B2C

Customer Product Matrix Management

Henrik Gabs Liliendahl2 Comments

A customer/product matrix is a way of describing the relationships between customer types and product types/attributes.  

Example:

Note: Please find some data quality related product descriptions in the post Data Quality and World Food.

Filling out the matrix may be based on prejudices, gut feelings, assumptions, surveys, focus groups or data.

If we go for data we may do this by collecting available historical data related to sales and inquiries made by persons belonging to each customer type regarding products belonging to each product type.  

In doing that correctly we need two kinds of master data management and data quality assurance in place:

  • Customer Data Integration (CDI) for assigning the accurate customer type in the real world related to the uniquely identified person in transactions coming from all sources – here based on location master data.
  • Product Information Management (PIM) for categorizing the relevant fit for purpose product type.

This reminds me about multi-domain master data management. Customer master data (or shall we say party master data), product master data and location master data used to figure out how to do business. I like it – both the master data management part and the mentioned product types.  

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Out of Facebook

Henrik Gabs Liliendahl7 Comments

Some while ago it was announced that Facebook signed up member number 500,000,000.

If you are working with customer data management you will know that this doesn’t mean that 500,000,000 distinct individuals are using Facebook. Like any customer table the Facebook member table will suffer from a number of different data quality issues like:

  • Some individuals are signed up more than once using different profiles.
  • Some profiles are not an individual person, but a company or other form of establishment.
  • Some individuals who created a profile are not among us anymore.

Nevertheless the Facebook member table is a formidable collection of external reference data representing the real world objects that many companies are trying to master when doing business-2- consumer activities.

For those companies who are doing business-2-business activities a similar representation of real world objects will be the +70,000,000 profiles on LinkedIn plus profiles in other social business networks around the world which may act as external reference data for the business contacts in the master data hubs, CRM systems and so on.

Customer Master Data sources will expand to embrace:

  • Traditional data entry from field work like a sales representative entering prospect and customer master data as part of Sales Force Automation.
  • Data feed and data integration with traditional external reference data like using a business directory. Such integration will increasingly take place in the cloud and the trend of governments releasing public sector data will add tremendously to this activity.
  • Self registration by prospects and customers via webforms.
  • Social media master data captured during social CRM and probably harvested in more and more structured ways as a new wave of exploiting external reference data.

Doing “Social Master Data Management” will become an integrated part of customer master data management offering both opportunities for approaching a “single version of the truth” and some challenges in doing so.

Of course privacy is a big issue. Norms vary between countries, so do the legal rules. Norms vary between individuals and by the individuals as a private person and a business contact. Norms vary between industries and from company to company.

But the fact that 500,000,000 profiles has been created on Facebook in a very few years by people from all over world shows that people are willing to share and that much information can be collected in the cloud. However no one wants to be spammed by sharing and indeed there have been some controversies around how data in Facebook is handled. 

Anyway I have no doubt that we will see less data entering clerks entering the same information in each company’s separate customer tables and that we increasingly will share our own master data attributes in the cloud.

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Social Master Data Management

Henrik Gabs Liliendahl3 Comments

The term ”Social CRM” has been around for a while. Like traditional CRM (Customer Relationship Management) is heavily dependent on proper MDM (Master Data Management) we will also see that enterprise wide social CRM will be dependent on a proper social MDM element in order to be a success.

The challenge in social MDM will be that we are not going to replace some data sources for MDM, but we are actually going to add some more sources and handle the integration of these sources with the sources for traditional CRM and MDM and other new sources coming from the cloud.

Customer Master Data sources will expand to embrace:

  • Traditional data entry from field work like a sales representative entering prospect and customer master data as part of Sales Force Automation.
  • Data feed and data integration with external reference data like using a business directory. Such integration will increasingly take place in the cloud and the trend of governments releasing public sector data will add tremendously to this activity.
  • Self registration by prospects and customers via webforms.
  • Social media master data captured during social CRM and probably harvested in more and more structured ways.

Social media master data are found as profiles in services as Facebook mainly for business-to–consumer activities, LinkedIn mainly for business-to-business activities and Twitter somewhere in between. These are only some prominent examples of such services. Where LinkedIn may be dominant for professional use in English speaking countries and countries where English is widely spoken as Scandinavia and the Netherlands other regions are far less penetrated by LinkedIn. For example for German speaking countries the similar network service called Xing is much more crowded. So, when embracing global business you will have to acknowledge the diversity found in social network services.

