The first of two 2023 postal rate increases take effect on Sunday, January 22nd. Price adjustments vary depending on the product, and a few rates remain unchanged. Postmaster General Louis DeJoy said that increases would be necessary to keep up with rising costs.
Click below to download a Marketing Mail Postal Rate Comparison chart.
A local dealership has been using our Lead Program for over 20-years.
Our Bankruptcy Leads help them sell an average of 15 additional cars a month.
They spent less than $60K (including postage) and generated over $360,000 in profit last year using our Bankruptcy Program.
We just completed a test program with another local dealership. We mailed 1,526 letters to people recently discharged out of bankruptcy. That generated 11 calls from 8 prospect which resulted in 3 cars purchased. That’s a 37.5% close rate! Cost per closed deal, including postage was only $522.
Our Special Finance Lead Program Works!
In the past, dealerships haven’t considered using their customer data as a source for advertising. That data was primarily used for sales follow-up calls or service inquiries.
That’s because third-party cookies (small pieces of text sent to your browser that remembers information about websites you visit on the internet) are becoming extinct. Privacy regulations and laws are driving digital giants to stop the use of them. Apple’s latest update allows users to opt out of ad tracking, Firefox and Safari have already stopped storing cookies, and Google will phase them out of Chrome by 2023.
The loss of cookies will make it more difficult to target people who previously visited dealership’s websites making digital ads less personalized.
The good news is that dealerships have a treasure of data of their own. Customers’ emails, addresses, phone numbers, details of their automobiles and more. This data is collected through CRM systems and dealership websites. It’s known as first-party data.
Social media companies can continue to track their user activity within their own platforms which helps to build an audience and allow for retargeting through advertising directly through the platform such as Facebook or Instagram. Social media is facing extreme challenges with the newly introduced privacy options as Apple user opt out of ad tracking, social platforms lose their ability to identify their user locations.
With the recent change in size regulation for First-Class postcards (from 6 X 4.25 to 6 X 9), dealers are beginning to revisit direct mail.
Data compilers can help to “fill in” missing data from dealers first-party data through a reverse append. You provide us with your data, name/address/city/state/zip and we can append phone numbers, email addresses, and run the data through cleansing software that will update records based on the National Change of Address Database, standardize the address information, and check the addresses for accuracy.
To learn more about reverse appends contact us today.
On July 19th the PRC (The Postal Rate Commission) ruled that the Postal Service’s unprecedented price adjustment request for Market Dominant products was consistent with applicable laws and approved the increase to take effect August 29, 2021.
Click on the items below to see rate comparison charts or download a handy BEBdata Postal Rate Guide:
NONPROFIT POSTAL RATE COMPARISON
MARKETING MAIL COMPARISON CHART
Artificial Intelligence (AI) is able to measure tone, tempo and other voice characteristics. Some systems compare those sounds to stored speech pattern libraries that define a plethora of human emotions to determine an individual’s emotional, mental or even physical health.
When this sound technology is used in conjunction with computer vision, the science that allows computers to gain a high-level understanding from digital images or videos, the applications become even more powerful. For example, imagine a vehicle that is able to hear a driver yawning and see the driving dozing off.
Research firm Gartner Inc predicts that within three years, 10% of personal devices will have emotion AI capabilities that include wearables (similar to a Fit Bit) that is able to monitor an individual’s mental health or video games that adapt to the players mood.
Last year, the US Postal Service entered into a contract with self-driving truck startup TuSimple to haul mail between Dallas and Phoenix. Founder, Xiaodi Hou says that this USPS pilot gives them fuel to help validate their system and expedite the technological development and commercialization progress.
TuSimple completed five round trips between May 28 and June 10 of 2019 while a safety engineer and licensed driver ride along in the cab. Its Level 4 self-driving system (see below for self-driving categories defined), uses 8 cameras to detect cars, pedestrians, and other obstacles over one-half a mile away, even in inclement weather.
TuSimple’s camera-based system allows it to achieve three centimeter (1.18 inch) precision for truck positioning even in inclement weather and tunnels with real-time decision making. By keeping aware of traffic flow ahead, trucks are able to maintain a given speed more consistently than human drivers which can cut fuel consumption by as much as 15%.
The USPS has been interested in self-driving technology for a long time. In 2017, a report published by the Inspector General detailed plans to add semi-autonomous mail trucks to its fleet as early as 2025. Placed into service on 28,000 rural routes, they would free up about 310,000 postal workers to sort and deliver packages.
TuSimple has R&D labs in San Diego and test facilities in Tuscon. It expected to close out 2019 with a 200-truck fleet in the US and a 300-truck fleet in China, making it the largest self-driving truck solutions company in the world.
Later the same year, TuSimple operated several self-driving trucks for 22 hours each along the I-10, I-20, and I-30 corridors through Arizona, New Mexico, and Texas. It says freight along the I-10 corridor accounts for 60% of the US’s total economic activity. It expects its semi-autonomous trucks to be a frequent sight along that route in the months ahead.
Self-Driving Systems are categorized by five-levels:
Level 1- Driver Assistance-Under specific conditions, the car controls either the steering or the vehicle speed, but not both simultaneously. The driver performs all other aspects of driving and has full responsibility for monitoring the road and taking over if the assistance system fails to act appropriately. Cruise control is Level 1
Level 2- Partial Automation- The car can steer, accelerate, and brake only in certain circumstances. Maneuvers such as responding to traffic signals or changing lanes largely fall to the driver, as well as scanning for hazards.
Level 3- Conditional Automation-The car is able to manage most aspects of driving, including monitoring the environment. The system prompts the driver to intervene when it encounters a scenario it can’t navigate. The driver must be available to take over at any time.
Level 4 -High Automation-The vehicle can operate without human input or oversight but only under select conditions defined by factors such as road type or geographic area. In a shared car restricted to a defined area, there may not be any. But in a privately owned Level 4 car, the driver might manage all driving duties on surface streets then become a passenger as the car enters a highway.
Level 5- Full Automation-The vehicle can operate on any road and in any conditions a human driver could negotiate.
See you there!
Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and prepare the data for further analysis. Descriptive analytics is sometimes said to provide information about the past or what happened.
The vast majority of the statistics used today fall into this category. (Like sums, averages, or percentages). For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.
Predictive models identify patterns found in historical or transactional data to identify risks and opportunities. Models capture relationships among multiple factors to allow assessment of risk or potential associated with a particular set of conditions. The defining functional effect is a predictive or probability score for each.
One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.
Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.
Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.