19 Steps to a Successful Direct Mail Campaign Timeline


ADVERTISING DIRECT MAIL

BY Craig Simpson

There are many details that go into setting up a direct mail campaign. If you establish a set of procedures to follow for each mailing and use a checklist to guide you, scheduling your campaign can be much faster and easier than you think.
Include the following items on your direct mail campaign checklist:
1. Write sales copy and sales copy design. Make sure your piece is written and laid out before scheduling the rest of your mail campaign.
2. Give list broker the mail schedule. Plan ahead with your list broker, and make sure your broker is aware of your mailing schedule. You’ll want your broker to make several list recommendations, and the more time you allow for the process, the more research he or she will be able to do. Provide your broker with a copy of the sales piece you’re mailing and, if possible, a copy of the product you’re selling. By doing these things, you’ll help your broker recommend the best lists for you.
3. Request printing quote for sales piece. Get price quotes from several different printers. Select the printer who offers the best combination of price and service.
4. Place list orders with broker. After you’ve selected the lists you want to mail, place an order with your list broker. Tell your broker what the list due date is and where you want the lists to go. Also ask your list broker to send back an order confirmation.
5. Give mailing schedule to data processing company. The company that’s going to handle your merge purge and data hygiene will need to know when the list due date is–i.e., the date that all the rented lists should be in. It will also need to know what lists you ordered and their order numbers to help it identify which lists it’s received and whether any are missing. You’ll also want to give the data processor instructions concerning what type of data hygiene you want to use so it knows exactly what’s needed to clean up your lists.
6. Artwork due at printer. This is the date on which you need to have the file containing your sales piece at the print shop. Have the print shop tell you how much lead time it needs to get your job done on time, and make sure you send the file in a format the printer can read.
7. Approve bluelines and color proof from printer. After the print shop receives your file, it will output a blueline and color proof, or email a PDF. The blueline is taken directly from the film that will be used to create printing plates. On the blueline, you need to check the copy, line breaks, page breaks, borders, cropping–in other words, everything. This is your last chance to make changes. Check the colors to make sure they’re exactly what you want. Any errors found on the blueline will end up on your printed material unless you make corrections.
8. Send data processor suppression files and seed list. The suppression files are names you want to omit from the mailing. For example, if you’re mailing an offer to sell a limited edition watch, you’ll want to omit all prior buyers of that watch. The seed list is the group of names and addresses you use to track delivery of the mailing. The list would have the names of individuals in different regions of the country who’ll inform you when they receive the sales piece and let you know what condition it’s in when it arrives.
9. Mailing list due date. This is the date all the lists are due at the data processor. If a list isn’t in by this date, you’ll need to cancel it. You don’t want to hold up the merge purge and possibly risk changing the mail date over one list not arriving on time.
10. Issue merge purge instructions and approve merge purge. These are the instructions you give to the data processor indicating what criteria you want to use for running the merge. After the merge purge is completed, check the results to see if there are any red flags. For example, if after the merge purge one of your 10,000-name lists is reduced to 2,000 names, you should investigate why.
11. Issue key codes and splits. The key codes are used to track the effectiveness of different elements of your campaign, such as lists, copy variations, sales package, etc. These codes will help you know what the response rate is for each list and tell you how well your test pieces are doing.
12. Issue lettershop instructions. These instructions tell the lettershop–the company assembling your mail pieces and preparing the mailing for the post office–how to process your job.

