What Did You Do on Data Privacy Day 2020? Do Tell Us.

Each year, Jan. 28 is known as “Data Privacy Day” in the United States and globally — also Data Protection Day in other jurisdictions. As business organizations — and marketers — we see that it’s a day when consumers are reminded to exercise their “privacy rights.”

Each year, Jan. 28 is known as “Data Privacy Day” in the United States and globally — also Data Protection Day in other jurisdictions.

As business organizations — and marketers — we see that it’s a day when consumers are reminded to exercise their “privacy rights” and take advantage of tips and tricks for safeguarding their privacy and security. In our world of marketing, there are quite a few self-regulatory and co-regulatory tools (U.S. focus here) that enable choices and opt-outs:

  • To opt out of commercial email, direct mail, and telemarketing in certain states, consumers can avail themselves of DMAchoice. For telemarketing, they can also enroll on the Federal Trade Commission’s Do Not Call database.
  • For data collected online for interest-based ads, consumers can take advantage of Digital Advertising Alliance’s WebChoices and Network Advertising Initiative consumer control tools, which are accessible via the ubiquitous “AdChoices” icon. DAA also offers AppChoices, where data is collected across apps for interest-based ads. [Disclosure: DAA is a client.]
  • Now that California has a new consumer privacy law, consumers there can also take advantage of DAA’s new “Do-Not-Sell My Personal Information” Opt Out Tool for the Web. Its AppChoices mobile app also has a new CCPA opt-out component for “do not sell.” Publishers all over the Web are placing “Do Not Sell My Personal Information” notices in their footers, even if others outside California can see them, and offering links to their own in-house suppression lists, as well as DAA’s. Some publishers are using new the Privacy Rights icon to accompany these notices.

Certainly, businesses need to be using all of these tools — either as participants, or as subscribers — for the media channels where they collect, analyze, and use personal and anonymized data for targeted marketing. There’s no reason for not participating in these industry initiatives to honor consumer’s opt-out choices, unless we wish to invite more prescriptive laws and regulations.

We are constantly reminded that consumers demand high privacy and high security — and they do. We also are reminded that they prefer personalized experiences, relevant messaging, and wish to be recognized as customers as they go from device to device, and across the media landscape. Sometimes, these objectives may seem to be in conflict … but they really are not. Both objectives are good business sense.

As The Winterberry’s Group Bruce Biegel reported while presenting his Annual Outlook for media in 2020 (opens as a PDF), the U.S. data marketplace remains alive and well. For data providers, the onus is to show where consumer permissions are properly sourced, and transparency is fully authenticated and demonstrated to consumers in the data-gathering process. It’s a rush to quality. Plainly stated, adherence to industry data codes and principles (DAA, NAI, Interactive Advertising Bureau, Association of National Advertisers, among others) are table stakes. Going above and beyond laws and ethics codes are business decisions that may provide a competitive edge.

So what did I do on Data Privacy Day 2020? You’re reading it!  Share with me any efforts you may have taken on that day in the “public” comments below.

Marketing Pros Provide Advice for Peers

When marketing pros provide advice, marketing practitioners listen. One of the high points of the New York marketing community calendar each year is the Silver Apple Gala hosted by the Direct Marketing Club of New York. The fete toasts the business and industry leadership success of honored individuals.

When marketing pros provide advice, marketing practitioners listen. One of the high points of the New York marketing community calendar each year is the Silver Apple Gala hosted by the Direct Marketing Club of New York. The fete, held this year on Nov. 7 near Times Square, toasts the business and industry leadership success of honored individuals, and at least one corporation or organization.

Each “Silver Apple” recipient has contributed for 25 or more years to our field, and since 1985, there have been 248 such honorees, including these four individuals in 2019:

Marketing, Career Wisdom They Share

So when more than 200 of your friends, family, and peers come together, what pearls of wisdom do you have to share?

Carl Horton, IBM

“The ability to execute against the dream in real time,” is what excites Carl Horton, Jr., in his current position in B2B marketing at IBM. Horton credits colleagues who have placed “personal investments in me” and dared to let him take crazy ideas (artificial intelligence applications don’t seem so crazy today) and make them reality, as well as the unconditional love of family.

