Keeping Sales & Marketing Data in Order: What startups must learn from the enterprise.
Nowadays it’s hard to find a startup CEO or CTO who’s not thinking about how their company can maximize the value of its data. Yet, most of the startups I speak with are shocked to learn how much money they’re leaving on the table as a result of poorly-managed customer data.
Discipline around data management is an area of practice that is very mature in large enterprises, but rather inadequate at many startups. Having worked with marketing departments of both large enterprises and startups, I’ve had the opportunity to see first-hand the differences in how each manages their leads and customers data, and the impact these differences have on the company’s ability to drive sales.
Marketing Data Mess?
Marketing data comes from and lands in a wide variety of sources.This is the case in all companies, large and small, and is the direct result of the fragmented nature of acquiring the data in the first place. It’s not uncommon for even the smallest of companies to manage customer data from sources as diverse as:
- inbound leads registering on the website and stored in marketing automation system like Marketo, Hubspot, Act-on or other.
- potential customers interacting with your brand in social media
- transactional records created as customers interact with your product
- conference attendee interactions captured via mobile devices and shared via CSV files and eventually loaded into the CRM system
- lists purchased from contact providers by the marketing department, also with a CSV-to-CRM lifecycle
- interaction and firmographic data manually added into the CRM system by sales reps (though most startups tell me they see this inconsistently if at all!)
The prospect insight represented by all this data–often referred to as a 360-degree view of the customer–can make the critical difference between sales execution, marketing campaigns and influencer relationships that hit the mark vs those that fall flat. But, that’s only the case if the data is usable!
Before you start actually using all this this data for marketing and sales operations, you’ve first got to bring order to the chaos… You need to do what enterprises call “Master Data Management of Customer Data.”
Master Data Management of Customer Data
Master Data Management of Customer Data, which we’ll refer to from here out as Customer Data Management (CDM) is a subset of the broader field of Master Data Management (MDM).
MDM is a technology-enabled discipline in which business and IT work together to allow an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. MDM is commonly applied to a variety of business data, including data describing the businesses customers, products, assets and employees. When properly done, master data management streamlines data sharing among the business’ personnel and departments.
From a marketing and sales perspective, Customer Data Management allows the business to build a Master database of customer data by defining the set of identifiers and attributes that describe its customers and leads. Every record in the Master database of customer data provides a single, uniform, 360-degree view of a customer or a lead.
How to Build a Master Database of Customer Data
MDM seeks to ensure consistency of the business’ data. To do this it distinguishes between master data and reference data. Master data is the data that actually describes the things that the business cares about. In the sales and marketing context, this is data about customers and prospects; while in manufacturing, this is data about product SKUs, parts and suppliers.
Reference data, on the other hand, is any kind of data that is used to organize and categorize the master data. In marketing and sales, reference data comes from a database of all companies, each given a unique identifier, called the reference database. There are specific data providers that provide reference databases–the well-known Dun & Bradstreet, and my company, Orb Intelligence, are examples. Dun & Bradstreet is a 150-year-old publisher whose business was built by manually collecting company information from sources such as newspaper mentions and credit reports. Orb Intelligence, on the other hand, has automated the process of collecting reference data from public government filings and the web, allowing us to deliver a modern SaaS offering that is more affordable for startups and easier for them to consume.
Independent of the reference database selected, the CDM process hinges on accurate matching. All the information about customers collected inside the organization is matched to a record in the reference database to determine a unique company identifier (D-U-N-S Number from D&B or Orb Number from Orb Intelligence) for each record. Businesses may be matched by their name (legal or fictitious name, or a brand name), address, or one of its URLs or email domains. For example, if there was a record in your marketing automation system with the name IBM and there is a record in the CRM with the name “International Business Machines Corp.” these two records will be matched onto a single company record in the reference database and so will be linked by the same unique identifier. Now, let’s say there is a lead with an email like firstname.lastname@example.org.The matching process will identify the company Clearleap, Inc as a subsidiary of IBM, so this linkage will exist in your master database as well.
In some domains, and at larger companies, master data is collected into a centralized master database that becomes the source of truth for all business objects in the domain. For your startup’s sales and marketing information, however, it’s better to think of the master database as a logical concept. In this case, master data management is the practice of matching and linking customer data in the CRM to the appropriate “golden record” in the reference database, and updating or appending the CRM record with trusted data from the reference.
The unique company identifier (i.e. the D-U-N-S or Orb Number) is thus the glue that holds pieces of information about a customer together. The unique identifier works like a social security number for businesses, so companies can track their customers across their own internal silos and also out onto the public Internet. The goal here is to get a more complete view of customers throughout the buying lifecycle, from prospect to contract to service and cross-sell and upsell – and be able to track relevant news, market movements and social media sentiment about a business and its employees.
Putting it All Together
Traditionally, enterprises accumulate batches of data records during the month and send the batches to D&B for matching on a monthly basis, receiving D-U-N-S numbers for the records with appended company information.
Of course, not all of the customers records will be matched, for two reasons:
- there may be no record for this company in the reference database (for example, D&B reportedly lacks about 1.5M small U.S. businesses)
- the record could not be matched, for example, because of some mistyping in the company name, or the use of unofficial company identifiers.
That is why it is very important that:
- the reference database contains all company’s names, aliases, DBA names, as well as all URLs and email domains
- The data provider has smart matching algorithms that can also process mistypes or incomplete names (“Adobe” with headquarters in San Jose, CA must be matched onto “Adobe Systems Incorporated”).
For CDM to be effective, a good match rate is very important. Data that is not matched effectively remains in data silos and can’t be used for analytics and marketing campaigns, resulting in lost revenue.
That is why enterprises like to use additional data providers to fill the gaps. For example, Orb Intelligence provides an alternative to D&B, with similar matching services. We see in some cases, especially when the enterprise sells to very small businesses, that Orb Intelligence is able to match 30%-40% of the records not matched by D&B. This increases the overall match-rate and the number of valid records in the master database.
Why Should Startups Care
It turns out that even at a small size, fast-growing B2B startups can benefit tremendously by “stealing” CDM ideas from their bigger enterprise contemporaries. Here are a few of the benefits they see:
- Reports are easier to produce, and make more sense. It never ceases to amaze us how many small companies require weeks of manual reconciliation to accurately report on who bought last quarter and how much. Without this data, marketing, services and customer success campaigns are flying blind.
- Analytics predict better on clean data. With clean data, predictive targeting and lead scoring are more accurate, and lead nurturing is more personalized and effective. The company identifier acts as a backbone to unify customer data. With this in place, companies can layer on additional information, adding third-party data, niche statistics and global market data.Buying data from third-party data providers now makes sense, because you have a framework where to plug-in the new data. For example, technology data provider HGData provides their data tied to DUNS numbers, so you can layer it onto your customer database easily.
- Marketing campaigns are more effective. Typical issues such as sending the same mail twice (or more) to the same customer reflects poorly on your brand and reduces deliverability for your campaigns. Now all customer records are deduplicated (the same customer could go twice under different names, for example, “Adobe” and “Adobe Systems Incorporated”) and you now have a single record for “Adobe Systems Incorporated” in your master database.
- Cross-sell and up-sell opportunities increase. Knowing how companies are related helps sales reps understand the scope of a business opportunity. Businesses can use this knowledge to identify related pools of independently subscribed users, or to extend a service from parent company to subsidiaries or vice versa.
- Account-based marketing becomes easier. Linking all data points related to a single account helps identify leads that are actually working at the same company, even though they may have different email domains.