How a data analytics consultancy gets recommended by AI

A data analytics consultancy gets recommended when an assistant can verify you somewhere other than your own site, and the only public registers available in this trade are the cloud and platform partner directories. Unlike an auditor or an empanelled security firm, you have no regulator, so a verified partner listing plus a published method and a published price is what stands in for one.

What buyers of a data analytics consultancy actually ask an assistant

Three buyers. A business head or finance controller who cannot get one reliable number. A CIO replacing a reporting stack. A founder who wants to add something intelligent to a product and has discovered there is no data foundation underneath it.

• "Our accounting system and our CRM give different sales numbers, how do we fix that"
• "Power BI implementation cost in India"
• "How long does it take to build a proper sales dashboard"
• "Should we hire an analyst or use a consultancy"
• "Snowflake, Databricks or BigQuery for a mid sized company"
• "Can someone pull reporting data out of Tally"
• "Who owns the code and the models when the project ends"

Notice that the first question is a business problem with no technology in it. Most consultancy websites are written for the last question instead of the first.

Which sources the assistants read for this trade

Platform partner directories. The Microsoft, AWS, Google Cloud, Databricks and Snowflake partner finders are structured, dated and verifiable, and they are the closest thing to a register in this trade.

Review directories and software marketplaces, which carry both your services and the tools you implement.

Vendor documentation and learning sites, which an assistant prefers whenever the question is about a tool rather than about a supplier. You will not outrank them and should not try.

Technical writing, talks and community answers, which is where an assistant learns that your consultants do the work themselves.

Professional network pages, for team size, certifications and locations.

The three fixes that matter most here

Get into every partner directory you genuinely qualify for, and keep the listing current. Name your certifications and their issuing platform. Do not state a count of certified consultants unless you can show the list.

Publish a scoped starter engagement. What it covers, what you deliver, how long it takes and what it costs. Most enquiries in this trade die on "it depends", and a fixed, small, dated first step is both a better sale and a quotable fact.

Write one page per source system you actually connect. Tally, Zoho Books, SAP, Salesforce, Shopify, a bank statement feed, a hospital or school management system, a warehouse system. Indian buyers search by the system they are stuck with, and almost nobody has written those pages. This is the largest open gap in the category.

Add a page on data handling: where data sits, who can see it, what happens at the end of the engagement, and how you work with the Digital Personal Data Protection Act 2023. That question now arrives from legal rather than from IT.

How to measure it

Ask three sets of questions blind on two or three assistants: problem shaped questions in business words, tool comparison questions, and source system questions. Score them separately, because you will probably be absent from the first, invisible behind vendor documentation on the second, and able to win the third quickly.

Read the citations. If the tool questions are answered entirely from vendor documentation, that is normal and not a gap to chase. If the source system questions are answered by nobody in particular, that is your opening.

Disclosure: we sell AI Knows Us, which runs this measurement on a schedule, and we cannot guarantee anybody a position in an AI answer.

A worked example of a source system page

This is the largest open gap in the category, so it is worth showing in full. Take one page about reporting from a widely used Indian accounting system.

Start with the problem in the buyer's words: the accounts team closes the month in the accounting system, the sales team keeps its own tracker, and the two numbers never match. Then describe the mechanics: where the data lives, whether it is on one machine or on a server, what can be exported and in what form, what an interface can read if one exists, and which fields matter for reporting, such as the voucher type, the ledger grouping, the cost centre, the tax fields and the party master.

Then name the five problems everybody hits: the same customer entered under three different names, ledger groupings that do not match how management thinks about revenue, entries posted to the wrong period and corrected later, credit notes and discounts recorded inconsistently, and branches maintaining separate company files. For each one, say what the fix is and whether it is a data fix, a process fix or a mapping decision.

Finish with what the finished state looks like: a nightly extract, a mapping table maintained by the finance team, one agreed definition of revenue written down, and a dashboard that reconciles to the trial balance. Say who has to own the mapping table, because it will not be you.

No platform's documentation will write that page, and no consultancy has. It is the most quotable page in your trade.

What this page does not cover

It does not cover competing with vendor documentation on tool questions. When somebody asks how a particular function works, the vendor's own documentation is the right answer and will be used. Do not spend effort there.

It also does not cover data science research or model building as a research activity. That is a different sale with different buyers and different proof.

And it does not fix the thing that most often kills these projects, which is that nobody inside the client company owns the definitions. A consultancy can write the definitions down. It cannot make a finance head and a sales head agree, and a page that pretends it can is selling a fantasy.

Common questions

How do we publish pricing when every project is different?

Publish one small, fixed, scoped engagement with a price and a duration, for example an assessment that maps the sources, documents the definitions and delivers one working dashboard. Then say what a larger programme depends on. A fixed first step is both a better sale and a quotable fact.

Are partner directories really worth the effort?

In a trade with no regulator, they are the only public verification available. They are also read by assistants as structured lists, which is exactly the shape a "who can do this" question needs.

Should we write tool comparison pages if we are not neutral?

Write them and declare the bias. Say which platforms you implement, then compare honestly on the things buyers care about: cost at their size, skills needed to maintain it, and what happens when data volume grows. A comparison that concludes that every tool is fine is useless, and one that always concludes in favour of your partner is discounted.

What about the artificial intelligence questions buyers now ask?

Answer them with the foundation, not the fashion. The honest answer for most mid sized Indian companies is that their data is not yet clean, joined or defined well enough for anything intelligent to sit on top of it, and that the first project is the boring one. That answer is unusual and therefore memorable.

How do we prove results without publishing client numbers?

Describe what changed structurally: a report that took three days a month now runs on its own, one definition of revenue instead of four, the finance team no longer maintains a parallel spreadsheet. Structural change is credible and needs no invented percentage.

What to do first

Write one source system page for the system most of your clients are stuck with. It is the page nobody in your category has written, and it brings buyers who have already described their problem to themselves.