Tag Archive for ‘customer data’

Has your name ever hurt you? – when nomen becomes omen

Published by Esther Labrie on 08-08-2011 in Data Quality and Names. 0 Comments Tags: , , , , , , .

Addressing clients with the right data often means the difference between making a profit and not making a profit. Working with data quality experts has made me ever more consious of the value personal data represents for people. In this respect names are especially intriguing to me, as owners appear to identify with their name a lot. So I decided to do a little research and determine if people really are what their name tells you. Can nomen indeed become omen?

Your parents probably gave a lot of thought to the name they once gave you, and as it turns out they were right to do so! Research tells us a name can do wonders for its owner, as well as a lot of damage for that matter. Let’s have a look at some remarkable results.

Peter for President!
Recent studies show that in the US a student called Fred is more likely to fail his exam than a student who just happened to be named Andrew: people tend to indentify with their name and, in general, have a positive feeling about letters that correspond with their initials. Consequently Fred is far more likely to settle for a meager F, while Andrew will have an extra motive to strive for an A. Continue reading ‘Has your name ever hurt you? – when nomen becomes omen’

Marketing? – Let your ingredients interact!

Published by Holger Wandt on 22-07-2011 in Data Quality. 0 Comments Tags: , , , , , .

 

Throughout the years Human Inference has carried out and supported research with regard to the importance, the impact and the perception of customer data quality in business environments. This reasearch shows that the phenomenon customer data quality is subject to a perception shift. In general, one could argue that, in the early years, data quality used to be perceived as “something that is being carried out by the IT-department”, whereas nowadays more and more companies and organizations are recognizing the importance of customer data and information quality. Issues and initiatives such as the value of a single customer view, data integration, fraud prevention, customer relationship management, operational risk management, compliance and anti-terrorism have become boardroom themes. Continue reading ‘Marketing? – Let your ingredients interact!’

New white paper: First Time Right – Turning your customer data into customer lifetime value

Published by Holger Wandt on 31-01-2011 in Data Quality. 0 Comments Tags: , , , , , , .

As promised in my previous post “First Time Right – The customer perspective“, I’m sending out this post to inform you about our new white paper. This paper describes the background, definition and business impact of the application of the First Time Right-principle in any organization. The First Time Right-principle is the basis of your upstream and downstream data management. Making sure that the input of data is correct, valid, complete and standardized, is the starting point of customer lifetime value. The application of the principle will take care of the quality of your data at the source, and will consequently have an increasingly positive effect on the total data quality in your organization.

The paper discusses business examples, the customer contact process, the reciprocity between people, process and technology, and the underlying concept of intelligent interpretation of customer data. In short, there are many ways to turn your data into customer lifetime value. The quickest, most efficient and most valuable is the implementation of the First Time Right-principle.

Please click here and dowwnload our white paper.

International data quality – Is a football always a football?

Published by Holger Wandt on 26-10-2010 in Data Quality. 0 Comments Tags: , , , , , , , , .

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football 1High quality customer data have become the prerequisite for successful business decisions. In order to reach the intended data quality level, a lot of money is being invested in solutions for input control, file merging, data enrichment and duplicate identification. But do these investments guarantee high quality data and information? For example, are the data quality tools and processes equipped for the inevitable internationalization of our business community? Is a football always a football?

Natural language processing

Why do we know that William Jones International Logistics Ltd and W. Jones Int. Transport Co. are probably different notations for the same company? How do we determine that Leonard is a given name in Leonard Peters and a surname in Leonard & Peters? Without being all that aware of it, we are using methods such as pattern recognition, context analysis and other linguistic considerations. To answer the question ”what is what in customer data?” people will use their knowledge of language and culture to interpret the data they will encounter in daily life. Continue reading ‘International data quality – Is a football always a football?’

High precision matching – apples, oranges or fruit salad?

Published by Holger Wandt on 21-10-2010 in Data Quality. 0 Comments Tags: , , , , , , .

apples-oranges In his excellent post “New matching engines go beyond apples and oranges”, Winfried van Holland states that traditional matching engines are based on atomic string comparison functions, like match-codes, phonetic comparison, Levenshtein string distance and n-gram comparisons. He further argues that the drawback of these functions is that it’s not always clear for what purpose one needs to utilize a particular function, and that these low-level DQ functions cannot distinguish between apples and oranges – you end up comparing family names with street names.

Good point! In essence, this is the basis of the discussion on the matching approach within customer data management: As intelligent automated matching of records distributed over various heterogeneous data sources is an essential pre-requisite for correct and adequate customer data integration, there are many opinions on how to achieve this.

In theories on data matching, there are in general two methods that prevail when customer data management is concerned: deterministic and probabilistic matching. Continue reading ‘High precision matching – apples, oranges or fruit salad?’

What’s the value of prospect data or ex-customer’s data?

Published by Ron Mulderij on 12-06-2009 in Data Quality. 0 Comments Tags: , , , , .

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Organisations often collect or buy prospect data to use for campaigns.
In most cases these prospects are stored in the CRM system and only the indicator “Prospect” distinguishes them from customers.
The same occurs with ex-customers who will remain in the CRM system although there is no contact anymore.
Prospects and ex-customers both have the same problem that their data is hardly maintained. Due to job changes the data usually already gets outdated after a year. Continue reading ‘What’s the value of prospect data or ex-customer’s data?’