Tag Archives: CRM

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Good-Bye to the old CRM data model

Today I stumbled upon a blog post called Good-Bye to the “Job” by David Houle, a futurist, strategist and speaker.

In the post it is said: “In the Industrial Age, machines replaced manual or blue-collar labor. In the Information Age, computers replaced office or white-collar workers”.

The post is about that today we can’t expect to occupy one life-long job at a single employer.  We must increasingly create our own job.

My cyberspace friend Phil Simon also wrote about his advanced journey into this space recently in the post Diversifying Yourself Into a Platform Business.

The subject is close to me as I currently have approximately five different occupations as seen in my LinkedIn profile.

A professional angle to this subject is also how that development will turn some traditional data models upside down.

A Customer Relationship Management (CRM) system for business-to-business (B2B) environments has a basic data model with accounts having a number of contacts attached where the account is the parent and the contacts are the children in data modeling language.

Most systems and business processes have trouble when following a contact from account (company) to account (company) when the contact gets a new job or when the same real world individual is a contact at two or more accounts (companies) at the same time.

I have seen this problem many times and also failed to recognize it myself from time to time as told in the post A New Year Resolution.

My guess is that CRM systems in the B2B realm will turn to a more contact oriented view over time and this will probably be along with that CRM systems will rely more on Master Data Management (MDM) hubs in order to effectively reflect a fast, but not equally, changing world, as the development in the way we have jobs doesn’t happen at the same time at all places.  

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More Social Master Data Management

Yesterday my American cyberspace friend Jim Harris was so kind to send an invitation for Google+ – the new social network service you must hook into. Thanks Jim, now I had to fill in yet a profile, upload the same picture as always and start networking from scratch once again :-)

As many people I have several profiles in different social network services as Twitter, Facebook and LinkedIn. As I’m doing business also with German speaking countries I also use XING as alternative to LinkedIn as told in the post LinkedIn and the other Thing.

In a comment to that post my Austria based French connection Olivier Mathurin noted: “Disconnected duplicated siloed professional profiles, mmm…”

In a post on this blog called Social Master Data Management made one year ago it is discussed how social CRM will add new sources from social networks to the external reference data sources we already know from old time CRM.

With all the different faces everyone are wearing in the social media realm this isn’t going to be easy and one may consider if social master data management is a wrong path giving the individual nature and built-in privacy in social networking services.    

Well, Gartner (the analyst firm) says that increasing links between MDM and social networks is one of the Three Trends That Will Shape the Master Data Management Market.

So, acknowledging that Gartner predictions are self-fulfilling, you better get moving into LinkedIn, Xing, Viadeo, Twitter, Facebook, (forget MySpace), Google+  and what’s next.

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Data Quality as Competitive Advantage

I always wanted to make the above headline, but unfortunately one of the hardest things to do is documenting the direct link between data quality improvement and competitive advantage. Apart from the classic calculation of the cost of returned direct mails most other examples have circumstantial evidences, but there is no smoking gun.

Then yesterday I stumbled upon an example with a different angle. A travel company issued a press release about that new strict rules requires that your name on the flight ticket have to be exactly spelled the same and hold the same name elements as in your passport. So if you made a typo or missed a middle name on your self registration you have to make a correction. Traditional travel companies do that for free, but low-cost airlines may charge up to 100 Euros (often more than the original ticket price) for making the correction.

So traditional travel companies invokes a competitive advantage in allowing better data quality – and the low-cost airlines are making profit from bad data quality.  

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Single Business Partner View

If you search in google for “single customer view” you’ll get over 20,000 hits. If you search for “single business partner view” you’ll get zero – until I just posted this blog post.

Some time ago I wrote about getting a 360° Business Partner View elaborating on extending the 360° Customer View or Single Customer View (SVC) to embrace all sorts of party master data managed within the organization.

In fact there is at least the same amount of similar techniques used between

  • managing supplier master data and business-to business (B2B) customer master data

as there is between

  • managing business-to-business (B2B) customer master data and business-to-consumer (B2C) customer master data.

If you look at Customer Relation Management (CRM) systems almost every package is aimed at managing B2B data as the data model and the functionality supports real world B2B structures and how the sales force and other employees interacts with B2B customers and prospects.

Interacting with B2C customers and prospects is much more diverse and often supported by operational systems specialized for the industry in question like solutions for financial services, healthcare and so on.

A business partner is a party acting in the role as customer, prospect, supplier, reseller, distributor, agent and other forms of partnership. Sometimes the same party is acting in several roles at the same time thus potentially being both on the Sell–side and Buy-side of Master Data Quality management.

