7 ways to use data analytics to improve your business.
Apr 10, 2026
Summary:
- Data analytics turns raw data into decisions by showing what happened, why it happened, what is likely next, and what to do about it.
- Descriptive, diagnostic, predictive, and prescriptive analytics each answer a different question, and digital experience analytics adds how customers actually experienced each journey.
- Seven practical uses include workflow efficiency, cybersecurity, operations, customer experience, market trends, marketing measurement, and business strategy.
- Customer results show how much time disappears between spotting a problem and fixing it.
- Analytics pays off when it is tied to business goals and focused on high-impact journeys, then shared across departments and tested continuously.
Updated September 28, 2026: This guide has been significantly expanded to cover not just the seven ways to use data analytics, but the types of analytics that make each one possible. New sections walk through descriptive, diagnostic, predictive, prescriptive, and digital experience analytics, each with a real customer example showing what the approach produced in practice. The seven use cases have been updated with new data from IBM's 2026 Cost of a Data Breach Report and Quantum Metric's 2026 benchmarks on AI-referred traffic and customer feedback gaps. A new strategies section has also been added covering how to align analytics with business goals, focus on high-impact journeys, reduce time from insight to action, break down data silos, and build a continuous testing loop.
A checkout server sat blacklisted for 23 months at one company before anyone connected the dots. The error had been dismissed as a general platform issue, and it kept costing revenue the whole time.
Data analytics is the practice of turning raw data into evidence a business can act on. Done well, it replaces gut feel, from operations to the customer experience.
Each of the seven ways below comes with evidence from IBM research and Quantum Metric's 2026 benchmarks, plus customers from telecom to healthcare.
What is data analytics in business?
Data analytics in business is the process of collecting, analyzing, and interpreting data to understand performance and guide decisions.
At its core, data analytics gets decision-makers past assumptions. Instead of relying on intuition or a single metric, leaders can see how operations, customer behavior, and digital experiences are actually performing.
Analytics also creates clarity when many factors move at once. Traffic sources shift. User behavior evolves. A small issue can affect performance at scale. When analytics works, the picture is clear: what changed, what likely caused it, and what to do next. That progression turns raw data into something actionable.
Digital environments raise the stakes. Even minor friction in a user journey can cause a measurable drop in conversion, engagement, or retention. Analytics makes those moments visible and shows what each one costs before anyone chooses a fix.
The six-step data analysis process sits inside that discipline as the method analysts follow, from defining a question to communicating what the findings mean.
What are the different types of data analytics businesses use to drive better decisions?
The four main types of data analytics are descriptive, diagnostic, predictive, and prescriptive, and digital experience analytics adds a fifth lens focused on how customers experience each journey.
Used together, the types move an organization from reacting to performance to improving it.
Descriptive analytics.
Descriptive analytics helps businesses understand what happened by analyzing historical data and performance trends.
Descriptive analytics answers the first question anyone asks when something changes: what actually moved? Instead of scanning dashboards or pulling multiple reports, you get a clear view of where performance shifted and by how much.
Every other type of analytics builds on this one. Skip it and you risk solving the wrong problem or chasing noise.
How descriptive analytics improves business performance.
Descriptive analytics gives everyone a shared source of truth. Stakeholders stop debating the data itself and start deciding what to do about it.
Example in practice.
Close to 35% of Vueling's sessions were loading slower than the airline's own benchmark, and sessions with a faster load time converted at three times the rate. Quantum Metric puts the value of fixing slow pages at a multi-million-euro scale. The same comparison works for any drop in conversion: descriptive analytics shows exactly when it started, which segments were affected, and how the numbers compare with normal performance.
Diagnostic analytics.
Diagnostic analytics explains why performance changes by identifying root causes in the data.
Once a change is identified, the next question is what caused it. That question stalls most organizations. Diagnostic analytics replaces manual guesswork by evaluating many variables at once and surfacing the likeliest drivers.
Digital environments make this harder because changes rarely come from a single source. Releases, traffic shifts, device differences, and user behavior all interact at once.
How diagnostic analytics improves business performance.
Diagnostic analytics shortens the gap between detection and action. Less time goes to investigating and more to fixing, so each issue costs less.
Example in practice.