A good way to integrate all these sources in business processes is using mashup’s. An example will be a mashup for entering customer data. If you are entering a business entity you may want to know:

  • What is already known in internal databases about that entity – either via a centralized MDM hub or throughout disparate databases?
  • Is the visit address correct according to public sector data?
  • How is the business account related to other business entities learned from a business directory?
  • Do we recognize the business contact in social networks – maybe we did have contact before in another relation?

If you are entering a consumer entity you may want to know:

  • Does that person already exist in our internal databases – as an individual and as a household?
  • What do we know about the residence address from public sector data?
  • Can we obtain additional data from phone book directories, nixie lists and what else being available, affordable and legal in the country in question?
  • How do we connect in social media?

Of course privacy is a big issue. Norms vary between countries, so do the legal rules. Norms vary between individuals and by the individuals as a private person and a business contact. Norms vary between industries and from company to company.

If aligning people, processes and technology didn’t matter before, it will when dealing with social master data management.

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What’s In a Given Name?

Henrik Gabs Liliendahl10 Comments

I use the term ”given name” here for the part of a person name that in most western cultures is called a ”first name”.

When working with automation of data quality, master data management and data matching you will encounter a lot of situations where you will like to mimic what we humans do, when we look at a given name.  And when you have done this a few times you also learn the risks of doing so.

Here is some of the learning I have been through:

Gender

Most given names are either for males or for females. So most times you instinctively know if it is a male or a female when you look at a name. Probably you also know those given names in your culture that may be both. What often creates havoc is when you apply rules of one culture to data coming from a different culture.  The subject was discussed on DataQualityPro here.

Salutation

In some cultures salutation is paramount – not at least in Germany. A correct salutation may depend on knowing the gender. The gender may be derived from the given name. But you should not use the given name itself in your greeting.

So writing to “Angela Merkel” will be “Sehr geehrte Frau Merkel” – translates to “Very honored Mrs. Merkel”.

If you have a small mistake as the name being “Angelo Merkel”, this will create a big mistake when writing “Sehr geehrter Herr Merkel” (Very honored Mr. Merkel) to her.

Age

In a recent post on the DataFlux Community of Experts Jim Harris wrote about how he received tons of direct mails assuming he was retired based on where he lives.

I have worked a bit with market segmentation and data (information) quality. I don’t know how it is with first names in the United States, but in Denmark you may have a good probability with estimating an age based on your given name. The statistical bureau provides statistics for each name and birth year. So combining that with the location based demographic you will get a better response rate in direct marketing.

Nicknames

Nicknames are used very different in various cultures. In Denmark we don’t use them that much and definitely very seldom in business transactions. If you meet a Dane called Jim his name is actually Jim. If you have a clever piece of software correcting/standardizing the name to be James, well, that’s not very clever.


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Merging Customer Master Data

Henrik Gabs Liliendahl3 Comments

One of the most frequent assignments I have had within data matching is merging customer databases after two companies have been merged.

This is one of the occasions where it doesn’t help saying the usual data quality mantras like:

  • Prevention and root cause analysis is a better option
  • Change management is a critical factor in ensuring long-term data quality success
  • Tools are not important

It is often essential for the new merged company to have a 360 degree view of business partners as soon as possible in order to maximize synergies from the merger. If the volumes are above just a few thousand entities it is not possible to obtain that using human resources alone. Automated matching is the only realistic option.

The types of entities to be matched may be:

  • Private customers – individuals and households (B2C)
  • Business customers (B2B) on account level, enterprises, legal entities and branches
  • Contacts for these accounts

I have developed a slightly extended version of this typification here.

One of the most common challenges in merging customer databases is that hierarchy management may have been done very different in the past within the merging bodies. When aligning different perceptions I have found that a real world approach often fulfils the different reasoning.

The fuzziness needed for the matching is basically dependent on the common unique keys available in the two databases. These are keys as citizen ID’s (whatever labeled around the world) and public company ID’s (the same applies). Matching both databases with an external source (per entity type) is an option. “Duns Numbering” is probably the most common known type of such an approach. Maintaining a solution for assigning Duns Numbers to customer files from the D&B WorldBase is by the way one of my other assignments as described here.

The automated matching process may be divided into these three steps:

During my many years of practice in doing this I have found that the result from the automated process may vary considerable in quality and speed depending on the tools used.

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What is a best-in-class match engine?

Henrik Gabs Liliendahl9 Comments

Latest in connection with that TIBCO acquires data matching vendor Netrics the term best-in-class match engine has been attached to the Netrics product.

