How do you leverage data analytics to improve decision-making and drive business value? Can you share a specific example?

Question

Data analytics has become a cornerstone for businesses seeking to make informed decisions and drive value. This article delves into practical strategies for leveraging data analytics, offering insights from industry experts on transforming complex information into actionable intelligence. Discover how to optimize your business processes, from marketing to customer retention, by harnessing the power of data-driven decision-making.

Neil Fried · Answer

For me, it's about turning complex information into insight that shapes strategy and guides decisions in real time. In high-growth markets, there's always a ton of noise, and the key is knowing which signals actually matter. I rely on data to highlight trends that can inform strategic partnerships, reveal new opportunities in tech adoption, or identify inefficiencies that impact sustainability goals. It's not about having every metric tracked but about focusing on the metrics that can move the business forward.
For instance, I was working with a team evaluating potential strategic partnerships across the marketing technology space. By analyzing adoption patterns, revenue growth trajectories, and even engagement with emerging tech tools, we were able to identify partners that not only fit strategically but also aligned with sustainability initiatives and operational efficiency. That data-driven approach allowed us to structure deals where both sides gained measurable value, while also integrating recycling and eco-friendly practices into the operational roadmap. The analytics gave us confidence to move quickly and negotiate from a position of clarity, avoiding costly assumptions. In the end, it's about translating raw data into actionable insight, and making every decision smarter, faster, and more aligned with both business goals and long-term environmental responsibility.

Elmo Taddeo · Answer

I place a significant emphasis on replacing guesswork with facts when making business decisions. Data provides a clear picture of what is happening and what might happen next. Examining past numbers establishes the baseline, delving deeper explains why something occurred, and forecasting aids in planning ahead. The final step is deciding on the appropriate action. Each stage reduces uncertainty and brings clarity to the choices we face.
At Parachute, I've witnessed the value firsthand. We once noticed a spike in support calls during specific times of the year. Descriptive analysis revealed the pattern, while diagnostic analysis showed it was tied to seasonal client expansions. Predictive models helped us plan staffing needs in advance. Prescriptive insights then guided us to adjust scheduling and training. That shift improved response times and client satisfaction, while also keeping costs under control.
A helpful example outside our company is Netflix. They analyze what people watch, when they watch, and even what they search for but don't play. These insights help them produce hit shows, personalize recommendations, and run targeted campaigns. The lesson is simple: collect the right data, ask the right questions, and apply the answers to both short-term actions and long-term strategy. Any business can start small with this approach and grow its decision-making confidence over time.

Taylor Humphries · Answer

For me, data analytics is less about dashboards and more about unlocking cultural precision. The numbers only matter if they tell a human story. At Ranked, we use analytics to understand not just who engaged, but why they engaged.
A clear example came during our campaigns with Roku. Instead of measuring success by impressions, we tracked how different creators, from nano-influencers in local zip codes to larger trendsetters, drove actual conversations and conversions. By analyzing engagement rates and cultural context side by side, we saw that smaller creators often delivered five times higher engagement per follower than bigger names. That insight shifted our strategy, moving more budget toward creators who delivered authentic resonance.
The business value was immediate: higher ROI, stronger brand loyalty, and campaigns that felt less like ads and more like culture. Data gave us the evidence to double down on authenticity, which is the only metric that truly scales.

Kevin Clancy · Answer

In real estate note buying, data analytics drives every aspect of our valuation process. I've built proprietary models that analyze not just property values, but also payment histories, regional economic indicators, and default risk factors across thousands of notes. For example, by cross-referencing county-level foreclosure trends with specific note characteristics, we identified a portfolio of seemingly risky second-lien notes that our data showed had only a 7% probability of default - allowing us to purchase them at discount prices while offering sellers fair value. This data-driven approach means we can confidently acquire notes others avoid, ultimately creating win-win scenarios for note holders who previously couldn't find buyers.

Josiah Roche · Answer

I cut a client's CAC by about 30% in six weeks by tracking performance at the keyword and landing page level. I looked at cost per conversion across campaigns, and saw that a lot of money was going into keywords that drove clicks but almost no good leads. So I paused those terms and put more budget into the ones that were converting. Then I matched those best keywords with better landing page versions, which boosted results even more and dropped CAC from about 140 to just over 95 without raising spend.  
In SEO I had a similar experience. I tracked conversions by blog post and saw that only about 20% of the content was bringing in leads. So once I knew that, I stopped writing broad low intent articles and focused on making more content around the high intent ones. That move grew organic leads by about 35% in a quarter.  
For me, analytics comes down to cutting waste and doubling down on what's already working. When I see clearly where revenue is coming from, decisions get made faster and tie straight to business value.  
Name: Josiah Roche  Title: Fractional CMO  Company: JRR Marketing  Website: https://josiahroche.co/  LinkedIn: https://www.linkedin.com/in/josiahroche

