For so long, the single view or 360-degree view or unified view of the customer was the centerpiece of the quest for CRM’s Holy Grail. That complete view or, in customer data lingo, the unified master-meaning a complete record in a single place of all transactions (sales) and operational interactions (marketing response or customer service tickets)-was what all companies drooled over.
The idea of that single customer record with the circular view was that it would provide a rich source of data when it came to making important decisions, especially around either optimized offers from the sales side or ticket resolution on the customer service side. Marketing folks could target the customer more specifically if they knew that not only was the customer a purchaser of full-length mirrors for their bathrooms to preen in front of, but called into customer service fairly regularly because they tended to break the mirrors because the mirrors didn’t exactly see them as the fairest of them all.
Knowing all this, the companies could more easily sell them reinforced glass with steel backing for their future mirrors-up-selling the customer and decreasing their likelihood of calling in again to replace a broken mirror.
However, all this really turned out to be was a dusty perspective that didn’t lead to an enormous amount of action beyond placement on wish lists. It’s not that businesses don’t recognize the value of a single customer record. They do. For example, in 2007, the Economist Intelligence Unit did a study called “Conquering Convergence: Focusing on the Customer” that found in the ICE industries-information, communications, entertainment-consumer pull is pushing convergence, meaning consolidated customer knowledge. They interviewed Peter Skarzynski, SVP for strategy at Samsung Telecommunications America, who said, “We are spending more and more time to understand the customer better. It’s become a very competitive market.”
This was bolstered in the study by 92 percent of the companies claiming they had a strategy for staying focused on their customers, though, frankly, I would doubt that, with most of them thinking that the strategy had been at least somewhat successful.
What makes it interesting, despite the skewing of the numbers toward highly successful, is that when the question was asked, “What are the main obstacles preventing your company from being as customer-centric as it would like to be?” (they could select three), the number one answer was “incomplete customer data” with 41 percent and then “lack of clarity about what customer data should be measured” with 32 percent. Fragmentation of customer data or inaccuracies of the same were the next two. In other words, it’s hard to aggregate the accurate customer data that you need when you’re not even sure what customer data you’re looking for.
That confusion about what customer data to look for is compounded by the availability of much more individual customer data than ever before and also by which of that highly personal profile data is valuable to a business.
Historically the idea of the single customer record was encapsulated rather easily into the three traditional CRM buckets. The 360-degree view of the customer would incorporate sales transaction data, marketing response data, and customer service inquiry data. That was that. It would all be in a singular place and just oh so easy to deal with.
But that actually never became the case. Even with all this commitment to customer centricity, the idea of what comprised the 360-degree view was decidedly old school. Here’s the way that EIU/Oracle framed the idea of that 360-degree customer record: “My company has a 360-degree view of customers, including purchases/contact history, preferences, and demographics.” That’s missing what we’re going to see is important for the new customer record in just a few. But even that particular approach generated only a 33 percent “we do have that for sure” response, with the rest being either neutral or denying they have it.
Things are getting more complex for businesses when it comes to customer data, because the historic transactional and demographic information is no longer sufficient for getting you what you need to ascertain what to do with that individual customer clamoring for personalized relationships with your company.
The New Customer Record
What should the new customer record include? Before I go through what should be a part of the record, here’s what the traditional customer record incorporated if it was considered complete:
Obviously, there are a lot of overlaps among the kinds of information listed here, but you get the idea. The totality of this in a customer record about you would comprise everything a company traditionally could or would want to know about you.
But that isn’t sufficient for a contemporary customer record. Just as we’re defining Social CRM as an extension of traditional CRM, the 21st century customer record is an extension of the traditional. It includes all of the above, but then goes to the more informal unstructured channels and looks for:
All of this can be harvested and incorporated into the customer record through text analysis, among other things, which effectively aggregates unstructured data and structures so it can be integrated into more traditional databases. There are many tools out there, such as InsideView, that integrate the unstructured data directly into CRM systems data, which makes it even easier to incorporate the data into the 360 degrees that the customer record purports.
The challenge that the new customer record presents isn’t so much in finding the data—with the proper tools, it’s doable—but using the new data. As in all data harvesting, there are issues that involve privacy and transparency (see electronic content “I Want this Chapter to Be on Privacy, But If I Wrote It, I’d Have to Blog About You”). Are you“stealing” information that is easily available on the Web, but still owned by the provider? But even more than that, what benefit does it provide to you when it comes to developing insights into individual customers? Is it just more noise or is it really valuable?
What that means is that you have to make decisions beforehand on what data is going to be important, whether it’s traditional or new.Ultimately, what data you use is based on how you want to use it. For example, if you merely need to know the transaction history of your customers, then don’t monitor the blogosphere for their conversations. While I think that’s insight suicide right now, you get the point. Use what’s valuable.
One of the other dilemmas that data mavens face in this deep dive into personalized customer data and the 360-degree view is how to deal with all the issues that are created by pulling in data from disparate sources. That, my friends, is what customer data integration (CDI) is for.
