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Let's talk definitions / Let's clarify some important terms Data Hygiene / Data Cleansing: Typically a disparate set of standardized processes, such as NCOA or Basic Deduping. Accepts a standard/required input and returns a static set of fields. Processes are typically rigid and do not accommodate the variety of data sources, inputs, and goals. Each step is done independently of the others and priced separately, making it difficult to understand the final cost. Often times laborious reformatting is required. Data Enhancement / Data Enrichment / Data Append: Matching input data to a large standard file consisting of consumers or businesses to add a variety of additional data points. Examples of added data include: Consumers - Demographics, such as Age, Income, Occupation; Psychographics, such as buying behavior; Contact information such is emails and phones. Business - Firmographics such as Revenue, employee size, Industry Codes, etc... Contact information such as emails and phones. Data Quality Solutions: Comprehensive end-to-end process to solve various data issues as a whole. Considers the input data sources, the goals, and desired output to create a unique customized and automated solution. Data quality tools: Tools that are installed in-house or software as a service. They are used to load your data for a wide range of standardization, reporting business intelligence, deduping, combining, etc... Typically they are higher priced and also require the expertise of the business needs and goals end of the data itself. These tools still require a large investment of time and learning. Master Data Management/MDM: is a technology-enabled discipline in which business and IT work together to maintain all aspects of the quality, consistency, and accountability of the data across the entire company. MDM supports the business initiatives and objectives. First-Party Data: First-party data collection is consent-driven and inherently more transparent than other collection methods, and advertisers who can act on collected data will have a leg up against competitors and the fast-changing industry. This will typically include your customer list people that have inquired and provided their information people that have opted in to receive newsletters and shared their information.

If you're only targeting vehicles four or five years old and newer for a buyback offer, how many opportunities are you missing out on? Here are insights into the used car inventory on Dealer Lots: Expanding your criteria to seven to ten-year-old vehicles can dramatically increase your universe for potential buybacks. This graph combines the last 3 years of traded-in vehicles: 2019 - 2021 These numbers show the used vehicles that were first posted for sale on dealers' lots across the country. This includes both franchises and independents. Typically, these are the vehicles that someone has traded in when purchasing another vehicle. The graph shows that the 2 most recent model years (2020 & 2021) have relatively low trade-ins Source: Xcelerated Data® I Sold It™ file (compiled daily)

For the last two years, the automotive industry has felt like a WWE wrestling SmackDown event. This current year is turning out to be WORSE than last year. Here are insights into the used car inventory on Dealer Lots: This graph compares the last 3 years: 2019 (normal), 2020 (COVID), and 2021 (Feb storms and chip shortage) These numbers show the used vehicles that were first posted for sale on dealers' lots across the country. This includes both franchises and independents. Typically, these are the vehicles that someone has traded in when purchasing another vehicle. The lines are a 7-day rolling average The lines show 2019 with fairly normal cycles which include a slow down towards the year's end. The COVID shutdown is evident in April of 2020. The normal industry cycles are evident at the end of 2020, although lower overall. Finally, we can see the effects of the February storms and now the chip shortage, which has caused a large divergence. Source: Xcelerated Data® I Sold It™ file (compiled daily)

The quality of your business decisions is only as good as the quality of your data. Poor quality data costs businesses a staggering $700 billion a year. What is poor data quality costing your business? Insights on your customers drive business decisions and marketing results. Even one incorrect record can translate to thousands of dollars in wrong decisions and lost sales. True data quality is an iterative and multi-step process that depends upon: The sources of your data Goals you have for your data Your industry Not sure where to begin evaluating your data quality? Here are 5 signs you may need help. 1. You only run NCOA Only running an 18-month NCOA might satisfy the Post Office requirements but does not constitute data quality. Do you really want this to be the only metric of accuracy you use for your customer database? NCOA is one small part of the entire multi-step process to create accurate insights from your data. Did you know that 20%-30% of new movers never fill out an NCOA card? Addresses can also contain inaccuracies and fail to match the NCOA database requiring additional hygiene and standardization.2. You haven't changed your processes in years Naturally, your data degrades over time without proper maintenance. Data quality tools and processes are continually evolving. How are you incorporating new technology and tools into your total data quality program? When was the last time you evaluated and updated your processes? Although there are many powerful data hygiene tools, attempting to combine these without some expertise will introduce additional flaws in your data. 3. You lack confidence in your data What keeps you awake at night? Multiple Processes Input fields with similar data (i.e. 3 phone fields, 2 addresses, 2 emails etc...) Countless returned fields How confident are you that you're keeping the "best records" for each project? DIY data disasters can easily happen if you don't take the time to evaluate the impacts of a flawed process. If you are unsure how to proceed you can start by contacting our experts or taking the free database challenge 4. The cost of your multiple data hygiene steps is complex Let's face it, pricing can be confusing when you are combining disparate tools, vendors, and processes. What is the real cost when you consider the different pricing schemes and order minimums? If you don't have a handle on this, it might be time to seek outside help from a data expert with one upfront price. 5. Your marketing content is great, but it's not getting the response you expected Hours and dollars are invested in marketing campaigns but the results are disappointing. What went wrong? Typically, the issue is poor-quality data. Data quality is the least expensive part of your marketing that drives the most expensive part. We can help you avoid these issues and boost your customer response with our proprietary process, Xcelerated ONE™.

