Hello, I X U, Won’t you tell me your name?

This is a guest post by Peter Hesselink of SDL Content Management Technologies

You want to address visitors in a proper way. Preferably by calling them by their name, showing that you know them, make them feel welcome, indicate you want to have a dialogue with them.

Hello, I welcome you, won’t you tell me your name?

When people visit your site they remain anonymous until they ‘reveal’ themselves. Registering, logging in, etcetera. Until that time they stay visitors or even ‘strangers’.

Hello, I like you, won’t you tell me your name?

You can tell a lot about people by their behaviour, from their interaction with your website. And if you like what you are seeing, you really would like to get to know them personally. Maybe they are already known to you, your customer, but you don’t know that, are unaware of the fact or unable to ascertain.

They even may like you as well, and their ‘like’ makes it possible to reach them, but not at that moment, with a personalized message.

Hello, I know you, won’t you tell me your name?

This is what happens all the time: people revisit your website, which you (can) know thanks to the wonderful invention of cookies. The whole day a lot of UVO’s (Unidentified Visiting Objects – people, robots and crawlers) come to your website. And you know them, because you recognize them by their cookie or even IP address. You can even serve them personalized content, but still can not address them by their name. You do not really ‘know’ these unidentified individuals, these UVP’s who may be VIP’s to your company.

Now it can become embarrassing. You probably also have had the experience of meeting somebody who you have spoken to before, but cannot remember their name anymore, I have had these experiences … Because you met them in a different place, and/or long ago (your memory is not what it used to be anymore).

This can happen on your website: you know they are there, because they have (re-)registered or logged-in, or from previous behaviour or characteristics. But you do not ‘recognize’ them or show this by personalizing the website content.

This all can be caused due to the fact that information is being collected online, through the website, email, or other means, on different moments, stages and places in the customer journey. And stored in different systems and databases. The total system cannot ‘recollect’ it.

It becomes annoying when for instance a customer has to resubmit information which he or she has provided before. They become frustrated and dissatisfied.

Having to ask again … Or not knowing that it is the same person as the one already stored in your database …

A registration could do the trick, after having logged with their user name and password in you are better able to meet their expectations. Or by letting the customer provide unique, identifiable information, like a customer number or something alike. But this does not make it more (user) ‘friendly’.

Hello, I love you, won’t you tell me your name?

That only works when you are somebody like Jim Morrison of the Doors …

Ambient recognition

Data is being collected at several touch points, moments, implicit and explicit (watch out for another post about implicit and explicit profiling) et cetera and stored in different systems, databases and so on. Having a ‘single view of the customer’ is a challenge. But customers expect that companies have this. They hate it when they have the idea that the company does not ‘know’ them, cannot recollect it.

From back in the days I was working at Acxiom Corporation here in The Netherlands I know the challenge in getting the right name and address information and getting it right (the area of DQM and MDM).

Recently I attended an event of the Dutch Dialogue Market Association, titled Data & Dialogue, chaired by Holger Wandt, principal advisor at Human Inference, an expert in the field of Data Quality and Integration (Gartner thinks so as well, named HI in their Magic Quadrant for data quality tools  as a visionary). We spoke afterwards about these challenges.

So how to achieve this? Well, by best combining ‘all worlds’. In a next post on www.EngagingTimes.com I will draw an outline of such a solution making use of Human Inference tools and the Tridion Ambient Data Framework (watch the video).

Short question, complex answer: Who is who and what is what in your database?


Any organization that deals with customer, prospect, supplier, distributor, product and service information, uses all kinds of data in their day-to-day business processes. Identification of a customer or a product within an automated system, using a specific id-number, the name or any other identifying feature, is a key issue in these processes. Furthermore, it is a task that needs considerable attention, since the collection and management of data is essentially error-prone. People make mistakes, names are understood incorrectly, numbers are typed in the wrong order; there are just too many reasons for defective data and poor information quality.

The collective term ‘business data’ is often used without a precise notion of what business data actually contain. It is not just the customer identification numbers and product codes. Naturally, the sort and the importance of data used in a business process will differ from organization to organization. However, a closer look at the seemingly endless variation will show that names and addresses of persons and organizations are as detailed and complicated as they are identifying. The following classification will show the details of names, addresses and complementary data.

* In personal names we will encounter: given (first) names, middle names, initials, surnames, surname prefixes, surname suffixes, forms of address, titles, functions, qualifications, professions, patronymics and nicknames.

* The name of an organization can consist of virtually everything: legal forms, fantasy words, natural language words, personal names, numbers, Roman numerals, ordinals, letters, acronyms, geographical indications, suffixes, articles, prepositions, conjunctions, indication of year of establishment and non-alphabetical signs.

