Many of the most effective database marketers take pride in the data they manage. To many, data is beautiful – if you don’t believe it then you will be surprised to learn that the #DataIsBeautiful sub-reddit on Reddit has almost 16 million members. That’s right, 16M people who think data is beautiful.
However, whilst data is beautiful it takes time and effort to get it to that point and then keep it at there. In the marketing World we often deal with prospect and/or customer data including the following company level fields:
- Company Name
- Company Address
- Company Description
- Company Size
- Company Website
And we commonly deal with decision makers so also manage the following common fields:
- First Name
- Last Name
- Job Title
- Email Address
The above fields make up the basic fields of most company prospect/customer databases however depending on how we collect data, the quality of the database can vary – for instance, if we collect data via a website and make all fields mandatory with validators running on all fields, then we might be able to build a very high quality database.
But for many companies this is not how it works, most companies collect data via different methods including:
- Website – Contact Form
- Website – Live Chat
- Face to Face
And even when we do collect data using a Contact Form, we are not always able to make all fields mandatory or use any validation tools.
As a result of the different collection methods we find data coming in to our database that is less than perfect.
For instance, many database managers would like to see the company name written in a standard format such as EMAILMOVERS LIMITED rather than EMAILMOVERS LTD. This simple difference is quite easy to rectify but what if was entered as just EMAILMOVERS or even worse, EMAIL MOVERS?
As you can see with the above example, there are a few different ways in which the company name can be written and this is where the problems begin.
Solving the problem
Agree on some common structure
To solve the problem we need to approach the it from 2 different angles, historical data and future data. Typically the first angle that needs dealing with is how future data is collected. First off, agree the structure you are most comfortable with then train your team to input new data according to that structure, for instance Emailmovers Limited, rather than Emailmovers.
Structural decisions are important and should not be taken lightly, bring in your marketing team, sales team and anyone else interacting with the data to get their input. For instance, the marketing team might ask that the company name is written Emailmovers rather than Emailmovers Limited because they will be including the company name in marketing messages to personalise those messages and would rather not have to change the name each time however the finance team might say that Emailmovers means nothing, only Emailmovers Limited means anything because Emailmovers Limited is the true incorporated entity and should be identified as such on any invoices. Only after getting input like this can you decide ultimately what structure your database needs to take.
Once you have agreed on a common structure, anyone inputting data must be trained on the common structure and given access to any tools that can help them understand and keep to that structure as they input data. For instance, giving the sales team access to Webcheck on Companies House to find the actual company name and registration number can be a very handy tool when it comes to identifying registered company names.
Monitor and review
After a short period of time, review the data that has been collected and look to see if it is correct – where it isn’t, further training may be required or forms updated. It is always important to note that with data the first 80% is typically quite easy to correct, the remaining 20% is difficult to correct. You need to decide how much time you spend on the 20%. If not, you may find you are working endlessly on a small amount of data and need to understand the real impact of that on the business.
Going back in time
Once you have agreed on your structure, trained up your team and reviewed their work to make sure everyone is happy with the output you can move back in time and focus on your historical data however this is where other problems can flare up. Going through manually data manually is typically ineffective and costly however never fear, companies exist to help with this specific exercise and if you are in the UK, with a focus on Business to Business marketing then Emailmovers is the logical choice.
Once we have a data processing agreement in place we can take a sample of your database and identify the correct legal entities using fuzzy logic that we have created over the years – for instance, Emailmovers = Emailmovers Limited or Emailmovers Ltd = Emailmovers Limited. Once we pass your file through this tool we are able to identify the match rate between your database and ours then apply the correct legal entity to yours. Typically match rates vary between 40% and 70%.
Once we have completed this process we can then standardise your historical data inline with your desired structure, quickly.
Filling in the blanks
One of the most significant assets to any data marketer is data itself. Imagine, you are the marketing manager for a waste disposal company located roughly 1 hour from London. One of your most successful campaigns is a free, no obligation site visit to companies within London and Greater London, however your customer database only contains a first name, last name, company name, telephone number and email address.
It is seemingly impossible to target those companies on your database located within Greater London with your offer unless you are somehow able to identify the location of each company on your database.
This is where we can append to those companies on your database that we match back to ours, a postcode. With this postcode you can then run a simple query to identify all companies within London and target those companies by email with your offer and as a result drive sales.
The above is a basic example of how targeting can work but it is only one such example, there are limitless campaign opportunities if you have the correct data.
What can be appended?
Once an initial match has been completed, the number of fields that can be added is extensive, for instance:
- Company Size
- Company Description
- Company Address
- Company LinkedIn URL
We can’t possibly list them all here – there are far too many to list, you can however download the full list here.
Need more help?
If you need help with your data then why not reach out to a member of our team for a chat on 0845 226 7181 or use the contact form on this page to schedule a call back. We also have live chat that is available during business hours.