As sell side and buy side has intersections within party master data, in some industries we may also go deeper into identity resolution and find intersections between B2B entities and B2C entities. I’ve described these matters in the post So, how about SOHO homes. The business case is that some products in some industries are aimed at the households of business owners and the small businesses at the same time. This is for example true for industries as banking, insurance, telco, real estate and  law.

All in all achieving a single view of business partners is a task going beyond traditional customer data integration (CDI) and stretching into areas traditionally belonging to Product Information Management (PIM). This is a business case for multi-domain master data management.

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Your Place or My Place?

We, and that’s including myself, often talk about multi-domain master data management as a marriage between party master data management (also called Customer Data Integration abbreviated as CDI) and Product Master Data Management (also called Product Information Management abbreviated as PIM).

The third most common master data domain is locations (or places). I like the term place, because then we have a P trinity: Parties, Products and Places. However there may be a fourth P involved, as I read a post today by Steven Jones of Capgemini telling that multi-domain MDM is a Pointless question.

The Premise of the Pointlessness is that Party and Product is an IT Perspective. The rest of the business sees the world from mainly either a customer centric perspective or a supply centric perspective.

I agree about that these perspectives exists too and actually made a blog post recently on sell side vs buy side master data quality.

I don’t agree about that this is an (pointless) IT versus business question, obviously also because I have a hard time recognizing the great divide between IT and business. From my perspective is IT a part of the business just like sales, marketing and purchase is it too. And from a product vendor perspective in the MDM realm you actually address the conjunction of business and technological needs a bit opposite to either being a database manager vendor aimed mostly at the IT part of business or a CRM or SCM vendor aimed mostly at the sales or purchase part of business.

Multi-domain MDM isn’t in my perspective a pointless place, but a meeting place between IT and all the other places in business and the core business entities being parties, products and places.

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Storing a Single Version of the Truth

An ever recurring subject in the data quality and master data management (MDM) realms is whether we can establish a single version of the truth.

The most prominent example is whether an enterprise can implement and maintain a single version of the truth about business partners being customers, prospects, suppliers and so on.

In the quest for establishing that (fully reachable or not) single version of the truth we use identity resolution techniques as data matching and we are exploiting ever increasing sources of external reference data.

However I am often met with the challenge that despite what is possible in aiming for that (fully reachable or not) single version of the truth, I am often limited by the practical possibilities for storing it.

In storing party master data (and other kind of data) we may consider these three different ways:

Flat files

This “Keep It Simple, Stupid” way of storing data has been on an ongoing retreat – however still common, as well as new inventions of big flat file structures of data are emerging.

Also many external sources of reference data is still flat file like and the overwhelming choice of exchanging reference and master data is doing it by flat files.

Despite lots of work around solutions for storing the complex links of the real world in flat files we basically ends up with using very simplified representations of the real world (and the truth derived) in those flat files.  

Relational databases

Most Customer Relationship Management (CRM) systems are based on a relational data model, however mostly quite basic regarding master data structures making it not straight forward to reflect the most common hierarchical structures of the real world as company family trees, contacts working for several accounts and individuals forming a household.  

Master Data Management hubs are of course built for storing exactly these hierarchical kinds of structures. Common challenges here are that there often is no point in doing that as long as the surrounding applications can’t follow and that you often may restrict your use to a simplified model anyway like an industry model.   

Neural networks

The relations between parties in the real world are in fact not truly hierarchical. That is why we look into the inspiration from the network of biological neurons.

Doing that has been an option I have heard about for many years but still waits to meet as a concrete choice when delivering a single version of the truth.   

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360° Share of Wallet View

I have found this definition of Share of Wallet on Wikipedia:

Share of Wallet is the percentage (“share”) of a customer’s expenses (“of wallet”) for a product that goes to the firm selling the product. Different firms fight over the share they have of a customer’s wallet, all trying to get as much as possible. Typically, these different firms don’t sell the same but rather ancillary or complementary product.

Measuring your share of given wallets – and your performance in increasing it – is a multi-domain master data management exercise as you have to master both a 360° view of customers and a 360° view of products.

With customer master data you are forced to handle uniqueness (consolidate duplicates) of customers and handle hierarchies of customers, which is further explained in the post 360° Business Partner View.

With product master data you are not only forced to categorize your own products and handle hierarchies  within, but you also need to adapt to external categorizations in order to getting access to external data available for spending probably on a high level for a segment of customers but sometimes even possible down to the single customer.

Location master data may be important here for geographical segmentations and identification.

My educated guess is that companies will increasing rely on having better data quality and master data management processes and infrastructure in order to measure precise shares of wallets and thereby gain advantages in a stiff competition rather than relying on gut feelings and best guesses.

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What are they doing?

A core attribute in customer master data when dealing with business entities is assigning values for your customers/prospects industry vertical (or Line-of-Business or market segment or whatever metadata name you like).

