AccuData Integrated Marketing http://www.accudata.com An Integrated Marketing Data Solutions Company Wed, 28 Jan 2015 14:25:06 +0000 en-US hourly 1 When Good Mailing Lists Go Bad http://www.accudata.com/good-mailing-lists-go-bad/ http://www.accudata.com/good-mailing-lists-go-bad/#comments Thu, 15 Jan 2015 16:33:55 +0000 http://www.accudata.com/?p=1005 As much as we don’t like to admit it, good mailing lists — well-targeted, high-quality mailing lists — go bad. Understanding the causes behind increasing inaccuracies and decreasing responses can help you navigate common roadblocks and lead you toward campaign success. Issue One: List Decay Frankly, mailing lists begin to decline in quality the moment […]

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As much as we don’t like to admit it, good mailing lists — well-targeted, high-quality mailing lists — go bad. Understanding the causes behind increasing inaccuracies and decreasing responses can help you navigate common roadblocks and lead you toward campaign success.

Issue One: List Decay

Frankly, mailing lists begin to decline in quality the moment they are produced. You have to think of a list as a living, breathing thing: as the people represented on the list change, the accuracy of the list declines. This is referred to as list decay. Here are two solutions to try:

1. Mail quickly.

Your mailing list is in its best condition when you first receive it from your list provider. Order your list as close to your need-by date as possible and begin your campaign quickly thereafter. Leading list compilers estimate that consumer direct mail lists decay at a rate of 1 – 2% monthly, so the longer you wait, the more likely you are to lose some of the benefits of the list you have selected.

2. NCOA multi-use lists.

Prolific mailers find value in multi-use lists, but with an estimated 15% of the American population moving annually (that’s just over 40 million moves), maintaining a quality, mailable list for long term use requires a service like NCOA. The USPS® NCOALink® service provides you with intelligence on moves (including updated addresses) while being Move Update compliant.

Issue Two: List Fatigue

Yes, absolutely, mailing lists get tired! Though performance may at one time have been optimal, with continued contact, and as recipients become desensitized to your offer, responses and conversions will decline. This is referred to as list fatigue. Here are two solutions to try:

1. Incorporate SourcePlus.

SourcePlus is a sophisticated list selection technology that seeks unique records among the nation’s leading consumer files. With SourcePlus, instead of only mailing to the consumers on your preferred list, multiple data sources can be tapped to identify new, fresh records that could be more receptive to your messaging and offers. For more information on ending list fatigue with SourcePlus, click here.

2. Test new mailing lists.

Perhaps a change in your target market will do your campaign good. A SnapShot descriptive model will use the intelligence within your list of current customers to create a report that will help to identify prospects that look just like your best customers. This process will not only provide you with a highly-targeted group of prospects, but also will help you glean valuable insight into the demographic make-up of your current customers.

Examine your current marketing plan to see if list decay and/or fatigue could be diminishing your results.  Implementing the aforementioned solutions could be just the fix you need!

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AccuData’s ALS Ice Bucket Challenge http://www.accudata.com/accudatas-als-ice-bucket-challenge/ http://www.accudata.com/accudatas-als-ice-bucket-challenge/#comments Fri, 22 Aug 2014 22:08:12 +0000 http://www.accudata.com/?p=827 We were nominated by Gregory Demetriou of Lorraine Gregory Corp to complete the ALS Ice Bucket Challenge… and we accepted! In addition to accepting the challenge, AccuData will be making a donation to the Lee Memorial Health System ALS Clinic. In order to pay it forward, we’d like to nominate a few other friends to […]

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We were nominated by Gregory Demetriou of Lorraine Gregory Corp to complete the ALS Ice Bucket Challenge… and we accepted! In addition to accepting the challenge, AccuData will be making a donation to the Lee Memorial Health System ALS Clinic. In order to pay it forward, we’d like to nominate a few other friends to take the challenge and make a donation to ALS. Dan Anglin and the Modern Postcard team, Eddie Faulkner from Bluewater, and Sandra Worm and the RME 360 team…You’re on deck! You have 24 hours to respond and donate!

 Watch Us Take the Challenge!

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Achieve Better ROI and Create Solutions with a Predictive Model http://www.accudata.com/achieve-better-roi-create-solutions-predictive-model/ http://www.accudata.com/achieve-better-roi-create-solutions-predictive-model/#comments Wed, 13 Aug 2014 20:45:23 +0000 /?p=542 From telecoms to finance, e-commerce to government, a predictive model is being utilized across various sectors to tackle all kinds of business problems. Companies that have yet to benefit from this practice need to examine the ways in which they can do so. For example, AccuData worked with an ad agency to help its client, a […]

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From telecoms to finance, e-commerce to government, a predictive model is being utilized across various sectors to tackle all kinds of business problems. Companies that have yet to benefit from this practice need to examine the ways in which they can do so.

