How to Identify A Small M&A Deal in the UK Technology Sector


I have been doing some thinking about how to screen for small M&A deals in the UK technology sector and have been working on creating a short list of potential targets.  I thought I would share some of this work with you to help you with your own acquisition searches.

My starting point was to create a database of the market I wanted to address and for this I turned to Companies House Data and have identified 1570 companies which are covered by the SIC code relating to Computer Services (including Software Development, Retail, Maintenance, Repairs, Training and Consultancy).  I restricted the companies to those with Revenues of £5m or less.

In addition, I collected the Pre Tax Profit data, Company Age and the Companies’ addressess.  I then derived the PBT Profit Margin to give me a relative measure of profitability.  I have also identified those companies who have received external funding which may in itself provide a useful short list of potential acquisition targets.

Using the Visokio Ominiscope, I then added the mapping coordinates for each Companies’ postcode so that I could create a Map of the Companies in Omniscope.  This is shown below.

Company Map


This enables me now to screen, using Omniscope, by Geography, simply by lassoing the area I want to keep.





The Omniscope enables me to map Turnover vs Profit and to look at the relationship between the two.  From this I could select unprofitable companies or select profitable companies.  This also throws up Outliers which could be anomalous and may need to be investigated further at a later point.

Turnover and Profit Graph









The next stage is to map Turnover against profit % to see the relative profitability of the Companies in the data set.   This shows that there is a very wide range of profit % which suggests that this may not be a particularly useful metric when dealing with small companies, however once the data set has been filtered to a smaller number of target companies, this data may provide useful information.

Turnover Profit Percentage Graph









I have also transposed the Axes in this graph to make it easier to read.

Within the SIC Descriptor, there are 46 Subcategories and we may not wish to keep all of these in the data set.  I can create a tile image to look at these in more detail and then decide which of these to screen out.  In the image below you can see both the tiles and the menu list on the right hand side which facilitates the selection or deselection of the categories.

SIC Code Tile









The last variable I can work with is Company Age.  In the technology sector, companies tend to be younger than average.  I use Company Age as a useful filter for trying to identify companies which may be approaching a sale as the founders seek to realise the financial rewards for their hard work.  This can only be a short hand proxy as everyone circumstances differ but it helps me to prioritise and create a short list.  I can then go and look more closely at the ages of the major shareholders.

A Worked Example

I want to show how I can bring these factors together.  Lets start by selecting only those companies involved in Software Publishing or Other Software Consultancy.   This reduces my data set to 487 from the initial 1571.

Let us then decide to focus only on Companies in the Southern half of the UK but we will leave in Wales.  I open the Map view and deselect the Companies in the northern half of England and the few companies in Scotland.  As this is based on Companies House data, most Scottish Companies are not in the data set.  My data set is now 426.

I now want to refine the data set on the basis of Turnover.  Let us assume that I am not interested in Companies with Revenues of less than £2m. I can use the Omniscope to select only those companies with revenues between £2m and £5m.  I now have 101 Companies in my Dataset.

Next I want to only look at Companies which are profitable or close to break-even, so I am going to eliminate those companies which show substantial losses.  Now I have 81 Companies from my original selection.  I am also interested to see which of these have External Funding so I am going to use the Tile View to subsegment these.  This is my Initial Short list and in the space of 10 minutes I have screened my data set and deselected 1490 Companies which do not meet my criteria.  This subset is shown in the image below.

Software Companies Worked Example








81 Companies is a workable Short List which I can now take forward for more detailed analysis.  I can at any time go back and either expand or further reduce my data set by adjusting my screening assumptions.

Next Steps

If you would like to know more about my Screening Methods and whether I can help you with your Acquisition Search call me on +44 7813 672 612 or email me john[at]jbdcolley[dot]com.

If you would like to know more about the Visokio Omniscope contact me for a personal introduction to the Company.

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