Tell them:
• Where the address label should appear on your sales piece
• What type of font you want for the address information
• What class of mail you want to use–first class or standard/bulk
• If you’re mailing a letter package, specify the insertion order for each component and in what direction you want everything to face
Ask the lettershop to presort the mail file into mail groups by ZIP codes to save the Postal Service time. It will give you a discount for doing some of the work.
13. Approve key codes and splits. After you’ve given the data processor your key codes and splits, it should send back a confirmation and sample. The data processor needs to apply the instructions you gave it and then let you approve.
14. Mail file due at lettershop. This is the date the data processor needs the final mail file at the lettershop.
15. Printing due at lettershop. Your sales material is due at the lettershop on the same day the mail file is due. The lettershop will then have everything needed to start processing your job.
16. Postage request from lettershop. The lettershop will take your mail file and calculate the total postage for your mailing. Then it’ll send you a request for postage. The Postal Service will not accept your mailing unless the postage is paid in advance.
17. Approve address panel(s). When the mail file is ready, ask the mailing facility to fax or email you a sample of how the names and addresses will appear. Check to see that everything looks exactly the way you want it, and that it appears in the correct place on the material.
18. Reports due from data processor. After the merge purge is complete, the data processor will send you reports from the merge purge process. Review these reports, and then, if necessary, send portions of them to your list broker so you can get the deductions you deserve on your list rental.
19. Postage due date. It’s necessary to get the postage to the lettershop the day before the mailing. You don’t want to miss your mail date because you didn’t get your postage in on time.
After your mailing has been dropped off at the post office, get a verification form confirming the number of pieces it received from you, the class of mail and the total cost. You also want the form to have the Postal Service “date stamp” for the day your mail entered the mail stream. This verification tells you whether the lettershop mailed the correct number of pieces and on the correct day.

For All Your Direct Mail and Data Needs Contact ApexDM.net

S. Tyler Stapley
Apex Direct Marketing – CEO / Owner
Skype – tyler.stapley1
E-mail – tyler@apexdm.net
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“The harder I work, the luckier I get.”
Samuel Goldwyn

 

8 Advertising Potholes Auto Dealers Should Avoid


In a drive to encourage truth in auto advertising, the FTC has announced Operation Steer Clear – a coast-to-coast law enforcement sweep focusing on deceptive TV, newspaper, and online claims about sales, financing, and leasing. If you have clients in the auto industry, the lessons of Operation Steer Clear can help keep them on the right track.

The companies named in the 10 lawsuits include four California dealers: Casino Auto Sales in La Puente, Rainbow Auto Sales in South Gate, Honda of Hollywood in Los Angeles, and Norm Reeves Honda in Cerritos. Also the subject of law enforcement action are Fowlerville Ford in Fowlerville, Michigan; Nissan of South Atlanta in Morrow, Georgia; Infiniti of Clarendon Hills in Clarendon Hills, Illinois; and Paramount Kia in Hickory, North Carolina. In addition, the FTC took action against Texas-based Southwest Kia companies, including New World Auto Imports in Dallas, New World Auto Imports in Rockwall, and Hampton Two Auto Corporations in Mesquite. A lawsuit against Courtesy Auto Group in Attleboro, Massachusetts is heading to trial before an Administrative Law Judge.

You’ll want to review the complaints to see the allegations in each particular case, but busy dealers can supplement their TO DO lists with these TO DON’TS, ad-related practices the FTC challenged as illegal in one of more of the cases:

Deceptive pricing. Some dealers lured prospective buyers onto the lot by advertising vehicles at a specific low price. But the real price was $5,000 more. (The complaint mentions that some of these ads involved a mix of English and Spanish.)

Deceptive teaser payments. In some cases, dealers advertised attention-grabbing low monthly payments. What they didn’t explain up front was that those were temporary teaser payments that would get jacked up after a short period. The FTC says dealers didn’t state the number of payments and how much they would be after those first few low monthly payments.
Undisclosed balloon payments. Another dealer advertised low monthly payments without clearly disclosing that buyers would owe a final balloon payment. What’s more, the FTC says the dealer didn’t disclose the amount of that balloon – in this case, over $10,000.

False $0 up-front leasing claims. Some companies advertised that consumers wouldn’t have to pay anything up front to lease a car. Not true, says the FTC. In fact, lurking behind those goose eggs were hefty fees and other amounts due up front.

Undisclosed lease terms. The FTC says some companies touted low up-front amounts and low monthly payments in their ads without clearly explaining that the transaction was actually a lease and involved substantial hidden fees.
Hidden rates. In one case, the FTC charged that the dealer claimed to offer 0% for 60 months. But as it turned out, the rate applied only if people bought a new car for up to a certain dollar amount – in one instance $12,000. If the car of a consumer’s dreams was, say, $18,000, the buyer would have to pay a higher rate, and that rate wasn’t clearly stated.