For example, AccuData worked with an ad agency to help its client, a nationally known health and fitness franchisee, increase response rates by over 100%. Their challenge: demographics alone revealed little insight.

With more than 700 stores, they wanted to partner with a direct marketing agency that could provide actual marketing intelligence – not just postcards and lists. The project began with a simple customer profile request as part of an effort to increase response rates. The client chose to work with a savvy, web-to-print direct marketing agency since they had a reputation for excellent creative strategy and back-end analysis.

The direct marketing agency recommended that the client create a descriptive clone model for each of its fitness stores. However, while this would help paint a picture of the various markets, demographic elements and relational penetration, it would not solve the problem of “how do we increase response?”

Matters became even more confusing when they got the results of initial tests between saturation data and scored data from the customer profile. It appeared that scored data actually offered no measurable lift in response – on average, profiled records responded virtually the same as saturation records. The reseller wondered, that perhaps the fitness stores were placed in market areas where immediate surrounding geography was populated by demographics very similar to those on the modeled client file.

That’s where AccuData came in. We provided an actual solution: Using a predictive model to uncover their prime prospects. While demographic elements looked very similar between the profiled records and those saturating the geography of the store sites, we wanted to identify key differentiators.

By implementing a predictive model process, which looked at over 200 individual demographic elements and compared responders verses non-responders, the resulting scored data became a tremendous resource for the client. They learned exactly which demographic traits – such as credit card usage, age, presence of children in the household, and interest in travel – were positive influences on the probability of an individual responding to an offer.

To further improve results, they also applied prospect suppression details – removing from the list non-responders and responders with low propensity to buy. By using a predictive model, the client was able to double their response rates and reduce the costs of their prospect acquisition marketing campaign.

A predictive model will find the statistical differences between responders and non-responders whereas a profile will only give you insight into your customers without regard to those that are not and will not be a customer. So you end up marketing to people that meet a general profile but statistically have a low probability to respond.

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There’s Nothing Wrong with Teasing in Predictive Analytics http://www.accudata.com/theres-nothing-wrong-teasing-predictive-analytics/ http://www.accudata.com/theres-nothing-wrong-teasing-predictive-analytics/#comments Wed, 13 Aug 2014 19:56:50 +0000 /?p=534 We have some national clients that are relatively new to applying predictive analytics models to their marketing campaigns. It is not uncommon to observe them pre-deciding markets without considering regional cultures. Consequently, their marketing efforts often excel in some markets and don’t do so well in others. How do we handle this? The starting point […]

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We have some national clients that are relatively new to applying predictive analytics models to their marketing campaigns. It is not uncommon to observe them pre-deciding markets without considering regional cultures. Consequently, their marketing efforts often excel in some markets and don’t do so well in others. How do we handle this?

The starting point is to implement a national model. The data set should include some sort of market or regional indicator (e.g. Metropolitan Statistical Area, ‘MSA’). After the results come in, the data can be parsed by the market/regional indicator and the campaign results analyzed on that basis. What does this do for you?

This approach will allow you to compare and contrast performance by market. You may find some similarities among markets. In this case, you can combine those markets for targeted treatment – starting with a predictive model built specifically for that market. Unique markets that do not act like others get “teased” out. Strategic decisions can then be made as to the feasibility of giving those unique markets special treatment. For example, using this approach for a national client, we were able to tease out underperforming markets that included smaller MSAs in Texas such as Austin and Portland, OR.

By taking this approach you can use your marketing budget most efficiently while continuously refining your targeted marketing for maximum performance.

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How to Complete a Puzzle that Has Missing Data Pieces http://www.accudata.com/complete-puzzle-missing-data/ http://www.accudata.com/complete-puzzle-missing-data/#comments Wed, 13 Aug 2014 19:18:43 +0000 /?p=529 As you know missing data is a problem for statisticians.  It is analogous to having missing pieces of a puzzle – you kind of know the full picture but you’re forced to somewhat guess at what the missing pieces might look like.  In the purest sense, records with missing data would be eliminated.  In the […]

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As you know missing data is a problem for statisticians.  It is analogous to having missing pieces of a puzzle – you kind of know the full picture but you’re forced to somewhat guess at what the missing pieces might look like.  In the purest sense, records with missing data would be eliminated.  In the real world of compiled and sourced data that means practically all records would be removed because a vast majority of them never have 100% of the fields populated. So how is missing data handled?

There are many schools of thought.  The most popular way is to impute by using an overall average.  However, that approach tends to be misleading and results in false positives.  Instead, mitigate risk by treating missing data as a “worst case” scenario based on the client’s needs.  For example, if a client is affluent prospects and a prospect record is missing data such as an income value, then we will assume a low income value.  There likely will be other affluent factors that are not missing that will improve the probability score.

By taking an approach such as this we’d rather lower the calculated probability than create a false positive that ends up getting this prospect record selected for a marketing campaign.  Although you may be overlooking a viable prospect, you’d rather save your client’s money by chasing the best prospects in a worst case scenario.

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