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[00] Data Analytics & Insights

Data Analytics Consulting Services That Turn Your Data into Decisions

Our data analytics consultants answer specific business questions with the data you already hold. Why did margins fall in one region? Which customers are likely to leave? How much stock will the next peak need? We clean the data, analyse it, forecast where it helps, and explain the answer in plain terms with the checks behind it.

Book a free consultation What we deliver
Case study35% higher conversion rate for a fashion and lifestyle e-commerce brand

built for

  • Founders and leadership teams
  • Operations and supply chain heads
  • Marketing and e-commerce teams
  • Finance and risk teams
  • Healthcare, logistics and manufacturing businesses
analysis.run

key facts

open tooling, so your team can rerun every analysis
Python & SQL
every forecast tested on data the model has not seen
Held-out data
each model compared with a simple rule, so you see what it adds
vs baseline
code, notebooks, models and documentation handed over
You own it

What we deliver

What you get with Data Analytics & Insights. Nine parts of the service, each scoped with you.

We start with the question, not the tool. A short discovery agrees what decision the analysis should support, which data exists and whether it is good enough. If it is not, we say so and suggest what to collect first, rather than building a model on weak foundations.

Forecasts and predictive models are tested on data they have not seen before anyone relies on them, and we report how far off they were. You receive the code, notebooks and documentation, so your own team can rerun and extend the work.

group.01 · Plan & prepare03 tools

Start from the decision, then check the data

We agree the question the analysis must answer, look at what your data can and cannot show, and clean it with rules you can rerun.

Discuss it with us
  1. t.01

    Data strategy

    We assess your current data, tools and skills, identify where analysis would pay off and recommend which technology to invest in, and which to skip.

  2. t.02

    Exploratory data analysis

    A structured first look at your data: distributions, gaps, outliers and relationships, written up so you know what the data can and cannot tell you.

  3. t.03

    Data cleansing and preparation

    Duplicates removed, formats standardised, missing values handled and sources joined, with every rule documented so the cleaning can be repeated.

group.02 · Analyse & predict04 tools

Separate real effects from noise

Statistical tests, forecasts and predictive models for sales, stock, customers and markets, each checked against a simple baseline.

Discuss it with us
  1. t.04

    Statistical analysis

    Regression, significance tests and cohort analysis that separate real effects from noise, such as whether a price change actually moved sales.

  2. t.05

    Predictive models and forecasting

    Time series forecasts, regression, decision trees, random forests and neural networks for demand, revenue, churn and risk, chosen to suit your data.

  3. t.06

    Customer analytics

    Segmentation, lifetime value and churn analysis from purchase and behaviour data, to personalise products, offers and marketing.

  4. t.07

    Market and competitor analysis

    Market trends, pricing and competitor activity combined with your own sales data, to inform product and pricing decisions.

group.03 · Share & sustain02 tools

Put answers where decisions are made

Results go into the dashboards and apps your teams already use, with ownership and quality rules that keep them trustworthy.

Discuss it with us
  1. t.08

    Dashboards and embedded analytics

    Executive, financial, operational and HR dashboards, and analytics embedded in your customer-facing apps, built from the models we develop.

  2. t.09

    Data governance

    Ownership, definitions, quality checks and access rules for your key data, so analysis stays trustworthy after the project ends.

Discuss your project

which approach fits

Match the method to the question.

Most questions need careful analysis, not a complex model. We start with the simplest method that answers the question, and only build a model when it clearly does better than a simple rule.

  1. Why a number moved

    Its history, plus what changed around it: prices, campaigns, deliveries

    Driver analysis and significance tests, before any model

  2. Whether a change actually worked

    Results before and after, ideally with a group that did not get the change

    An A/B test or a controlled before-and-after comparison

  3. How much you will sell or need

    A year or more of history, with promotions and holidays marked

    A time series forecast, measured against last year's figures as a baseline

    Predictive analytics, explained

  4. Which customers will leave or buy again

    Customer records, orders and usage over at least a year

    Segmentation first, then a churn or propensity model if it pays off

  5. What to recommend to each customer

    Purchase and browsing history

    A recommendation engine, tested against best-sellers

    Recommendation engines

  6. How to score every case, every day

    Labelled history and a system the score can feed

    A production machine learning model, monitored for drift

    Machine learning development

  7. What happened last month

    Figures you already report

    A dashboard, not an analysis project. We will point you there.

    Business intelligence

Why it matters

What changes once it is live. Each outcome is measured, not promised.

Data Analytics & Insights is planned around these outcomes, measured against a baseline we agree with you up front.

Discuss your project
  1. 01

    Decisions based on evidence

    Pricing, stock and marketing choices are backed by analysis of your own data rather than the loudest opinion in the room.

  2. 02

    Risks spotted earlier

    Churn signals, cost overruns and slow-moving stock show up in the data before they show up in the accounts.

  3. 03

    Forecasts you can check

    Every model comes with its measured error on held-out data, so you know how much weight to give it.

  4. 04

    Skills that stay with you

    Documented code and walkthroughs let your team maintain and extend the analysis without depending on us.

results

What changed for a fashion and lifestyle e-commerce brand

From our published case study: results within six months.

result.01Fashion and lifestyle e-commerce

35%higher conversion rate
  • Mid-sized company
  • Multiple regions
Read the case study
  1. ckpt_01$68 → $921average order value
  2. ckpt_0222% → 41%1customer retention
  3. ckpt_0371% → 49%1cart abandonment

input the challenge

High traffic but low conversion: generic recommendations, poor product discovery, high cart abandonment and few repeat purchases.

output what we delivered

A custom machine learning recommendation engine that personalises products in real time from browsing, search, wishlist and purchase history.

