#95. Build Software That Does More Than Serve Users — Build Software That Learns From Them
For many businesses, software begins with a simple objective:
Make something easier.
Accept bookings.
Process payments.
Connect buyers and sellers.
Manage customers.
Track inventory.
Deliver a service.
Generate invoices.
Allow people to submit requests.
And there is absolutely nothing wrong with that.
Good software should solve a problem.
But if that is all your software does, you may be leaving one of its most valuable capabilities unused.
Because every time someone interacts with your software, something else is happening in the background.
They are creating data.
They are telling you what they want.
They are showing you what they ignore.
They are revealing what they are willing to pay for.
They are showing you where they live.
They are showing you which products or services interest them.
They are showing you what time they are most active.
They are showing you which features they repeatedly use.
They are showing you where they abandon the process.
They are showing you what they search for.
They are showing you what they compare.
They are showing you what frustrates them.
They are showing you what your business should probably build next.
The question is:
Is your software learning anything from all of this?
That is where the difference between ordinary software and strategically valuable software begins.
Software Should Not Only Process Transactions
Imagine you own a marketplace connecting customers with service providers.
A customer visits your platform.
They search for photographers.
They filter by location.
They view five profiles.
They contact two providers.
They eventually book one.
Most founders would consider the job completed.
The software worked.
The customer found a provider.
The transaction happened.
But from a business intelligence perspective, the platform may have learned much more.
It may now know:
- Which location generated the request
- Which service category attracted the customer
- Which price range received the most attention
- Which profiles received the most clicks
- Which vendors converted views into enquiries
- Which search filters customers use most frequently
- Which categories generate enquiries but few successful matches
- Which locations have high demand but low supply
One transaction can therefore produce dozens of useful signals.
Now imagine that happening 10,000 times.
Suddenly, you do not simply have a marketplace.
You have a growing intelligence system.
And that intelligence can become one of the most valuable assets in your company.
Data Accumulation Changes the Quality of Your Decisions
Many businesses operate by instinct.
They ask questions such as:
“What should we advertise?”
“Who should we target?”
“Which feature should we build?”
“Which city should we expand into?”
“What product should we promote?”
“Which type of customer is most valuable?”
And very often, the answer is based on opinion.
The founder thinks one thing.
The marketing team thinks another.
The sales team has another theory.
The loudest person in the meeting sometimes wins.
But once your software has accumulated enough structured data, you no longer need to depend entirely on opinions.
You can look at what users are actually doing.
That difference is enormous.
Your software can begin telling you:
“Most paying customers come from this demographic.”
“This location has the highest conversion rate.”
“This service receives three times more searches than the others.”
“People who use this feature are more likely to become paying customers.”
“Customers in this segment spend more.”
“Users frequently search for something we currently do not offer.”
“This step causes the highest abandonment rate.”
“This category is receiving demand faster than supply is growing.”
Those are not guesses.
Those are business signals.
And businesses that learn to recognise those signals can improve much faster than businesses that rely only on intuition.
Your Real Target Audience May Be Different From the Audience You Imagined
This is one of the biggest advantages of accumulating data.
When launching a software business, founders usually begin with assumptions.
You may believe your ideal customer is between 25 and 35 years old.
You may believe most customers will come from Lagos.
You may believe small businesses will be your biggest users.
You may believe one particular industry will generate most of your revenue.
You need assumptions when starting.
You cannot launch without them.
But assumptions should eventually be replaced by evidence.
After thousands of users interact with your platform, the software may reveal something completely different.
Perhaps users aged 40 to 50 convert better.
Perhaps Abuja customers spend more.
Perhaps medium-sized businesses retain longer than very small businesses.
Perhaps a secondary industry you barely considered becomes your fastest-growing customer segment.
Without data, you might continue advertising to the audience you believe wants your product.
With data, you can advertise to the people who have already demonstrated that they want it.
That difference can dramatically improve marketing efficiency.
Your Software Can Tell You What to Advertise
Many businesses treat advertising as something separate from software.
The software team builds the product.
The marketing team runs advertisements.
But the two should be deeply connected.
