Data Analysis & Reporting

What do we solve with AI data analysis?

In most businesses the data is not missing; it is scattered. Sales sit in the e-commerce panel, customer details in the CRM, visitor behavior in Google Analytics and stock in an Excel file, and nobody can see them in the same table. Our data analysis service starts by fixing this scatter: we bring the sources together with automated flows, build reliable dashboards and use AI on top of this clean data for interpretation and forecasting.

The aim is not to produce more charts but to make visible the patterns a business would struggle to spot on its own: which customers are about to be lost, which product’s demand has changed, which branch is behaving unusually. For that, the data first has to flow by itself, then be reliable and only then be interpreted.

Time spent collecting numbers

• Most of the time a team sets aside for reporting goes to collecting numbers, and only a small part to interpreting them.
• By the time the report is ready, the data is out of date; a problem is seen only weeks later.
• Small mistakes in tables merged by hand flow into decisions unnoticed.
• Nobody has time to look at relationships between numbers, such as the effect of stockouts on sales.

Example questions by sector

Data analysis helps most with the questions a business asks every day but can answer only with a guess. A few examples from different sectors:

• E-commerce: Which products are bought together, and which customers disappear before placing a second order?
• Cafe and restaurant chains: Which products sell at which hours by branch, and does the staffing plan match demand?
• Manufacturers and B2B companies: Which dealer or customer group’s orders are falling, and which deals are lost at the quote stage?
• Clinics and service businesses: How many appointments actually take place, and which services are chosen again?

What these questions have in common is that the answer requires combining data from more than one system. The first value of a data analysis project often comes from this merging itself.

Scope of this service

The work we describe on this page is infrastructure and analysis covering all of a business’s operational data: sales, customers, stock and website behavior. Measuring the return of ad campaigns and the split of the marketing budget across channels is a separate field of expertise; we cover it in our marketing analytics and reporting service. The two projects can share the same data infrastructure.

Data infrastructure: bringing sources together in one place

Solid data analysis rests on a solid data infrastructure. At this stage we map out which systems hold which data and make sure this data flows to a shared place regularly and automatically. When manual copying goes away, you save time and reduce the margin of error.

Data inventory: first, see what exists

We start by listing which data is kept where, who updates it and how far back it goes. This inventory shows what is ready and what is missing for data analysis. Sometimes the most important finding is information that is never recorded at all; for example, where the customer heard about you or why a quote was lost. Adding such gaps to recording processes increases the depth of data analysis in the months that follow.

Sources that can be connected

• E-commerce platforms: Order, product, return and campaign code data.
• CRM systems: Customer records, quote stages and the sales team’s notes.
• Google Analytics 4: Visitor behavior, traffic sources and conversion events.
• ERP, accounting and point-of-sale systems: Invoices, stock and store sales.
• Messaging and call records: Requests coming from WhatsApp, Instagram or phone channels.
• Spreadsheets: Targets, budgets and manual records not kept in any other system.

Whether a source can be connected is mostly decided by whether it offers an API. If there is no API, we consider alternatives such as working with regularly exported files or a direct read-only connection to the database.

Automated data flows

We build flows that pull the data at set intervals, transform it and write it to a shared data store. For small and medium-sized businesses, workflow tools such as n8n or small integrations we write can be enough for this job. As data volume grows, a data warehouse such as BigQuery becomes more suitable; Google Analytics 4 data can also be exported to BigQuery and examined at the raw event level.

We add error alerts to the flows as well. When a source’s connection drops or the expected data does not arrive, you should see this as a clear warning, not as a missing number on the dashboard.

Analysis-ready data

The least visible but most labor-intensive part of the data analysis process is getting data ready for analysis. The same customer kept under different records, sales history broken by changed product codes and fields left blank mislead even the best model. We clean these problems up one by one and put the definitions of the metrics to be used in writing together with you.

• Stockouts: Sales that do not happen while a product is off the shelf do not mean demand fell; these periods need to be flagged separately in forecasting models.
• One-off large orders: A corporate bulk purchase can distort averages and trends.
• Product code changes: Opening the same product under a new code resets its history; old and new codes need to be matched.
• Campaign periods: Putting discounted weeks on the same footing as ordinary weeks makes normal demand look higher than it is.

Dashboards and automated reporting

Once the data comes together, we carry data analysis results into dashboards that decision makers can read easily. These dashboards are built not to run daily operations but to see trends and analysis findings; each screen aims to help a specific person find the answer to a specific question.

