Crossing the (Other) Chasm: To Bridge the Gap Between Sales and Marketing, Customer Understanding Is Key

Sales and marketing. Cats and dogs. Chalk and cheese. Oil and water.

For as long as there have been sales teams and marketing departments, there’s been friction. And yet, there’s magic in a simple vinaigrette, Vouvray and an aged chevre, even a cross-species best friendship. The same kind of synergy can and should exist between sales and marketing.

So why do they seem further apart than ever before?

Two words: technology distraction. The hundreds, even thousands of technology tools that are supposed to make the work of selling and marketing easier are driving a wedge between the two, not bringing them together.

Do any of these conversations sound familiar?

Sales: Why are these random names appearing in my pipeline?
Marketing: They’re marketing qualified leads.
Sales: What exactly makes them “qualified”?
Marketing: Well, they opened eight emails, clicked on three of them, downloaded a whitepaper, and joined a webinar in our six-week campaign.
Sales: So how do we know they’re actually ready to buy?
Marketing: That’s your job.
--
Marketing: Hey, you didn’t give us any attribution for that big deal you just closed. We gave you leads on that one. What’s up?
Sales: We knew everybody who had a role in making that decision and none of them were the names you gave us.
Marketing: Yeah, but we still had an influence on the outcome.
Sales: (silence)
--
Sales: Hey, I’ve got this deal I’d really like to accelerate and close. What can you tell me about key messages and conversations that will help?
Marketing: Well, we can tell you what pages on our website people from that company domain have visited in the last month.
Sales: Guess that’s better than nothing.
--
Marketing: Hi, can you put me in touch with a senior contact in your customer organization? We want to put together a case study on their use of our new offering.
Sales: Uh…now’s not really a good time. The relationship is a little sensitive at the moment. Come back to me in a few weeks.
(Three weeks later)
Marketing: Hi again! So, I can see in the system that all of the trouble tickets seem to have been cleared in your customer account. Can you put me in touch?
Sales: Yeah, sure. Let me get back to you on that. I’ve got a bunch of meetings today.
Marketing: (to voicemail) Me again…I’ve left you a bunch of messages and sent emails, but haven’t heard anything back. Can you please, please, please give me a contact in your customer account for a case study??
--
The problem here isn’t (necessarily) the technology. It’s the fact that we’ve lost sight of what really matters. We’ve been way too distracted by gaming the system and creating new metrics. Marketing is off doing one thing while sales is focused on another. Meanwhile, customer service is busy dealing with the everyday realities of customer issues.

Where do we go from here? The key is to focus on the most important fundamental of all: understanding customers.

The relationships and handoffs between marketing, sales, and customer service can be much smoother and more effective if everyone shares a clear, coherent understanding of customers. Truly understanding them means knowing who they are (organizations and individuals), their priorities, their competitors, their motivations and obstacles, where they are, how they think, and how they make decisions. Whether they’re current customers, dormant customers, or potential ones. It means knowing them well enough to anticipate their needs, sometimes even before they do themselves.

Customer Understanding

So what exactly is Customer Understanding?

As a concept, it is the construct behind an integrated, cohesive, holistic, and shared view of customers. This shared view of customers supports marketing, sales, and customer service, as well as strategy and product/offering development. Ultimately, it feeds into almost every aspect of a business.

Why do we need this concept? Because to really engage customers, we need to more clearly recognize that there are multiple elements to the customer relationship, and that they change over time. While this is equally true for both consumer and enterprise customers, the relationship elements and how they change can differ significantly.

In principle, Customer Understanding encompasses all of aspects of customer interaction. It also incorporates insights—both quantitative and qualitative—that build a clearer picture of customer priorities, needs, and preferences.

Building that understanding requires input from across all customer interactions. Using that understanding to best effect means creating feedback loops that inform how marketing, sales, and customer service operate. That includes inputting to strategy and product or offering development.

If Customer Experience describes the relationship from the customer’s perspective, Customer Understanding describes the flip side of the coin—the cross-enterprise experience of the same relationship.