Alec Loeb · Answer

Data drives almost every decision I make. When I started, some locations were outperforming others despite having nearly the same marketing budget. By analyzing transaction data alongside foot traffic insights, we spotted distinct patterns. Some communities engaged more with sustainability-focused offers, while others prioritized quick service and convenience.
We reallocated spend, tested new copy in those regions, and saw immediate lifts. That wasn't theory; it was hard numbers showing us where to lean in. I've carried that approach into every campaign since. For me, the value isn't just in collecting data; it's in translating it into actions the team can actually execute. And when the numbers validate that choice, you build real confidence in the strategy.

Niclas Schlopsna · Answer

At Spectup, I've found that data analytics is most powerful when it reveals counterintuitive insights that challenge business assumptions. In one instance, we helped a B2B SaaS company prepare for Series A funding by analyzing their funnel data, which revealed that their highest Customer Acquisition Cost was directly linked to their lowest retention customer segments. By realigning their marketing spend based on these insights and refining their pitch narrative with data-backed conclusions, we helped the company close an oversubscribed funding round in just six weeks.

Edward Piazza · Answer

Data analytics serves as the foundation for informed decision-making across our organization, enabling us to identify trends and optimize strategies in real time. I particularly value A/B testing for marketing campaigns, as it provides clear comparative data on what resonates with our audience. In one notable instance, we identified an underperforming campaign through careful analysis of testing data, which prompted our team to pivot their approach completely. This data-driven decision ultimately saved significant resources and demonstrated how analytics can transform business outcomes when teams are willing to adapt based on what the numbers tell them.

Casey Ryan · Answer

My engineering background taught me to build systems that track what actually works. For instance, we analyzed SMS campaign response patterns and discovered that texts sent on Tuesday evenings had double the engagement rate. By restructuring our outreach around this insight, we boosted lead conversions by 30% without increasing ad spend - that's pure efficiency translating directly to more homeowner connections and deals closed.

Andrew Dunn · Answer

At Zentro, I leverage data analytics by establishing clear metrics that align with business objectives and regularly reviewing these insights to make informed decisions. A specific example was our analysis of the marketing team's project completion rates and collaboration patterns, which revealed workflow bottlenecks affecting productivity. By tracking these key performance indicators (KPIs) and adjusting our team structure based on data insights, we significantly improved campaign delivery times and saw a measurable increase in employee satisfaction scores.

Piotr Zabula · Answer

Data analytics has been instrumental in our decision-making process, particularly when we analyzed Customer Lifetime Value against Customer Acquisition Cost in our SaaS business. This analysis revealed that mid-sized companies were actually unprofitable for us, leading us to implement a new segmentation-based pricing model. The strategic shift was informed by metrics like Average Revenue Per User and conversion rates, ultimately increasing our profitability by 20%.

Vivian Chen · Answer

We leverage data analytics by implementing AI systems that function as on-demand research analysts, pulling important signals from multiple data streams including customer feedback, competitor activities, and internal performance metrics. This comprehensive approach allows us to make faster, more informed decisions based on a complete picture rather than fragmented insights. The result has been a significant reduction in strategy reversals and a more agile response to market changes. By consolidating these diverse data points into actionable intelligence, we've been able to drive measurable business value through more consistent and confident decision-making.

Justin Smith · Answer

Data doesn't make decisions for you, but it makes your decisions smarter. It shines a light on where your effort creates the most impact.
I see data analytics as a compass. It doesn't replace instinct or experience, but it points you in the right direction with clarity. One example that stands out is when we analyzed usage patterns to see which features our customers engaged with most. The data showed us a few tools were driving the majority of value, so we doubled down on improving those instead of spreading resources thin. That decision not only boosted retention but also accelerated growth because we were focusing on what mattered most to our users.

Carl Fanaro · Answer

At our company, we leverage data analytics by strategically partnering with specialized technology providers to implement advanced analytics solutions that deliver measurable business outcomes. In one case, we collaborated with a tech startup to integrate their analytics platform, which significantly reduced our data processing time by 40%. This implementation not only streamlined our operations but also generated substantial business value, resulting in a 20% increase in revenue and a 15% reduction in operational costs.

Adam Garcia · Answer

Data analytics has become fundamental to our decision-making processes, particularly through implementing machine learning techniques that provide deeper insights than traditional methods. In a recent analytics project, we successfully reduced forecasting errors by 40%, which significantly improved our planning accuracy and resource allocation. This faster, more accurate approach to data analysis allowed our teams to identify market trends earlier and adjust strategies accordingly, creating measurable business value across departments.

