9	Pivot 3: The Data Handshake

AT THE END OF THE DAY, ENGAGING IN LEVEL 3 AND LEVEL 4 operating models profitably resolves down to one enabling capability: consumption analytics. With this capability, suppliers can help customers achieve unprecedented business outcomes from their employees and equipment. Without it, such assistance is simply not possible; it’s too full of friction and labor.

Obviously, the analytics are dependent on data. And unfortunately, we seem to have another Goldilocks problem on that topic. We often hear Level 1 and 2 suppliers complain that they don’t have enough usage data. We hear Level 3 and 4 suppliers, such as SaaS companies, say they are overwhelmed by it. We hear some customers say they won’t allow any of their data out in the cloud and won’t let suppliers connect to their on-premise products. We hear others say that they feel vulnerable because they are completely reliant on a cloud XaaS provider to handle their data. They may nervously have their financials, their new products designs, or the identity of their big deals for the next quarter all in the hands of a Level 3 or Level 4 supplier whom they have never met.

So why would we devote an entire pivot to just this one area? Compared to Land + Expand selling or adoption services, behavioral data or consumption analytics might seem like a pretty small topic. But we view consumption analytics as a crown jewel that will differentiate the winners from the losers in executing everything else we’ve talked about in this book. On the back of rapidly decreasing compute and storage costs, top suppliers are fundamentally transforming both their own operating models and the basis of competition in their industries. Suppliers simply cannot get from Levels 1 or 2 to Levels 3 and 4 without this new capability.

The Data Handshake

So if you’ve gotten this far in the book, you are intrigued by the power of B4B. In the B2C world, the analytics train has already left the station. Apple knows more about music consumption than does any musician or music company. Amazon knows more about shopping than does any traditional retailer. Google knows more about the topics people are interested in than does any news organization. Facebook knows more about the people in our lives than we do. LinkedIn knows what company you might work for next before you think about making a change. How? These suppliers have engineered a data handshake with their consumer customers. Now we need one in B4B.

FIGURE 9.1 The Data Handshake

Although certainly more complex than the exchanges that take place in the B2C world, the data handshake that is needed between business customers and their Level 3 and 4 suppliers (shown in Figure 9.1) is going to be conceptually very similar. In essence, the data handshake represents a set of mutual agreements between a supplier and its customers for data sharing, monitoring, and usage intervention with either employees or machines.

In Chapter 8, we talked about the critical consumption analytics capability that a supplier must master. We identified seven sources of data inventory to feed their analytic techniques:

•Product

•Environment

•Interactions

•Usage

•Process

•Customer

•Industry

The point of origin for virtually all of that data is the equipment or the customer—specifically, the customer’s employee end users. They are the ones who are using the technology product. They turn the equipment on and off, program it to perform functions, select features, and conduct transactions. Somehow, some way, those actions and the resulting data that they originate need to make their way to the supplier. There the supplier can apply various techniques to turn that data into useful and actionable insight. This insight then triggers actions that benefit both the customer and the supplier. Because these insights are linked to the supplier’s business processes within their consumption monitoring, optimization, and operate service organizations, the supplier can efficiently intervene to help optimize the customer’s outcome.

But this cycle, shown in Figure 9.2, means that two things must be agreed to: what data will be shared and what actions will be taken.

FIGURE 9.2 Critical Agreements

Without these critical agreements, the true power of Level 3 and Level 4 operating models never even begins.

The Supplier Side of Big Consumption Data

Leading Level 3 and Level 4 suppliers are already harvesting user behavioral data from their installed bases and turning it into profitable insights for themselves and their customers. This has been particularly true of the early SaaS companies. Their cloud-deployment models and multi-tenant architectures have enabled them to heavily instrument all aspects of the behavior of all users. For some, this has been taken down to the individual mouse click of every end user. They can build this behavioral tracking natively into their solutions. These SaaS companies can then analyze patterns across very large user communities, extract the specific user behaviors that lead to the fastest time-to-customer business value, and use this knowledge to differentiate themselves from their competitors. They can also run their adoption-led expand selling model with facts—and not just gut instincts. They know what the next module that a given customer or even a department within a customer is going to need based on the patterns observed in other customers—somewhat like Amazon knowing what the next book is that a particular consumer will want to read.