Conversion dipped right after Six Flags added ticket insurance to its checkout. Session replay showed why: guests kept clicking Continue without choosing Yes or No, and the error message that followed disrupted the flow and led to abandoned carts. A Six Flags manager took the replays to the insurance partner, who fixed the problem within 24 hours. Conversion recovered, and Six Flags avoided an annual opportunity cost of at least $1.8 million.
Predictive analytics.
Predictive analytics forecasts what is likely to happen next by applying historical data and patterns to current trends.
Most analytics look backward. Predictive analytics looks ahead. It identifies the patterns that signal what is coming and buys time to prepare instead of react.
Predictive analytics is especially useful where small changes compound quickly, such as high-traffic digital experiences or seasonal demand cycles.
How predictive analytics improves business performance.
Predictive analytics makes early action possible. Change costs less when it comes sooner, and fewer surprises reach customers.
Example in practice.
Customers at one top 20 bank tended to fail the same workflow several times before they contacted support. That pattern works as an early warning, and it let the bank reach out before the call came. Support tickets for key workflows dropped 10%.
Prescriptive analytics.
Prescriptive analytics recommends what to do next, based on what the data shows.
Knowing what happened and why only goes so far. The payoff comes from knowing what to do about it, and prescriptive analytics connects insight directly to action.
Prescriptive analytics ranks changes by expected impact, so effort stays off low-value improvements.
How prescriptive analytics improves business performance.
Prescriptive analytics builds confidence to act. It reduces hesitation and improves prioritization, so effort goes to the changes most likely to drive results.
Example in practice.
Fatal checkout errors were pushing lululemon customers to abandon their carts, so digital leaders ranked the biggest areas of opportunity and fixed them in order. Its chief digital officer says that work has had a revenue impact in the tens of millions of dollars.
Digital experience analytics.
Digital experience analytics shows how customers actually experienced a journey by analyzing real user behavior across digital platforms.
Traditional reporting shows what users did. Digital experience analytics adds how they experienced it, capturing real interactions across sessions and revealing the friction and confusion that reporting misses.
Instead of aggregated metrics, you see how issues affect real users in real time.
How digital experience analytics improves business performance.
Digital experience analytics connects behavior to business impact. Analysts can find the moments that drive conversion or cause drop-off, then fix those first.
Example in practice.
During peak holiday traffic, a checkout error kept resurfacing at Highlights for Children and customer complaints climbed with it, so Highlights used session replay to watch how direct mail leads moved through the order form and compared the replays of affected customers with backend logs. One of its three checkout servers had been blacklisted by its order management system for 23 months. That error was worth multiple millions of dollars.
The value comes from what people do with what they see. Knowing what happened, why it happened, and what to do next only matters when it changes a decision.
How to use data analytics to improve your business.
Data analytics improves a business when it is aimed at a real problem, from operational efficiency to customer experience.
The data usually exists already, so the real work is turning it into action before performance suffers. Each of the seven ways below answers one question: how quickly can an organization get from insight to action?
Below are seven ways data analytics can improve business performance:
- Optimize workflows and efficiency
- Enhance cybersecurity measures
- Streamline operations with data-driven insights
- Enrich customer experiences with analytics
- Monitor market trends for business growth
- Measure marketing campaign performance
- Inform business strategies with data insights
1. Optimizing workflows and efficiency.
Workflow gains start with seeing where work slows down and where automation can take over manual tasks. Data analytics provides that view. From there, bottlenecks get easier to find, processes get easier to streamline, and people are freed for higher-value work.
The same data shows how each group is performing. Managers can use it to coach people and build a healthier work environment.
The first hour of an escalation at one top five telecom provider often went to gathering context and agreeing on what had happened. Support, product, and engineering each held a different piece of the picture. The company embedded session replay links directly inside its Salesforce cases, so everyone sees the same customer evidence in one click. Manual case troubleshooting fell 50%, and more than 500 employees across product, engineering, service, and retail now use the workflow.
2. Enhancing cybersecurity measures.
Businesses use data analytics to spot threats earlier and direct security effort where it counts. Speed matters here. IBM's 2026 Cost of a Data Breach Report found that the global average breach now costs $4.99 million, up 12% and a record high, while AI-driven attacks grew 56%. Organizations that used AI and automation extensively in security saved $1.93 million compared with those that used none.
By pairing data capture with analysis, organizations can strengthen their security in three ways:
- Experience alerts: Real-time alerts flag unusual user behavior that may point to a technical error or a security incident, letting responders act before the risk spreads to users.
