If you’re looking for a little respite from the constant changes that have befallen our not-so-humble species, sorry to inform you that this is not going to happen anytime soon.
Artificial Intelligence (AI) is here and it's already turned half our world around – at least for digitalized businesses. That's not bad, but it takes some time to accept.
For product teams whose lives are an endlessly iterative process, this shouldn't be a shock. Or at least we hope so because they will see the wheel turning faster and faster now.
AI data analytics, as a basis for better strategic decisions, will be the driving force behind this quickened pace. In this article, we'll discuss how this happens – from the basics to the nitty-gritty.
What is AI data analytics?
AI data analytics applies AI and advanced algorithms to analyze large amounts of data. With machine learning (ML), it uncovers patterns and insights so you can make the right decisions in every context to improve your product.
AI can automate data processing, identify anomalies, predict outcomes, and offer actionable recommendations in the form of text or graphs. This lifts a significant weight off the shoulders of any product manager and their whole team.
But there’s a catch: the quality of the data is very important.
If you cannot ensure your data is accurate, complete, valid, consistent, unique, timely, and fit for the purpose you’re using it’ll be bad news for your organization. Imagine having to base a business-critical decision on the insights from a dataset that doesn’t meet these requirements.
We might argue that it’s better to operate with partial, clean data rather than unlimited, flawed data.
The basics: 4 pillars of AI data analytics
While it all begins with data collection, AI analytics is about how to process data to extract the gold nuggets that bring tangible value to your bottom line.
Natural language processing (NLP) is a branch of AI that focuses on the interaction between computers and human language. NLP creates the conditions for machines to process and generate human language in a meaningful and useful way.
Product teams can use NLP to evaluate large volumes of customer feedback, reviews, and other textual data to glean information about user preferences and needs. You can also use NLP to automate tasks such as sentiment analysis and categorization of customer feedback. That saves time and improves the team’s efficiency.
Put simply, NLP helps you tune in to the needs and wants of your customers and prioritize which of them are more important than others.
2. Machine learning
Machine learning involves training algorithms to learn from data and make predictions or decisions without explicit programming. They can analyze vast amounts of data and uncover patterns or trends that human analysts may overlook.
Machine learning algorithms have shown their value to product teams. Just like with NLP, you can use them to automate tasks and improve efficiency, but they also reduce costs and detect fraud. Some teams even use them for predictive maintenance of infrastructure.
3. Neural networks
Neural networks are machine learning models trained to replicate the structure and functionality of the human brain. They can process extensive amounts of information to find patterns, trends, and insights that might not be apparent to human analysts. Neural networks excel in tasks such as image recognition and predictive analytics.
This technology advances design iterations and uncovers UX ideas that the human team misses.
4. Deep learning
The “deep” in deep learning comes from the depth of the neural networks used. More than three layers are considered deep.
This type of artificial neural network can be trained to make predictions and decisions and use numerous hidden layers and large amounts of information for image recognition and natural language processing. Deep learning has already revolutionized whole industries by extracting actionable insights from sizable datasets.
Product teams depend on deep learning to improve user experiences, optimize pricing strategies, and sustainably promote business growth.
3 benefits of using AI data analytics for your product team
AI data analytics offers numerous benefits to product teams, although we’re just covering three.
The first is enhanced decision-making through valuable insights and predictive analysis. It also makes it easier to understand customer behavior, preferences, and needs.
Next, AI analytics transform processes by automating repetitive tasks and increasing efficiency and productivity.
Lastly, it provides a competitive advantage by looking out for trends, predicting market demands, and delivering innovative products.
“AI analytics can take the hard work of dredging through vast amounts of data and come up with short summaries that hold the essence of the dataset.”
Mariya Ivanova continues, “Then, a model trained to spot specific patterns can help team members with recommendations for further analysis (segmentation and funnel analysis recommendations), as well as UX and UI tips that serve as a starting point in devising product iterations and even new features.”
AI algorithms can analyze large amounts of data – many times larger than even the largest team of human analysts.
The algorithms identify patterns and correlations, lifting decision-making to a whole new level. Imagine what a product manager could do with a few strategic optimization tips as opposed to staring blankly at a monstrous Excel spreadsheet with rows upon rows of raw data.
Understanding your customers gives you the chance to develop a product roadmap tightly following your target audience’s needs without any stray features or components that were supposed to be great but...aren't.
What’s more, real-time performance tracking provides continuous improvement feedback so you can pivot more easily and way faster. Automating manual data analysis tasks saves time and resources, allowing you to focus on strategic initiatives.
Creating a robust product strategy and roadmap based on the right KPIs will be easier. Hopefully, it’ll be just as easy to meet and exceed your objectives.
2. Improved efficiency and productivity
With AI data analytics, product teams can set up a machine to do repetitive and time-consuming tasks.
For a busy product manager, this means more time to focus on more strategic activities, while for an engineer it might mean faster turnaround of tasks. AI also helps optimize product development processes by identifying areas for improvement, such as UX bottlenecks or infrastructure inefficiencies.
At the end of the day, knowing your clients leads to the development of more customer-centric products without the need to expend as much effort on customer interviews, surveys, or usability tests.
3. Enhanced user experience
Neglecting to take the time to get to know their customers in detail is a major mistake that many small businesses make.
The extraordinary insights into user behavior and trends that AI brings afford you a better understanding of your target audience while using fewer resources.
