We’ve made a small change to how Lists that are uploaded as Global are handled within the system.
The list must contain a column that is described as “Country” if Address type (Address, City, State etc) descriptors are also used in the list.
An additional step in the List load process has been added to validate the values in the described Country field.
Any values that Match2Lists doesn’t recognise as a valid world Country are displayed for further user verification.
This is your chance to assign the appropriate Country to the values Match2Lists failed to recognise.
This “learning” is then stored, so in the future those previously unknown values will not have to be matched again.
From then on the process and usage of the List stays exactly the same.
As always your feedback helps make changes. Any suggestions or improvements you think we can make to Match2Lists please let us know, we discuss and read every one. Many thanks.
Until the next time, happy matching.
You’ve got a ton of data. This you know.
The trouble is, there’s a lot more that you don’t know.
For example, how to pull more usable insights from all that data in less time. You’re already working hard, but you know you could do better with more timely, relevant information.
You’re Not Alone
Marketing spend on data management is rising.
In a survey sponsored by Infogroup Targeting Solutions and Yesmail Interactive, 68% of marketers said they expect to increase data spending simply to keep up with the incoming flood of customer data. 56% intend to take on more employees in data roles.
More than half of the survey’s respondents cited data cleansing, analysis or application as their biggest challenge. But, worryingly, 26% couldn’t recall the last time they carried out quality control of their customer data.
Given that 78% of these marketers reported plans to make greater use of social media data for their 2013 campaigns, quality control is more vital than ever.
That’s from the marketer’s point of view, of course. From your target market’s point of view, quality isn’t demonstrated merely by the absence of duplicate messaging or basic spelling errors. It’s something you also show by treating your customers like individuals, even when you can’t give them individual attention.
How do you do that? With data matching and segmentation, so that you can customise messages for different demographics and different points in your sales funnel. Insight shows.
Speed It Up
The biggest problem facing marketers isn’t data. It’s time.
If you had all the time in the world, you could examine all your data personally, one record at a time, until you had all the insight you needed. But you haven’t got a ton of time. You’ve barely got enough time to do what you’re already doing.
So you’ll be glad to hear we’ve fixed that.
One of the things that makes Match2Lists the go-to tool for data matching, merging and deduping is that it all happens lightning fast, in the Cloud. Upload your data, start a project running, take a coffee break. Come back to your desk, approve the results, and download your data. That fast.
Give it a try for free. You’re welcome.
You must have heard this business mantra before: “Keep It Simple, Stupid”, or KISS for short.
I always thought it funny that they didn’t simplify further by leaving out the “Stupid”.
But that’s a classic example of how we overcomplicate business with clever ideas that, in the end, aren’t well aligned with our defined objectives. A cute, memorable acronym is just one more shiny object to chase.
Shiny Objects in Data Quality
Every field of endeavour has its own holy grail, and data quality is no different.
But along the path to data purity and excellence, there are many shiny objects to distract you from your mission. They sound smart, exciting, even fun. The only problem is, they don’t move you in the right direction, and it takes a while to find that out.
So the question is, how do you spot these time-wasting shiny objects and avoid falling under their spell?
It’s really simple. All you need to do is keep your eye on the goal…
If your goal is to keep your data complete, accurate, relevant, timely and consistent, do you really need to use an app that scrapes email addresses or social media handles from online networks? No, because their timeliness and accuracy is famously poor.
Do you really need to buy an expensive, overhyped project management application just to keep track of who’s doing what with your data? Probably not; whatever your business is already using for project management is likely to be more than adequate for your data quality projects too.
Do you need to order every book, ebook and training course that mentions the words “Data Quality”? I doubt it. Some of those books are out-of-date by the time they go on sale, and many of those courses will teach you very little in exchange for a shockingly high fee.
How to Dodge Shiny Objects and Get the Job Done
Look at every investment in your business data quality with a critical eye. If it won’t help you make your data richer and more powerful, then it isn’t necessarily what you need.
If you’re undecided about a new product or service that sounds interesting but potentially shiny, check it out by taking a free trial or a live demonstration. I don’t mean sign up and then forget about it – instead, really put it to work for you. Don’t spend money on shiny objects until you’ve proved to yourself that they’re worth it!
