The terminology gets conflated all the time in conversations with deal teams. Someone will ask for help with "sector mapping" and mean that they want a list of potential acquisition targets filtered by size and geography. Or they will say they need to "screen the market" and mean that they want to understand how a sector is structurally organised before they can decide which sub-segments are worth pursuing. The distinction matters because the two activities start from different questions, use different inputs, and produce outputs that are not interchangeable.
This is not a pedantic taxonomy exercise. In practice, conflating the two leads directly to the situation where a deal team runs a database screen, receives a list of companies matching their financial criteria, and then struggles to build a coherent investment thesis because they have no model of how those companies relate to each other within the competitive landscape. They have answers without having asked the right question first.
What Sector Mapping Actually Is
Sector mapping is fundamentally a structural question. Before you can assess which companies are attractive acquisition targets, you need a working model of how the sector is organised: what the meaningful sub-segments are, who the dominant players are in each, where the fragmentation sits, what the typical ownership profile looks like (founder-led, PE-backed, subsidiary of a larger group), and where consolidation has already occurred versus where it remains dispersed.
A useful sector map for a UK business services deal team looking at environmental testing would not start from a financial filter. It would start from a functional decomposition: what is environmental testing actually? There is ambient air monitoring, water quality analysis, soil contamination assessment, asbestos and hazardous materials surveying, noise and vibration monitoring, and ecological assessment. Each of these sub-segments has a different competitive structure, a different customer base, and different regulatory drivers. A company operating across all of them looks very different from one that is deeply specialised in a single function.
Getting that structural picture right is a prerequisite for intelligent target screening. Without it, a financial screen will return a mixture of companies from different sub-segments that look similar on revenue and headcount but are not genuinely comparable, and will miss companies that are structurally interesting despite not appearing prominently in any database.
What Target Screening Is
Target screening is the application of criteria to a known universe to produce a prioritised list. Once you have a sector map that tells you the sub-segments exist and who populates them, screening applies filters: revenue threshold, growth rate, ownership type, geography, specific capability or accreditation. The output is a ranked list of companies worth approaching.
The important word in that description is "known universe." Screening is only as good as the universe it operates on. If the universe is derived from a financial database that has incomplete coverage of small UK private companies (which most do, given the abbreviated accounts filing regime), the screening output will systematically miss a portion of the genuinely interesting companies. If the universe has not been organised by meaningful sub-segment, the screening will produce a list that looks coherent on financial criteria but lacks strategic logic.
This is why sector mapping is a prerequisite for screening, not an alternative to it. The two activities are sequentially dependent. Mapping defines the universe and provides the structural frame. Screening then applies criteria within that frame to produce a prioritised list.
Where Standard Tools Get This Wrong
Most commercial deal sourcing tools are built around the screening paradigm. They are databases with filter interfaces: select a sector code, a revenue range, a geography, and receive a list of companies. Some overlay signals like ownership status or recent fundraising. These tools are useful for screening within a defined universe, but they do not produce a sector map. They return data; they do not explain structure.
The sector code problem is illustrative. Standard industry classification codes (SIC, NACE, or the UK's condensed SIC2007 system) are designed for statistical aggregation, not for the granular sub-segment distinctions that are meaningful for deal sourcing. UK SIC 2007 code 71.20, Technical Testing and Analysis, covers everything from environmental testing laboratories to materials testing for aerospace certification to product safety testing for consumer goods. These businesses share a classification code but are not in the same competitive market. A sector map built on SIC codes will group them together; a properly constructed sector map will separate them.
We are not arguing that existing databases are useless. For a deal team with a clear thesis in a sector it knows well, a database screen is a fast way to identify the obvious candidates. The problem arises in new sector coverage, early-stage thesis development, or any situation where the deal team is trying to understand a space they have not previously worked. In those situations, screening without mapping produces a list that looks comprehensive but has significant structural gaps.
The Mapping Process: What Goes Into It
Building a genuine sector map for a UK private company sector involves several layers of work that are distinct from database queries.
The first layer is sub-segment definition. This comes from understanding how the sector actually operates: what are the distinct customer segments, what are the different service or product categories, where are the operational models genuinely different? This is partly desk research and partly inference from the filing and hiring data that describes what companies in the sector are actually doing day-to-day.
The second layer is entity identification. For UK private companies, this means going beyond the obvious database-covered names to include companies that appear in filing data but not in standard research databases, companies that operate as subsidiaries of larger groups and therefore do not file as independent entities, and companies that have recently changed their registered activity descriptions or ownership structure.
The third layer is relationship mapping. Within a sector, competitive relationships, customer-supplier relationships, and ownership linkages structure the landscape in ways that a flat list of companies does not capture. A cluster of three founder-led businesses in the same sub-segment with similar revenue profiles looks very different from three companies if one of them is a recent PE-backed roll-up vehicle. The map needs to capture those structural differences.
The fourth layer is positioning each company within the sub-segment structure, annotated with the evidence signals that explain their current state: growth or contraction signals from filing data, capacity signals from hiring, ownership transition signals from director appointment patterns.
An Illustration From UK Facilities Management
Consider a deal team setting coverage on UK facilities management with a particular interest in the specialist technical services sub-segment: companies that provide mechanical and electrical maintenance, building fabric maintenance, and related hard services, as distinct from the broader soft services segment (cleaning, security, catering).
A database screen for FM companies in the UK with revenue between five and fifty million pounds might return two hundred companies. This list will include hard services specialists, soft services businesses, integrated FM operators, and a number of businesses that have migrated between these categories. Without a structural frame, the list is difficult to work with. The team cannot tell from financial data alone which companies are genuinely comparable.
A sector map would first partition the space by service model, then by technical specialisation within hard services, then by customer base (public sector framework contracts versus private sector open-market contracts). The same two hundred companies, when organised this way, reveal that the interesting hard services consolidation opportunity sits in a sub-segment of perhaps forty companies that have not been previously rolled up, while the rest of the list is either already consolidated or belongs to a different competitive market entirely. Those forty companies become the screening universe. The criteria applied to them are materially different from those that would be applied to the broader undifferentiated list.
How Thema Separates the Two
When we build a sector deliverable for a deal team, the mapping layer comes first and is separated from the ranked target output. The map describes the sub-segment structure of the sector, explains the basis for that partition, identifies the known players across all sub-segments with evidence signals attached to each, and notes where coverage gaps exist in the public record. That is a standalone analytical product.
The ranked target output then applies the deal team's specific criteria within the structural frame the map provides. The criteria might be ownership type (founder-held, not PE-backed), estimated revenue band (five to thirty million pounds), hiring signal profile (growing organically), and sub-segment position (operating in the highest-margin technical specialisation). Companies are ranked by the combination of these criteria, not by any single metric.
The two outputs serve different purposes. The map is the analytical frame that the deal team will return to as they develop their thesis and revise their criteria. The ranked list is the operational output that drives near-term contact activity. Both are necessary. Neither is a substitute for the other.