Suppliers that have grown up in the traditional CapEx models of Levels 1 and 2 have been much slower to move. They have not yet made the investments to instrument their products down to the user level. They have not yet built a flexible data aggregation model in which behavioral data are aggregated behind customers’ firewalls in some deployments and on suppliers’ servers in others. They have not yet adjusted their customer contracts to reflect who gets what rights to what data. They have not built out a road map of the behavioral consumption analytics that will maximize customer business value for their solutions. Some suppliers have made a small step toward solution monitoring and instrumentation, but mainly for their own interests. For example, many Level 1 and 2 suppliers have placed “collectors” in their customers’ environments for installed-base management. These systems track the deployment of that supplier’s component products, including serial numbers, software versions, operating status, and so on. The suppliers then use this information to ensure that every piece of their equipment is covered by a maintenance contract. Although this adds some value for customers, it ensures that they are “covered” for ongoing maintenance; however, it does nothing to instrument the actual usage of the solution. It is becoming increasingly clear that this slow progress toward becoming a big consumption data expert is putting these suppliers at a significant disadvantage relative to their IaaS, PaaS, and SaaS competitors. They must speed up their progress.

The Customer Side of Big Consumption Data

Many customers have already figured out the strategic value of behavioral data analysis capabilities for themselves and have been investing in them for years. They are capturing and internally analyzing the consumption behavior of their employees as they interact with the high-tech and near-tech solutions they have deployed. Some might even track the behavior of their strategic partners’ employees who have access to their systems. These customers are then leveraging the insights from behavioral analytics to streamline and improve their operations.

This notion is not new. Way back in 2004, for example, UPS had Symbol Technologies build 90,000 advanced rugged mobile computers for its global delivery fleet. Given that every UPS driver carried this device at all times, the GPS within the device enabled UPS to track the specific driving and walking route that each driver took when delivering each day’s packages. UPS was able to analyze this behavioral data relative to the best possible route and to identify inefficiencies in an individual driver’s actions. Based on these insights, UPS was able to give drivers specialized training to help them get their work done with a minimum amount of fatigue, minimum risk of injury, and maximum on-time performance. There are many such examples of this kind of optimization activity across almost every industry.

But usually these activities were conducted internally. The original consumption data that enabled the optimization never went outside the customers’ firewalls. But that meant that customers paid the whole bill. They paid to have the capability developed, and they paid internal staff to conduct the analysis and implement the recommendations. In Level 3 and Level 4 operating models, suppliers take on those development costs. They can deliver the same kinds of benefits to customers for a tiny fraction of doing it all in-house. However, this means that customers must agree to let the data leave their control in some form.

We think that the question of what form that data transfer takes will be a critical one with which both sides will wrestle. What options can suppliers present to the customer to assuage their concerns? Can they anonymize the data? Can they aggregate the data? Can they segment and separate the data? Then, for whatever data the customer is willing to share, how will suppliers protect it, archive it, or dispose of it?

When customers are uncomfortable with having some or all of their raw data out of their direct command and control, we can imagine an arrangement in which customers provide their Level 3 and 4 suppliers with insider access to carefully selected behavioral and consumption data of their employees on a “one-to-many” basis. This will enable their Level 3 and 4 suppliers to add the maximum amount of business value for them in the least amount of time. The customer could achieve this best-of-both-worlds state by engineering two sets of APIs (application programming interfaces): a “public” set that many of its suppliers could connect to, and a larger “private” set available only to a select few strategic partners.

