How big data and correlation analysis help build a potential customer profile

Why a standard target audience description is not enough

Many companies describe their target audience with broad attributes: industry, business size, region, and the decision-maker role. This helps at the start, but quickly reaches a limit: companies within one industry can have different economics, urgency, and readiness to buy.

That is why an ideal customer profile should not be built only on sales or marketing assumptions. It must be validated by data: who has already bought, who reached a deal, who returned after touchpoints, which companies share similar attributes, and where real demand appears more often.

What data is used for analysis

Work starts by combining several data sets. The external market shows which companies exist and how they differ. The client’s internal data shows who has already bought, which deals were successful, and which inquiries did not convert.

The unified database includes attributes that help compare companies with each other:

• industry, geography, company scale, and structure;
• revenue, growth dynamics, vacancies, tenders, and public activity;
• technologies used, licenses, equipment, or infrastructure;
• history of leads, deals, repeat touches, and inquiries;
• positions and roles of people who influence the purchase.

What correlation analysis provides

Correlation analysis shows which attributes appear more often among the best customers and deals. It is not magic and not an attempt to guess the buyer from one parameter. The point is to find stable combinations of factors that increase purchase probability.

For example, analysis may show that not all manufacturing companies respond well, but companies of a certain size, in specific regions, with active hiring, similar procurement structures, and expansion signals do. This segment is much more precise than a broad ad campaign setup.

At this stage, we create not an abstract “target audience,” but a set of testable segments: from the most precise and profitable to broader groups for scaling.

How the ideal customer profile is built

After data analysis, we create the ICP — ideal customer profile. It describes companies that are highly likely to fit the product, have clear deal economics, and can move from first touch to sale.

The profile includes more than demographic or industry parameters. The important attributes explain potential need: growth, company changes, procurement activity, recurring pains, budget availability, decision structure, and similarity to current best customers.

This profile becomes the working foundation for the company database, acquisition channels, sales scripts, and further lead support.

How the profile turns into acquisition

Once segments are defined, we build a database of potential customers and decision-maker contacts from them. Then we launch a connected touchpoint system: Yandex, email, LinkedIn, telemarketing, DMP, website, forms, quizzes, or chatbots.

The key principle is that channels do not work separately; they work around one database and one segmentation logic. A company may see an ad, receive an email, read expert content, get a call, and return to the site with preserved context.

Why the model improves after launch

The system does not end after the first touches. New leads, reactions, rejections, deals, and sales team comments return to analytics. This helps refine the model: which attributes truly work, which segments are overheated, and where another offer or channel is needed.

This creates a managed growth cycle: data → ICP → company database → acquisition channels → leads → sales → new data. The more quality feedback, the more precise the next acquisition cycle becomes.