Data integration
Connect app and website events with CRM, orders, payments and operational records. Define shared metrics, access permissions and data quality checks.
Data Analytics & AI-Ready Data
Make your business data useful for everyday decisions
technine.io connects data across your apps, websites and business systems, builds clear dashboards, and develops analytics and AI capabilities so your team can understand customer behaviour and identify opportunities to improve operations.
Technology partner, not only IT vendor
A wider lens than IT
We review the system stage, operating needs, architecture, workflow, and support model before shaping a practical roadmap.
Brand and customer experience
Marketing and growth context
Data and AI readiness
Sustainable operations
Team adoption and support
Connect customer activity to what happens in your business
Visitor numbers alone cannot explain repeat purchases, lost enquiries or the cost of serving customers. We connect behaviour data with records from CRM, payment and operational systems, so your team can investigate these questions and decide which system or workflow changes to test.
01
Data integration across apps, websites and business systems
02
Dashboards and visualisation built around team decisions
03
Customer retention, conversion and operational analysis
04
AI-assisted insights linked to source data for review
What we build
We extend new or existing systems with the data connections and reporting your team needs. The scope starts with a business question and the records available to answer it.
Dashboards and visualisation
Build conversion funnels, customer retention views and operational dashboards, with filters and links to the records behind each figure.
Customer and business analytics
Analyse repeat usage, enquiry drop-offs and revenue by customer group. Include agreed cost data when investigating margins or profitability.
AI-assisted insights
Prepare structured data for plain-language questions and change summaries. Link answers to supporting records and metrics for human review; possible causes remain hypotheses to investigate.
Business questions your data can help answer
Use analysis to identify a specific opportunity, make a change and assess its effect. What we can establish depends on the quality and coverage of your data.
Where do customers drop off
Compare the steps from enquiry or registration to booking or payment. Investigate friction and prioritise changes to forms or workflows.
Which customers return
Compare repeat purchases or usage across customer groups and time periods. Use the findings to plan and test onboarding or service improvements.
Where does revenue become profit
Connect sales and refunds with agreed acquisition, product or service costs. Revenue alone does not establish profitability.
What is slowing operations down
Track processing times, repeated exceptions and service demand. Identify where a system change or automation is worth testing.
A reliable path from source records to decisions
Each report needs clear definitions, appropriate access and a way to trace its figures. We build those controls into the data flow and document who maintains them.
Source systems and tracking
Connect business records with sources such as Google Analytics and Cloudflare analytics where relevant. Their metrics differ: HTTP requests are not people and must not be added to analytics user counts.
Data pipelines and quality
Build scheduled data transfers with validation, duplicate checks and retry handling. Document metric definitions, refresh frequency, permissions and data owners.
Reporting and AI access
Provide dashboards and permission-controlled AI access to prepared data. Keep sources and missing data visible so the team can check findings before acting.
Modules for your existing systems
Select the components needed for the first business question, with room to extend the reporting as new needs emerge.
Event tracking
Conversion funnels
Customer retention views
Revenue and cost reporting
Operational dashboards
Data pipelines
Data quality checks
AI-ready datasets
From a business question to a measured change
Start with one decision your team needs to make. Agree how progress will be measured before building the reporting or changing the workflow.
01Define
Choose the question and baseline
Agree the business question, metric definitions and a starting comparison period. Identify missing records and who owns each source.
02Connect
Build and validate the data flow
Connect the required systems, check tracking and reconcile records. Set access permissions and refresh rules before building dashboards.
03Analyse
Investigate patterns with your team
Review customer behaviour and operational trends. Separate observed changes from possible causes, then choose a practical improvement to test.
04Measure
Implement changes and review the effect
Make agreed software or workflow changes and compare results against the baseline. Hand over dashboards, metric definitions and maintenance guidance, with ongoing support scoped as needed.
Example: understanding repeat purchases
Illustrative example using fictional data. This is not a client result or a forecast.
Do customers return after their first purchase?
Connect customer IDs with order dates, payment status and refunds. Group customers by the month of their first completed, non-refunded purchase and allow every customer a full 30 days of follow-up.
| First purchase | Customers | Returned | Return rate |
|---|---|---|---|
| January 2026 | 100 | 24 | 24% |
| February 2026 | 80 | 28 | 35% |
What the figures show
24 of 100 January customers returned, compared with 28 of 80 February customers: 24% and 35%. This comparison alone does not explain the difference.
What the team could do next
Break down the groups by product and acquisition source, and check discounts or changes to onboarding. Choose an improvement to test, then compare equally mature groups and account for other changes before attributing an effect.
FAQ
Frequently asked questions
What data do we need for customer and profitability analysis?
Start with the business question. Retention analysis needs records that distinguish returning customers over time, such as orders, subscriptions or app activity. Profitability analysis also needs relevant costs and an agreed calculation method; revenue alone does not show profit. We assess data coverage and access before defining the scope.
Can we start if our website or app tracking is incomplete?
Yes. We can review the current setup, define the events needed for your business questions and validate new tracking. Existing orders or system records may support some historical analysis. Events that were never recorded cannot be recovered from a new tracking setup, and reports should make those gaps clear.
Can you use our existing CRM, payment and business systems?
We assess the available APIs, exports and database access, then agree which records to connect and how they should match. The integration plan covers permissions, refresh schedules and failed updates. The scope depends on what each system makes available and the quality of its records.
What does an initial project deliver, and is ongoing support available?
We agree the deliverables around a specific business question. An initial project can include a data and tracking review, metric definitions, validated data connections, a dashboard and handover documentation. Optional ongoing support can cover data checks, reviewed analysis and agreed system improvements, with scope and frequency defined separately.
Relevant work
Related client use cases
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Talk through scope, timeline, and next steps
Which business question do you need to answer
Tell us what your team needs to understand and which apps, websites or business systems hold the relevant data.
We can assess the available records and integration requirements, then define a practical first scope for reporting and system improvements.