This difference between public and private APIs has some history behind it. Decades ago, Walmart took on this strategy with its supply chain. Walmart wanted to enable all of its suppliers to be more efficient in ensuring that they maintained just enough inventory in its stores at all times. It was focused on maximizing inventory “turns,” that is, its sell-through revenue versus its inventory levels. To do so, it made available to all suppliers its basic views of its point-of-sale (POS) data so that each consumer products company and wholesale distributor could better plan their actions to ensure Walmart was never out of stock and never had excess inventory. In parallel, Walmart was an early investor in business interlock technology called EDI, or electronic data interchange. With its best suppliers, Walmart made available a much more granular view of its sell-through data and even tied its ordering, receiving, and inventory management systems directly into those of its strategic suppliers. Procter & Gamble (P&G) was an early example. This enabled those strategic suppliers to add even more value in areas like vendor-managed inventories. That is, P&G actually managed the inventories in Walmart’s distribution centers and the merchandising in Walmart’s stores for certain categories of its products. If you think about it, this is an exact parallel of a high-tech or near-tech supplier stepping up to managed services or adoption services of their products in B4B.

In return for providing data, customers can demand that their suppliers share the behavioral data and analytics with them as part of their B4B relationship. This is really what the consumption monitoring service is all about. So think about the data handshake as a two-way street: customers sharing access to their raw data and suppliers repaying them with optimal ROI from their solutions.

Key Capabilities for the Data Handshake

There are many challenges facing Level 3 and 4 suppliers. There are technical issues such as security, multi-tenancy, and latency. But as we said, success at these levels is about much more than remote hosting or subscription pricing. Taking advantage of the power of these new operating models requires many new capabilities. Here are two more that are tied to successfully utilizing consumption analytics to improve business outcomes.

New Customer Agreements

The potential that lives inside B4B is exciting to consider. But it will be all for naught if customers will not agree to provide the enabling data or to allow suppliers to intervene with its employee end users or the product itself.

In the world of B2C e-commerce, suppliers rely on strong brands and high consumer confidence to earn their customers’ data trust. They supplement their own brands by earning third-party trustmarks such a TRUSTe, obtaining security certifications from familiar sources such as McAfee, or by linking to separate payment entities such as PayPal. These encourage consumers to part with critical data such as credit card numbers and home addresses. Consumers are also given opt-out options. They can elect to not house cookies or to not have credit card numbers stored. The bottom line is that leading B2C websites find a way to reach data handshakes with their consumer customers. Level 3 and Level 4 suppliers must find a way to do the same with their business customers.

These agreements will address both of the critical topics we identified in Figure 9.2. They will delineate what data will be shared and how the process will work. The agreement may also define what KPIs the customer wants tracked by that supplier. Last, the agreement must identify what intervention actions—either with technology or with end users—the supplier is authorized to perform. Although we think that this is a good standard practice for all customer-supplier partnerships, it is particularly essential at Levels 3 and 4. This agreement gives both parties a common understanding of when, where, and how the operating model of the supplier will touch the customer’s operating model. It frames in the supplier’s value proposition and protects both sides from potential legal or expectation conflicts. It may even govern and regulate which kinds of fee-based end-user consumption are commercially preapproved and which are not.

There is no doubt that the scope of these agreements will vary widely. Some customers may not even require one from certain suppliers in certain roles. In other cases, such agreements will be central to the overall partnership agreement. In any case, being able to negotiate these successfully will be an important capability for suppliers.

The R&D Balancing Act

Level 1 and 2 suppliers rightly focus on product features. At Levels 3 and 4, they have a second focus: the service platform. Throughout this book, we have talked about all the great things that suppliers at this level can do with their operating model. But doing all those great things profitably and at scale requires that suppliers sit on top of a powerful platform of technology.