  • Python
  • TensorFlow
  • Machine learning
  • Recommendation algorithms
  • Big data analytics
  • Cloud infrastructure
  • API integration

source: fashion and lifestyle e-commerce case study

How we work

How we deliver Data Analytics & Insights

A clear five-step process, so you always know what happens next and what you will get.

Every project starts with a free, no-obligation consultation.

Start with discovery Call us
run.logappsinai / project

$ appsinai run --pilot-first

  1. Define the question

    time 1–2 weekscost free

    We agree the decision the analysis must support, the data available and how the answer will be judged.

    you get agreed question · data check · fixed quote

    we need the decision you are trying to make · a sample or description of your data

  2. Audit and prepare the data

    time 1–2 weekscost fixed price

    We profile each data set, fix quality problems and join sources, flagging anything that limits what the analysis can show.

    you get data quality report · documented cleaning rules · joined data set

    we need read-only access or an export of each data set · someone who knows what each field means

  3. Analyse and model

    time 2–4 weeks

    Exploratory analysis comes first, then statistical tests or predictive models where the question calls for them.

    you get exploratory findings · tests or models · notebooks and code

    we need time to review early findings

  4. Prove it and explain it

    Models are tested on held-out data and compared with a simple baseline, and findings are presented in plain language with their limits.

    you get measured error vs baseline · findings walkthrough · go / no-go

    we need the people who will act on the findings

  5. gatego / no-go on the results before “Deploy and support”

  6. Deploy and support

    time ongoingcost monthly support plan

    Models go into production or dashboards, are monitored for drift and are retrained as new data arrives.

    you get model or dashboard in use · drift monitoring · retraining

    we need access to the systems the results feed

↺ results feed the next improvement cycle$

Security & control

Your data, handled with care

Analytics often touches customer and financial records. We agree what data is used, where it is processed and when it is deleted before any work begins.

Ask us about security
  • 01

    Only the data we need

    We ask for the fields the question requires and pseudonymise personal data where we can, so names and contact details rarely leave your systems.

  • 02

    Work in your environment

    Where you prefer, analysis runs inside your cloud account or on your servers, with our access limited to that project.

  • 03

    NDAs and access controls

    We sign an NDA before you share any data, and access is limited to the named people working on your project.

  • 04

    Deleted when we finish

    Copies of your data are deleted at the end of the engagement, and we confirm it in writing.

where this fits

Analysis turns clean data into answers. Labelled rows in, findings out.

Good analysis needs good inputs, and its findings need somewhere to live. We can collect and label the data first, and turn what we find into dashboards your managers will use.

  1. stage.01 · collect

    Data collection & web scraping

    Exports from your own systems, plus public or partner sources gathered into clean, structured rows.

    out → raw rows
  2. stage.02 · label

    Annotation & labelling

    People tag text, images and records to an agreed guide, and a second reviewer checks the hard cases.

    out → labelled rows
  3. stage.03 · analyseyou are here

    Data analytics

    We clean and profile the data, find what actually predicts the outcome and spot gaps before anything is built on it.

    out → findings
  4. stage.04 · report

    Business intelligence

    Dashboards your managers will open, showing the numbers the work is meant to move.

    out → dashboards

FAQ

Questions about Data Analytics & Insights

Straight answers to what buyers ask us most. Can't find what you need?

Email sales@appsin.ai or call +91-9025555584.

Ask us directly
What does a data analytics consultant do?

We help you turn a business question into an analysis, prepare the data, run the analysis or build a model, and explain what it means for the decision in front of you. We can also set up the data strategy and governance that keeps analysis reliable.

How is data analytics different from business intelligence?

Business intelligence reports what is happening through dashboards and agreed KPIs. Data analytics looks for why it happened and what is likely next, using statistics and predictive models. The two share the same data, and we often build both.

What data do we need to get started?

Usually the data you already have: sales, orders, customer records, website or app events, finance and operations data. Forecasting needs enough history to show patterns, often a year or more for seasonal businesses. The discovery step tells you whether your data is enough.

What if our data is not good enough?

The discovery step tells you before you pay for analysis. If key fields are missing or the history is too short, we say so and suggest what to start collecting, or a narrower question your data can answer now.

How long does an analytics project take?

Defining the question and checking the data takes one to two weeks, and a first set of findings usually two to four weeks more. Forecasts and models that go into daily use take longer, and the quote sets that out milestone by milestone.

How accurate will the forecasts be?

That depends on the data and how predictable the thing you are forecasting is, so we do not promise a number up front. We test every model on data it has not seen, compare it with a simple baseline and tell you its measured error before you use it.

Which tools do you use?

Mostly Python and SQL, with libraries such as pandas and scikit-learn, and Power BI, Tableau or custom dashboards for presenting results. We can work in your existing stack, such as Databricks, BigQuery or Snowflake, if you have one.

Can you work alongside our in-house team?

Yes. We can lead a project end to end, or join your analysts and engineers as a dedicated team, sharing code and reviews so the knowledge stays in-house.

How much does a data analytics project cost?

It depends on the number of data sources, how much cleaning they need and whether the result is a one-off analysis or a model in daily use. After the discovery step you get a fixed quote or a dedicated team proposal.

Estimate your project cost

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