First: I have no doubt that the Netrics product is a capable match engine – I know that from discussions in the LinkedIn Data Matching group and here on this blog.

Next: I don’t think anyone knows what product is the best match engine, because I don’t think that all match engines have been benchmarked with a representative set of data.

There are of course on top the matching capabilities with different entity types to consider. Here party master data (like customer data) are covered by most products whereas capabilities with other entity types (be that considered same same or not) are far less exposed.

As match engine products are acquired and integrated in suites the core matching capabilities somehow becomes mixed up with a lot of other capabilities making it hard to compare the match engine alone.

Some independent match engines work stand alone and some may be embedded into other applications.

These may then be the classes to be best in:

  • Match engines in suites
  • Embedded match engines (for say SAP, MS CRM and so on)
  • Stand alone match engines

Many match engines I have seen are tuned to deal with data from the country (culture) where they are born and had their first triumphs. As the US market is still far the largest for match engines the nomination of best match engine resembles when a team becomes World Champions in American Football. International/multi-cultural capabilities will become more and more important in data matching. But indeed we may define a class for each country (culture).

In the old days I have heard that one match engine was best for marketing data and another match engine was best for credit risk management. I think these days are over too. With Master Data Management you have to embrace all data purposes.

Some match engines are more successful in one industry. The biggest differentiator in match effectiveness is with B2C and/or B2B data. B2C is the easiest, B2B is more complex and embracing both is in my eyes a must for being considered best-in-class – unless we define separate classes for B2C, B2B and both.

As some matching techniques are deterministic and some are probabilistic the evaluation on the latter one will be based on data already processed in a given instance, as the matching gets better and better as the self learning element is warmed up.

So, yes, an endless religious-like discussion I reopened here.

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Create Table Homo_Sapiens

Henrik Gabs Liliendahl19 Comments

Create Table is a basic statement in the SQL language which is the most widespread computer language used when structuring data in databases.

The most common entity in databases around must be rows representing real world human beings (Homo Sapiens) and the different groups we form. Tables for that could have the name Homo_Sapiens but is usually called Customer, Member, Citizen, Patient, Contact and so on.

The most common data quality issues around is related to accuracy, validity, timeliness, completeness and not at least uniqueness with the data we hold about people.

In databases tables are supposed to have a unique primary key. There are two basic types of primary keys:

  • Surrogate keys are typically numbers with no relation (and binding) to the real world. They are made invisible to the users of the applications operating on the database.
  • Natural keys are derived from existing codes or other data identifying an entity in the real world or made for that purpose. They are visible to users and part of electronic, written and verbal communication.

As surrogate keys obviously don’t help with real world uniqueness and there are no common global natural key for all human beings on the earth we have a challenge in creating a good primary key for a Homo Sapiens table.

Inside a given country we have different forms of citizen ID’s (national identification number) with very varying terms of use between the countries. But even in Scandinavia where I live and we have widespread use of unique citizen ID’s most tables that could have the name Homo_Sapiens cannot use a Citizen ID as (unique) primary key for several reasons as well as that data is not present in a lot of situations.

Most often we name the tables holding data about human beings by the role people will act in within the purpose of use for the data we collect. For example Customer Table. A customer may be an individual but also a household or a business entity. A human being may be a private consumer but also an employee at a business making a purchase or a business owner making both private purchases and business purchases.

Every business activity always comes down to interacting with individual persons. But as our data is collected for the different roles that individual may have acted in, we have a need for viewing these data related to single human beings. The methods for facilitating this have different flavours as:

  • Deduplication is the classic term used for describing processes where records are linked, merged or purged in order to make a golden copy having only one (parent) database row for each individual person (and other legal entities). This is usually done by matching data elements in internal tables with names and addresses within a given organisation.
  • Identity Resolution is about the same but  – if a distinction is considered to exist – uses a wider range of data, rules and functionality to relate collected data rows to real world entities. In my eyes exploiting external reference data will add considerable efficiency in the years to come within deduplication / identity resolution.
  • Master Data Hierarchy Management again have the same goal of establishing a golden copy of collected data by emphasising on reflecting the complex structure of relationships in the real world as well as the related history.

Next time I am involved in a data modelling exercise I will propose a Homo_Sapiens table. Wonder about the odds for buy in from other business and technical delegates.

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55 reasons to improve data quality

Henrik Gabs Liliendahl9 Comments

The business value in data quality improvement is an ever recurring topic in the realm of data quality.

