Postal Rate Increase: The Role of Data Analytics in Optimizing Direct Mail Campaigns

Posted on by Camille Lehmann

 

USPS postal rates are continuing to climb, with the latest increase set to take effect on July 9th, 2023. As a result, direct mail marketers are becoming progressively concerned with the rising costs of campaigns. However, increased postal costs do not mean that your direct marketing budget needs to go out the window.  

The Current State of Postal Rates 

The proposed postal rates were approved by the Postal Regulatory Commission on May 31st, 2023. This rate increase is the fourth of its kind since the post office moved to the twice per year cadence, and the Postmaster General has been clear that increases will continue to occur twice per year at least through 2025. 

As a company deeply invested in postal ongoings, two of our very own, Brandon Jacklin, Postal and Logistics Manager, and Kelly Marthaler, Sales Representative, went to Washington D.C. to collaborate with the ACMA (American Catalog Mailers Association) and Vogel Group to bring awareness around the impact of rising postal costs and tax laws to the Postmaster General Louis DeJoy. 

Direct Mail Optimization Amidst Postal Increases 

What can you do about rising postal rates? Optimize, optimize, optimize! By being wise about your data, you can be strategic in mailing less, but to highly responsive consumers, lowering your postage costs.  

Audience Segmentation 

The more people you mail to, the more postage you must spend, plain and simple. To save on postage, marketers should perform an audience audit. Who is your responsive audience? What is your key demographic and what is their past behavior?  Understanding better who your audience is will help you narrow the net you mail to. Less mail to a more responsive audience equals a higher ROI. 

Predictive Modeling

Data analytics allows marketers to leverage predictive modeling techniques to identify those most likely to respond. By analyzing historical promotional data and customer files, companies can build models that predict customer behavior, response rates and conversion rates. Predictive models optimize various aspects of direct mail campaigns, such as selecting the most responsive target audience, and determining the optimal timing for mailings.  By using data-driven insights, marketers can make informed decisions that maximize the impact of their direct mail campaigns and minimize the costs associated with postage. 

Testing

Data analytics enables marketing to conduct testing and optimization of their direct mail campaigns. By testing different variables such as messaging, design, offers, and call-to-action, companies can measure the effectiveness of each element and make data-driven decisions to improve their campaigns. Through continuous testing and optimization, businesses can identify the most impactful strategies, refine their mailings, and achieve better results while mitigating the impact of rising postal rates. 

Integration with Digital Marketing

Data analytics also plays a crucial role in integrating direct mail campaigns with digital marketing efforts. By leveraging customer data from various sources, such as online interactions, social media, and previous email campaigns, businesses can create a comprehensive view of their customers’ preferences and behaviors. This integrated approach allows for a cohesive and consistent customer experience across different marketing channels. Data analytics helps in identifying the most effective touchpoints, enabling businesses to create a seamless and personalized omnichannel experience that maximizes engagement and conversion rates. 

Don’t let rising postal rates get you down. Instead, focus on ways to optimize your direct mail campaigns to ensure a high ROI. Being smart about your data is one of the necessary tools marketers can use to achieve this optimization.  

If you’re ready to test smarter and drive ROI for your program, fill out the form below for the full guide to direct mail testing today! 

 

The Importance of Data in Direct Mail Marketing

Posted on by Camille Lehmann

Author: Alan Sherman, VP of Marketing Strategy

Quite often, when we work with clients in direct mail marketing, creative development is the first focus. But, just as in any marketing channel, who we target is just as important, if not more so for driving increased direct mail response and a successful direct mail campaign. For a full-service direct marketing company like Nahan, using data in direct mail marketing is a crucial component of an integrated success chain that includes strategy, data, creative and production execution.

Direct mail provides more data points to target against than any other marketing channel. The typical national data compiler manages over three thousand data points per person.*  Combine a multi-sourced wealth of data with sophisticated predictive analytics, and we can precisely rank prospects based on their propensity to respond (or other desired outcomes).

Let us take a look at typical data used by various direct mail industry clients. In the interest of time and space, what follows is not an exhaustive list.

