Why Segmentation Really Matters

Updated: Oct 27, 2020

This image shows a tree in a forest and the words Data 101 Big Tree, Most providers will sell you a tree when you need to buy kindling
Don't buy a tree when you need kindling!

Buying data for the first time can be daunting. It's like if you'd never made a fire before, you might not realise that a big tree won't be the ideal fuel to start that blaze.

Your starting point is the forest. Look at all the big trees. Which one will you choose to start your fire? How are you going to chop it down? And even if you do manage to do all these steps, will a big piece of un-seasoned wood make a fire?

With data, your starting point is your own database. Who is your customer? Have you analysed and segmented? Knowing your customer is critical when it comes to maximising your marketing efforts.

Many businesses collect data, but it is amazing how many choose to overlook the goldmine of information at their fingertips.

Start by looking for patterns that match your business. Do your customers reside in a certain part of the world? Does your business require a minimum spend? These are some of the factors to consider when trying to define your customer.

Here are some standard points to consider when segmenting your database:

  • Geographic location

  • Industry - Standard Industry Code (SIC)

  • Product group / range and how is the product used

  • Organisation size - number of employees

  • Organisation turnover

  • Product delivery

  • Special use / needs

Once you know what you are looking for, then you can truly engage with your customer; improve your current product or service to fully serve that customer; focus your marketing strategies to maximise impact; and acquire new customers through business development and data.

Make your "Big Tree" manageable

This image shows a picture of stacks logs in an open field
Trees are easier to stack when they are cut into smaller logs

Now that you have identified your target customer with segmentation, you can now employ a targeted marketing and growth strategy with data matching your current customer profile. When your business looks at purchasing data, there are standard business fields that you will be able to search and specify:

  • Company name, address, contact name

  • Email address

  • Company registration number

  • Turnover

  • Thomson classification

  • No. of employees on site

  • Date established

  • Head office indicator

  • Legal Status

  • Telephone number

  • No. of employees in business

  • Age of business

  • Premises type

  • Start-ups

Ask your data provider to help you with which business fields would help you target the best prospects - they should be able to guide you through the complexities.

Most big data providers are only really concerned with selling you bulk amounts of data - because that's their business model. They don't really care whether this is the best data strategy for your business.

Now this is fine if your business has the capacity to deal with large amounts of data. But you might want to think about smaller and more manageable amounts of data that your business can process. If you take on a big list, you may find that it's no longer up-to-date and relevant by the time you use it. Call centre teams would call this 'bad' data, and the'd be right, because you haven't been sold the data that was right for your business.

You also have to remember that most data isn't a qualified lead either. It is cold. Your sales team might be great at closing a sale, but are they any good at warming up potential customers? It might be a better idea to pass a cold list to your marketing department first to start qualifying leads before passing to through to the sales department.

If cold calling isn't in the DNA of your business, you may have a higher conversion rate with super-refined data. Can your data provider give you specific business intelligence add-ons? Could they pre-qualify the data before passing it to you? There may be a higher cost in this process but it may save you time and money in the long run.