In the following I will list the first 55 reasons that comes to my mind for improving data quality related to the single most frequent data quality issue around, which is duplicates (and unresolved hierarchies) in party master data – names and addresses.

It goes like this:

1.  It’s a waste of money sending the same printed material twice or more times to the same individual consumer.

2.  Allowing the same customer enter twice or more times for an introduction offer challenges the return of investment in such campaigns.

3.  When measuring churn and win-back two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

4.  Sending the same promotion eMail twice or more times to the same individual consumer looks like spam even if different eMail addresses are used. Spam has more offending than selling power.

5.  It’s probably a waste of money sending the same printed material with presentation and offerings to a household already having a customer.

6.  Assigning different credit terms for two or more unrelated accounts for the same business hierarchy will make uncontrolled financial risk.

7.  When measuring cross selling results two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

8.  When measuring life time value two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

9.  It’s probably a waste of money sending the same printed material twice or more times to the same household.

10.  When measuring life time value two or more unrelated accounts for the same individual being a consumer and a business owner will produce an incomplete result leading to a wrong decision.

11.  When wanting a 1-1 dialogue two or more unrelated accounts for the same individual consumer will not lead to a 1-1 dialogue.

12.  Having companies represented in two or more unrelated accounts for the same company with a different line-of-business assigned will produce an incomplete segmentation.

13.  When trying to point at your best customers being households in order to find similar households two or more unrelated accounts for the same household will produce an incomplete segmentation.

14.  When measuring cross selling results two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

15.  It’s a waste of money sending printed material with presentation and offerings to an individual consumer already being a customer.

16.  When wanting a 1-1 dialogue two or more unrelated accounts for the same business hierarchy will not lead to a complete 1-1 dialogue.

17.  When measuring life time value two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

18.  Assigning different credit terms for two or more unrelated accounts for the same individual consumer will increase financial risk.

19.  When measuring cross selling results two or more unrelated accounts for the same individual being a consumer and a business owner will produce only an incoherent result leading to a wrong decision.

20.  When wanting a 1-1 dialogue two or more unrelated accounts for the same household will not lead to a true 1-1 dialogue.

21.  Assigning different credit terms for two or more unrelated accounts for the same business entity could increase financial risk.

22.  Having activities related to companies attached to two or more unrelated accounts for the same company will show an incomplete customer history with the risk of taking damaging actions.

23.  It’s a waste of money and credibility sending printed material with presentation and offerings to an individual business decision maker in a business entity already being a customer.

24.  When buying from a supplier having two or more unrelated accounts despite being the same business entity you may miss discount opportunities.

25.  Having companies represented in two or more unrelated accounts for the same company with a different lead source assigned will produce a false measure of marketing and sales performance.

26.  Sending the same promotion eMail or newsletter twice or more times to the same individual business decision maker looks like spam even if different eMail addresses are used. Spam has more offending than selling power.

27.  When measuring  churn and win-back two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

28.  Having activities related to influencers attached to two or more unrelated business contact records for the same person will show an incomplete business partner history with the risk of retaking already made actions.

29.  When buying from a supplier having two or more unrelated accounts despite they are belonging the same business hierarchy you could miss discount opportunities.

30.  Having activities related to households attached to two or more unrelated accounts for the same household will show an incomplete customer history with the risk of taking insufficient  actions.

31.  When trying to point at your best customers being individual consumers in order to find similar individuals two or more unrelated accounts for the same individual consumer will produce a wrong segmentation.

32.  Having companies represented in two or more unrelated accounts for the same company with a different address assigned will produce an incomplete segmentation.

33.  When measuring life time value two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

34.  Having activities related to decision makers in companies attached to two or more unrelated contacts for the same person will show an incomplete customer contact history with the risk of not taking appropriate actions.

35.  When wanting a 1-1 dialogue two or more unrelated accounts for the same business entity will not lead to a real 1-1 dialogue.

36.  When trying to point at your best customers being companies in order to find similar companies two or more unrelated accounts for the same company will produce a false segmentation.

37.  Maintaining data related to two or more unrelated accounts for the same real world entity will probably be more costly than necessary when exploiting external reference data.

38.  It’s probably a waste of money sending printed material with presentation and offerings to a business entity already being a customer at a higher or lower hierarchy level.

39.  Having individual consumers represented in two or more unrelated accounts for the same individual consumer with a different lead source assigned will produce a wrong measure of marketing and sales performance.

