An Introduction to AI Analytics for Product Teams

An Introduction to AI Analytics for Product Teams

Elena Doynova

Nov 29, 2023 • 7 min read

An Introduction to AI Analytics for Product Teams

With artificial intelligence already all around us, it’s a no-brainer to ask yourself whether your trusted analytics partner can benefit from a little nudge. But what can it possibly do to make the lives of product teams easier? Let’s dive deeper into AI analytics: what it is, why product teams need it, and how to use it to improve your product faster.

First up: What is Artificial Intelligence? 

Machine learning algorithms, neural networks, and other technologies in the AI field are rapidly evolving and transforming various industries, including digital analytics. At its core, AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition. AI systems have the ability to learn and and problem-solve (or at least do so to an extent that looks eerily human-like). 

The growing significance of AI in analytics can be attributed to its capacity to handle immense volumes of data, automate repetitive tasks, and generate accurate predictions. With AI, every business can get a better understanding of its customers, detect patterns in user behavior, and enhance its decision-making processes. This enables various stakeholders to identify opportunities well ahead of time, mitigate risks, and drive innovation.

What is AI Analytics?

Following the definition above, AI analytics refers to the application of artificial intelligence technologies, such as machine learning, natural language processing, and deep learning, in the field of data analysis. It combines advanced algorithms with large datasets to find patterns, synthesize insights, and make predictions with a higher level of accuracy than traditional analytics methods. Needless to mention it’s a lot faster than having to use an Excel spreadsheet…

Unlike traditional analysis approaches that rely on predefined rules and queries, artificial intelligence systems can learn and adapt from available information on their own. These systems can analyze large amounts of data (both structured and unstructured) from various sources, such as customer interactions, social media, website traffic, product interactions, and more.

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What are the benefits of using AI analytics?

The benefits of using AI analytics include improved accuracy, faster and more efficient data analysis, identification of patterns and trends that humans may miss, real-time insights for better decision-making, and the ability to automate repetitive tasks, freeing up time for more strategic work.

  • Enhanced customer insights: empowers businesses to personalize their offerings, improve customer satisfaction, and drive customer loyalty.
  • Real-time decision-making: provides businesses with the agility they need to respond to changing market dynamics, optimize digital customer experiences, and seize opportunities in real time.
  • Improved operational efficiency and increased revenue: helps boost operational efficiency, optimize costs, and optimize conversion rates and revenue by automating repetitive and time-consuming tasks.
  • Enhanced competitiveness: helps organizations stay ahead of trends, adapt to market changes, and innovate digital experiences to match the evolution of customer-brand relationships.

Let’s Take AI Analytics Apart!

Dissecting technology is always fun. In this case, it will take us a lot of time to describe the precise inner workings of an artificial intelligence algorithm (and the humble author of this article will have to complete a data science degree) – but let’s try to summarize the main components that make AI analytics AI

Data collection: on the importance of gathering relevant and accurate data

The quality of the raw data directly impacts the insights derived from the artificial intelligence processes. This includes both structured data (such as transactional data, customer demographics, and website interactions) and unstructured data (such as behavioral data and customer reviews).

The accurate and reliable information ensures the validity of the analysis and prevents skewed results. It is essential to establish data governance practices, including cleansing, validation, and integration, to maintain data quality throughout the analysis process.

Data processing: how AI algorithms analyze and interpret data

Once the relevant information is collected, AI analytics uses advanced algorithms to process and analyze the data. AI algorithms excel at handling large datasets, extracting meaningful patterns, and identifying correlations that may not be immediately apparent to the human brain (sometimes, even a team of human brains might not be enough).

During the processing stage, techniques such as natural language processing (NLP), image recognition, behavior data, and sentiment analysis are applied to extract insights from unstructured data sources. 

Machine learning: automation techniques that enhance traditional capabilities

Machine learning algorithms can automatically identify patterns, make predictions, and adapt to changing circumstances without explicit programming. This capability allows us to make the most of historical data to build predictive models and take the data-driven part of data-driven decisions to the next level.

Predictive analysis: can AI forecast future trends and behaviors?

Predictive analytics, a subset of AI analytics, uses historical and real-time data to forecast future trends and behaviors. By applying machine learning algorithms, predictive models can be created for customer preferences, demand patterns, and market trends. We can then take these predictions into consideration to optimize marketing strategies, improve customer targeting, and enhance overall business performance.

Predictive analysis can be a driving force for identifying opportunities, mitigating risks, and optimizing resource allocation. 