Financial Services and Insurance – Credit Data

For financial and insurance clients, we see widespread use of credit bureau prescreened data – both in terms of trigger (credit or insurance inquiries by consumers) and broad market (often dictated by credit score and other data points) campaigns.

As a direct marketing service provider, Nahan partners with credit data agents, which can provide unique sources of value. Credit data agents typically receive and maintain real-time data from all 3 main credit bureaus, providing a comprehensive picture of all credit behavior across bureaus. More data across all 3 bureaus means more net qualified names, typically 15-20% more, and improved credit decisioning.

It also means more flexibility in terms of FCRA regulations, allowing for counts to be more easily run before actually pulling a file. Typically, when one pulls a complete prescreened credit file, one is obligated to make everyone on that list a firm offer of credit. Credit data agents have more flexibility in this regard. Custom models can make use of both credit and non-credit data for increased predictive power.

While credit data is usually the go-to data source for most financial and insurance acquisition mailers, it can often be supplemented by Invitation to Apply (ITA) data, which is primarily driven by a lifecycle event – such as college graduation, marriage, having children and buying a home. While ITA prospects are typically not as responsive, it is less expensive, and can be tested and paired with credit data as an effective supplemental data source.

Multiple Industries – Modeled Compiled Data

Compiled data is just that – data compiled from multiple sources and then linked to individuals and households. It’s typically used in travel, healthcare, retail, telecom, and auto direct mail.  There are a number of medium and large-sized data compilers that we partner with to provide the best data for our clients. Compiled data typically includes demographic, psychographic, and attitudinal data. 

Demographic data includes data elements like age, gender, income, occupation, and more.

Psychographic data is focused upon people’s interests and hobbies, often obtained via surveys, donations, and specialty lists.

Attitudinal data reflects attitudes and belief systems, typically from surveys and donations made to non-profits.

Compiled data is best paired with predictive analytics to identify the data elements that will give the greatest response.

Catalogers, Non-Profits, E-tailors and Others – Cooperative (Co-op) Data

Co-op data is customer purchase data collected from thousands of co-op members and maintained in a database. Typically, a member marketer must provide their customer data on a regular basis to join and participate. Co-op members include companies from the catalog, retail, etail, continuity, non-profit, publishers, finance, insurance, and business-to-business industries. Some co-ops focus on non-profit donation behavior specifically.

Co-ops collect over 1500 data elements for a given household and cover 190MM U.S. consumers. The depth and granularity of the data can vary by co-op. Given that customer behavior is often the most predictive of future behavior, this data is very powerful in its ability to predict the future response and purchasing.

Using marketer-provided customer purchase data, the co-ops use predictive models to find prospects elsewhere in the database with similar product and purchase behavior. Co-op data has long been a go-to data source for catalogers, replacing many of the more expensive specialty, “vertical” lists that exist, such as magazine subscriber files.

Business-to-Business (B2B) Data

B2B direct mail data used to come from two main data sources – Data Axle (formerly InfoGroup) and Dun & Bradstreet. They are still major players providing excellent data. Both, along with a continual flow of new players, now offer much more than the traditional data points like annual sales, number of employees, SIC code, and NAIC Code. Data points such as B2B buying behavior, public filings and linked consumer information all provide additional targeting insights. Because people change jobs much more frequently than they change addresses, B2B data is more challenging and labor intensive to maintain and keep up to date, resulting in a higher cost.

The Role of Analytics

The performance of all data mentioned here can and should be enhanced by predictive analytics. We simply can’t leverage any of these types of data to their full potential without the use of modeling to prioritize prospects. While a predictive model adds to the cost, it usually pays for itself in the first direct mail campaign with the increased direct response it produces. Depending on the circumstances, the model can be re-used until market conditions change. Machine learning and artificial intelligence have sped up the modeling process, and in certain cases, such as co-op data or credit models, new models may be built with every direct marketing campaign.

Our Data Role

Nahan has deep and long-time relationships with many types of direct mail data providers and list brokers. We can determine which source is the right fit for our clients’ objectives. Typically at a lower cost than our clients can obtain on their own. Our expertise ensures the best possible data at the best possible price. For any questions about data, please feel free to reach out to me at alan.sherman@nahan.com.