“Keep it simple” is still excellent advice.
If what you really need is a way to match business data, clean up duplicate records, and merge datasets neatly together, then don’t waste your time chasing after shiny objects. Seek out a simple solution, and free yourself to get on with the rest of your business.
Now, there’s something I’d like you to see. Match2Lists can match, merge and deduplicate your data in minutes, but don’t take my word for it – claim your free evaluation account and see it for yourself!
Recently I came across a question online about ‘How do I do a Fuzzy Match of Company Names?
It reminded me of the days before Match2Lists, when we used to suffer with exactly this problem.
Having spent years developing Business Intelligence solutions for multi-national ITC enterprises, I’ve been constantly faced with the task of linking together company information from disparate datasets so as to provide a complete 360 degree view of a customer.
This often involved integrating the Financial Sales History from Oracle or SAP, the CRM data from Siebel or SalesForce, often matching this to information from external Data Providers like Dun and Bradstreet, enabling us to build buying propensity models and provide insightful analytics to business leaders.
Why is Matching Company Names such a Big Issue?
Well, let’s start by determining what is considered successful.
Is it satisfactory to say you matched 80% of the records? What if the 20% that remain unmatched are your biggest customers and responsible for 80% of your revenue? How do you assess the importance of the unmatched data? Does the unmatched data contain strategically important Customers or Prospects?
By matching the data with external data sources you can bring in information to help you determine the importance of these customers or prospects. Looking at Employee Numbers, Industries and Subsidiary or Parent Company Hierarchies can help you to prioritise your efforts.
How do we Match the Company Names?
Matching data that is exactly the same is easy, but what about the non-exact matches? We can use Fuzzy Logic for those, right?
In the forum discussion I came across, they were discussing the different types of algorithms and which was best to use, including Soundex, MetaPhone and Levenshtein. It was a pretty typical discussion that I’ve come across countless times, but the more important issue –and one that is rarely, if ever, discussed– is how this logic is to be applied, regardless of which specific algorithm or algorithms are chosen.
Let me demonstrate what I mean. Here’s an example of Soundex applied to some Big Company Names.
|Bank of Canada||B521|
|Canada, Bank Of||C531|
|Bank of India||B521|
|Bank of Ireland||B521|
|Bank of England||B521|
OK, before we start deriding Soundex, and I know that it’s a much maligned function, let’s take the same Company Names and look at MetaPhone. The arguments are valid for the MetaPhone derivatives such as Double MetaPhone.
|Bank of Canada||BNKFKNT|
|Canada, Bank Of||KNTBNKF|
|Bank of India||BNKFNT|
|Bank of Ireland||BNKFRLNT|
|Bank of England||BNKFNKLNT|
So you can see that with Soundex we would have got some false positive matches for Bank of Ireland, Bank of England and Bank of India, which is not the case with MetaPhone. But the MetaPhone algorithm didn’t suggest that ‘Samsung Electonics’ and ‘Samsung Elec.’ could be matched, and it didn’t match the ‘Bank of Canada’ with ‘Canada, Bank Of’.
Ok, what about Levenshtein? Well, in case you’re not familiar with it, the Levenshtein function returns the number of characters that you would need to change to turn one string into another. The lower the number, the more probability that the data matches.
- Levenshtein(‘Bank of Canada’, ‘Canada, Bank Of’) returns a value of 10 (poor match)
- Levenshtein(‘Bank of India, ‘Bank of Ireland’) returns a value of 5 (poor match)
- Levenshtein(‘Samsung Electonics’, ‘Samsung Elec.’) returns a value of 7 (poor match)
- Levenshtein(‘Visa’, ‘Vista’) returns a value of 1 (Potential match)
- Levenshtein(‘Visa’, ‘Visa Card Services’) returns a value of 14 (poor match)
In practice none of these functions can be used in this way without serious scrutiny of the results, and I found that using matching software that was built on this type of logic produced far too many potential candidates and missed lots of what we would consider perfect matches.
So, What’s the Answer?