When handling this particular data element you will come across many of the classic different options in data and information management.

Unstructured versus structured

Many early CRM (Customer Relationship Management) implementations offered a free text field for the industry vertical. While this approach may have been good for the free flow in data entry it of course has created havoc when business intelligence was applied to the CRM data. Countless cleansing projects have been done (and is going on) around in order to fix this basic mistake.

Most data entry forms today having an industry vertical value has a value list to choose from.

Your list versus an external standard

When having a value list it may be a list of your own creation or be based on an external standard list, for example SIC or NACE codes.

Having a list of your own tends to fulfill the data quality principle of fit for purpose of use while an external standard tends to fulfill the data quality principle of reflecting the real world construct.  

The main weaknesses of a list of your own are that it requires continuous manual based maintenance and may cause conflicts.  Deep down into a discussion on the Initiate MDM blog Julian Schwarzenbach offered a good example saying:

“I have also come across ‘flip-flop’ data – which is typically subjective data where two users cannot agree what the correct value is and it keeps getting changed between two values. This could be the classification of a customer by market sector where two different territories are reflecting different capabilities in their territories.” – Link here.

The main weaknesses of an external standard are that they seldom offer the granularity you need and for global data the different standards (SIC versions and different national NACE implementations and others) are a pain in the…

One versus several values

Many companies have more than one distinct activity. Catching only one (the primary) value for each company is keeping it simple, stupid. Having more than one value in relevant cases is adding complexity but may lead to better decisions.

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Data Quality is an Ingredient, not an Entrée

Fortunately it is more and more recognized that you don’t get success with Business Intelligence, Customer Relationship Management, Master Data Management, Service Oriented Architecture and many more disciplines without starting with improving your data quality.

But it will be a big mistake to see Data Quality improvement as an entrée before the main course being BI, CRM, MDM, SOA or whatever is on the menu. You have to have ongoing prevention against having your data polluted again over time.

Improving and maintaining data quality involves people, processes and technology. Now, I am not neglecting the people and process side, but as my expertise is in the technology part I will like to mention some the technological ingredients that help with keeping data quality at a tasty level in your IT implementations.

Mashups

Many data quality flaws are (not surprisingly) introduced at data entry. Enterprise data mashups with external reference data may help during data entry, like:

  • An address may be suggested from an external source.
  • A business entity may be picked from an external business directory.
  • Various rules exist in different countries for using consumer/citizen directories – why not use the best available where you do business.

External ID’s

Getting the right data entry at the root is important and it is agreed by most (if not all) data quality professionals that this is a superior approach opposite to doing cleansing operations downstream.

The problem hence is that most data erodes as time is passing. What was right at the time of capture will at some point in time not be right anymore.

Therefore data entry ideally must not only be a snapshot of correct information but should also include raw data elements that make the data easily maintainable.

Error tolerant search

A common workflow when in-house personnel are entering new customers, suppliers, purchased products and other master data are, that first you search the database for a match. If the entity is not found, you create a new entity. When the search fails to find an actual match we have a classic and frequent cause for introducing duplicates.

An error tolerant search are able to find matches despite of spelling differences, alternative arranged words, various concatenations and many other challenges we face when searching for names, addresses and descriptions.

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Dealing with annoying customers

No, this is not a blog post about how to handle customers that unjustly complaints about everything.

This is a blog post about how to maintain high quality data in customer databases.

When doing that, there are some types of party entities that are more difficult to handle than others. In general B2B (business) entities are more complex than B2C (consumer/citizen) entities. Some of the B2B types I have spent more time with than others are the following:

Restaurants are some of the more demanding guests in our databases:

  • They do change owner more often than most other business entities making them a new legal entity each time which is important for some business contexts like credit risk.
  • On the other hand it’s the same address despite a new owner, which makes it being the same entity in the eyes of other business contexts like logistics.
  • In many cases you may have a name (trade style) of the restaurant and another official name of the business – a variant of this is when the restaurant is franchised.

Public sector bodies can’t be sliced the same way as private entities:

  • Often it is hard to state if a business partner belongs to a narrow defined or a broader defined unit within a governmental or local authority.
  • Public sector bodies tend to have long names that may be used with different inclusion of words, sequence of words and abbreviations of words.

Global enterprises may be seen as one or as thousands of customers:

  • The need for hierarchy management is obvious when it comes to handle data about business partners that belongs to a global enterprise – risk management, 1-1 marketing, sales force automation and so on will use the same data in many different ways.
  • Company family trees are useful but treacherous. A mother and a daughter may be very close connected with lots of shared services or it may be a strictly matter of ownership with no operational ties at all.

These are some of the facts of life that make it fun and not trivial when you are conducting data matching and other activities in order to achieve and maintain high quality of customer master data.

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