Pitney Bowes Introduces The Knowledge Fabric

Pitney Bowes Introduces The Knowledge Fabric

You might not think of Pitney Bowes as a place that a data scientist would want to spend their time, but they are putting together a full data science and digital marketing ecosystem with tools that range from data processing to predictive analytics to business delivery (e.g. direct mail, email, marketing videos). They have introduced a concept called “The Knowledge Fabric,” for describing their data products.

I recently spent time at their analyst relations conference in San Diego. Here are some highlights.

First let’s start with the challenges. Pitney Bowes has some challenges to overcome:

  1. Is location precision that important?  Pitney Bowes has 190 million U.S. addresses. The United States Post Office only has 140 million. Not surprisingly they are very very good at finding correct addresses and locations. This is critical for some applications but not all.  It will be important for them to convince us that location precision is critically important for digital transformation (clearly it is a part of the mix). They need to continue to understand and serve the industries and application areas that can best utilize their superior location processing as a competitive advantage.
  2. Will customers pay for a one stop shop for data? They’ve introduced a data marketplace, but they’ll need to prove that a well-run and organized data marketplace is worth paying for rather than the vagaries of free and open source data or putting together individual data agreements with individual vendors. My guess is that organization and curation will be worth the convenience for many companies.
  3. Any privacy issues looming? Part of Pitney’s value proposition comes from the combination of information from a variety of sources. While all of the sources are privacy compliant, Pitney is only collecting address information, it remains to be seen whether combining the data might run into some legitimate privacy concerns or at least cross the line of ‘creepy’ and have some risk of "trial by public opinion." Pitney Bowes has their lawyers on it and feel confident.
  4. Can they leverage the upsell? Pitney Bowes has a tremendous advantage because it can leverage its existing customer base and pursue a strategy of: “you’ve already bought mail services from us why not buy a data science solution as well…”. This will likely work but Pitney will need to find ways to transition existing customer relationships to other parts of the client organization.

Here’s the good news. Pitney Bowes is doing these things well:

  1. A prioritization of user experience. For a big company they have a uniquely strong focus on building a world class user experience in their products. They have a centralized team responsible for UI and UX, and it is already paying off in unifying diverse products through consistent interfaces and intuitive controls. I have not seen many other companies with this kind of thoughtful coordinated strategy.
  2. A focus on partnerships and APIs. Pitney is about two years into a strategic push towards building strong partnerships that can leverage existing client relationships. They have smartly created generous commission plans to incent both their own and their partner’s sales forces and focused on APIs that make it easier for partners to use Pitney’s offerings.
  3. Good with names. Pitney is, not surprisingly, good at name and address matching. They repeatedly demonstrated being faster and achieving higher matching rates than competitive products. This is only becoming more important as companies push for a 360 degree view of their customer. They will also need to aggressively incorporate ‘digital customer location’ into their offerings but have partnered with companies like Full Contact to do just that.
  4. Cool product naming. Let’s not underestimate the value of wrapping their offerings up into coherent and futuristic branding. The knowledge fabric (their virtual data landscape on which rich customer information is located) and pbKey (a universal customer / address identifier) are great ideas and memorable.

Here’s some thoughts on the future. Four years from now:

  1. Partnering with agencies will not go well. Some of Pitney’s offerings look a lot like they want to be the plumbing supporting a marketing agency’s offerings. Pitney will be tempted to do it themselves (a quasi-agency service will grow up within Pitney) or try to work with the agencies (which won’t work as agencies fundamentally want billable hours, not efficiencies). They will, however, be successful with their partnership model for building these core competencies within companies.
  2. Physical mail will continue to be an important marketing channel. Pitney has convinced me that, while my digital fingerprint is growing and becoming more important, my physical fingerprints are still critical for many marketing interventions. High quality location services will not go away, and mail will continue to be an important marketing channel that Pitney will excel at.
  3. New initiative for digital fingerprint overlays. They will need to continue to enrich the view of people from their digital behavior. I imagine that they will find ways to do this that are privacy-safe - possibly with a more sophisticated version of the classic Claritas Prizm codes that assign demographic information to zip codes.
  1. Privacy issues might bite them but eventually be good for them. Pitney won’t be the first company caught by the GDPR polar bear (ref. “you don’t need to outrun the polar bear you just need to outrun the slowest person in your hiking party …”). But their innovations will fall within the privacy crosshairs at some point. The good news is that eventually regulations like the GDPR and others will catalyze the need for better permission management which will make services like those provided by Pitney Bowes all the more valuable.

Stephen J. Smith is the research leader for data science at the Eckerson Group. His unique perspective comes from his real-world experience in building the predictive analytics products Darwin, Discovery Server and Optas as well as writing the highly-rated business technology books “Data Warehousing, Data Mining and OLAP” and “Building Data Mining Application for CRM” with McGraw-Hill. If you are an expert in applying data science to business he’d like to hear your ideas: [email protected]

More Writing from Stephen J. Smith:

“Operationalizing Data Science: 13 Challenges” https://www.eckerson.com/articles/operationalizing-data-science-13-challenges

 “Data Science is Plutonium: Powerful, Dangerous and Handle With Care” https://www.eckerson.com/articles/data-science-is-plutonium-powerful-dangerous-and-handle-with-care

“The Demise of the Data Warehouse” - https://www.eckerson.com/articles/the-demise-of-the-data-warehouse

“Ok, I Was Wrong, MDM is Broken Too: Insular, Dictatorial MDM Doesn’t Work” - https://www.eckerson.com/articles/ok-i-was-wrong-mdm-is-broken-too-insular-dictatorial-mdm-doesn-t-work

Stephen J. Smith

Stephen Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance...

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