Bogus prize promotions. One dealership used a mailer to get folks in the door, falsely claiming the consumer had won a sweepstakes prize.
Credit and leasing violations. In many of the cases, the FTC charged that companies violated the Truth in Lending Act (TILA), Reg Z, the Consumer Leasing Act, and Reg M – long-standing laws that any dealer should be familiar with. One common thread: the failure to disclose key credit- or lease-related terms in ads.

To settle the FTC lawsuits, the companies have signed proposed orders that will change how they do business in the future. Notable terms in these legally binding settlements: a ban on ads that misrepresent the cost to buy, lease, or finance a vehicle and a prohibition on other deceptive claims about pricing, sale, leasing, or financing. When charged in the complaint, the orders mandate that dealers abide by TILA and the Consumer Leasing Act.

Also forbidden: bogus claims about sweepstakes, prizes, or other incentives.
The FTC is accepting comments about the proposed settlements by the February 10, 2014, deadline.

• By Lesley Fair

Data Brokers and the Driving Force behind the Data Economy


TARGETED DATA LISTS

 

With all the talk of data out there, who is actually using it and what are they using?  Turns out, most data used in the enterprise today comes from internal applications. That is starting to change, and the trend will accelerate.  But for now, when asked which data types were important to their firm’s overall business strategy, the majority of business intelligence users and planners cited internal sources such as transactional data from corporate apps or other customer data. Only about 1/3 of respondents reported the importance of external sources such as scientific data, partner data or other 3rd party data.

Fewer still used unstructured external data such as Twitter feeds or other social media sources. Current data sources are limited.  Yet both business and IT decision-makers recognize the need to improve their use of data:  56% of business and IT decision-makers surveyed by Forrester see improving the use of data and analytics to improve business decisions and outcomes as a top priority. And, that potentially includes expanding the use of external data… if they can find it.

Where do they go for external data?  What types of data might complement their transactional and other internal data? How can corporate strategists and market research teams identify new sources of information? Where can they find them, and how can they acquire and consume them? Can they be combined with internal data? Are the sources safe? reliable? sustainable?

We’re kicking off research here at Forrester that looks at data providers and enablers in the new data economy. Some are veterans of the data industry like Lexis Nexis  or Dunn & Bradstreet.  But others are new players who have entered the market to help facilitate the exchange and use of data.

For example, Enigma is a new search and discovery platform for public data – note they do not say “open data” but rather any data that is available to the public whether enterprise data, scientific data, academic data or open government data.  They describe the “public data paradox” in which data is out there and available but not accessible.  Public data remains in diverse formats – although that will slowly change with new government mandates for API access – and is not yet indexed and searchable.  As they put it “you are limited to the data you know about” and you can’t see the connections among different data sets.  At Enigma, they have built an infrastructure for acquiring, indexing and searching public data.  It makes it easy to find data on a particular subject, and most importantly find data sets you didn’t even know about. As they say, “a lot of people have been pioneering how we analyze the world with data” but what was missing was how to find that data.  They want to be the “Google” for public data.

Another example on my side of the pond is Data Publica. Like Enigma, they help businesses acquire data, yet with less emphasis on self-service or a “Google” approach.  Data Publica will help identify data sources, will extract and transform the raw data into a usable structure, and will deliver data as a service.  Delivery mechanisms range from dashboards and reports to data sets or streams, either as a one-off purchase or through subscription access.  Customer solutions include regional dashboards used to determine market opportunity and RFP notifications alerting potential bidders of a new request.  Small businesses who might not have the internal resources to watch the websites of multiple government agencies on a daily basis benefit from the RFP aggregation Data Publica provides by pulling data on public RFPs from over 100 sources.  Getting a timely alert that an RFP has been issued can be the key to winning the bid.

DataPublica also assists the other side of the equation, the producers/owners of public data.  In the City of Nantes, DataPublica provided guidance in the launch of the open data initiatives, advising on data structure, as well as the delivery of data through both visualizations and APIs.  Public organizations may produce data, but they face the perennial challenge of bringing it to market.

