You’ve done some great analysis and you're able to define your target audience. Now all you need to do is find them and make sure you have their up-to-date name and address details. Caroline Kimber, head of data planning at Stephens Francis Whitson, provides an insight into how to get started.

Sources of data

Contact a list broker. As well as having a comprehensive understanding of all the lists in the marketplace, a good list broker will give you valuable informal feedback on factors such as list quality, recency, list overuse and responsiveness.

Speak directly to a list supplier. If your target audience is very niche and you need in-depth information about how the list has been sourced, it’s often valuable to speak directly to the data supplier. The DMA has a useful list of data suppliers.

List swapping

Another cost-effective way to acquire data is to broker a swap with another organisation whose target market is similar to your own. Swaps are particularly prevalent in the charity, mail order and publishing sectors. List brokers are a good source of advice on list swap options.

Setting objectives

Sounds obvious, but it’s important to define your objectives and your success criteria, as different datasets and lists have different strengths and weaknesses. Are you looking to increase profit, generate traffic or improve response and conversion?

Testing and control groups

Direct mail is the ideal medium for testing the impact of different creative treatments or targeting mechanisms.
Tip Ensure you have a solid test matrix with large enough sample sizes for valid analysis and a control group so you can measure any uplift.

Selection criteria

There are a number of different attributes that you can use to select data. A good rule of thumb is to map out your target audience on each of the ten selection criteria listed below:

• Recency.
• Data source – for example, data that has been compiled from lifestyle surveys, prize draw responders, mail order buyers, share registers, the Electoral Roll, geo-demographic models.
• Customer attributes – such as age, gender, marital status, income, demographics, interests.
• Business attributes – including standard industrial classification (SIC) code that categorises business by the type of economic activity in which they are engaged, turnover, number of employees.
• Business contact – such as IT director, decision-maker for purchasing stationery.
• Product attributes – including have a credit card, are interested in a short break, renew their motor insurance in March, spend more than £x on electronic gadgets.
• Geography – for example, TV region, postcode, branch catchment.
• Channel – such as direct mail with email addresses for follow-up, phone numbers.
• Exclusions – for example, previous campaign mailed, agricultural businesses, age 60+.
• 1 in n. If you're only selecting a proportion of the data available, ensure you specify that a 1 in n representative sample is used.

Cold versus warm data

Previous responders, former or lapsed customers and current customers will always be much warmer to your offer than cold prospects who have never had a relationship with your brand.
Tip Always explore options for selecting from your prospect and customer database before you buy cold data. Consider the following:

• Prospects – ensure you select the relevant ones by applying the ten selection criteria listed above.
• Lapsed or inactive customers for whom the offer may be relevant.
• Any specific client defined selection criteria, such as:
  - Propensity models (these score customers from best to worst, for example, on their likelihood to respond or purchase a particular product).
  - Customer segmentation.
  - Purchase/transaction history.

Data quality

Request information about data quality, update cycles and goneaway levels before you order. The DMA issues accredited list providers with a list warranty number for each of their lists.

Legal obligations and data protection

Your direct mail needs to adhere to the eight principles of the Data Protection Act, the Privacy and Electronic Communications Directive (if it includes telemarketing or email) and the British Code of Advertising, Sales Promotion and Direct Marketing (CAP) code.
Tip The DMA’s Code of Practice outlines legal requirements, while its Best Practice guides summarise what’s good practice but not a legal requirement.

Volumes and pricing

Deduplication and suppressions can have a big impact on volume.
Tip It makes sense to over-order data and request a net name deal (you pay for net names used rather than gross names ordered). Request a multiple-use deal if you want to mail data more than once. Check any output and delivery charges.

Campaign processing and delivery options

Having specified your target audience, the next stage is to determine your output options.
Tip The following is a list of eight processing criteria that you need to stipulate:

• Suppressions – for example, deceased and goneaway screening, MPS, TPS, records without postcodes, client do not mail file, county court judgments (CCJs). All good data processing bureaux offer these services and can advise on how they can best be applied to your data.
• Enhancements – for example, postcode address file (PAF) verification and address enhancement, tele-appending, mailsorting.
• Deduping – if you buy data from multiple sources, there will inevitably be duplicate names and addresses. Ensure you specify at what level your data should be deduped (for example, individual or household) and specify a dedupe hierarchy. Your data processing bureau can advise on this.
• Salutation – for example, Forename, Mr, Mrs, Ms. Be especially careful if your mailing is aimed at titled people.
  Tip Have a default for people with unknown gender.
• Seeds – track your mailing by inserting seed records (that is, you and colleagues, friends, family).
• Output format – for example, comma-separated variables (CSV), fixed field, mailsort 3. Check requirements with your mailing house or email bureau.
• Coding or tagging – for example, unique reference numbers (URNs) need to be added, each list needs an individual media code.
• Output format – for example, file layout, fields, file splits, encryption required.

And finally

Getting the data right is key to making your campaign work. The ten data planning steps outlined above take you through the whole process from defining your audience to outputting a file to your data bureau or mailing house. Developing a data segmentation strategy will help fine tune your data and target your audience even more successfully.  

Remember that data planning is an iterative process: the most successful campaigns are based on the lessons learned from building and refining criteria and plans over time. Data that is as clean and up to date as possible is key to any successful marketing campaign, and once you've defined and found your audience, you should make sure that you keep on top of keeping your data current.

Please note, before purchasing or using any personal data you should consult with a data protection expert to ensure the data and the proposed use of it complies with the Data Protection Act 1998.