Moving a manually prepared report to automation

Most businesses have a report that is prepared by hand every week or at the end of each month. When we move this report’s logic to an automated flow, we run the two reports side by side for a while, and only after confirming the numbers match do we drop the manually prepared one. When a difference appears between the numbers, it often reveals a mistake in the old report that no one had noticed or a rule that was never written down. This comparison also builds the team’s trust in the new data analysis infrastructure.

Which dashboards should be built first?

Building everything at once leads to a system full of screens nobody opens. That is why we recommend starting with a few core dashboards:

1. Management summary: Sales, profitability indicators, and the numbers of new and returning customers.
2. Customer behavior dashboard: The size of segments and the movement between them, repeat purchase rate, cohorts.
3. Demand and forecast dashboard: Sales trends by product group, forecasts and actual figures.

After these three dashboards settle into daily use, the detailed analyses you need are added. We also track whether the data analysis dashboards are really used and simplify screens that go unopened.

Reporting rhythm

Not all information is needed at the same frequency. Operational data such as stock and orders is more meaningful with a daily view, sales and customer trends with a weekly view, and profitability and segment shifts with a monthly view. Organizing data analysis outputs around this rhythm keeps the team from drowning in unnecessary notifications.

Choosing a tool

For most businesses, ready-made dashboard tools are a good start. Google’s free Looker Studio connects directly to sources such as Google Analytics, Google Ads and Sheets; other sources may need extra connectors or an intermediate data layer. Tools such as Power BI are also often preferred in corporate settings.

If the dashboard has to work inside your own software, with your own user permissions, ready-made tools may not be enough. In that case we consider developing a custom panel for you under admin panels and reporting.

AI data analysis: behavior, forecasting and interpretation

AI data analysis creates value when it is added on top of a clean, well-ordered data infrastructure. A model fed with scattered and faulty data presents wrong results in a more convincing voice. That is why we always set up the AI layer after the infrastructure and definitions are settled.

Behavior analysis and segmentation

One of the methods often used in data analysis to understand customer behavior is RFM analysis: customers are grouped by how recent their last purchase was, how often they buy and how much they spend. That way you see loyal customers, those at risk of being lost and one-time buyers separately. Cohort analysis, in turn, shows how customers acquired in the same period behave over time.

Products bought together, steps abandoned on the website and the time customers take to place a second order are also among the topics of behavior analysis. To turn these findings into concrete actions, we can pass the segments to personalized marketing flows.

Customers at risk of churn

A regular customer stepping outside their habit is often the first sign before they are lost. A model that looks at past order intervals can calculate the expected next purchase time for each customer and flag those who clearly exceed it. The list that comes out of this data analysis is a concrete starting point that shows whom to reach and when.

Forecasting models

We build models that estimate the demand, sales or stock needs of the coming weeks from past data. This kind of AI data analysis needs a history that is long enough and consistent; to see seasonality, it asks for data covering at least one full year, and generally a few years of history. Campaign periods, price changes and stockouts also need to be introduced to the model separately.

In Turkey, Ramadan and Eid al-Adha move about eleven days earlier on the calendar each year, so the effect of these periods needs to be modeled by the holiday dates rather than by a fixed month. We present forecasts not as a single number but as a likely range; a forecast is not certainty but a probability that helps you decide.

On the stock side, a forecast makes sense when it is considered together with lead time. When a product’s demand for the coming weeks is put next to the supplier’s delivery time, it becomes clearer when and how much to order. Here data analysis turns into a decision tool that directly affects cash flow and empty shelves.

Tracking the model’s accuracy

Once a forecasting model is built, it should not be left on its own. Within AI data analysis, we regularly compare forecasts with actual figures and look into the periods where the deviation grows. A new sales channel, a change in pricing policy or a widening of the product range may require the model to be retrained.

Anomaly detection

Noticing unusual changes is one of the most practical contributions of AI to data analysis. When a category’s sales drop below the expected range, when errors rise on a certain payment method or when a page’s traffic suddenly collapses, the system flags it. That way your team focuses on a few topics that need attention instead of searching through thousands of rows. Flagged cases can be passed to the relevant person by email or through the messaging channel your team uses.