CDI, Not Miami
This is exactly what it sounds like and truly is sans David Caruso. It is the use of technology, services, and best practices to consolidate customer data, including names, addresses, phone numbers, company names, and so on (often called entities in the analytics world) so that there is a single clean reference to a specific individual rather than a larger number of incorrect, duplicated, and possible but not certain references to that individual.
The process for doing this is straightforward. It works something like this:
As an example, let’s presume that we have four records in four separate databases:
Effective CDI would clean up these four records and come up with two individual records by first consolidating Will Smith and Willard C.Smith with the information it has. But then it would have to have rules set up to deal with the specific case of William Smith. Even though it’s clear that he’s from LA and married to Jada Pinkett Smith, which would indicate it was Will Smith, Will Smith is not 38, he’s 41, and his name is Willard, not William. So what do you do?
There is a lot of ambiguity in customer records due to data input incorrectly at the beginning or from siloed databases with duplicate but slightly different information on the same person, such as a donor and a volunteer database for a nonprofit with the same person donating and volunteering. CDI becomes an important technology for cleaning all that data, since even exfoliating soaps won’t do the trick. Once cleansed, then combined.
But is that enough? Just taking care of the data? What about the use of customer data by multiple sources? According to Ray Wang, one of the top enterprise analysts in the world, in his 2007 Forrester Research report “Wave on Customer Hubs, ”traditional CDI (wow, already a traditional version exists) is being superseded by what he calls customer hubs. These go beyond the traditional use of customer data integration and administer how the data is arranged for specific sources through the use of business rules and event management following the technical cleansing, deduplication, and structuring of the data. The customer hub takes the data and applies business rules that tell it how to organize the data according to the preferences of the systems that want to use it. There are a number of well-known vendors providing CDI services that are focused around the customer hub, such as Oracle with its Customer Data Hub, Siperian’s HubXT, and IBM’s Websphere Customer Center, among others.
What makes the customer hub valuable now? According to Wang, now a partner at Altimeter Group, in an article in SearchCRM, January 2007: “While everyone is moving toward [a master data management] solution in general, it’s very hard to implement that kind of change across an organization. Starting with a smaller target like customer or product is a good way to make sure that you will ultimately succeed.”
Master Data Management: Better Read than Dead
Ray Wang, of course, aside from being a convenient segue, raises another question or two. Why is everyone moving to master data management (MDM)? What is MDM? What does it have to do with CRM now? Actually, that’s three questions, all of which I’m going to answer briefly before we get on to business processes.
What Is MDM?
MDM was a drug that was used illegally in the ’60s and ’70s. Oh, wait, that was PCP. Actually, MDM is an increasingly popular paradigm for taking all data in an enterprise, whether it is customer data, product data, or supplier data, and linking it to a single file that provides a common reference point. They call the single file a master file. Then, depending on what you need, you can access the data in the way that you need it.
On the surface, MDM’s definition eerily mirrors the original definition of CRM. Aaron Zornes, perhaps the leading MDM guru, uses these definitions and subdefinitions at his MDM Institute.
Master Data Management (MDM): The authoritative, reliable foundation for data used across many applications and constituencieswith the goal to provide a single view of the truth no matter where it lies. These are his sub-definitions:
Operational MDM: Definition, creation, and synchronization of master data required for transactional systems and delivered via service-oriented architecture (SOA); examples: near real-time customer data hubs and securities masters.
Analytical MDM: Definition, creation, and analysis of master data;examples: counterparty risk management applications and financial reporting such as global spend analysis or chart of accounts consolidation.
Collaborative MDM: Definition, creation, and synchronization of master reference data via workflow and check-in/check-out services; examples: product information management (PIM) data hubs and anti-money laundering (AML).
If you remember the original MetaGroup definitions of CRM, they are based around three distinct “types” of CRM: operational, analytical, and collaborative. The master CRM definition focused around the single view of the customer as its centerpiece.
But reality points in another direction. If you reference Chapter around the collaborative value chain, then master data management (MDM) makes a lot of sense when dealing with large or mid-sized data stores that might emanate from multiple sources and be of multiple types. The collaborative value chain is the core of a wellintegrated enterprise value chain engaged with the personal value chains of individual customers. That means the customer is partnering with the company either to help them innovate or to receive information that allows the customer to make intelligent decisions on how they deal with that company. For that to work effectively, there have to be high degrees of data integration and the ability to call up that data in ways that allow companies to make their own operational decisions or to reach out to the customer with what the customer needs from them.
For example, the combination of product data, supplier data, and customer data allows you to tell a high-value customer with some precision that his shipment of equipment is in inventory, will be shipped on Thursday from a warehouse in Richmond, Virginia, to his location with an expected arrival of the following Monday, all units contained in a single package. You’ll also be able to identify how much this will cost you, how much it will cost the customer, what kind of history you’ve had with this customer when it comes to on-time delivery, and whom to flag in the event that a problem surfaces And now you can also see if the customer has been complaining to his peers about your company.