Your software should help your marketing team understand what deserves advertising investment.
Imagine an online marketplace with 20 service categories.
Without data, the company might divide advertising spend equally across all categories.
But the platform’s data might reveal:
- Category A receives the most searches
- Category B generates the highest transaction value
- Category C converts visitors into paying customers most efficiently
- Category D has enormous demand but too few suppliers
- Category E attracts many visitors but very few purchases
Now advertising becomes much more intelligent.
You might advertise Category A because demand is already strong.
You might promote Category D to recruit more suppliers.
You might investigate Category E before spending additional money.
You might create separate campaigns for customers with different buying patterns.
This is how software data begins influencing marketing strategy.
Advertising becomes less about guessing what people might want and more about amplifying patterns that already exist inside your business.
Every Search Is Market Research
One of the most underrated sources of business intelligence is search behaviour.
When someone searches inside your application, they are telling you what they want.
That is incredibly valuable.
Imagine thousands of users repeatedly searching for a feature, product, location, category, service, or keyword that your platform does not currently support.
That is not merely a failed search.
It might be a product roadmap.
If 2 people search for something, it may mean nothing.
If 20 people search for it, it becomes interesting.
If 2,000 people search for it, you should probably investigate.
Your users may be telling you exactly what they want you to build next.
Companies sometimes spend enormous amounts of money conducting market research while ignoring the market research happening inside their own software every day.
Every Abandoned Process Is Also Data
Businesses naturally focus on successful actions.
Completed purchases.
Successful registrations.
Bookings.
Subscriptions.
Payments.
But unsuccessful actions can be equally informative.
Suppose 10,000 people begin creating an account.
Only 6,000 complete registration.
The question should not simply be:
“How many registrations did we get?”
A better question is:
What happened to the other 4,000 people?
Where did they leave?
Was the form too long?
Did they encounter an error?
Were they asked for information they were uncomfortable providing?
Did they reach the pricing page and change their minds?
Did the payment process fail?
Did they struggle to understand the next step?
When your software captures these behavioural signals properly, failure becomes useful.
You can begin identifying friction.
And removing friction is often one of the fastest ways to grow a software business.
Your Best Customers Leave Clues
Not every customer has the same value.
Some users register and disappear.
Some use the platform occasionally.
Some become regular users.
Some become paying subscribers.
Some upgrade repeatedly.
Some refer other customers.
Some become extremely valuable accounts.
Your software should help you understand what makes these groups different.
For example, perhaps customers who upload at least three products within their first week are significantly more likely to become paying subscribers.
That is an important discovery.
Instead of merely waiting and hoping new users upgrade, your onboarding system can encourage them to add products immediately.
Perhaps customers who invite a second team member are more likely to remain subscribed.
Your software could encourage collaboration.
Perhaps customers who use a particular report are significantly less likely to cancel.
You could introduce that report earlier during onboarding.
This is what it means for software to learn.
The product begins revealing behaviours that correlate with success.
And once you understand those behaviours, you can design the software to encourage more of them.
Data Can Tell You What to Build Next
One of the most expensive mistakes in software development is building features nobody truly needs.
A founder gets an idea.
The team spends three months developing it.
The feature launches.
Almost nobody uses it.
The problem was not necessarily poor engineering.
The problem may have been poor prioritisation.
The company built from imagination instead of evidence.
A data-aware software business approaches product development differently.
Instead of asking only:
“What would be cool to build?”
It asks:
“What are users repeatedly trying to accomplish?”
“What features receive the most engagement?”
“Where are users getting stuck?”
“What requests appear repeatedly?”
“What manual processes are customers performing outside the software?”
“What are users searching for that does not yet exist?”
“Which features correlate with higher retention?”
“Which features generate revenue?”
Now your development roadmap becomes connected to real customer behaviour.
That does not mean data should make every decision.
Vision still matters.
Creativity still matters.
Founder intuition still matters.
But data gives those instincts something extremely valuable:
evidence.
Software Data Can Reveal New Business Opportunities
Sometimes the most valuable discovery is not improving your current product.
It is discovering another business entirely.
Imagine operating logistics software.