* Postal Address data combine recipient information with delivery points: countries, regions, towns, districts, proximate towns, delivery service indicators, delivery service qualifiers, postcodes, addressee and mailee indicators, thoroughfare names, thoroughfare types,  house or plot  numbers, house number additions, building names, building types and delivery point access data, such as wing, floor or door.

* Complementary data used in business processes include: phone numbers, fax numbers, e-mail addresses, dates of birth, contract dates, social media account id’s, product and brand names, product codes, product numbers, gender indication, financial data, lifestyle data and transaction data.

Defining the data groups as precisely and as detailed as possible, is the first step towards useful interpretation. People, applying their natural language processing capabilities, structure the information as they interpret it. They will use their frame of reference, which includes their knowledge dictionary, their linguistic repository, statistical information and mathematical information.

Knowledge-based interpretation, incorporated in an automated system to solve data quality issues, must work in exactly the same way. Consider the following examples: Continue reading ‘Short question, complex answer: Who is who and what is what in your database?’

Know your customer to trust your data


The success of many business processes is linked directly to the quality of customer data. This is not only an obvious fact, but a recurring conclusion of many field studies: Incorrect, incomplete and inaccurate data will have a direct impact on your business succes rate. The symptomatology of this increase is established in inefficient marketing and sales processes, customer dissatisfaction, difficult cross- and upsell, unreliable analyses and many other disturbances in the day-to-day business of almost every organization dealing with customer, supplier and/or partner data.

In essence, it all comes down to knowing your data, in order to be able to trust your data. If you trust your data, you are definitely doing something right. So, how do you establish that trust? For this, you first have to answer a short, yet rather complex question: What is what in my database(s)? In other words: You have to identify and interpret the data you are working with .

A robust customer data identification solution intelligently interprets the details of both natural and legal persons. That process has to take account of the significance of words in a specific context, usage of company names, abbreviations, synonyms, acronyms, spelling mistakes, notation methods, standards and phonetic similarity of words. All in all, this is not a simple task; it more or less mimics the capabilities that humans show when interpreting data … Continue reading ‘Know your customer to trust your data’

Why there are maximum of (fe)males in a country

Within Europe there is no such system as European Social Security Number or European Identification Number. A lot of countries have their own system, and other countries are struggling to get a system into place.

The struggle of some countries has to do with historical reasons and with privacy aspects. Unique identifiation is not always used in favour of the community. And some of the used identification systems contain privacy-sensitive information, among others date of birth, gender and/or place of birth, where older systems might even contain religious or other privacy-senitive information.

A wide range of countries use the combination of date of birth, gender identification and the political region where you are born. In such a mechanism it is most common that part of the identification number is a 2-digit or 3-digit serial number to identify the unique male or female born on a specific date (or born on a specific month). Some countries provide odd serial numbers for male, and even for female. Bulgaria is the only one that wants “odd” females. Some countries like to divide on range (0-499 male, 500-999 female). And some countries like Norway make nice combinations to include the century of birth or period of birth in the serial number. Continue reading ‘Why there are maximum of (fe)males in a country’

The “miracle” of customer data integration

mulitple view

The more a company knows about its customer’s wishes, needs and habits and the more that company is able to tailor its proposition accordingly, the greater the value it will eventually provide for its customers. We all know that there are countless examples where defective, fragmented, or just plain poor customer data cause unnecessary costs, decrease in revenue, employee dissatisfaction or frustation, damage of the corporate image and many other unsdesirable or painful consequences.

Customer data quality and integration problems impact every area of the value chain of organisations. Far too often companies have a multiple view of their customers. Customer Data Integration (or MDM for Customer Data) is the key to providing companies with a single view of their customer. Continue reading ‘The “miracle” of customer data integration’

Any close encounters with the FBI terrorist watchlist?

tsc080105aJust before this summer the U.S. Department of Justice filed a report about the FBI Terrorist Watchlist. This watchtlist serves as a critical tool for screening and law enforcement personnel for alerting them when they come across a known or suspected terrorist. It is used by personnel at airports, harbours and the borderline. Also when you apply for a visum you are matched against this watchlist. The Terrorist Screening Center, a subsidiary of the FBI, is responsible for maintaining the watchlist.

This watchlist was created in 2004 from several other lists and at that time it consisted of about 68.000 entries. I use the word entries, because in the years after it became fuzzy if one record is the same as one individual. By the end of 2008 the list had grown to over 1,1 million entries. In 2008 after the American Civil Liberties Union (ACLU) mentioned that the list had passed the 1 million, the government came with an explanation. Although we have recorded over 1 million entries in the database, the net result is that these records correspond to about 400.000 individuals. Terrorist often use different and thus multiple identities, use several (falsified) passports etc. But adding entries with only the first initials and last name, while an entry of the full first names and last name already exists will result in unwanted side-effects. Continue reading ‘Any close encounters with the FBI terrorist watchlist?’