Making a Firmographic Analysis

What demographics are to people, firmographics are to organizations.

I am currently working with starting up a Business-to-Business (B2B) service. In order to assess the market I had to know something about how many companies there are out there who possibly could be in need of such a service.

The service will work word-wide, but adhering to the sayings about thinking globally/big and starting locally/small I have started with assessing the Danish market. Also there are easy and none expensive access to business directories for Denmark.

My first filter was selecting companies with at least 50 employees.

As the service is suitable for companies within ecosystems of manufacturers, distributors and retailers, I selected the equivalent range of industry codes. In this case it was NACE codes which resembles SIC codes and other classifications of Line-Of-Business used in other geographies.

There were circa 2,500 companies in my selection. However, some belong to the same company family tree. By doing a merge/purge with the largest company in a company family tree as the survivor, the list was down to circa 2,000 companies.

For this particular service, there are some other possibly competing approaches that are stronger for some kinds of goods than other kinds of goods. For that purpose, I made a bespoke categorization being:

  • Priority A: Building materials, furniture, houseware, machinery and vehicles.
  • Priority B: Electronics, books and clothes.
  • Priority C: Pharmaceuticals, food, beverage and tobacco.

Retailers that span several priorities were placed in priority B. Else, for this high level analysis, I only used the primary Line-Of-Business.

The result was as shown below:

Firmographic

So, from my firmographic analysis I know the rough size of the target market in one locality. I can assume, that other markets look more or less the same or I can do specific firmographics on other geographies. Also, I can apply first results of dialogues with entities in the breakdown model and see if the model needs a modification.

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Related Parties, Products and Locations

Managing relationships between entities is a very important part of Master Data Management (MDM) as told in the post Another Facet of MDM: Master Relationship Management.

puzzleThere are relationships between entities within the single MDM domains and there are relationships between entities across multiple MDM domains.

Related Parties

Within customer (or rather party) MDM establishing the relationships between entities heavily increases the value of the data assets. Examples are:

  • In B2B (Business-to-Business) environments knowing about company family trees supports both analytic and operational challenges. That knowledge is often provided by enriching data from third party data providers, but as most things in life there is no silver bullet available, as the real world is quite complex and in no way fully covered by any provider I know about.
  • In B2C (Business-to-Consumer) environments knowing about how individuals are related in households is key to many analytic and operational issues too. Here having high quality location data is a necessity.

Related Products

In today’s multi-channel world there is a rush for getting product entities enriched with a myriad of attributes to support customer self-service and thus as a minimum mimicking the knowledge of the traditional sales person in a brick and mortar store.

But we also need to mimic that sales persons knowledge about how products relates. That knowledge can be collected in different ways:

  • From the manufacturer of the product. This source is often good when it comes to product relationship types as accessory and replacement (succession).
  • From the customer. We know this approach from the online sales trick prompting us with the message “People who bought A also bought B”.
  • From internal considerations. Facilitating up-sell can be done by enhancing product data with that kind of product relations.

Multi-Domain Relations

Here we may have:

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Leads, Accounts, Contacts and Data Quality

business partnersMany CRM applications have the concepts of leads, accounts and contacts for registering customers or other parties with roles in sales and customer service.

Most CRM systems have a data model suited for business-to-business (B2B) operations. In a B2B environment:

  • A lead is someone who might become your customer some day
  • An account is a legal entity who has or seems to become your customer
  • A contact is a person that works at or in other ways represent an account

In business-to-consumer (B2C) environments there are different ways of making that model work.

The general perception is that data about a lead can be so and so while it of course is important to have optimal data quality for accounts and contacts.

However, this approach works against the essential data quality rule of getting things right the first time.

Converting a lead into an account and/or a contact is a basic CRM process and the data quality pitfalls in that process are many. To name a few:

  • Is the lead a new account or did we already have that account in the database?
  • Is the contact new or did we know that person maybe at another account?
  • How do we align the known data about the lead with external reference data during the conversion process?

In other words, the promise of having a 360-degree customer view is jeopardized by the concept of most CRM systems.

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Using External Data in Data Matching

One of the things that data quality tools does is data matching. Data matching is mostly related to the party master data domain. It is about comparing two or more data records that does not have exactly the same data but are describing the same real world entity.