This is an important step in product development because the intimate knowledge of user pain points informs feature prioritization decisions, marketing strategy, and sales initiatives. Apart from traditional analytics being augmented by AI to provide quantitative insights, qualitative data sources such as session replay also benefit from the input of AI in the form of summaries and UX optimization tips.
All of these benefits come together to help you better meet customer needs and stay ahead of the competition no matter what.
Top industry use cases that showcase the power of AI analytics
As with any new technology, the first question you might ask is, “How do I use this?”
Let's name just a few of the many AI use cases for product teams.
Predictive analytics: Forecasting customer behavior and trends based on historical data and open-source industry data can save you tons of costly assumptions about products and features that are worth pursuing.
Quality assurance: Identifying patterns and anomalies in product data helps engineers address quality issues proactively and saves valuable resources from costly bugs that show up in production.
Market research: You can use AI analytics to gather and analyze market data to find deeper insights for product development and strategy at every stage – from ideation and launch to optimization.
Competitive analysis: Product teams can track the competition’s performance and market trends to stay ahead.
Pricing optimization: Paired with the other use cases, analyzing pricing strategies and customer behavior puts companies on the road to maximum profitability.
Let’s also look at some industry-specific use cases.
AI analytics empowers SaaS product teams to get the most out of their platforms by exploring user behavior and preferences.
As a SaaS product manager, you can pinpoint the features your customers care about the most so you can focus your development efforts. Recognizing trends in customer usage data is the basis of many business-critical decisions, and having an automated scanning system in place that also recommends actionable items in real time is mind-blowing.
Not only that, but you’ll be able to integrate AI capabilities into various workflows, like personalization algorithms and targeted marketing campaigns.
AI data analytics is already transforming the fintech industry by pushing for more accurate financial forecasting and more efficient risk management.
In customer service, AI-powered chatbots enhance the overall experience by providing personalized assistance at all times. Lastly, AI models can be trained to flag potential fraud, identifying suspicious transactions for further investigation much more quickly than human verification efforts.
AI-powered recommendation systems can personalize product suggestions, thus increasing conversion rates and sales.
“A critical element of personalization is building better data and insights on customers, an asset that also generates additional value across the value chain. Our research suggests the ROI for personalization will quickly outpace that of traditional mass marketing.” - McKinsey & Company
Additionally, AI analytics draws on customer feedback and reviews to present product managers with suggestions for targeted enhancements.
In this fast-paced market, AI can be the cutting-edge tool that gives you a competitive advantage.
Healthcare is one of the most promising fields around this topic. Feeding AI algorithms with medical data such as x-rays, CT scans, and biopsy slides helps spot trends or shifts in specific populations that will inform preventative activities.
This type of analysis can also be used in case-by-case scenarios to find medical conditions before they manifest physically. The result is better outcomes, especially with threats like cancer or cardiovascular diseases.
Bonus use case: managing cloud and infrastructure costs
Cloud costs are not cheap. You’re probably aware that their prices tend to sour any growth when you haven’t included them in your calculations. If that’s your situation, use AI analytics to identify areas of inefficiency or waste and optimize spending.
Analyzing raw data from cloud usage is an AI specialty that offers cost-saving recommendations like rightsizing resources or implementing reserved instances.
How to start using AI analytics in your product workflow
The process is straightforward, but you’ll need to pay attention to which factors are appropriate for your business.
You need to identify which areas will benefit the most. The UX optimization part? The resource improvement aspect? Customer support? Sales? All of them?
Then, research and select a tool that suits your business needs and goals. Be mindful of how well the tool you choose integrates with your data sources and whether you’ll need to increase your spending as you grow. Figure out if you’ll be able to course correct if you decide to choose a new vendor.
Next up, practice with small datasets. When you integrate the AI analytics tool into your workflow, best practices suggest that you first practice with small datasets that can be easily verified by your data analysts. That way, you’ll know that everything runs smoothly before you move to large-scale operations.
Finally, don’t forget to train your team. Your team must know how to effectively use and interpret the insights generated by the AI analytics tool. As much as we’d like to go all-in on AI, it’s not a useful idea to forego the expertise of well-trained people.
Should your team worry about redundancies?
Data analysts are probably not overly concerned about becoming redundant in the near future. They know the intricacies of the tasks at hand and understand the limitations of AI models, but what about other jobs?
Some junior positions on product teams or people who handle repetitive tasks might become obsolete at some point, but this is what automation has always been about – and it’s been around for quite some time.
AI is far away from substituting human intelligence and as far as analytics is concerned, you wouldn't want to leave it up to AI to make strategic decisions anyway. The worst thing that can happen is you forget what a spreadsheet looks like. Awful, right?
Riding the wave or drowning: It's up to you
AI data analytics is revolutionizing product teams by providing them with powerful tools to extract value from their mixed bag of data and make better-informed strategic decisions.
Industries such as SaaS, fintech, and e-commerce are already reaping the benefits of AI data analytics and many others are getting up to speed.
To stay ahead, embrace AI analytics in your product team’s workflow. From engineers to product managers, the quality of the team’s output can improve based on the insights an AI tool generates. It’s up to you to decide whether you’ll be left behind when this tide of innovation rolls out.
Elena Doynova is a digital marketing professional with a penchant for blogging and social media, currently focusing on building the marketing strategy for
SessionStack - a leading session replay vendor venturing into the world of AI.
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