The bottom line is this: No algorithmically derived insight will make a difference if suppliers cannot efficiently interlock it back into their business. By this, we mean changing what a supplier’s personnel, process, and technology do and when they do it. That could be changing which subsequent module the expand sales team focuses on with a specific customer. It could also be what adoption service is launched for a specific customer at a specific point in its adoption of a solution. We have already talked about how a solution might need to be engineered to allow in-the-workflow suggestions and commerce. The platform will also need new consumption dashboards to see the status of the customer’s progress toward maximum business value.

None of these is a “feature.” These are a whole second thought. Beginning at Level 3, the suppliers’ R&D teams don’t just have one thing to keep them up at night; they have two. They must keep their features competitive and keep their operating platform effective. In the SaaS world, that may all be one huge software labyrinth with customer end users and supplier service employees accessing the same applications from different angles. In the world of connected but on-premise equipment, the end customer may never actually touch the platform; instead, he or she may just touch the technology product that is talking with the platform.

In any case, this service platform represents the business process interlock that sits between the enabling data and a successful outcome-driven operating model. R&D must either acquire much more funding to be able to support both activities or, more realistically, develop a higher bar for new feature additions. They must ask the hard questions about whether they really need to add feature number 1,344. Or are 1,343 features enough? In order to balance the new demand for far greater involvement of R&D resources into the construction and development of the operating model platform against the constant stream of new feature requests, R&D must become more judicious in their decisions. Adding even more pressure on them is the growing need to engineer out much of the current product complexity that customers are rebelling against.

All in all, building, negotiating, and operating the data handshake—and the platform it runs on—is harder than it sounds.

Organizing to Make the Data Handshake Pay Off

For most suppliers and customers, the actions required are new. They are “off the road map” from the existing strategy of either organization. Therefore, the right way to get started is likely a Center of Excellence (COE). In the case of suppliers, that COE may be their new Success Science team. If the customer plans to keep its data in-house and build its own adoption services organization, the customer may house the data and analytics as a COE within its existing IT or operations organization. In any case, a special-purpose organization meant to work across all other business units and functions may achieve the most substantial progress in the shortest time.

This type of COE is where a supplier will vest the responsibility to work across every product line, engineering team, sales region, and customer segment to develop and execute the strategy described earlier. The COE will need the direct sponsorship of the CEO, as they are going to have to use some “silver bullets” to get things done. This could mean working with engineering to prioritize deep instrumentation of user behavior above speeds, feeds, features, and functionality in the next major product release or technology deployment. It could also mean working with sales leadership to rewrite its sales methodology to shift from fact-free sales targeting to fact-based sales targeting. It might also mean working with legal advisors to rewrite standard contract terms and conditions to cover the data handshake described earlier. It almost certainly means substantial investments in incremental data storage and analytic tools to power your platform.

This type of COE must also win the war for talent among data scientists. Data scientists are going to be as in demand in the next five years as user experience engineers have been in the last five. There are going to be shortages. A supplier’s COE will need to take a global approach to tap into the best data scientists regardless of their country of residence or education.

The COE approach is not unprecedented in this context. GE is becoming the clear leader in what it calls the Industrial Internet using this exact organizational model. They formed their software COE two years ago. This organization has jump-started a $150 billion, 110-year-old company’s leverage of granular operational and behavioral data to achieve breakthrough economics for its customers. It is optimizing both the performance of the GE assets its customers have purchased as well as the operation of those assets by the employees of GE’s customers. It is doing this across multiple industries, including aviation, rail, health care, oil and gas, and energy. It is doing an incredible job of winning the war for talent. If fact, its COE has expanded to 500 people in 24 months and is on track to hit 1,000 people in 2014. In short, the COE organizational approach has put GE on the map as an analytics powerhouse in the industries it serves in a very short time. Customers and suppliers alike would do well to study what GE has achieved and replicate its COE model.

So we have covered a lot of ground in the three pivots. We think these are all actionable and urgent conversations that should be taking place inside progressive suppliers and their customers. We want to end our discussion by talking about what these trends really mean for technology markets. We would like you to ask yourself: “Is your market really heading into the new era of B4B?”