Common approaches for that is to compare data records in internal master data repositories within your organization. However, there are great advantages in bringing in external reference data sources to support the data matching.

Some of the ways to do that I have worked with includes these kind of big reference data:

identityBusiness directories:

The business-to-business (B2B) world does not have privacy issues in the degree we see in the business-to-consumer (B2C) world. Therefore there are many business directories out there with a quite complete picture of which business entities exists in a given country and even in regions and the whole world.

A common approach is to first match your internal B2B records against a business directory and obtain a unique key for each business entity. The next step of matching business entities with that unique is a no brainer.

The problem is though that an automatic match between internal B2B records and a business directory most often does not yield a 100 % hit rate. Not even close as examined in the post 3 out of 10.

Address directories:

Address directories are mostly used in order to standardize postal address data, so that two addresses in internal master data that can be standardized to an address written in exactly the same way can be better matched.

A deeper use of address directories is to exploit related property data. The probability of two records with “John Smith” on the same address being a true positive match is much higher if the address is a single-family house opposite to a high-rise building, nursery home or university campus.

Relocation services:

A common cause of false negatives in data matching is that you have compared two records where one of the postal addresses is an old one.

Bringing in National Change of Address (NCOA) services for the countries in question will help a lot.

The optimal way of doing that (and utilizing business and address directories) is to make it a continuous element of Master Data Management (MDM) as explored in the post The Relocation Event.

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Completeness is still bad, while uniqueness is improving

In a recent report called The State of Marketing Data prepared by Netprospex over 60 million B2B records were analyzed in order to assess the quality of the data measured as fitness for use related to marketing purposes.

An interesting find was that out of a score of maximum 5.0 duplication, the dark side of uniqueness, was given the average score 4.2 while completeness was given the average score 2.7.

The STaTe of MarkeTing DaTa

This corresponds well with my experience. We have in the data quality realm worked very hard with deduplication tools using data matching approaches over the years and results are showing up. We are certainly not there yet, but it seems that completeness, and in my experience also accuracy, are data quality dimensions currently suffering more.

In my eyes the remedy for improvement in completeness and accuracy goes hand in hand with even better uniqueness. It is about getting the basic data right the first time as described in the post instant Single Customer View and being able to keep up completeness and accuracy as told in the post External Events, MDM and Data Stewardship.

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From B2B and B2C to H2H

I stumbled upon an article from yesterday by Bryan Kramer called There is no more B2B or B2C: It’s Human to Human, H2H.

H2H

The article is about the implications for marketing caused by the rise of social media which now finally seems to eliminate what we have known as business-to-business (B2B) and more or less merges B2B and business-to-consumer (B2C).

As discussed here on the blog several times starting way back in 2009 in the post Echoes in the Database a problem with B2B indeed is that while business transactions takes place between legal entities a lot of business processes are done between employees related to the selling and buying entities. You may call that employee-to-employee (E2E), people-to-people (P2P) or indeed human-to-human (H2H).

Related to databases, data quality and Master Data Management (MDM) this means we need real world alignment with two kinds of parties:

While B2B and B2C may melt together in the way we do messaging the distinction between B2B and B2C will be there in many other aspects. Even in social media we see it as for example two of the most used social networks being FaceBook and LinkedIn clearly belongs mainly to B2C and B2B respectively for marketing and social selling purposes.

The different possibilities with B2B and B2C in the H2H world was touched in an interview on DataQualityPro last year: What are the Benefits of Social MDM?

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Third-Party Data and MDM

A recent blog post called Top 14 Master Data Management Misconceptions by William McKnight has as the last misconception this one:

“14. Third-party data is inappropriate for MDM

Third-party data is largely about extending the profile of important subject areas, which are mastered in MDM.  Taking third-party data into organizations has actually kicked off many MDM programs.”

business partnersIndeed, using third-party data, which also could be called big external reference data, is in my eyes a very good solution for a lot of use cases. Some of the most popular exploitations today are:

  • Using a business directory as big reference data for B2B party master data in customer data integration (CDI) and supplier master data management.
  • Using address directories as big reference data for location master data management also related to party master data management for B2C customer data.
  • Using product data directories such as the Global data Synchronization Network (GDSN®) services, the UNSPSC® directory and heaps of industry specific product directories.