Over time, you notice thousands of customers repeatedly shipping products between two specific cities.
That pattern could justify launching dedicated logistics capacity on that route.
Imagine running a property platform and discovering that users repeatedly search for properties within a certain price range in a particular area.
That could influence partnerships, financing products, advertising, or expansion.
Imagine operating business management software and discovering that thousands of customers repeatedly struggle with collecting overdue invoices.
That might justify building payment reminders, automated collections, credit services, or financing tools.
The opportunity may already exist inside your data.
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You simply need software capable of revealing it.
The Bigger the Dataset, the More Interesting the Questions Become
During the early stages of a product, data may appear relatively simple.
You might track:
Registrations.
Logins.
Payments.
Purchases.
Subscriptions.
But as usage grows, much richer questions become possible.
Which user behaviours predict conversion?
Which actions predict churn?
Which customer segments generate the highest lifetime value?
Which locations are growing fastest?
Which services are becoming more popular over time?
Which products are usually purchased together?
Which acquisition source produces the most profitable customers?
Which customer behaviours happen before an upgrade?
Which activities frequently happen before cancellation?
Which categories experience seasonal demand?
Which features attract users but do not generate revenue?
The answers become increasingly valuable as the dataset grows.
This is one reason data accumulation can create a powerful competitive advantage.
A new competitor may copy your interface.
They may copy your pricing.
They may copy your features.
They may even hire excellent developers and build something technically impressive.
But they cannot instantly copy years of accumulated behavioural knowledge about your customers and market.
That knowledge takes time to build.
This Is Where AI Becomes Much More Powerful
Artificial intelligence gets far more interesting when it has meaningful business data to work with.
Without useful data, AI is often just another interface.
With useful data, AI can become an intelligence layer over your business.
Imagine asking your software:
“Which customer segment grew fastest this quarter?”
“Which service should we advertise next month?”
“Why are customers cancelling?”
“Which users are most likely to upgrade?”
“Which locations are showing increasing demand?”
“What products are frequently purchased together?”
“Which customers should our sales team contact today?”
“What unusual changes happened this week?”
“Which users appear likely to churn?”
“Summarise the biggest behavioural changes in the last 90 days.”
Now AI is not simply generating text.
It is helping you interpret your own business.
That is a far more powerful application.
First Build the Data Foundation, Then Add Intelligence
There is an important mistake businesses should avoid.
Do not rush to add AI before building the data foundation.
An AI system cannot magically understand information your software never collected.
If important events are not tracked, AI cannot analyse them.
If customer records are inconsistent, the conclusions may be unreliable.
If information exists across disconnected systems with no structure, analysis becomes difficult.
If historical data was never stored, you cannot suddenly recreate years of user behaviour.
This is why data architecture should be considered early.
Your software should intentionally capture meaningful information from the beginning.
Not everything.
More data is not automatically better data.
The goal is not to collect information simply because you can.
The goal is to collect information that helps you understand:
Users.
Demand.
Supply.
Transactions.
Behaviour.
Conversion.
Retention.
Revenue.
Performance.
And opportunities.
Build Dashboards That Answer Business Questions
Another mistake businesses make is collecting enormous amounts of data without making it usable.
The database may contain millions of records.
But management still cannot answer simple questions.
That is not enough.
Your software should translate raw data into understandable business intelligence.
A useful dashboard should answer questions such as:
How many new users joined?
Where did they come from?
What are they doing?
Which features are growing?
Which customer segments generate revenue?
Where are transactions happening?
Which categories are increasing or declining?
Where are customers abandoning the process?
Which acquisition channels are producing valuable customers?
What changed compared with last week, last month, or last year?
Executives should not need to understand database queries to understand their businesses.
The software should make the answers visible.
Start Thinking in Events, Not Just Records
Traditional business software often focuses mainly on storing records.
Customer created.
Invoice created.
Order completed.
Payment received.
Those records matter.
But modern software should also understand events.
A customer viewed a product.
A customer searched for something.
A customer abandoned checkout.
A customer returned after 30 days.
A customer opened a feature five times.
A customer attempted payment but failed.
A customer changed a filter.