Who can use AI analytics?

Anyone. While many people think this is a technology reserved for large enterprises, the democratization of data and technologies makes it possible for even the smallest businesses to take advantage of these promising new technologies.

The obvious use cases would be healthcare (for early disease detection or treatment personalization), FinTech (for fraud detection or customer support enhancement), or retail (for inventory management). But even the smallest SaaS can introduce an element of artificial intelligence – for example, SessionStack’s AI functionality can speed up session replay analysis and provide insights and suggestions for user journey optimization.

Of all the roles within an organization, product managers are likely the ones who will be able to derive the most from AI analytics – both in terms of everyday processes and communication with different stakeholders. If we can predict one thing, it’s that AI will make it much easier for product managers to get buy-in for important new features or resource allocation if they have a set of robust predictions at hand…

How to Use AI Analytics for Growth: Step-by-Step Guide

Incorporating new technologies into your monitoring and management routine is never easy as most tools require a learning curve, but hopefully, these tips will help you align your goals and get the insights you need faster.

Step 1: Align AI analytics with your business goals

You will first need to identify clear business goals and align them with the capabilities of artificial intelligence. Defining specific objectives you want to achieve through analytics (all types), such as improving customer satisfaction, increasing revenue, optimizing operational efficiency, or enhancing product recommendations, will be helpful when you craft your overall analysis strategy. Then, you can align some of these goals with specific analytical initiatives so that you can better prioritize your efforts and measure the impact of AI analytics on achieving the desired outcomes.

Step 2: Choose the right tools and technologies

There are various platforms and solutions available, each with its own strengths and features. Some of them are not necessarily new – for example, Google Analytics introduced predictive analysis some time ago as part of its existing suite. The best way to choose is to take a hard look at what you already have and upgrade/downgrade your stack according to the objectives you set in the previous step.

When choosing, you should consider factors such as scalability, ease of integration, ease of use, and cost.

Did you know that you can also use AI with your UX analytics platform?

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Step 3: Ensure the accuracy, reliability, and ethical use of data

What use is it to add an AI analytics tool to your stack if you don’t have enough data (or it is compromised)? You need to ensure that the data used for analysis is accurate (check whether your setup is up-to-date), consolidated (some organizations’ data is heavily fragmented), and representative of the target audience (segmentation of large datasets can help you get better insights).

N.B. Implementing data governance practices, including privacy and security measures, is essential to protect sensitive data and comply with regulations. Ethical considerations, such as transparency, fairness, and bias mitigation, should also be addressed.

Step 4: Integrate AI analytics into your decision-making workflows

You need to set up mechanisms to operationalize the outputs from day 1 (and make sure they are actually followed). This may involve integrating AI analytics into existing business intelligence systems, creating automated reporting dashboards, or implementing real-time decision support systems. 

What are the challenges and how can we address them?

As with every new technology, this one also poses a set of challenges that we are yet to fully unpack and address – here’s an attempt to summarize the biggest…

  • Data quality and availability

As mentioned earlier, ensuring that data is accurate, relevant, and accessible for analysis can be a significant challenge. You should invest in governance practices and infrastructure to address quality issues in a timely manner and secure the constant availability of reliable data.

  • Skill gaps and talent acquisition

While certain platforms offer out-of-the-box solutions and visualization dashboards, an AI analytics solution might require specialized skills owned by data scientists, machine learning specialists, and AI experts. You may face challenges in acquiring and retaining talent with the necessary skills (building a team of skilled professionals or partnering with external experts can help bridge this gap).

  • Change management and organizational readiness

Implementing new technologies often requires changes to existing processes, workflows, and organizational culture. Resistance to change, lack of buy-in from stakeholders, and inadequate change management strategies can block successful implementation. You should prioritize change management efforts to ensure smooth adoption and help every team member feel comfortable with what’s to come.

Monitoring and measuring the impact of AI data analytics on business outcomes

A set of Key Performance Indicators (KPIs) aligned with your business goals will help you measure whether you’re moving in the right direction. These KPIs can include metrics related to customer satisfaction, revenue growth, cost savings, operational efficiency, and other relevant factors.

To put it in a nutshell…

While AI is still in its infancy, we are already hooked on the benefits it brings. But we need to make sure we’re setting a healthy foundation in terms of data quality and processes, as well as training and preparation for what the future might hold. We at SessionStack are already embracing artificial intelligence and adding it to our UX analytics stack – how about you?

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