One key takeaway from Horton:

“The importance of diversity in leadership and innovation: The NextGen of innovation may come from someone of experience, income, race, gender, gender identity, very different from our own.”

Here, here, we need to foster it.

Britt Vatne, ALC

Britt Vatne, who leads the data management practice at ALC, talked about a career pivot 15 years ago, when she worked with a nonprofit client for the first time, March of Dimes, and it showed to her how critical acquiring, retaining, and growing donors are. She also credited industry luminaries, such as the late Bob Castle and the energetic Donn Rappaport (in the room) – as well as her father, who came to America from Norway, never finished primary school, and taught her “there is no substitute for hard work.” She was the first of her family to go to college.

“Being human, being respectful, and having integrity are non-negotiable,” she said. “Be a positive role model, and you’ll have the love and loyalty of family.”

And probably, quite a few colleagues and clients, too.

Joe Pych, NextMark & Bionic Advertising

Joe Pych, who is the startup founder of two companies — NextMark and Bionic Advertising, says his “go-to metric is sales growth.” CRM [customer relationship management] is so much more of an opportunity than simply managing costs, he says. Set a goal, uncover an idea, execute, and measure results.

”I feel selfish standing alone with so much support I’ve received over the years,” he said, referring first to his mother, who put four children through college on an electrician’s salary – and then went and got a masters herself.

He also thanked many of his client data businesses that helped make his first company take off — companies, such as MeritDirect, ALC, Worlddata, and Specialists Marketing Services (SMS), among others – who took a chance on a Hanover, NH-based enterprise. To his wife, Robin.

“Those missed vacations, I’m sorry … again.”

Gretchen Littlefield, Moore DM Group

Gretchen Littlefield, CEO of Moore DM Group for the past two years, also served at Infogroup for 14 years, where she helped develop its nonprofit, political, and federal government marketing practice – which propelled her into her current role atop Moore.

In 2018, she co-founded the Nonprofit Alliance, where she serves as vice chair, to advance in Washington the interests of nonprofit and charitable organizations.

“I fell into this business like everyone else,” she said, starting from data entry and advancing to “getting data [insights] out of the industry.”

She thanked many industry leaders among her mentors and influencers, among them Jim Moore, Larry May, and Vin Gupta.

“It seems as if on every innovation, we are working together and competing all the time. Coopetition,” she said. “The flow of data – from list rentals, to coops, to marketing clouds. We share data for growth.”

Littlefield also emphasized investment in education, citing Marketing EDGE and Direct Marketing Club of New York, for their respective roles in attracting bright students to the marketing field.

“Time goes by faster than we expect — Joe [Pych] and I were Marketing EDGE Rising Stars back in the day. I’m just as excited today as my first day in direct marketing, but mostly grateful for the friendships.”

In addition, there were three special honors bestowed, among them a first-time “Corporate Golden Apple” to Marketing EDGE for its more than half-century of creating and connecting market-ready college students for careers in marketing. And two Excellence Apples:

  • 2019 Apple of Excellence, Advocacy:
    Tony Hadley, SVP, Regulation and Public Policy, Experian (Washington, DC)
  • 2019 Apple of Excellence Disruptor:
    Mayur Gupta, CMO, Freshly (New York, NY)

There’s more to share – but that likely will be another post! Stay tuned …

Think of Food Nutrition Labels. Now, There’s Audience Data Labeling

This summer — this “nutritional” label for commercially available audience data, which vendors, agencies, advertisers and publishers can use to understand the sourcing of targeting data and how it is prepared for market — is ready for marketplace use.

Last fall, I reported briefly on an industry initiative related to “data labeling” a bid to provide transparency of data sourcing for audience data used in digital and mobile marketing. DataLabel.org is an initiative of the Interactive Advertising Bureau (IAB) and the IAB Tech Lab. (At the time of inception, the Data & Marketing Association now the Data Marketing Analytics division of the Association of National Advertisers was also at the table.)

This summer this “nutritional” label for commercially available audience data, which vendors, agencies, advertisers and publishers can use to understand the sourcing of targeting data and how it is prepared for market is ready for marketplace use.  (From a June 27 IAB Tech Lab press release🙂

“Data transparency is a table-stakes requirement to ensure responsible and effective use of audience data and companies that provide consistent access to detailed information about their data will attract more business,” said Dennis Buchheim, EVP and general manager at IAB Tech Lab. “Taking part in the corresponding compliance program will further differentiate an organization, affirming their full commitment to the highest standards.”