Illustrious Espiritu · Answer

For a long time, we were making decisions based on intuition and gut feeling, but it was holding us back. We were leaving a lot of money on the table, and our business was not growing as fast as it should have been. The data was there, but it was siloed. We knew we had to find a way to connect the dots.
The way we leverage data analytics to improve decision-making is by analyzing customer support calls and live chat conversations. The key is to see our data not as numbers, but as stories about our customers' problems.
A specific example is in our content strategy. Our operations team was getting a lot of calls from customers who were having a specific, recurring problem. The old way would have been to just tell them to call us. My new approach was different. Our marketing team used a simple tool to analyze the customer support transcripts. The data gave us a ton of insights that we couldn't get from a traditional marketing report. It showed us that many of our customers were having a problem, but no one was talking about it.
The impact this had was a massive increase in our brand's credibility and profitability. We were able to create content that was a direct solution to that problem. The biggest win is that we learned that the best business decisions are the ones that are based on data.
My advice is that the best way to leverage data analytics is to find a way to get a real, honest look into what's happening in your business. Stop just making decisions based on intuition. You have to be a person who is a problem-solver, and a data-driven approach is a great way to do that.

Nikita Sherbina · Answer

I leverage data analytics by treating it as both a diagnostic and a decision-making tool. Instead of looking at metrics in isolation, I focus on patterns across customer behavior, sales performance, and operational efficiency to uncover insights that actually move the business forward.
One example was with our subscription churn problem. At first, the data showed only surface-level patterns—customers canceling after three months. By digging deeper into engagement metrics, I noticed a sharp drop in feature usage after the first two weeks. That insight shifted our focus: instead of trying to win customers back at cancellation, we invested in an onboarding overhaul with personalized tutorials and automated check-ins during the first month.
The result was a 20% improvement in retention and higher lifetime value per customer. The experience reinforced for me that analytics is most powerful not just in reporting what happened but in asking why it happened and using that answer to shape strategy.

Hillel Zafir · Answer

We use analytics to spot patterns that people miss. One example was rebate liability—many firms underestimated accruals, hurting cash flow. By tracking data in real time, we showed the true exposure and helped leaders plan with confidence. That insight turned what was once a risk into a point of control, giving both stability and stronger growth.

Sara Levy-Lambert · Answer

At Unison, I found that focusing exclusively on key value metrics was essential when managing our $150 million portfolio. By identifying and prioritizing the most impactful data points, we could filter out noise and make decisions based on what truly drove financial outcomes. This targeted approach to data analytics allowed us to maintain clarity in our decision-making process while ensuring we stayed focused on metrics that directly correlated with business value.

Raj Baruah · Answer

Customer success pattern analysis to predict and prevent churn transformed our retention strategy - specifically, identifying behavioral indicators that predicted client cancellation 60-90 days before explicit dissatisfaction, enabling proactive intervention that saved 89% of at-risk relationships.
The Analytics Framework:
I analyzed 18 months of client interaction data including project response times, meeting frequency, scope change requests, payment timing, and communication sentiment patterns. The goal was identifying subtle warning signs that preceded client departures rather than waiting for obvious dissatisfaction signals.
Predictive Pattern Discovery:
The analysis revealed specific behavior combinations that predicted churn with 87% accuracy: declining meeting attendance plus delayed project feedback plus increased scope modification requests within 30-day periods. Individually, these seemed like normal business fluctuations, but combined they indicated relationship deterioration.
Data-Driven Decision Implementation:
Based on these insights, I created an automated alert system that flagged clients showing 2+ risk indicators simultaneously. When alerts triggered, we initiated proactive strategic reviews focusing on relationship health and service optimization rather than waiting for problems to escalate.
Intervention Strategy:
Instead of generic check-ins, we addressed specific concerns revealed through behavioral analysis. If data showed delayed feedback patterns, we streamlined approval processes.
Measurable Business Value:
Retention Improvement: Client retention increased from 78% to 94% because we resolved relationship issues before they became cancellation decisions, maintaining revenue that would have been lost through reactive approaches.
Revenue Protection: The early warning system prevented approximately $340,000 in potential churn during the first year of implementation, far exceeding the analytics development investment.
Relationship Enhancement: Proactive intervention often strengthened client relationships beyond original levels because clients appreciated our attention to their evolving needs and communication preferences.
Strategic Expansion:
Success with churn prediction led to developing similar models for upselling opportunity identification and optimal service delivery timing, creating data-driven frameworks for multiple business decisions.

Dan Taylor · Answer

Data analytics is most valuable when it helps organizations process information at scale and make proactive rather than reactive decisions. During COVID, we worked with several e-commerce and FMCG brands to develop forecasting models that could handle the market disruptions caused by the pandemic. These models allowed companies to analyze trends and prevent knee-jerk reactions during a highly unstable period. This approach ultimately helped those organizations maintain more stable operations despite unprecedented market conditions.