*Source: WebFX

Bio: Alan Sherman is our Vice President of Marketing Strategy. Alan enhances Nahan’s current value proposition with strategy solutions that support new/existing client relationships. For clients, he leverages market, customer, and competitive intelligence to build achievable strategies for omnichannel marketing success. His marketing plan strategies include targeted data, predictive analytics, testing and creative that drive ongoing client performance improvement. In his spare time, Alan enjoys spending time with his family, traveling, going to concerts, watching sports (he’s a fan of the NY Giants, Boston Red Sox and Celtics) and walking the dog, even though it was just out.

How To Build an Effective Direct Marketing Strategy

Posted on by Camille Lehmann

Author: Alan Sherman, VP of Marketing Strategy

How many times have we in business or marketing heard the word “strategy?” or “strategic?” or “strategic direction?”  It’s one of those common business buzzwords that we hear all the time, but when it comes to direct marketing, what does “strategy” really mean? Merriam-Webster offers up several definitions of “strategy,” but the one I think is most relevant to direct marketing is “The art of devising or employing plans towards a goal.” After all, successful direct marketing strategy requires effective planning – putting the components of a winning program together.

From Our Perspective, What is Strategy?  

Nahan provides Strategic Planning, which is most simply about enabling clients to achieve their direct marketing goals.  This means improving direct mail gross response, while lowering cost without corresponding drops in response. This can be a challenge, as adding package components, increasing package size or paper quality will often lift response, but add cost. Ultimately, improving upon both response and cost leads to an improved cost per acquisition and superior return on marketing investment. So how do we do this?

A Step by Step, Rigorous Approach Gets Results

We recommend initially approaching this from a macro level. Who will we target and why? If we are focusing on direct mail, what kind of package format is needed? What does our creative and messaging need to look like? What are our target metrics?

The answers to these questions can be found in a step-by-step approach to direct marketing performance improvement. We suggest beginning with a marketing assessment to best understand everything that has been done in the past, from creative, targeting, cadence and offers to the competitive landscape.

Based on our knowledge of data and analytics and an analysis of the marketer’s data, we will likely propose specific types of data and the right predictive analytic tools for data testing. On the creative side, although budget is usually a factor, when we can test more than one creative, we create more opportunities for success.

Finally, based on what we see in the data and from our knowledge of direct mail creative best practices, we design the creative, with the target audience in mind. The data analysis tells us quite a bit about our prospects. We develop messaging, the package design, articulate the offer, supporting benefits and Call to Action.

How Strategy is Rolled Out Against Data, Analytics, and Creative

The available universe is determined. Because we take a “test and learn” approach, a test plan that minimizes risk while testing creative, data, and analytics is developed. When results come back, we measure them and make recommendations for further improvements in targeting and creative. Feeling more comfortable in what works, we can invest more dollars in reaching more people, often expanding to an omnichannel campaign, particularly where we can simultaneously target the same direct mail recipients with online campaigns, can substantially lift results further.

A subsequent results analysis leads to additional improvement – ideally, a never-ending improvement process. Which is necessary, given that costs like paper and postage are always rising!

The Results

We can almost always improve response through our knowledge of what creative and data works, creative and data testing, and predictive analytics. We can also test less expensive materials to bring down package costs. When we work these “levers” in the right way, cost per acquisition drops.

Questions To Ask Yourself and Your Team

What are your acquisition goals for the year? Where do you think your program is working and where might improvement be needed? Are you testing creative and data on a regular basis? Do you rotate your creative to keep it fresh and performance up? If you would like to achieve better results or simply discuss your challenges and receive suggestions, give us a call.

Bio: Alan Sherman is our Vice President of Marketing Strategy. Alan enhances Nahan’s current value proposition with strategy solutions that support new/existing client relationships. For clients, he leverages market, customer, and competitive intelligence to build achievable strategies for omnichannel marketing success. His marketing plan strategies include targeted data, predictive analytics, testing and creative that drive ongoing client performance improvement. In his spare time, Alan enjoys spending time with his family, traveling, going to concerts, watching sports (he’s a fan of the NY Giants, Boston Red Sox and Celtics) and walking the dog, even though it was just out.