Transparency in Data Sourcing Matters

I remember hearing IAB CEO Randall Rothenberg admonishing the ad tech ecosystem in early 2017 to get out of the “fake anything” business, and arguably the effects of fraud, brand safety, and other concerns have led many advertising and marketing professionals to scour their data sourcing, permissions, stacking, integrating, and statistical analyzing to make sure that an otherwise reputable company is not engaged with anything untoward on the data front.

DataLabel.org supports this objective, in part, and goes further.  While it does not assign a quality score to any particular data source, it does enable apples-to-apples comparisons in important areas, (Opens as a PDF) which inform where media dollars based on audience data are committed:

Data Labeling label
Source: DataLabel.org

Yes, it’s an agnostic nutritional data label for data sourcing. Through IAB et al, dozens of companies were part of a working group that led to the Data Transparency Standard, Version 1.0 (a PDF download] led by Meredith Digital, Lotame Solutions and Pandora, among its supporting cast.

Does ‘Table-Stakes’ Mean Traction? You Look Good Dressed, in Responsible Data

According to the IAB, “completion of the program requires an annual business audit to confirm that the information provided within the labelling is reliable, that the organization has the necessary systems, processes, and personnel in place to sustain consistent label completion at scale, and that a label can be produced for all in-market segments available. Engagements typically range between [two to five] months, depending upon the size and complexity of the company’s business.”

So now that’s the Data Label is available to the data-driven marketing marketplace, is there real traction to see its use?  From the data provider side, at least, I’d say so.  While some may be taking a wait-and-see approach, some data marketing companies are moving forward with data labeling and transparency certification.

“The digital ecosystem tends to focus on areas like inventory and traffic,” said Chris Hemick, senior product marketing manager, Alliant, whose company is now in the onboarding process. “Alliant is an advocate for bringing the same level of focus to the data marketplace. We firmly believe that IAB’s efforts to spotlight data provider practices around audience creation will be a positive for the entire industry.”

Another data provider, Audience Acuity, echoes these sentiments. “The concept of the Data Transparency Label was introduced in the fourth quarter of last year, after it was developed by the ANA’s Data Marketing Analytics (DMA) division, the IAB Tech Lab, the Coalition for Innovative Media Measurement (CIMM), and the Advertising Research Foundation (ARF),” said Riad Shalaby, CMO of Audience Acuity. “We are aligned with their perspective on this important topic, and we are delighted to be one of the first major data companies in the United States to provide this level of transparency.”

There are many things we, as data marketing professionals, need to concern ourselves with in best practices, ethics, and even legal compliance. Brand safety, ad measurement, piracy, privacy and security, and fake anything are among them. Proper data governance is related to all of these concerns. The more we spotlight our roles as stewards of and for data integrity, the better we can achieve marketplace confidence and trust in the very information that helps make brand-consumer engagement succeed.

Consumer Marketers, Looking to Test New Data Categories? Try These

We are all trying to create and sustain customers, using data to discover new patterns, new audiences, and new prospects — and that requires a lot of testing, and innovative data sets to explore (responsibly). Let’s make it experiential, as well as experimental.

We in the data marketing business love to test — at least, we should. And what we should test for is new data categories.

Expanding the marketing universe — and stretching the marketing budget — depends on higher efficiency in our lists, offers, and creative. We should be eager to test new proofs of concepts and new categories of data sources as they enter the market … if only to know whether or not they produce incrementally or otherwise.

I’m still surprised when I hear some of my data-vendor friends say that a good number of their clients pass on testing — and just go all-in on new lists and data sources. It seems like testing is still too much work for some, or they feel the only way to test is with an entire data source. Guess these client-side folks have money to burn, or are operating very much on-the-fly.

In some ways, digital marketers have it all over offline marketers in their ability to test, cycle, test again, and so on — often, many times over by the time a direct mail or direct-response print or broadcast test cycle has run its course. Yet, in this speed, have we sacrificed some quality in our prospecting strategies?