The next wave of exploiting external data, which is just kicking off as Social MDM, is digging into social media for sharing data, including:

  • Using professional social networks as LinkedIn in B2B environments where you often find the most timely reference data not at least about contact data related to your business partners’ accounts.
  • Using consumer oriented social networks as Facebook for getting to know your B2C customers better.
  • Using social collaboration as a way to achieve better product master data as told in the post Social PIM.

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Social Events and Master Data Management

Recently I changed one of my job titles on LinkedIn resulting in a number of likes, congrats and messages. And thanks for that.

Probably I have a record in a number of CRM systems out there where I am registered as a contact for an account with an attached job title. As a guy working at several places at the same time I am a bit complicated, I have to admit, so I guess many of these records aren’t up to date about where I work carrying what title and having what means of contact.

Complicated or not, I have no doubt about that many CRM implementations will benefit from digging into social networks in order to be up to date and complete as told in the post Social MDM and Complex Sales.

New job (title)As discussed in the post Multi-Facet MDM we may divide master data management into handling these facets:

  • Entities
  • Relations
  • Events

Within party master data management events may be captured during interacting with your (prospective) customers and other business partners, as an update from a third party reference data provider or in an increasing way by monitoring social networks which are often the first to know certain things, not at least when it’s about contacts in Business-to-business (B2B) activities.

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In the future, data quality will be more social

Every time I walk in and out of a plane at London-Gatwick Airport I always nod at an advert from the HSBC bank saying that in the future, selling will be more social:

Selling will be more social

A natural consequence of this will also be that data quality improvement (and master data management) will be more social.

One example is how complex sales, being sales processes typically in business-to-business (B2B) environments, will be heavily depended on integrating the exploitation of professional social networks as discussed on the DataQualityPro interview about the benefits of Social MDM.

Traditional Master Data Management (MDM) and related data quality improvement in B2B environments has been a lot about a single view of the business account and the legal entity behind. As Social Customer Relation Management (CRM) is much about the relations to the business contacts, the people side of business, we need a solid master data foundation behind the people being those contacts.

The same individual may in fact be an important influencer related to a range of business accounts being the legal entity with who you are aiming for a sales contract. You need a single view of that. So many sales contracts are based on a relation to a buyer moving from one business account to another. You need to be the winner in that game and the answer to that may very well be your ability to do better social MDM and embrace the data quality issues related to that.

Social selling of course also relates to business-to-consumer (B2C) activities and in doing that we will see new data quality issues. When exploiting social networks, both in B2B and B2C activities you need to link the traditional attributes as name and address with new attributes in the online and social world as explained in the post Multi-Channel Data Matching.

Besides exploiting social networks we will also see social collaboration as a mean to improve data quality. Social collaboration will go beyond collaboration within a single company and extend to the ecosystems of manufacturers, distributors, resellers and end users. A good example of this is the social collaboration platform called Actualog, which is about sharing product master data and thereby improving product data quality.

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Psychographic Master Data Management

As told in the post Psychographic Data Quality marketers are moving from demographic marketing to psychographic marketing where a lot more data than before are used to getting the right message, to the right suspect at the right time. This affects the way we are working with data quality around customer master data and eventually how we do multi-domain master data management.

Using data for building psychographic profiles not only deals with lead generation. It’s usable throughout the whole customer master data life cycle by for example:

  • psychographic MDMFinding the best suspects at the right moment
  • Keeping the prospects on the optimal track coordinated with the prospects need
  • Ensuring a well received customer experience and facilitating up-sell and cross-sell.
  • Preventing churn
  • Making win-back possible

These opportunities apply to business-to-consumer (B2C) and business-to-business (B2B) as well.

Location master data management is essential in this quest as well, because we are not abandoning the basic demographic attributes in the physiographic world. We are building a deeper data universe on top of the traditional demographic (and firmographic) data. Having accurate location master data only helps here.

Mastering product master data is essential in the psychographic world too. This does not only apply to having your product hierarchies well manages for your own products, but will eventually also lead to a need for handling data on your competitors products and services in order to listen to social data streams.

Master Data Management (MDM) will extend to Social Master Data Management and must support wider exploitation of big data sources by being the hub for the psychographic customer profiles and the reference for descriptions of the product and service realm related to the psychographic attributes.

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