- Predictive analytics: Comparing current activity with historical baselines helps security staff anticipate likely threats and put preventive measures in place before an incident occurs.
- Data analysis: Analysis of logs, network traffic, and user behavior surfaces anomalies and potential vulnerabilities, and shows where to focus security resources first.
3. Streamlining operations with data insights.
Data analytics streamlines operations by showing how the business actually runs, so wasted steps and inefficiencies become visible and fixable at the source.
Data visualization presents findings clearly enough to speed up decisions, while descriptive analytics shows the current state of operations and diagnostic analytics points to the root causes of inefficiency. Together, they point to practical actions for improving how the business functions.
The data analysis process makes that repeatable. Working from the framing of the question through to communicating the finding turns operational data into streamlined workflows and moves people off slow manual ones.
At a top 20 bank, the payoff was speed. Diagnosis used to depend on delayed screen shares and vague customer descriptions, and a single fix could take one to two days. With shared visibility into customer sessions and Felix AI, average issue resolution time dropped from more than 24 hours to 15 minutes. Nobody waits on a screen share to agree on what happened anymore.
4. Enriching customer experiences with analytics.
Customer experience improves once the people running it know what customers need, where they struggle, and which changes will raise satisfaction. Data analytics supplies all three. It also makes personalization practical by tying interactions to real behavior instead of guesses.
Here are three ways data analytics can enrich customer experiences:
- Data mining: Searching customer data for preferences and behavior patterns supports personalized recommendations and targeted campaigns.
- Analyzing data: Reading interaction logs, transaction histories, and feedback together shows where customers get stuck and what they ask for most.
- Identifying trends: Analytics surfaces emerging shifts in customer preferences early, so businesses can adapt their offerings before demand moves.
Listening is a common gap. Quantum Metric's 2026 Customer Voice Benchmark found that half of consumers haven't given a brand direct feedback in the past six months. It also found that 56% have left a brand that kept ignoring the feedback they did give. Pairing feedback with behavioral data shows what customers experienced without waiting for a survey response.
5. Monitoring market trends for business growth.
Data analytics helps businesses monitor market trends by collecting and analyzing data on customer preferences and industry shifts. It also exposes competitor and market gaps that a strategy can be built around, and statistical analysis supports lower-risk decisions on product development, marketing campaigns, and business strategy.
AI-assisted discovery is a current example. Quantum Metric's 2026 AI Experience Benchmark Report found that 1 in 5 consumers now start their search on an AI platform, and AI-referred traffic is growing about 111% year over year. The same research found that 98% of consumers will buy again from an AI-recommended brand after a positive experience, but they are twice as likely to abandon after a single instance of friction.
6. Measuring marketing campaign performance.
Measuring a campaign means connecting its activity to results: which messages, channels, and experiences actually drive conversions. Analyzing the data each campaign generates shows how well it worked and informs the next round of decisions.
Here are three data analytics techniques for measuring marketing campaign performance:
- Set KPIs: Define key performance indicators (KPIs) that match your marketing goals, such as conversion rate, click-through rate, or customer acquisition cost.
- Gather descriptive data: Collect data on your target market, distribution channels, and industry trends to understand the context your campaigns run in.
- A/B testing: Test variations of ads or content to find the messaging and visuals that perform best.
Highlights for Children tracked conversion rate, units per transaction, bounce rate, and average order value for its 2024 fall gift direct mail campaign. It then used session replay to see where visitors struggled in the order form, and conversion for that campaign rose 6.4% after Highlights acted on what it found.
7. Informing business strategies with data insights.
Business strategy gets sharper when data from many sources feeds one clear picture of customers, markets, and campaign results. Analytical methods turn large datasets into conclusions leaders can act on, and that supports precise decisions, new product ideas, and a better customer experience.
Combining sources takes real work. Wyndham Hotels & Resorts brought Datadog, Medallia, Adobe Analytics, Adobe Target, Amperity, and UserTesting data together into a single source of insight. Embedding session replay links in Datadog logs let its staff correlate backend issues with user frustration in real time. Mean time to resolution fell from 60 to 30 minutes, and Wyndham found six persistent issues behind more than 45,000 daily console errors.
Data analytics strategies for improving business performance.
Data analytics strategies turn insight into execution by focusing on how data is used to prioritize work and improve experiences.
Insights only pay off when someone acts on them fast enough to matter. Here is what to consider when building and applying a data analytics strategy in your business.