Online audience algorithms can produce some highly categorized niche segments, based on site visits and app usage — much of it de-identified, from a personal perspective. But how do these segments really stack up against a transaction database, or response lists, or even compiled lists, based on personally identifiable information? Thankfully, we can test for this, or even overlay data! (I am not advocating re-identification here, nor should you. Oh California, please don’t force us to identify non-PII. It’s soooo anti-privacy.)

Recently, the Direct Marketing Club of New York (DMCNY) held a very interesting breakfast program titled “Beyond Demographics: The Data You Need to Max Out Marketing Performance.”

Some Fresh Categories for New Reach and Affinity Discovery

Consider some of these data sources for testing:

  • Values Data — Test cohorts based on “shared values,” rather than simply choosing audiences based on demographics or psychographics. David Allison, principal, David Allison Inc., and author of “We Are All the Same Age Now,” pointed to his firm’s internal research that shows that popularly defined age groups rarely (or barely) match on what they agree upon, or value, as a generation. For example, Baby Boomers agree with each other about 13% of the time; Gen X, about 11% of the time; and Millennials, 15% of the time. Thus, targeting based on demographics alone can be extremely wasteful if the marketer is assuming some sort of shared attribute among them, other than age.However, when targeting based on shared “values” — Adventurers, Savers, and Techsters, and the like — all of a sudden affinities jump sky-high. In these cases, 89%, 76%, and 81%, respectively. These “valuegraphics” are based on “big data” segments — rather than small data (response lists, for example). Still, when compared to demographics targeting alone, shared-value targeting offers an eight-time lift!  Well, that’s worth testing.
  • Attitudinal Data — Another perspective on “beyond demographics” came from Mark Himmelsbach, co-founder, Episode Four, a creator of “brand hits,” such as this one for Charles Schwab. We often have stereotypical views of many demographic and other audience categories — and too many algorithms, he said. But analyze the data for unusual patterns, and suddenly you can find “who knew?” commonalities among certain audience segments that would wow any of us.Who knew that ultra-high net worth individuals are electronic dance music enthusiasts? Who knew that African-American married women are high on the e-sports genre? Or that young Hispanic/Latino adventurers are really into escape rooms? These discoveries give brands new advertising, product placement, and sponsorship opportunities, for example, which might otherwise go untapped. I’m still trying to get my head around these reported affinities, based no doubt by my own preconceptions.
  • Location Data — According to the World Economic Forum, 90% of the world will soon have or already has a supercomputer in their pocket — a smartphone. We’re actually closing in on four connected devices per person, reports Jeff White, founder and CEO, Gravy Analytics. With smartphones alone, as constant companions, we have a huge opportunity to leverage responsibly use of location data. Location can provide huge “affinity” targeting opportunities.A casual wine user might search and buy online his or her wine. But a wine aficionado visits a winery (Location X), or attends a wine tasting (Location Y), and now you have a true affinity opportunity. Granted, location data has a level of sensitivity that carries, more often than not, an opt-in requirement — but the marketing lift can be a significant reward for the advertiser who strategically applies such insights from it. Makes me want to tag every latitude and longitude for some hobby or interest!
  • Experiential Data — Live Nation may own concert venues, Ticketmaster, online game communities and music/culture festivals — but across these many first-party experiences, the company can provide deep analytics that help monetize its various audiences through enriched second-party relationships, said Anubhav Mehrotra, VP, Live Nation. Hilton, American Express, and Uber are just some of the brands Live Nation has teamed up with to enrich brand users with engaging experiences, such as backstage tours and “meet the artists.”

We are all trying to create and sustain customers, using data to discover new patterns, new audiences, and new prospects — and that requires a lot of testing, and innovative data sets to explore (responsibly). Let’s make it experiential, as well as experimental: I sure hope to meet some ultra-high-net-worth individuals at the next Electronic Dance Festival I attend. Or not.

How to Find New Customers, Based on Current Customers, With a Targeted Mail List

When you need to acquire new customers, purchasing a targeted mail list is the way to reach them. However, some lists are better than others. We talked about five types of prospecting lists in the last post; now, we will discuss analytics for profiling and modeling lists.

Your mailing list is critical to your mailing results. When you need to acquire new customers, purchasing a targeted mail list is the way to reach them. However, some lists are better than others. We talked about five types of prospecting lists in the last post; now, we will discuss analytics for profiling and modeling lists.

