The Definitive Guide to Mailing Lists

If you do a Google Search on “Mailing Lists” you get 1,540,000 results. Everything from vendors who offer lists for sale to free lists. So how do you choose the right vendor for your list purchases?

There are two main categories of data vendors you will find with specific attributes you should consider.

Data Service Bureaus

Service Bureaus provide data processing services for a fee with some functions such as data entry, data conversion and batch processing. Service Bureaus offer modeled data as well. Extracting useful information from data sets while maintaining data integrity is the forefront of a Data Service Bureaus core operating agenda.

Data Broker

In today’s digital age organizations need a lot of better data which is usually externally sourced. So where do they turn to get the data?

According to Gartner they estimate there may be up to 5,000 data brokers worldwide and nearly 10 million datasets. This raw data which is published by government agencies and non-government organizations (NGOs).

Data brokers are businesses who aggregate all this information from multiple sources. They are able to process, enrich, cleanse or in some cases analyze it. They then in turn licence the data to other organizations for a fee.

Data brokers are usually categorized by the type of data they provide. Examples are consumer data, commercial data (company info), scientific, and technical data. There are types of data such as real estate and geolocation data.

To get a competitive edge data brokers will often specialize in certain industries so they can gain a competitive advantage.

Choosing a data broker is often done based on the services they provide.

Simple data services are very common. Data is collected from multiple sources and put into datasets for use.

Smart data services provide conditioned and calculated data. Analytical rules are applied to gather further insight.

Adaptive data services take a more custom approach to specific data combined with data in context and this is the most advanced form of service.

When choosing data from a broker there are three pricing models.

• Free: Data is accessible without a charge or cost.
• Freemium: This is a combination of using part of the free data with another part that cost.
• Paid: The paid model offers the data after you pay for it. Many times subscriptions, pay per use or combinations of these are available.

Researching the type of vendor you need depending on your data initiative will save you time and headaches. Make sure your vendor offers the capabilities you need, are financially viable and have good references. Big Data is forcing the market to evolve and put much higher demands Brokers. Some will adapt and some will lose market share.

There are three main categories of lists used for marketing. Many times these list types are used in conjunction or separately.

Compiled Lists

Compiled lists come from multiple sources. These are often proprietary and public. Examples are telephone listings, voter registrations, self-reported surveys etc. Lists with these attributes are broader and offer higher volumes of data. Normally these lists are subdivided into categories for easier segmenting. Lists such as new movers, vehicle specific, homeowners, apartment dwellers etc allow targeting that will likely yield a much more cost effective approach and higher ROI for the strategy you use.

In-House Lists

In-house lists are proprietary customer or subscriber lists of a company. Managing and keeping these lists up-to-date offer some of the most useful data for businesses.

Response Lists

Opt-in lists are a collection of individuals who respond to specific offers. They can be a combination of buyers, subscribers, inquirers etc.

With so much data available it’s important to recognize you are sourcing and using quality data. Whether it’s for marketing or scientific research, the quality of your data greatly affects the outcome of your model.

Data quality is composed of five components that derive how robust it is.

1. Relevancy of your data takes into account the time period, location and / or demographics that correlate with your analysis. If you are interested in marketing to Ford owners for a client in a specific zip code then the data set should comprise of Ford and be within that zip code so it’s specific to the goal of your outcome.
2. Accuracy of your data can make or break you. Datasets full of typos, transpositions, and outdated information not only wastes the time you invest using it but has a high financial cost in regards to lost opportunities.
3. Complete data covers all the relevant aspects of the goal you’re using it for. It will encompass all the “selects” you need to target the dataset completely. For example if you plan on a marketing campaign to advertise a new Mini Van to expecting mothers, then you must make sure they are a targeted select for your data. Simply compiling a set of females based on geography will yield a completely different outcome than targeting with all the set criteria. Performing data appends will also help update your data to a complete set.
4. Recent data is based on how current the data is. Depending on the purpose of your data the recency can affect the relevancy.
5. Clean data refers to data that is free of duplicates and is organized, standardized, and structured. Many forms of data are unstructured meaning it does not fit into fields of a data table. Examples of this are social media, emails, videos, images etc. However even this data can be documented and set to a standardized format.

According to the Harvard Business Review bad data costs the U.S. $3 Trillion per year. Whether you’re a decision maker, manager, business owner or data scientist the data you use can have plenty of errors. Review the data you plan on using and use the five components to grade your data insurring better success with the endeavor you are using it for.

Individual pieces of data are the starting point for any field of discipline whether it is business, marketing or science. Everything starts at data collection in order gather and measure the information in a systematic fashion.

Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes.

There are two type of data that are collected depending on the discipline or field and the object or goal of the user.

1. Quantitative Data. As the description notes, this is data dealing with quantities, values, and numbers. This type of data is measurable as they are expressed in a numerical form such as size, amount, length or price. Statistics uses this type of data and because of this it is viewed as more reliable and objective.
2. Qualitative Data. . Just like the description this type of data deals with quality, so it is more descriptive than numerical. This data for the most part is not measurable and is usually gained through observation. Things like appearance, color, texture etc are examples of qualitative data.

QUALITATIVE DATA COLLECTION METHODS

Since qualitative data is based on insights, reason and motivations the type of data collection used are more time consuming and expensive to conduct.

They include:

• Fact-to-Face Personal Interviews
• Qualitative Surveys, questionnaires and forms
• Focus Groups
• Observation
• Longitudinal Studies
• Case Studies

QUANTITATIVE DATA COLLECTION METHODS

This data is generated in numerical form processed into information mathematically. Differing from the qualitative methods, these techniques use larger sample sizes.

They include:

• Quantitative Surveys
• Interviews, both face-to-face, by telephone and online
• Observation
• Experiments

What are segmented lists?

One of the most powerful techniques you can use is to break up your list into smaller segments. This can be done by your list vendor when you order your list or even on an in-house list. The segments are normally cut and combined  by: psychographics, demographics, industry, company size etc. The segmented results allows you to personalize the content you send to the most pertinent people at an ideal time.

According to DMA, 77% of email marketing ROI came from segmented targeted, and triggered campaigns.

When ordering your list decide on the segments that will allow your message to be more relevant to the data. Here is a breakdown of some common segments used when ordering lists.

• Geography
• Age
• Gender
• Persona
• Organization Type
• Industry
• Job Function
• Education Level
• Seniority Level
• Past Purchases
• Purchase Interests
• Buying Frequency
• Purchase Cycle
• Content Topic
• Content Format
• Interest Level
• Buyer Behavior

These are just a handful of ways to segment lists so your messaging resonates better with the data you are targeting. So think through your list segmentation when ordering your list. Targeting the right audience at the right time is a great recipe for marketing success.