Analyzing customer reviews and messages

An important part of data is text, not numbers: product reviews, support requests, survey answers and WhatsApp or Instagram messages. AI data analysis can classify these texts by topic, separate positive and negative statements and bring out the most frequently repeated complaints. That way you can see in numbers how topics such as shipping delays or size mismatches rise and fall over time.

We test the accuracy of the classification by checking randomly chosen samples by hand. Because texts may contain personal information such as names, phones or addresses, this information needs to be removed before analysis.

Summaries and questions in natural language

Large language models can be used to turn the numbers on a dashboard into a short written summary or to answer questions such as “Which branch’s return rate went up last month?”. However, these models can make mistakes in calculation and can describe a relationship that does not exist as if it did. That is why we do the calculations with queries and formulas and have AI interpret only the calculated results; the numbers a summary is based on can always be seen on the dashboard.

In practice, this means a manager starts the week by reading a summary of a few paragraphs before opening the dashboard: the standout changes, unusual movements and topics that need attention. AI data analysis is built so that, while preparing this summary, every sentence shows the number it rests on; that way the summary is not an opinion but a verifiable shortcut.

Data security and KVKK

When data analysis works with customers’ personal data, legal obligations come up as much as technical matters. You can find guides and announcements about Personal Data Protection Law No. 6698 (KVKK) on the Turkish Personal Data Protection Authority (KVKK) site. The headings below are for general information; we recommend assessing the practice together with your legal counsel.

• Data minimization: Not moving personal information that is not needed for analysis into the data store at all.
• Using pseudonyms: Replacing information such as name, phone and email with a customer number in the analysis.
• Access permissions: Deciding from the start who can see which dashboard and which level of detail.
• Location of data: Knowing where the data store and the AI service in use process data.
• Provider terms: Checking whether the AI service keeps the data sent to it and whether it uses it for model training.

Before you start: preparation and scope

What we need from you

• The list of systems you use and read-access to each of them.
• Examples of the reports you prepare today and the decisions made with them.
• Questions you struggle to find the answer to.
• Your targets, budgets and seasonal campaign calendar.
• The people who will use the dashboards and what each of them needs.

What determines the scope

The scope is determined mostly by the number of sources to connect, whether those sources offer an API, how clean the data is and how many different dashboards will be prepared. Forecasting and behavior models are a separate layer of work; for most businesses it is healthier to start with infrastructure and dashboards and add the AI data analysis layer once the data has become reliable.

Common mistakes

The mistakes we see most often in data analysis projects are not technical but related to planning. The main ones are these:

• Starting with the tool: Choosing a dashboard tool before it is clear which question needs an answer.
• Mistaking sales for demand: Building a forecasting model without separating the periods of stockouts.
• Trusting an AI summary blindly: Making decisions based on the interpretation without checking the number it rests on.
• Building a model on dirty data: Forecasts built on uncleaned data give false confidence.
• Setting it up and leaving it: As business processes change, data flows and definitions need to be updated too.

Frequently asked questions

Do you need to be a big company for data analysis?

No. A business with a single e-commerce site and an accounting program also benefits from seeing sales, customer and stock data on one dashboard. The scope scales with the number of sources and the depth you need.

Does AI data analysis make my decisions for me?

No. AI flags unusual changes, produces probabilistic forecasts and interprets numbers; you make the decision with this information. Forecasts are not certainty but ranges that help you decide.

Which dashboard tool will we use?

That depends on your sources, your budget and who will use the dashboards. For most businesses working with Google sources, Looker Studio is a good start; for dashboards that need to work inside your software, custom development may be needed.

How much historical data does a forecasting model need?

To see seasonality, you need data covering at least one full year; a few years of consistent history gives more reliable results. If the data is short or irregular, it is more sensible to start with infrastructure and reporting.

What is the difference between data analysis and marketing analytics?

Data analysis covers all of a business’s operational data, such as sales, customers, stock and website behavior. Marketing analytics focuses on measuring the return of ad campaigns and channels; the two projects can use the same data infrastructure.

Is our data sent to AI services?

It is sent only as far as needed and, as far as possible, under pseudonyms. We assess together before setup which service will be used, where the data will be processed and what the provider’s retention terms are.

Let’s talk about your data analysis needs

Tell us which reports you prepare today and how, and which questions you struggle to answer; let’s work out together your sources, what the first dashboards should show and where AI can contribute. A few example reports you already have are enough for a first call. You can write to us through our contact page.