The better your list is targeted, the better your response rate will be.

  • Descriptive Analytics: Is a profile that describes common attributes of your customers and helps to target, based on demographic lookalikes. The market penetration of each attribute shows the comparison between customers and overall population living in the same geo area, with the same attributes, where each element is examined separately. Basically, you will see who your best customers are and find prospects just like them.
  • Predictive Analytics: Is a model that finds how two or more groups are similar or dissimilar. For example, buyers vs. non-buyers; or responders vs. non-responders. Then it assigns a score that represents a probability-to-act, based on the interaction of several attributes. That way, you can get a better idea of who buys what in order to find more people like them.

So why would you want to try one of these options? You can expect an improved response rate, more effective cross-sell and up-sell opportunities, and the ability to build better loyalty programs, because you understand people better. These processes help you identify prospects who “look like” your best customers.

Profiling allows you to profile your best customers (B2C or B2B) and find out what makes them different from others in your target market. You can target new prospects who are the most likely to respond, purchase, or renew, based on your customer data. You can gain precise information about your customers, based on the statistical analysis of key activities. Finally, you will understand the lifetime value of customers, including their probability to respond and purchase products, with a highly advanced model.

Predictive modeling is a process that analyzes the past and current activities or behaviors of two groups to improve future results. This is accomplished between the comparisons of two groups of data. The differences are assessed to identify whether a pattern exists and if it is likely to repeat itself. Scores can be applied to prospect data purchases, or to segment client data for marketing.

Both provide great opportunities for you to target and reach prospects who are more likely to be interested in what you are selling. This way, your offer resonates with them and compels action. This is another way to increase your ROI, as well as save money. You are mailing to only qualified people, so there are less pieces to print and mail. Keep in mind that your customer list is going to get the best response rates, but a highly targeted list like these will have higher response rates than an average purchase list. Are you ready to profile and model your list?

 

Need Prospects? 5 Direct Mailing List Types to Help You Find Them

Direct mail is a great way to reach targeted prospects to turn them into customers, but how do you select the right prospects? There are so many mailing list options, it can feel overwhelming. Let’s look at the various list options.

Direct mail is a great way to reach targeted prospects to turn them into customers, but how do you select the right prospects? There are so many mailing list options, it can feel overwhelming.

Let’s look at the various list options.

Prospect data is marketing data that has been collected and compiled for the purpose of new customer acquisition. This data is compiled from a variety of public record sources, including deed recordings, surveys, telephone directories, self-reported and more.

5 Prospect List Types

  1. Residential/Occupant: This list is compiled from USPS intelligence carrier route-level demographics, and you can segment businesses. The purpose of this type of list is to saturate an area and have names associated with it, along with census demographics, unlike EDDM. The advantage of this list is deep postal discounts. The disadvantages are the ability to only target to the ZIP-carrier route level, there are fewer options for personalization, and it uses only postal data.
  2. Consumer: This list can be selected by demographics, psychographics, life stage, lifestyles, behavioral, new mover, new homeowner, new borrower, new connect, pre-mover, mortgage/loan, and property data. The purpose of this type of list is to target consumers at their home addresses. The advantages of this list are: controlling who receives your offer; rich demographics selects for enhanced targeting, so you can use variable data for creative optimization; you can use look-alike targeting through the use of demographic profiles; and there are multi-channel opportunities.
  3. Business: This list can be selected by contact names, job titles, company size, ownership status, square footage, own vs. rent, years in business, business expenses, credit rating, SIC, and NAICS. The purpose of this list is finding businesses and/or business professionals. The advantage of this list is you can target specific types of businesses and key contacts within them.
  4. Specialty: This list can be selected by many things. Here are some of them: automotive, hospitals, doctors and nurses, education, government, voters, clubs/nonprofits, insurance agents, pilots, realtors, churches, or pool owners. The purpose of this list is to be able to target consumers or businesses based on specific niche attributes most commonly related to occupation/profession. The advantage of this list is a highly targeted audience.
  5. Managed: This list can be selected by niche marketing, RFM, subscriber files, specific purchases, past purchases, hotline buyers, multi-buyers, responders, or donors. The purpose of this type of list is the ability to identify consumers and businesses by their actions and affinities; to benefit from RFM (recency, frequency, and monetary value). The advantage of this type of list is significant targeting.