A customer invited another user.
A customer downloaded a report.
A customer upgraded.
A customer downgraded.
A customer stopped logging in.
These events tell the story behind the records.
And when those events accumulate across thousands or millions of users, powerful behavioural patterns begin to emerge.
Data Should Improve the Customer Experience Too
There is another important point.
Your data strategy should not only benefit your company.
It should make the software better for the customer.
For example:
A marketplace can recommend more relevant service providers.
An ecommerce platform can show better product recommendations.
A financial application can highlight unusual spending patterns.
A business management platform can warn customers about overdue invoices.
A logistics application can predict delivery delays.
A learning platform can recommend lessons based on progress.
A recruitment platform can improve candidate matching.
A real estate platform can recommend properties based on search behaviour.
A booking platform can suggest available times based on previous patterns.
The software becomes increasingly personalised.
That creates a better user experience.
And better experiences often lead to higher retention.
But Data Must Be Collected Responsibly
Building data-driven software does not mean collecting everything possible about everyone.
Businesses must take privacy seriously.
Users should understand what information is being collected where appropriate.
Sensitive information should be protected.
Access should be controlled.
Security should be designed into the system.
Data should be collected for legitimate purposes.
Companies should follow the privacy and data-protection requirements that apply to their markets and users.
Trust matters.
A business can spend years building customer trust and destroy it extremely quickly through irresponsible data practices.
So the objective should never be:
Collect everything.
The objective should be:
Collect the right information, protect it properly, and use it to create legitimate value.
Think About the Software You Are Building Today
If you are planning a new software product, ask yourself something beyond the usual feature questions.
Do not ask only:
Can customers register?
Can customers pay?
Can customers create profiles?
Can administrators manage users?
Can we send notifications?
Can customers place orders?
Also ask:
What will this system learn after 1,000 users?
What will it know after 100,000 transactions?
What patterns will we be able to identify?
Which customer behaviours should we capture?
What information could improve our marketing?
What information could improve the product?
What information could help us understand our most valuable users?
What information could reveal new markets?
What information could help AI become useful later?
That changes how you think about software architecture.
You stop building only for today’s transaction.
You begin building for tomorrow’s intelligence.
The Most Valuable Software Gets Smarter With Usage
A basic software product can remain almost exactly the same whether 100 people use it or one million people use it.
Users enter information.
The system processes it.
The system returns an output.
But a more strategic software product becomes more useful as its dataset grows.
Every new customer contributes another signal.
Every search provides another clue.
Every transaction strengthens your understanding of the market.
Every interaction helps you understand behaviour.
Every year of operation gives you historical context your competitors may not have.
Eventually, the software becomes more than infrastructure.
It becomes institutional intelligence.
Your Database Is Not Just Storage
This is perhaps the most important mindset shift.
Your database is not simply the place where your application stores information.
It can become the memory of your business.
It remembers:
What customers wanted.
What they purchased.
What they ignored.
What they searched for.
What worked.
What failed.
What changed.
Where demand increased.
Where demand disappeared.
Which experiments succeeded.
Which customers stayed.
Which customers left.
Over time, that memory becomes extremely powerful.
But only if you design your software to learn from it.
Build for Today, But Design for What You Will Know Tomorrow
When building software, it is natural to focus on immediate functionality.
You need customers to register.
You need the payment system to work.
You need the dashboard.
You need the marketplace.
You need the booking engine.
You need the mobile application.
Those things matter.
But the smartest founders think one level further.
They ask:
What will this software teach us after people start using it?
That is where extraordinary long-term value can emerge.
Your software should serve customers today.
But every interaction should also help you understand tomorrow.
It should help you identify your real audience.
It should help you decide where advertising money belongs.
It should help you understand what customers truly want.
It should help you discover where your product is failing.
It should help you decide what to build next.
It should help you identify opportunities before they become obvious.
And eventually, it should provide the foundation for intelligent automation and AI systems that understand the unique reality of your business.
The businesses that build this way will have something much more valuable than software.
They will have software that continuously teaches them how to build a better business.
And that may become one of the strongest competitive advantages they can own.