Given the skills shortages and difficulty in recruiting data expertise, enablers like Data Publica and Enigma are key to the development of the data economy. Stay tuned on more Forrester research on these enablers of the data economy.  Posted by Jennifer Belissent, Ph.D.

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Crush the Complexity of Cowardice


Wonderful advice to follow. Reducing ones “drag” by eliminating things that are not working instead of just adding adding adding is something I don’t see too often.

3 Reasons Why “Big Data” Isn’t Really All THAT Big


Quality Payday Leads

Over the last couple of years, Big Data has been unavoidable. It’s not just big, it’s massive. If you throw a stone down the streets of London or New York, you’ve got as much a chance of hitting a big data guru as you do a social media guru.

Undoubtedly, there is great power in data, but is Big Data all it’s cracked up to be?

50% of my brain thinks Big Data is great, and 50% of me thinks it’s a neologism. I’ve found it difficult to reconcile all of the varying information out there about it.

So join me for the first part of a two-part series looking at Big Data. In part one, I’ll look at Three reasons why Big Data is a big load of baloney. And next week in part two, I’ll look at Three reasons why Big Data is awesome.

1. Big trends are trendy

My pet rock still hasn’t moved, and my Tickle-Me-Elmo still won’t shut up. And also, Big Data is big, at least according to Google Trends:

Targeted Data
Source

Some other terms once synonymous with the inter-web were pretty trendy too. Remember this one?

Auto Finance Leads
Source

The adoption curve of the term “web 2.0” looks quite similar to where we are now with Big Data. And yet, if you still use the term “web 2.0” in your job, then you probably think the Fresh Prince still lives in West Philadelphia. (He doesn’t.)

The thing about Big Data is that it really isn’t anything new. Cluster analyses, propensity modelling, neural networks and the like have been in use in the marketing sphere for quite some time.

The phrase used a few years ago for this sort of stuff was ‘business intelligence’

STUDENT LOAN DEFAULT LEADS
Source

But now, we don’t care about business intelligence anymore. Who needs intelligence? It’s over-rated. Like Goethe said, “All intelligent thoughts have already been thought”.

And yet, Big Data is everywhere. Why shouldn’t it be? It’s BIG. However, you ask 10 people what Big Data means, you’ll get 10 answers, none of which make much sense.

Maybe it’s because of this:

EDU LEADS
Source

We’ve all seen Moneyball and read Nate Silver’s blog. There are people out there who are better at statistics than you. And this is scary.

So what’s the solution? Throw a bunch of money at Big Data, whatever it is, and sleep soundly knowing that you’ve gainfully employed a math graduate.

And therefore, Big Data is a big load of baloney.

2. Missing one V

Gartner defines Big Data as requiring Three V’s: Volume, Velocity, and Variety. So let’s look at this a bit deeper.

Volume of data: for sure, there’s loads of data out there. Huge amounts. Check.

Velocity of data: yep, data is moved around in large quantities faster than ever before. Check.

Variety of data: in most digital marketing ecosystems, there are the following types of data (yes, I know there are more, but for the sake of argument bear with me):

  • Site stats.
  • Email engagement stats.
  • Mobile/SMS stats.
  • Past purchases.
  • Demographics, preferences etc.

And within each of these, the options are finite. For example, in email, most people measure (at the very least) opens, clicks and conversions. That’s three types of data.

And for all of the other areas above it’s the same. For the sake of argument, let’s say that we’ve got 30 types of data in total.

This is the thing. 30 types of structured data. Processing this data doesn’t require a super-computer, it simply requires robust statistical methodology.

So, if you’re a digital marketer, what you actually have is ‘a few sets of structured, small data’, not ‘Big Data’.

And therefore, Big Data is a big load of baloney.

3. You can perfectly predict the past

With the beginning of the National Hockey League’s 2013-14 season fast approaching, I’ve been spending a lot of time lately trying to determine the best bets to place on the eventual winner.