Keep in mind that the cost of prospecting lists goes up, the more targeted you get. However, by targeting correctly, you can send fewer mail pieces to more people who are most likely to buy from you. You can save money, send to the right segment of people, and increase your ROI when you mail to the right people.

There is also the option of profiling your current customers and then finding like people in your marketplace, which we will discuss in the next article. Are you ready to select your prospect list?

Media Outlook 2019: Spell Marketing with a ‘D’

The January marketing calendar in New York has included for the past decade or so a certain can’t-miss event of the Direct Marketing Club of New York. In 60 fly-by minutes, 100-plus advertising and marketing professionals hear a review of the previous year in marketing spend, a media outlook for the current year and macro-economic trends driving both.

The January marketing calendar in New York has included for the past decade or so a certain can’t-miss event of the Direct Marketing Club of New York. In 60 fly-by minutes, 100-plus advertising and marketing professionals hear a review of the previous year in marketing spend, a media outlook for the current year and macro-economic trends driving both.

Bruce Biegel, senior managing director at Winterberry Group, keeps everyone engaged, taking notes and thinking about their own experiences in the mix of statistics regarding digital, mobile, direct mail, TV and programmatic advertising.

“We will be OK if we can manage the Shutdown, Trump, China, Mueller, Congress and Brexit,” he noted, all of which weigh on business confidence.

Suffice it to say, marketing organizations and business, in general must navigate an interesting journey. Biegel reports estimated U.S. Gross Domestic Product (GDP) growth of 2.3 percent in 2019 down from 3 percent in 2018, while total marketing spending growth in 2018 had dipped below its historic level of exceeding two times GDP growth.

In 2019, we are poised for 5.3 percent growth in advertising and marketing spending a slight gain from the 5.2 percent growth of 2018 over 2017.

Watch the Super Bowl, By All Means But Offline Dominance Is Diminishing

Look under the hood, and you see what the big drivers are. Offline spending including sponsorships, linear TV, print, radio, outdoor and direct mail will spot anemic growth, combined, of 0.1 percent in 2019. (Of these, direct mail and sponsorships will each post growth of more than 3 percent, Winterberry Group predicts.)

But online spending growth display, digital video, social, email, digital radio, digital out-of-home, and search will grow by 15.5 percent. Has offline media across all categories finally reached its zenith? Perhaps. (See Figure 1.)

Figure 1.

Credit: Winterberry Group, 2019

Digital media spend achieved 50 percent of offline media spend for the first time in 2018. In 2019, it may reach 60 percent! So who should care?

We do! We are the livers and breathers of data, and data is in the driver’s seat. Biegel sees data spending growing by nearly 6 percent this year totaling $21.27 billion. Of this, $9.66 billion will be offline data spending, primarily direct mail. TV data spending (addressable, OTT) will reach $1.8 billion, digital data $7.85 billion, and email data spend $1.96 billion (see Figure 2.)

Figure 2.

Credit: Winterberry Group, 2019

Tortured CMOs: Unless She’s a Data Believer

Marketing today and tomorrow is not marketing yesterday. If marketing leadership does not recognize and understand data’s contribution to ad measurement, attribution and business objective ROI, then it’s time for a new generation to lead and succeed. Marketing today is spelled with a D: Data-Driven.

Unfortunately we don’t have all the data we need to manage Shutdown, Trump, China, Mueller, Congress and Brexit. That’s where sheer luck and gut instincts may still have a valid role. Sigh.

How to Take Your Direct Mail List Targeting to a New Level

There are some new technologies out there that can help you better define your direct mail targeting by adding attitudinal data. What is attitudinal data? It is the attitudes, preferences, motivations and beliefs that are behind the consumer decisions driving response. When you are able to add this to your list, you can really drive response.

Your direct mail list targeting is extremely important. The better you are able to target the right people, the better your response rate will be. There are some new technologies out there that can help you better define your targeting by adding attitudinal data. What is attitudinal data? It is the attitudes, preferences, motivations and beliefs that are behind the consumer decisions driving response. When you are able to add this to your list, you can really drive response.