Why Is Everyone Moving Toward MDM?
The MDM industry is by no means gigantic yet, but the promise is. It’s a new idea, which is why the market spend was only $730 million in 2007. It’s projected by the MDM Institute to go to $6 billion for software and services by 2012, though, because the need for it is becoming more apparent as the amount of data available and the amount of work required to parse it and analyze it, and establish the relationships between different data points and types, increases by the day.
The value proposition is not all that difficult to grasp. A centralized master data source makes governance far easier, since it’s far easier to comply with rules involving a single source than those involving multiple data sources.
Business process integration becomes a lot more efficient. With a data hub to work from, business rules can determine and direct where and how the data will be used—emanating from that central source rather than from a variety of different locations—which would mean different sets of business rules, which conceivably could conflict with each other. Rules that involve privacy preferences or pricing discounts are one kind that MDM serves; rules that involve results leading to action are another, such as a problem with delivery needs to be routed to a supervisor for a preemptive call.
There is another interesting wrinkle. All the data I’ve spoken about so far is structured data. What about the mass of unstructured data that’s out there currently? How do you deal with that?
The Disruptive Nature of MDM
MDM seems to have an answer—at least fourth-generation MDM solutions do. First, they’ve centralized all data around process hubs, which allows the enterprise to manage the business rules and processes that are needed to use the data in specific and appropriate ways. Then they are integrating enterprise search. Zornes foresees the complete fourth-generation MDM solution looking like this (from his “Enterprise Master Data Management Market Review and Forecast for 2008–2012”):
While the majority of contemporary [author’s note: third generation] MDM solutions focus on the structured data held in CRM and ERP applications, the reality is that a plethora of valuable customer, product, supplier, employee, etc., information resides in what is characterized as “unstructured” information, e.g., emails, instant message log files, voicemails, etc. To provide a robust “universal customer view” . . . it is clearly desirable to incorporate these valuable information sources as part of the composite view.
While that is a lovely vision, is there anyone who’s actually doing that now?
MDM Market Leaders
Not entirely. There are market-dominant MDM forces, which include a Big Four who are beginning to investigate the fourth generation of MDM, though still a long way from implementing that enterprise search capability: Oracle, SAP, Teradata, and IBM. However, they are still aimed at improving and have made some surprising efforts. In the second quarter, 2009 SAP and Teradata announced an MDM-like partnership to integrate SAP’s NetWeaver and BW with varying Teradata solutions so they could actually satisfy the requests of their customers, rather than try to up their market ante. It seems that there was an 80 percent overlap between SAP and Teradata customers, according to Teradata EMEA CEO Herman Wimmer. Many of those customers, like Hershey’s in Pennsylvania, welcomed the move.
As far as fourth generation capabilities like enterprise search, one of the more promising approaches has been the integration of Microsoft SharePoint with their MDM solution. Though I’m no fan at all of SharePoint, this at least points in the right direction for the future of MDM.
What in Heaven’s Name Does MDM Have to Do with CRM, Except Sharing an M?
Even though technically you could call CDI a subset of MDM, the value of MDM is that it provides a complete and integrated view of all enterprise data, not just the customer data. It subjects that data to the expected analytics tools, and also to business rules and workflow so you can make the post-analysis results actionable and also direct them to the appropriate parties to take that action. Because it’s SOA based, it integrates well with applications that would need to access it.
It has a potentially disruptive—good—effect on contemporary business models also. It supports Social CRM self-service efforts by making available the data that customers need to handle their own service requests or purchases, and then automates the transactions regardless of what systems are involved in the process. It gives both the company and the customer (with the permission of the company) visibility into what Zornes calls the “hyper-integrated 21st century chain, ”which means a supply chain that involves internal manufacturing, warehousing, distribution, logistics, and the outsourced pieces of that too.
At the early 2009 MDM Summit, Zornes (my man!) introduced three reasons for companies to consider MDM, especially as it evolves into the fourth generation of solutions:
For Social CRM, item 1 particularly concerns us, as it impacts customer lifetime value and how the social customer is identified, valued, and interacted/ transacted with.
The ability to identify relationships using data that spans multiple locations and multiple data sources is a major leap forward, especially for the largest enterprises when it comes to their customers. This allows them a multidimensional look at their customers and those around them. It also provides them with the information they need to optimize the offers they are going to provide to those customers and determine what kind of investment the customer needs to get. This means that the transaction history is the supreme arbiter of net present value (NPV), the core metric in a customer lifetime value calculation.The relationships matter.
Before you get too complacent, this is only a beginning stab at how you measure the social customer—the technology that underpins the effort. There is much more to this, but you’re going to have to wait until next Chapter to hear anything more on it. This chapter is on data and process. That chapter is on value.
So I think the benefit of MDM is apparent to you all. Yes? No? Let me know at the site that I’ve listed in the introduction or on my blogs. If there are any doubters among you, let me put your doubts to rest by introducing you to Jill Dyché, who’s going to provide you with what you should see as the key benefits of MDM. Believe me, she knows.
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