And of course, it seems Big Data is the best route to my next million dollars. (Btw if anyone is interested in joining my hockey pool then drop me a line – go-live is 1st October!)

I downloaded as many team statistics as I could from last season and embedded them into a spreadsheet. It included rudimentary statistics such as Goals For and Goals Against, right through to Winning % when trailing after two periods, CORSI 5v5, and defensive zone exit rate.

Then I ran a multiple regression and removed non-causal variables. I perfected the model such that the formula spat out expected point totals that were on average within 0.5 points of the actual result.

When I plugged in the raw data from the previous season, the outputted expected results weren’t even close to the actual results.

This is a perfect case of what is called ‘over-fitting’.

When you have a lot of data, the urge is to use all of it and create an uber-complex, bullet-proof formula. Take all of your data points and find the trendline that touches everything. But there’s an inherent problem with this – all you’ve done is create a formula to perfectly predict the past.

The risks that come with an over-fitted model are twofold:

  1. You are assuming that the future will      be the same as the past.
  2. Adding or removing variables becomes      extremely difficult and risky.

So despite there being lots of data out there, the dominant strategy is to focus on the causal variables. In the hockey allegory above, while I won’t reveal my secrets, two of the stronger predictors of eventual success are goal differential and shot differential.

Not rocket science, I know – if you take more shots than your opponents you’ll generally score more goals than your opponents. However, I did learn to remove strictly correlative variables (such as Faceoff Win %, PDO and punches thrown).

Instead of focusing on Big Data and its billions of variables, I’m instead focusing on a small amount of variables that actually matter.

Within your organisation, what are your causal variables? By looking at all the Big Data available to you, you run the risk of the truly valuable signals being obfuscated by irrelevant correlates.

And therefore, Big Data is a big load of baloney.

Disagree?

I do too. Well, 50% of me does. Feel free to elaborate on your point of view in the comments section below.

Parry Malm is Account Director at Adestra and a guest blogger on Econsultancy. Connect with him on LinkedIn or Google+.

Topics:Data & Analytics

by caesararum

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Student Loan Defaults at an All Time High: This May be the Unexpected Solution


Student Loan Default Leads

 

More than 7 million consumers are in default on federal or private student loans, according to a new study, and standardized financial literacy is on the horizon to stop it in future generations.

The Consumer Financial Protection Bureau (CFPB) found that student loans now outpace credit cards as the highest level of consumer debt.

In the U.S., existing student loan debt is estimated at $1.2 trillion.

Default typically occurs when a loan receives no payment for 270 days. New collection costs are then added to the loan’s balance, and the loan becomes more expensive than its original principal. The added costs can only be reduced or eliminated through negotiation or legal action.

According to the U.S. Department of Education, 37 million Americans currently have outstanding student loans and 11% of all student loans are at least 90 days delinquent.

“We strongly believe in the importance of communication and financial education that helps students better understand the serious ramifications of defaults, delinquencies and forbearance,” said Pat Morris, CEO of ACA International, an association of credit and collections professionals.

Problem is many Americans are illiterate when it comes to finances.

A John Hancock Survey found that 46% of respondents who answered a literacy quiz earned a failing grade with 22% earning a D and 23% receiving an F.

While some were able to select correct answers to questions about financial concepts or product definitions, most exhibited significant knowledge gaps.

For example, only 37% were able to choose the correct answer when asked about an optimal retirement savings strategy.

About 94% of those surveyed properly identified the definition of asset allocation and 85% understood dollar cost averaging but only 62% understand that the price of a bond or bond fund decreases as interest rates rise.

“It is critical that children learn the grammar of economics and finance, the specialized language that describes how our economy operates,” said Nan J. Morrison, president and CEO of Council for Economic Education (CEE). “Without that proficiency, they are likely to remain on the periphery of their own potential.”

Currently, only four states require a minimum of one semester of financial literacy education in primary and secondary school and only 20 states require that the topic be taught within another subject area.

That will soon change if Rep. Matt Cartwright has his way. He introduced the Financial Literacy for Students Act, HR 2920 with the support of 23 Representatives earlier this month.