Consider this, if you’re trying to drive donations or acquire new members, you will want to identify potential donors who are highly predisposed to your specific cause and likely to donate regularly. You can also find people who are attracted to your specific campaign message and have the highest propensity to respond. Just because two people might look the same based on demographics and behavior does not mean they are. Let’s say you have two people who live in the same neighborhood, drive the same type of car, are in the same income bracket and have kids under 10. Traditionally, these people are sent the same message and expected to respond the same way. However, consider that they are individuals and may not find the same creative attractive. They also do not share the same motivations, or like the same offers or causes.

Check out these list targeting options:

Standard List Rental

How it works:You already know your customers’ demo or behavioral profile, so you buy a list that matches that profile (Women aged 25-45 with children at home & HHI $80k+).

Why you’d use it: It’s cheaper, it’s simpler, and that really matters to you.

Syndicated Lifestyle Segments (e.g., Claritas, Mosaic)

How it works: You match your customer file to syndicated lifestyle segments to provide broad demo, behavioral, lifestyle and attitudinal profiles.

Why you’d use it: Superior to standard list rentals for most applications, and provides valuable profiling insights on your customers.

Standard Response Model

How it works:You match your customer file to a consumer database, and your analytics provider builds a custom response “look-alike” model to find consumers with similar characteristics to people who’ve responded in the past.

Why you’d use it: You want to reliably and consistently repeat your past success.

Custom Attitudinal Modeling (e.g., Twenty-Ten)

How it works: Matches your responder file to a custom consumer attitudes database and builds a customer hybrid attitudinal/behavioral model that identifies consumers most likely to have the attitudinal predisposition to respond to your ad.

Why you’d use it: You want to build on past successes and realize significant improvement in targeting accuracy.

The ability to differentiate between people and only send your mailer to the person most likely to donate or purchase from you will significantly increase your campaign response and ROI. The same thing applies if you are selling products or services.

One of the common misconceptions about data targeting is that it is expensive. When you consider the lift you can get with targeting accuracy that increases your results, you can find that it more than pays for itself. So whether you choose syndicated segments from Claritas, or purchase intent data from Experian, including this additional layer to your list creation will help you optimize targeting, increase campaign engagement and boost response.

Marketers have considered it hard to budget for new list testing. However, with the right vendors, you can try out altitudinal data with a test run at no cost. The typical response increase with custom attitudes in your direct mail targeting is 20% to 80%. Are you ready to get started?

The Power of Purchase List Targeting

It’s important to have a trusted purchase list source. You should be informed of where the company gets its data, how often the data is updated and its policies on bad data. Once you have a good source, you need to take on the challenge of choosing your list options.

targetaudSince your response rate is directly related to who you are sending mail to, purchasing a mailing list can be a real challenge. There are so many options to choose from that it can be overwhelming. But first, it’s important to have a trusted purchase list source. You should be informed of where it gets the data, how often the data is updated and its policies on bad data. A couple of big purchase list players are Experian and Acxiom — you can check them out, as well as many other reputable list brokers. Once you have a good source, you need to take on the challenge of choosing your list options.

Top industry list option examples include:

  • Nonprofit: Income, net worth, age, children, causes donated to in the past, organization membership, fundraising engagement, location
  • Retail: Number of children, income, age, gender, apparel purchase habits, brands, online shopping habits, location
  • Political: Children, homeownership, voting propensity, location, age, political party affiliation
  • Entertainment: Age, income, children, hobbies, purchase history, location, marital status
  • Healthcare: Age, income, number of children, location, gender, homeownership
  • Education: Age, income, gender, highest level of education, location, interests

You may pick from demographics as well as psychographics. There are so many options, make sure to give yourself time to look over what will target your best potential customers. You want to get the right offer to the right people — the more targeted your list, the better response you are going to get. Marketing personas are fictional representations of your ideal customers, so if you have mapped personas beforehand, it will be easier to make your selections.

You can pre-map customer personas by taking a look at your best customers: Who are they? The more details you have, the more accurate the persona will be. Look for trends in how your customers find you and what they buy. Make sure you are capturing important information about customers in your data so that you can use it to build your personas. You should also interview customers to obtain key answers directly from the source. Too many assumptions can cause you to create an inaccurate persona.