Our approach with Match2Lists includes the following;
- Data Standardisation
- Probabilistic Logic
- Fuzzy Logic
- Extensive Knowledge Base
- Ability to Learn from Experience
- Leveraging of Corroborative Information
- Iterative Approach to Matching
- Powerful Visualisation
Data Standardization helps address issues with common abbreviations such as Ltd, Limited, Corp and Corporation, Inc and Incorporated. Unlike some matching systems, we do not advocate removing the legal entity information, as we would rather match ‘Siemens Ag‘ exactly where available rather than ‘Siemens Inc’ or ‘Siemens Corporaton’; we use Probabilistic Logic to manage this.
With Probabilistic Logic we examine the Company Name and determine which elements are of most relevance for matching, and prioritize these. For example in ‘The Procter and Gamble Company’ we would determine ‘Procter’ and ‘Gamble’ to be more important keywords in a matching context, especially when used in conjunction with each other.
By combining fuzzy logic with probabilistic logic, we are better able to ascertain the probability of the data matching. For example, ‘Proctor & Gamble’ would be seen as a very probable match even with the misspelling of ‘Proctor’. Fuzzy Logic is very powerful when used in conjunction with Probabilistic Logic, as it substantially limits the quantity of False Positive matches that Fuzzy Logic is prone to provide.
Extensive Knowledge Base
With our experience of B2B Data Matching over many years, we have built an extensive library of knowledge for common acronyms used for various large businesses. Some examples include ‘GSK’ = ‘GlaxoSmithKline’, ‘BBC’ = ‘British Broadcasting Company’, ‘GE’ = ‘General Electric’, and many many more.
For a large corporate business this knowledge is a must, as it’s not acceptable to leave ‘HP’ unmatched beacuse we had ‘Hewlett Packard’ listed instead.
This is an ever growing library, and covers international and national companies as acronyms can mean different things in different countries.
Leveraging of Corroborative Information
By using other data such as address information, telephone numbers, Longitude/Latitude coordinates, City etc. we can find potential matches where the company name is very different but where a human being can use their own common sense or local knowledge to confirm that this is indeed the same business.
By way of an example, I have recently been matching inventories of Hotel names and in some instances the Hotel name would be the parent chain name such as ‘Best Western’ in one list, and a completely different name in the other list.
Iterative Approach to Matching
Most matching applications run a single pass on the data using one set of criteria, and then output the results for scrutiny. We found this resulted in many good matches being overlooked, and in some cases inferior matches being produced while better matches were overlooked.
By implementing functionality to dynamically change the matching criteria, adjusting the importance of the collaborative information and being able to continually append the results to the same project, we were able to find more matches and ensure we approved the best possible matches.
Lastly, and a real game changer for us, is the implementation of a powerful User Interface to visualize the match candidates. By being able to visually scrutinize the results we were able to determine the best criteria to use, and to automatically approve many more results than before. This Visualization meant that we achieved significant reductions in the cost of our matching projects and were able to deliver the results to our customers in record breaking time.
Get in touch if you would like to learn more about our approach to B2B matching, or if you have any questions.
Be honest: how reliable is your B2B marketing data?
If you’ve been a B2B marketer for more than 5 minutes, then you already know some of the data quality problems that can drag your campaign results down.
According to NetProspex, only 2% of B2B companies have functional contact databases for their marketing. The other 98% are flying part-blind, using databases rated as questionable or unreliable.
Why B2B Marketers Are Flying Blind
Take telephone numbers as one example of data that lets you down. More than half of the prospects in an average B2B marketing database either have no associated phone number, or can’t be reached at the number recorded.
Before 2008, this would have been unthinkably bad for business. In the last five years, though, email and social networks have become the new standard marketing media. Now that more than 58% of B2B enquiries are made online, the accuracy and completeness of phone numbers in marketing contact lists has dwindled rapidly.
This all brings us to the B2B marketer’s pet peeve: leads whose corporate identity is hidden by an email address with a free service like Hotmail or Gmail, depriving you of vital information about who you’re marketing to. In the IT industry, more than 70% of buyers use a second email address for online registration forms rather than give away their business email address.