The Act would create incentive grants to states who agree to provide financial literacy education in Title I public elementary and secondary schools.

“The importance of consumer sophistication on financial matters has never been more important than it is in today’s economy,” said Cartwright. “The key to improving the financial literacy of all Americans is ensuring that our students have access, at all appropriate stages of their education, to formal financial literacy education.”

Under current law, individual states are left to create and implement financial literacy education curriculum and courses in their districts and schools.

Below are 5 financial terms that improve your financial literary

  • 1. Dollar cost-averaging: Purchasing the same dollar amount of investments each month so when      share prices are low you get more shares, and when share prices are high      you get fewer shares.
  • 2. Asset allocation: A method of assigning your financial contributions to different      risk classes of investments, such as equities and bonds and cash.
  • 3. Index funds: Seek to match the investment returns of a specified stock or bond      benchmark, such as the S&P 500
  • 4. Forbearance: An agreement that allows graduates to temporarily postpone or      reduce their federal student loan payments.
  • 5. Delinquency: Student loan default is a state of delinquency on student loans      occurring after a missed payment exceeds 270 days. Student loan default      remains on a graduates credit report for seven years.

–Written by Juliette Fairley for MainStreet

Payday lenders covered by FTC Act


Magistrate Judge’s finding: Payday lenders covered by FTC Act even if affiliated with American Indian Tribes

 

In an FTC action challenging allegedly illegal business practices by a payday loan operation affiliated with American Indian Tribes, a United States Magistrate Judge just issued a report and recommendation on the scope of the FTC Act.  Attorneys will want to give the order a careful read, but here’s the need-to-know nugget:  Over the defendants’ vigorous opposition, the Magistrate Judge concluded that the FTC Act “gives the FTC the authority to bring suit against Indian Tribes, arms of Indian Tribes, and employees and contractors of arms of Indian Tribes.”  Most importantly, the Judge’s finding confirms that the FTC’s consumer protection laws apply to businesses regardless of tribal affiliation.  The FTC sees that as a key step in protecting consumers from deceptive and unfair practices.

The FTC sued a web of defendants — including AMG Services, Inc., 3 other Internet-based lending companies, 7 related companies, and 6 individuals, including race car driver Scott Tucker and his brother Blaine Tucker — for violating Section 5 of the FTC Act, the Electronic Fund Transfer Act, and the Truth in Lending Act in their payday loan practices.  Some of the defendants tried to get the FTC case dismissed, claiming that their affiliation with American Indian Tribes makes them immune from those federal statutes.

Not so, urged the FTC.  True, the FTC Act makes no specific reference either way to its applicability to tribal entities.  But citing Supreme Court and Ninth Circuit precedent, the FTC reasoned that “statutes of general applicability that are silent on tribal issues presumptively apply to tribes and tribal businesses.”

The defendants responded that the FTC Act isn’t a “statute of general applicability” because Congress wrote certain exemptions into the law.

“Exemptions alone aren’t dispositive,” said the FTC, quoting the Ninth Circuit’s Chapa De case.  As the Court held in Chapa De, “The issue is whether the statute is generally applicable, not whether it is universally applicable.  We have previously held that other federal statutes that contain exemptions are nevertheless generally applicable.”

Citing that decision and others, the Magistrate Judge’s report and recommendation rejected the defendants’ immunity theory and concluded that “the FTC Act has a broad reach and is one of general applicability.”  The order reserves judgment on whether the defendants are “not for profit” corporations

for purposes of the FTC Act, but held that TILA and EFTA apply regardless of the defendants’ disputed for-profit status.

The Magistrate Judge’s report and recommendation is now subject to review by United States District Judge Gloria M. Navarro.

A related update:  The FTC reached a partial settlement with the principal defendants in the case.  Under the terms of the order, those defendants will be barred from using threats of arrest and lawsuits as a tactic for collecting debts, and from requiring all borrowers to agree in advance to electronic withdrawals from their bank accounts as a condition of getting credit.  The FTC continues to litigate other counts against the AMG defendants, including that they deceived consumers about the cost of their loans by charging undisclosed charges and inflated fees.

By Lesley Fair