Once you know the personas you are looking for, choosing the right selections for your list becomes easier. Select the options that best represent your customers. The more characteristics you pick, the better targeted your list will be. But keep in mind that more selections often result in a higher-priced purchase list. So make sure you only use the options that really reach your target.

Your list is now ready! Your final ingredients for successful direct mail are your creativity and your offer. Don’t spend all your time on the list and forget these other two components — without all three working together, your direct mail will not generate the response you are looking for. Make your offer clear and concise. Make your creative design catching, but not overwhelming. Give people a reason to read your direct mail.

Good News: B-to-B Prospecting Data Is More Accurate Than You May Think

Business marketers are always complaining about their customer data. “It’s pretty bad,” they’ll say. “It’s a mess.” But our latest study shows that prospecting data is surprisingly accurate — well over 90 percent.

PE0214_dataBusiness marketers are always complaining about their customer data. “It’s pretty bad,” they’ll say. “It’s a mess.” Over the last decade, my colleague Bernice Grossman and I have studied this issue, producing a series of five research reports on the quality of the data B-to-B marketers can rent or buy for prospecting purposes. Our latest study, published this week, shows that prospecting data is surprisingly accurate — well over 90 percent. We actually verified the accuracy by outbound phone, thanks to the call center at PointClear.

In past studies, our focus has been on both data quantity and quality, with the goal of giving marketers a sense of how likely they will be to reach all the prospects they want, with minimal waste, using the prospecting data provided by U.S. vendors today.

We were generally satisfied with the method we used to get at data quantity, where we asked vendors to provide company counts in specified sample industries and contact counts at specified sample companies.

But when it comes to data quality, we have long wished for a better method of verifying the accuracy of the company records provided by data vendors. Fortunately, an opportunity arrived with a generous offer from Dan McDade of PointClear to televerify the data samples. PointClear provides lead generation and management services, and houses a sophisticated and efficient call center run by Karla Blalock.

So we invited vendors of B-to-B prospecting data to participate, and we structured a research study to get at the accuracy of a statistically projectable sample of company records from the vendors. The five participants who agreed, and contributed a data sample, are Equifax, Harte Hanks, Infogroup, Lake B2B and Salesforce. Our sincere thanks to them all. (Harte Hanks has since sold its prospecting data business.)

The televerification process took place immediately on receipt of the names, but the analysis was more complex than we expected. Eventually, the skilled analyst David Knutson generously volunteered to work on the research data for us.

Methodology and Process
We asked the vendors to supply records as follows:

  1. All firms located in PA, GA, WI, OH and CO, with $25+ million revenue, HQ locations only.
  2. Company name, address and URL.

We planned to televerify firms that were common to all five participants. PointClear conducted a merge, and called the common companies in random order, stopping when 103 companies had been contacted successfully. The televerification took place during the period of August 28 to September 15, 2014.

The Research Results
Having asked for all headquarters sites of $25+ million revenue companies in five states, we found the company-level data to be generally accurate, above 90 percent.B-to-B Prospect Data Accuracy

Overall accuracy by vendor ranged from 92.9 percent to 97.8 percent. When looking at the accuracy by data element, company name was the most likely to be inaccurate, at 91.2 percent overall. There were some minor (less than 5 percent) accuracy problems with the street address, zip codes and URLs. The state data reports at a perfect 100 percent because the companies were selected on a state level.

Marketers can feel fairly comfortable that the prospecting data they get from vendors is likely to be reasonably accurate when it comes to company names, postal addresses and URLs.

Advice to Business Marketers Ordering Prospecting Names
B-to-B marketers should be prepared for a certain number of errors, due to the inherent limitations of merge/purge software, and software variations among vendors. Business addresses are complicated, with variations like P.O. box versus street address; headquarters versus divisions and subsidiaries; and legal name versus trade name. Marketers need to examine how their vendors maintain data at the company level, and then specifically ask for data to be pulled the way they want it.

Other suggestions for marketers to consider:

  • Take a sample of records for testing, and do your own televerification, before placing a large order.
  • Examine the incoming records for problems.
  • Use a trusted list broker who has a thorough knowledge of the particular vendor’s file.

We hope our research is useful to business marketers who are renting or buying data for finding new prospective accounts. The data may be a lot more accurate than you think.

A version of this article appeared in Biznology, the digital marketing blog.