Ydette Florendo · Answer

Data analytics becomes valuable when it translates patterns into action. We used it to address rising overtime costs in one department. By analyzing time-tracking data alongside patient volume trends, we identified scheduling gaps that consistently led to overstaffing on slower days and shortages during peak demand.
Adjusting shift structures based on those insights reduced overtime expenses by 18 percent within a quarter while improving response times for patients. The example reinforced that analytics is not about producing more dashboards but about aligning information with operational decisions that directly affect both cost and quality outcomes.

Matt Lasker · Answer

I utilize data analytics in a way that considers the distinction between surface metrics and actionable insights. It is not about collecting data for the sake of having the most data possible, but about gathering data to ask the right questions and use analytics to make decisions with clarity.
As an example, I worked within outdoor advertising, where my focus was less on looking at raw traffic counts and more on examining dwell times, or how long drivers were actually exposed to a billboard. The data indicated that some mid-traffic corridors could actually be seen better than higher traffic intersections because of the slower speed of the vehicles and the lighter weight of the traffic light cycle. Using that insight allowed me to get into locations that others did not think were valuable placements.
This produced real business value because clients demonstrated improved ad recall, while simultaneously our inventory made a case for being higher quality than other higher traffic count locations, yet the placements were less obvious. The key learning was that analytics provides value when it alters or reframes assumptions about data, so that it has potential competitive advantage instead of simply rehashing the anticipated known.

Justin Mauldin · Answer

When people talk to me about "data," the first thing I think about is a spreadsheet dashboard with a bunch of numbers on it. While that might work for some, to me data is information made clear, a way to slice through noise to focus on, and act on, the information that will make you a better decision maker, quicker.
Analytics play a central role in any PR and operations effort, but specifically looking at the data that shows activity that's actually driving the numbers we care about: PR that's driving qualified leads, campaigns that are turning into customers, or time/money being lost on the front end.
Take the BuildOps $127M Series C, for example. Instead of approaching the release as just a press release, tracking a few phrases across different outlets, geographies, demographics, and audiences told the real story of where the piece gained the most ground. Instead of simply clicks, these numbers represented tangible engagement: customer conversations and recruiting results. More than a PR win, we were able to focus on the future of skilled trades story and actively grow business.
Data to me isn't an after-the-fact report; it's signals that should be built into your strategy as you go. That's where data has business value.

Kristin Marquet · Answer

I leverage data analytics by integrating it directly into my proprietary systems, so decisions aren't based solely on intuition, but on measurable insights. Instead of tracking surface metrics like likes or impressions, I focus on the data that ties back to visibility, credibility, and conversion. For me, analytics isn't just reporting; it's a strategic lens for refining messaging, allocating resources, and scaling what works.
An example: when refining one of our frameworks, I used analytics to measure not just engagement but language resonance. I tracked which headlines, hooks, and phrases in our social and email content drove higher click-through and inquiry rates. The data revealed that phrases around "clarity" and "credibility" outperformed "growth" and "influence." That insight directly shaped how we positioned offers, which increased opt-ins by 30% and shortened sales cycles for PR services. It confirmed that the market responds best when I anchor content in confidence and trust-building—an insight I would have missed if I had relied on intuition alone.

How to Leverage Data Analytics for Better Decision-Making and Business Value

How to Leverage Data Analytics for Better Decision-Making and Business Value

Transform Complex Data into Strategic Insight

Leverage Analytics to Replace Guesswork with Facts

Unlock Cultural Precision Through Data-Driven Storytelling

Proprietary Models Drive Real Estate Note Valuation

Optimize Marketing Spend with Targeted Data Analysis

Translate Data into Actionable Regional Strategies

Challenge Assumptions with Counterintuitive Data Insights

Adapt Strategies Based on A/B Testing Results

Boost Efficiency by Tracking Engagement Patterns

Align KPIs with Business Objectives for Success

Implement Segmentation-Based Pricing Using Data Analytics

Consolidate Data Streams for Agile Decision-Making

Focus Resources on High-Impact Features through Analysis

Partner with Tech Providers for Advanced Analytics

Reduce Forecasting Errors with Machine Learning Techniques

Analyze Support Data to Solve Customer Problems

Uncover Patterns to Improve Customer Retention Strategies

Turn Financial Risks into Points of Control

Prioritize Key Value Metrics for Portfolio Management

Predict and Prevent Churn with Behavioral Analysis

Develop Forecasting Models for Market Disruptions

Align Staffing with Patient Demand through Analytics

Reframe Assumptions About Data for Competitive Advantage

Drive Business Growth with Targeted PR Analytics

Refine Messaging Using Data-Driven Language Insights