Even if you have plenty of valid email addresses, the average across industries is that 28% of B2B marketing emails don’t get delivered. In some industries the average bounce rate is even higher; for internet and media companies, it’s 36%.
So, you can’t get through to your marketing contacts by phone and their email address isn’t working for you either.
You’re not alone. The average B2B company’s records are only 50% to 60% complete, and 25% of the average B2B marketing database is inaccurate.
How to Fill the Gaps in Your Marketing Data
There are several ways to increase the completeness of your data. One of the simplest is to match and merge records across more than one dataset you already hold: contact lists from two different sources, for example.
That’s all it takes to fill in some of the blanks and create a more complete master list from your validated match results! Do consider the date of data capture, though – it’s no use completing the record with data you obtained years ago, as a large proportion of your older data will be inaccurate. If you bring in fresh, timely data for your matching projects, you’ll see better results.
Keep Your Data Clean
Businesses with consistent, ongoing data hygiene processes create 4 times as many leads as those whose data is neglected. As well as matching, merging and enriching your data, there’ll be times when you need to deduplicate it.
This is one area of data quality in which most B2B companies are coping better, with average duplication rates of 10% or less. Still, in a database of millions, 10% is a lot of duplicate records! Bring that duplicate rate down for your B2B marketing data by resolving duplicates on a regular basis.
To see the results of matching, merging, deduplication and enrichment using your own data, take advantage of a free evaluation account with Match2Lists. You’ll soon realise how much easier your marketing campaigns can be when your data’s healthy and ready for action!Source for all statistics: NetProspex
Are you getting the results you want from your email marketing?
The majority of businesses, from small startups to global enterprise, use email marketing to reach new customers and maintain their relationship with existing customers. It’s easy and effective.
At least, it should be.
But low-quality data holds a lot of organisations back from releasing the full potential of their email lists. Let’s make sure your business isn’t one of them!
Bounce Rate and Timeliness
When you see a high bounce rate on an email marketing campaign, you know it’s likely that your distribution list is out-of-date. If you buy new data, you’ll want to replace outdated information with the latest data but at the same time you still need those old records that are accurate.
It’s best to enrich and refresh your data just before launching a new email campaign. Importing new lists and merging them with the old doesn’t take long with our cloud-based tools.
Personalised Email Marketing
Increasingly, the emails you send out are highly personalised. Not just with a FirstName and LastName, but with insights from big data analytics.
Relevance is everything. Small segments, tightly focused niche marketing, and reliance on the data make it all work. There are many excellent service providers who help you get this part done.
But what do you do if it’s the data quality that’s letting you down? Problems with record duplication, entry error, or data standardisation can be a plague on your email marketing campaigns, draining them of their power to convert and retain customers.
Improve Data Quality – Fast!
A common solution to data quality issues in contact data is to pay an agency to clean it up for you. Or, you can instal expensive data quality software and train your staff to use it.
But there’s a simpler option: self-service data quality tools in the Cloud.
Cloud computing makes Match2Lists lightning fast and accessible from any internet connection. We designed it to be fully self-service, though we can manage your project for you on request.
You can hit the ground running with no specialist training required. Just upload your lists, choose to match, merge, deduplicate or enrich your data, and the intuitive visualiser helps you validate your project results before downloading them.
Go ahead and open a free evaluation account today to check it out!
Did you buy a new marketing database and need to de-dupe and merge the contacts / companies with your existing database?
Then I think you’ll welcome the new Merge2Lists feature we have just launched within Match2Lists.
With the Merge2Lists feature you can now match & merge your new data against your existing master file, removing duplicates, filling blanks and combining fields with different names to create your new master list, all with the usual simplicity you expect from Match2Lists.
The data matching process uses our ground breaking Match Visualiser as normal, but you will be presented with new options to customise and select how you would like to merge the 2 lists and determine the structure of your new master file when you choose the download option.
We are currently in the process of putting together a video tutorial on this fantastic new feature, in the mean time you will find guidance on screen when merging your data.
Feel free to ping us any questions or comments. Thank you for using Match2Lists.