It happens… You’re under a time crunch to deliver data for a business project, and a few duplicates catch your eye. What now? Luckily, Excel provides tools to dedupe data in a pinch. WAIT!! Before you click that Remove Duplicates button, and before you delete any records, follow these critical steps: Step 1: Make a copy of your data first! Step 2: Standardize your data fields Step 3: SORT your file Step 4: View your duplicates! Step 5: Choose the correct fields (If this is overwhelming - we are a phone call away 877-236-9155) Step 1: Make a copy of your data first! It goes without saying, but we're saying it anyway: Take that 1 minute to click FILE --> SAVE AS and make a copy. You will thank yourself (and us) later. Step 2: Standardize your data fields The Remove Duplicates functionality in Excel uses EXACT matches. This is very different than what a human eye will recognize as duplicates. Here are the steps for standardizing the most common fields: 1. Standardizing Addresses: Utilize CASS standardization or even better, NCOA. (Need help processing CASS or NCOA?) Attempting to fix a mixture of Address1 and Address2 without a postal solution requires a complex formula and may still result in inconsistencies. 2. Standardizing Split Names: Names have become more creative over the years, making it nearly impossible to 'standardize' a person's name. Tom may not like to be called Thomas; maybe it is Tomas, or maybe it really is just Tom. Incorrectly modifying a person's name can be insulting. To be safe, the most you should do is remove punctuation such as commas, periods, and apostrophes for matching purposes. The following describes the steps of a file that has a split name field of First Name and Last Name. If your data is a mixture of Full Name and Split Name, we can help with a more sophisticated approach. a. Copy the name columns: The best option for deduping on names is to copy the name columns for matching while leaving the ORIGINAL names intact. Highlight the "First Name" and the "Last Name" column and right-click Select Copy on the drop-down menu Scroll all the way to the right and highlight the first empty column Right-click on the column header Click on the first clipboard icon, which is the basic Paste option (With the two name fields copied to the end of your file, you can now remove punctuation using those copied columns) b. Highlight the two new name columns c. Click on Home --> Find & Select --> Replace In the "Find what:" box, type a period Leave the "Replace with:" box BLANK Click Replace All The periods are now gone d. Repeat this for each of the punctuation types: commas, apostrophes, and any other that exist Final result after punctuation is removed 3. Standardizing Phone Numbers: To Standardize a phone field, use the same steps as above to copy your phone fields. a. Copy the phone number column b. Highlight the new phone column c. Click on Home --> Find & Select --> Replace d. Repeat this for each of the punctuation types that exist: This will take several steps to remove EACH one SEPARATELY. Here is the typical list of characters in a phone field: ( ) - , . (If your phone field has additional characters that are not numeric, continue to step through the "Find & Replace" command box.) Step 3: SORT your file This next step is CRITICAL. Sort your file to include the field(s) that are causing the duplication. In some cases, multiple fields will need to be sorted. Example: ZIP Code, Address 1, and Address 2. Why is sorting critical? When Excel removes duplicates, it will keep the first record in the set and remove the rest. There may be cases where you sort a field in descending order to preserve the best record. Sorting helps ensure the most desired record is first. The below example prioritizes Vehicle Year to keep the record with the newest vehicle and deletes the rest. 1. Sorting Address Fields: a. Highlight all columns including the headers. (Caution - if you only highlight and sort a few columns, you will skew your file). b. Click Home --> Sort & Filter --> Custom Sort c. Choose the fields for sorting In the Custom Sort dialog box, use the dropdown arrows to choose the fields for sorting. The sort order matters. For addresses, choose ZIP Code FIRST, then Address 1, then Address 2. Keep these in the default sort order ("A to Z"). d. Set the Priority Choose the field within the duplicates that will determine the priority. In this case, we want to keep the record with the most recent vehicle the person owns. Add the Vehicle Year to the sort box, and change "Smallest to Largest" to "Largest to Smallest" Your file is now sorted in the correct order and ready for deduping by address. 