40.  Allowing the same customer re-enter for an offer already turned down (e.g. credit services) will create unnecessary double validation work.

41.  When measuring churn and win-back two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

42.  When wanting a 1-1 dialogue two ore more unrelated accounts for the same individual being a consumer and a business owner will not lead to a sensible 1-1 dialogue.

43.  When measuring cross selling results two or more unrelated accounts for the same business entity will produce a false result leading to a wrong decision.

44.  Having activities related to individual consumers attached to two or more unrelated accounts for the same individual consumer will show an incomplete customer history with the risk of taking wrong actions.

45.  When measuring life time value two or more unrelated accounts for the same household will produce an incomplete result leading to a wrong decision.

46.  Having activities related to customers attached to two or more unrelated accounts for the same real world entity may lead to that different sales representatives are working against each other.

47.  Allowing sales representatives creating new accounts for already existing customers may create time consuming commission disputes.

48.  Having households represented in two or more unrelated accounts for the same household with a different lead source assigned will produce an incomplete measure of marketing and sales performance.

49.  Maintaining data related to two or more unrelated accounts for the same real world entity will consume more manual work than necessary.

50.  When measuring churn and win-back two or more unrelated accounts for the same individual consumer will produce a wrong result leading to a wrong decision.

51.  When buying from a supplier having two or more unrelated accounts despite being the same business entity you may have multiple unnecessary inventory costs.

52.  It’s a waste of money and credibility sending the same printed material twice or more times to the same individual business decision maker.

53.  When measuring churn and win-back two or more unrelated accounts for the same individual being a consumer and a business owner will produce only an incoherent result leading to a wrong decision.

54.  Assigning different credit terms for two or more unrelated accounts for the same household may increase financial risk.

55.  When measuring cross selling results two or more unrelated accounts for the same business hierarchy will produce an incomplete result leading to a wrong decision.

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Process of consolidating Master Data

Henrik Gabs Liliendahl4 Comments

stormp1

In my previous blog post “Multi-Purpose Data Quality” we examined a business challenge where we have multiple purposes with party master data.

The comments suggested some form of consolidation should be done with the data.

How do we do that?

I have made a PowerPoint show “Example process of consolidating master data” with a suggested way of doing that.

The process uses the party master data types explained here.

The next questions in solving our business challenge will include:

  • Is it necessary to have master data in optimal shape real time – or is it OK to make periodic consolidation?
  • How do we design processes for maintaining the master data when:
    • New members and customers are inserted?
    • We update existing members and customers?
    • External reference data changes?   
  • What changes must be made with the existing applications handling the member database and the eShop?

Also the question of what style of Master Data Hub is suitable is indeed very common in these kinds of implementations.

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So, how about SOHO homes

Henrik Gabs Liliendahl3 Comments

This post is the 3rd in a series of challenges in Data Matching with Party Master Data hierarchies.

80 % of all business entities are one-man-bands operated from so called SOHO’s (Small-Office-Home-Office). The home part is very often seen as a business is sharing a private residence address with a household.

farm

Examples are:

  • Farmers
  • Healthcare professionals
  • Small shops
  • Small membership organisation administrations
  • Fawlty Towers
  • Independent Data Quality consultants

Here we have a 3 layer relationship:

  • An ADDRESS occupied by a HOUSEHOLD and a BUSINESS (if not several)
  • The HOUSEHOLD consists of one or several CONSUMERS
  • The BUSINESS(s) has an EMPLOYEE being the Business Owner / Representative

One of the CONSUMERs and the EMPLOYEE is the same real world individual.

(About party master data entity types please have a look here.)

This very, very common construction creates some challenges in Data Matching and Master Data hierarchy building such as:

  • If you focus on B2B (Business-to-Business) you want to include the Business and Owner in that role, but not the same individual in the consumer role.
  • If you focus on B2C (Business-to-Consumer) you want to include the consumer role of that individual, but not the business (owner) role.
  • If you do both B2B and B2C you may want to assign either a B2B or a B2C category, and that’s tricky with those individuals
  • In several industries business owners, the business and the household is a special target group with unique product requirements. This is true for industries as banking, insurance, telco, real estate, law.

In my previous post on B2B (E2E) and B2C hierarchies methods for solving this is fuzzy matching, exploiting external reference data and other investigations – and so it is with this challenge as well. This makes Data Matching and Master Data hierarchy building a very exciting profession were you need both business and technology skills – and a real world perspective – to go all the way.

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