- Align analytics with business goals
- Focus on high-impact journeys and metrics
- Reduce the time from insight to action
- Break down data silos across teams
- Continuously test, learn, and iterate
Align analytics with business goals.
Analytics works best when it is anchored to a clear objective such as increasing conversion, reducing churn, or improving customer satisfaction. Without that anchor, time goes to metrics that do not influence outcomes.
About 2 million people a year use the sign-in page that opens 20 digital platforms for the American Medical Association (AMA), and it was frustrating and losing them. AMA's customer experience group counted how many abandonments came from login frustration, and the count decided which fixes came first. Sign-in abandonment fell 67%, membership sales rose 12%, and auto-renewal rose 51%.
Focus on high-impact journeys and metrics.
Not every metric deserves equal attention. Focus on the journeys that directly influence revenue and retention, such as checkout, onboarding, and account access.
A small error in its online booking form was stopping about 2.5% of reservations at Radisson Hotel Group. Fixing it brought failures under 1% and raised annualized revenue. Removing unnecessary steps from front-desk check-in also cut at least 51 seconds from each transaction, all of it spent in front of a guest.
Reduce the time from insight to action.
In many organizations, insights sit in dashboards longer than they should. Every day between discovery and response raises the cost of a problem.
A digital issue at BMO used to take a day or more to fix. It now fixes them in under an hour, and the time to identify customer frustrations fell 50% in the first year. The gap is common. Quantum Metric's 2026 Customer Voice Benchmark found that only 1 in 4 digital leaders have daily access to recent customer feedback, and 52% have no formal process for acting on it.
Break down data silos across teams.
Product, marketing, engineering, and customer experience groups often work from different datasets and perspectives. When data is siloed, a decision in one area can quietly cause a problem in another.
Engineers at Alaska Airlines receive tickets with replay links, so nobody has to reproduce a problem before fixing it. Product, engineering, support, the call center, and design all work from the same session replays, and call center agents find a caller's session by mileage number to replay it. Helpdesk agents log issues with a linked replay and use a "see more like this" feature to report how many guests hit the same problem. An eCommerce manager there says the platform helps product teams decide where to prioritize.
Continuously test, learn, and iterate.
Analytics works as a loop: identify an opportunity, test a change, measure the result, and repeat.
Monthly releases replaced years-long development cycles at the American Medical Association, and insights from digital monitoring now feed straight into its agile sprints. Platform errors fell 76% and its digital audience grew 140%.
Use data analysis and digital analytics from Quantum Metric.
Quantum Metric's digital analytics platform connects digital experience data to business outcomes. You can see where customers struggle and what each problem costs, then fix it before it spreads. Customers use it to trace a failed checkout, a broken sign-in, or a stalled booking back to the exact session where it happened.
If you want to see it with your own data, request a demo and explore how digital experience analytics can help you improve conversion and decide faster.
See how Quantum Metric brings it together in practice.
Frequently asked questions about data analytics in business.
What is the main purpose of data analytics in business?
The main purpose of data analytics in business is to help decision-makers understand performance, spot patterns, and make informed decisions. By analyzing data, organizations can see what is working, where issues exist, and how to improve outcomes across operations and customer experiences.
What are the four main types of data analytics?
The four main types of data analytics are descriptive, diagnostic, predictive, and prescriptive analytics. Each type answers a different question, from what happened to why it happened and what actions to take next, and together they give a more complete view of performance and decision-making.
How do businesses use data analytics to improve performance?
Businesses use data analytics to identify inefficiencies, understand customer behavior, and optimize key processes. For example, Vueling found that close to 35% of its sessions loaded slower than its own benchmark and that faster sessions converted at three times the rate. Other common uses include improving digital experiences, refining marketing strategies, and resolving performance issues before they affect revenue or customer satisfaction.
What is the difference between data analytics and digital experience analytics?
Data analytics is the broad practice of analyzing data to understand trends and performance across a business. Digital experience analytics is a specialized branch that focuses on how people interact with websites and apps, capturing behavior such as clicks, errors, and hesitation to show where journeys break down.
What challenges do businesses face when using data analytics?
Common challenges include data silos, slow analysis, and difficulty turning insights into action. Wyndham Hotels & Resorts, for example, had to integrate Datadog, Medallia, Adobe Analytics, Adobe Target, Amperity, and UserTesting to pull fragmented data into one source of insight.