2. Sorting Name Fields: When deduping by name, follow the same steps as above: a. Highlight all columns including the headers. b. Click Home --> Sort & Filter --> Custom Sort c. Choose the fields for sorting Choose Last Name as your first sorting field, then, First Name. d. Set the Priority Again, choose the field that determines the record you would like to keep. In the above example, this is vehicle year. 3. Sorting Phone Fields: Same steps as sorting address and name: a. Highlight all columns including the headers. b. Click Home --> Sort & Filter --> Custom Sort c. Choose the fields for sorting For sorting phones, simply choose the field containing the phone numbers. d. Set the Priority As in the above examples, choose the field that determines the record you would like to keep. in this case, vehicle year Step 4: View your duplicates! Now that you have standardized and sorted your data, take a few minutes to scroll through the duplicates. Do a spot check to determine if; There is additional punctuation that needs to be removed The duplicates contain exact matches Further standardization is needed Other anomalies exist that need to be addressed Step 5: Choose the correct fields for deduping 1. Removing Duplicates Based on Addresses a. Select the entire spreadsheet. Make sure all of the columns are highlighted. b. Click Data --> Remove Duplicates c. Click the My data has headers box if it's not already selected. d. Resize the "Remove Duplicates" dialog box. Drag down the bottom border to enlarge the dialog box. This will allow you to easily view the entire list of fields. e. Click Unselect ALL so that NO boxes are selected. By default, all fields are selected when the dialog box is opened. f. Check ONLY the boxes that are causing the duplicates. In this case, that is ZIP Code, Address1 and Address 2 g. Click OK to remove the duplicates After clicking OK, a new dialog box will appear showing the number of records removed, and how many unique records remain. You can always click UNDO, if needed, to revert back. Below is the before and after comparison for the current example. You can see that the first record in the duplicate set remains, while the others are removed. 2. Removing Duplicates Based on Names Use the standardized fields that were created for the names in Step 2, not the original. (Once the dedupe is completed, you can then delete these extra columns if desired). a. Select the entire spreadsheet. Make sure all of the columns are highlighted. b. Click Data--> Remove Duplicates c. Click the My data has headers box if is not already selected. d. Resize the remove duplicates dialog box. Drag down the bottom border to enlarge the dialog box. This will allow you to easily view the entire list of fields. e. Click Unselect ALL so that NO boxes are selected. By default, all fields are selected when the dialog box is opened. f. Check ONLY the boxes that are causing the duplicates. In this case, that is Last Name and First Name g. Click OK to remove the duplicates After clicking OK, a new dialog box will appear showing the number of records removed, and how many unique records remain. You can always click UNDO, if needed, to revert back. 3. Removing Duplicates Based on Phones Use the standardized phone number field, not the original. (Once the dedupe is completed, you can then delete this extra column if desired). a. Select the entire spreadsheet. Make sure all of the columns are highlighted. b. Click Data--> Remove Duplicates c. Click the My data has headers box if is not already selected. d. Resize the remove duplicates dialog box. Drag down the bottom border to enlarge the dialog box. This will allow you to easily view the entire list of fields. e. Click Unselect ALL so that NO boxes are selected. By default, all fields are selected when the dialog box is opened f. Check ONLY the boxes that are causing the duplicates. In this case, Phone Number. g. Click OK to remove the duplicates After clicking OK, a new dialog box will appear showing the number of records removed, and how many unique records remain. You can always click UNDO, if needed, to revert back. Finally... Check your results. If the duplicates did not remove as expected, it is possible they were not exact matches and require further standardization, or perhaps advanced matching algorithms are required. *Frequent question: Can you use Conditional Formatting to remove duplicates? NO, see our previous blog AVOID These 3 Deduping DIY Disasters