It unifies data from a variety of sources, including internal systems, social media, the web, and customer surveys. This data is then used to power customer-focused analytics and to drive customer engagement.

The CDP is essential for a couple of reasons. First, it provides a single view of the customer. This is important because it allows you to see how the customer interacts with your brand across all channels. It also helps you to identify customer trends and preferences.

Second, the CDP makes it easy to extract customer insights. With a CDP, you can quickly and easily analyze customer data to understand how it impacts the customer experience. This allows you to make data-driven decisions about how to improve the customer experience.

Customer Experience Management (CXM) Platform

A CXM platform is essential for managing the customer experience. It enables you to track customer interactions and to collect and analyze customer data. This data can then be used to improve customer interactions and to drive customer engagement.

There are a variety of CXM platforms available, but not all of them are created equal. Make sure to choose a platform that has the features you need to manage the customer experience.

Data Integration Tools

Data integration tools are essential for a good data stack. They allow you to connect to a variety of data sources and to unify the data into a single database. This makes it easy to access and analyze the data.

There are a variety of data integration tools available, including ETL (extract, transform, and load) tools, data connectors, and data warehouses. ETL tools are the most popular type of data integration tool. They allow you to extract data from a variety of sources, including databases, files, and web pages, and then load it into a single database.

Data connectors are used to connect to social media platforms and other online services. Data warehouses are used to store large amounts of data in a single database.

Analytics Tools

Analytics tools are used to analyze data and to understand how it impacts the customer experience. There are a variety of analytics tools available, including business intelligence (BI) tools, data mining tools, and predictive analytics tools.

BI tools allow you to analyze data to understand how your business is performing. Data mining tools allow you to analyze data to identify trends and patterns. Predictive analytics tools allow you to predict customer behavior.

AI-Powered Analytics

AI-powered analytics is a recent addition to the analytics toolkit. It uses machine learning algorithms to analyze data and to identify customer trends and preferences. This allows you to understand how customers are interacting with your brand and to identify opportunities to improve the customer experience.

The Key Component That's Often Forgotten: Public Data

A good data stack is important, but there's one key component that's often forgotten: public sources. Public sources are the unsolicited data that comes from the internet, social media, and other sources. This data is unstructured and doesn't fit into traditional data models.

Public sources are a goldmine of information for companies. They can provide insights into customer trends, preferences, and behavior. They can also help you predict future trends.

Unfortunately, most companies don't use public sources to their fullest potential. One of the reasons is that it can be difficult to access and process this data. Data integration tools and analytics tools don't always have the capability to handle unstructured data.

This is where Commerce.AI comes in. Commerce.AI specializes in using Deep Learning to extract insights from public sources. By using Commerce.AI, you can access and process the unstructured data from public sources to get a deeper understanding of your customers.

Use AI and Unstructured Data to Supercharge Your CX

AI is a tremendously broad category of technology that can be used for a variety of purposes, such as recognizing images or objects, understanding natural language, or making recommendations. In the context of data solutions, AI can be used to perform tasks such as data mining, pattern recognition, and predictive modeling.

AI applications can be used to analyze data in order to find patterns and insights that would otherwise be difficult to detect. AI can also be used to make predictions about future events based on past data. This allows businesses to identify opportunities and threats that they would not otherwise be aware of.

The quality of AI models depends on the data used to train them. The more data that is fed into an AI model, the better it will be at predicting outcomes. This is where Commerce.AI comes in.

Commerce.AI For Product Strategy

Commerce.AI has a massive data engine that has scanned over a trillion unstructured data points. This data is used to train the Commerce.AI algorithms and to power the AI applications used by businesses.

This massive data set is extremely valuable for businesses that want to improve their product strategy. The data can be used to identify trends in customer behavior, understand what products customers are interested in, and determine what products should be developed and launched next.

Commerce.AI For Competitive Strategy

Commerce.AI can also be used for competitive intelligence. The data collected by Commerce.AI can be used to track the activities of competitors, understand what products they are developing, and determine their strengths and weaknesses.

Using this data, businesses can tailor their own strategies to keep up with the activities of competitors. This allows them to stay competitive and develop products that customers will buy over those offered by other brands.

Commerce.AI For Market Intelligence

AI is also useful for market research. AI tools can be used to gather information about customer activities, such as the products they are interested in and which ads they interact with. This data can then be mined to determine which new markets should be explored and what products or services should be launched there.

Commerce.AI For Conversation Intelligence

AI can also be used to understand customer conversations on social media. Using Natural Language Processing (NLP), AI applications can analyze online chatter, determine the dominant topics and sentiments, and identify the most influential people taking part in these conversations.

This information can then be utilized by businesses to better communicate with customers and suggest products that would interest them. It is a win-win for both brands and shoppers as it allows companies to offer relevant products and services to their target audience without requiring too much information from the customer at the point of sale.

Commerce.AI For AI-Assisted Content Optimization

AI can also be used to optimize the content of online stores and other sites with user-generated content. By analyzing blog posts, articles, comments, forum threads, and more about a brand, product, or service, AI applications can provide businesses with insight into how to improve their copywriting and more effectively attract customers.

Not only is this useful for optimizing websites and blogs but it could also be applied to advertising as well. AI applications could analyze the success rates of different advertising methods compared to cost to determine which are most effective.

Commerce.AI can even optimize your product catalogs and product descriptions to boost your sales to the next level.

Commerce.AI For Digital Transformation

AI can also be used to transform the digital operations of businesses. The data collected by Commerce.AI could help companies visualize, depict and monitor critical metrics for their website and other digital applications. This can then be used to make changes that improve customer experience and generate revenue.

Takeaways

Data is fundamental to providing great customer experiences. If you don't know which data to collect and where to find it, your customer experience will suffer.

A CX data stack, therefore, should enable you to collect, manage, and process all types of data, including public sources.

Commerce AI is essential for extracting insights from unstructured data. By using Commerce AI, you can get a deeper understanding of your customers and stay ahead of the competition.

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