CRM, customer engagement, sales effectiveness, customer service, and field service are critical elements of generating Customer Understanding and delivering Customer Experience. So is the ability to aggregate and analyze data from all of these different sources, as well as from customers themselves. Customer Understanding informs each of these areas and more. It is the unifying concept that determines the appropriate responsibilities and activities across the enterprise. It defines the criteria to design effective processes and make effective technology investments.

Done well, a shared Customer Understanding ensures offerings that meet customer needs, compelling messages delivered when customers are receptive to hearing them, and little or no friction in the sales process. It translates into happier, more loyal customers. It goes a long way toward bridging the gap between sales and marketing, too.

With thanks, and apologies, to Geoffrey Moore

Big Privacy and Data Infrastructure for an Orderly Digital Economy

Steve Wilson

Former Vice President and Principal Analyst
Constellation Research

Steve Wilson is former VP and Principal Analyst at Constellation Research, leading the business theme Digital Safety and Privacy. His coverage includes digital identity, data protection, data privacy, cryptography, and trust. His advisory services to CIOs, CISOs, CPOs and IT architects include identity product strategy, security practice benchmarking, Privacy by Design (PbD), privacy engineering and Privacy [or Data Protection] Impact Assessments (PIA, DPIA). Coverage Areas: -  Identity management, frameworks & governance-  Digital identity technologies-  Privacy by Design -  Big Data; “Big Privacy”-  Identity & privacy innovation Previous experience: Wilson has worked in ICT innovation, research, development and analysis for over 25 years. With double…...

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Two new Constellation Research reports of mine cover data protection, looking at the heightened need for privacy in the face of artificial intelligence and Big Data, and the sorts of systemic infrastructure needed to safeguard data supply in future.

Big Data and AI are infamously providing corporations and governments with the means to know us "better than we know ourselves". Businesses no longer need to survey their customers to work out their product preferences, lifestyles, or even their state of health; instead, data analytics and machine learning algorithms, fueled by vast amounts of the "digital exhaust" we leave behind wherever we go online, are uncovering ever deeper insights about us. Businesses get to know us now automatically, without ever asking explicit questions.

What are the privacy implications? The good news for consumers and privacy advocates is that general data protection and privacy laws are technology neutral; they extend essentially the same protections to Personal Data that is automatically generated as they do data collected manually. Long established privacy laws have been applied to curb the excesses of digital companies on the cutting edge of data processing. My report Big Privacy Rises to the Challenge of Big Data and AI examines the strengths and weaknesses of classical privacy laws.

The future of the digital economy depends on reasonable and equitable use of data as a resource. The early years of the Internet Age has seen significant exploitation of individuals by digital entrepreneurs, and stark imbalances in the riches that can be made from mining and refining information. But privacy laws are being reinforced in Europe (with the EU's General Data Protection Rule, GDPR) and extended to places like California. This is surely a sign of the law-and-order to come.

The early oil rush is instructive for how the digital economy should probably evolve from here. To bring oil safely to market, the petroleum industry organised itself into complex new supply chains, for moving and processing petrochemicals. Technical standards, enforceable rules, and even social norms (like good habits for handling gasoline) developed to help keep the new supply chains orderly.

Obviously data is quite different from oil, and the comparison isn't meant to be taken too far. So what practical lessons are there from the petrochemical experience for the future organisation of the digital economy? What would data supply chains actually look like? It seems likely to me that new laws and jurisprudence will emerge to deal with data as a intangible asset class, but that's another story. For now, I start to tease out the more technological aspects of data protection in How Data Supply Chains Must Be Safeguarded in the Digital Economy.

I start with the challenge of how to be sure about the people and entities we try to deal with in the digital environment. The Digital Identity industry has grappled with this for two decades, and its successes can be leveraged.

In the so-called "real world", commerce and government services revolve around established facts and figures about people (account numbers, customer reference numbers, employee numbers, professional qualifications, memberships, social security entitlements, driver licenses, and personal attributes like age, residency, health conditions and so on). But all these critical pieces of information lose their reliability and provenance online: we cannot tell where the information is supposed to have come from, much less can we distinguish clones and counterfeits from "originals". Nor can we be sure that data presented online truly belongs to particular individuals.

But in another setting, this is a solved problem. The susceptibility of Digital Identity data to fraud is very similar to that of credit card numbers, and we've secured them with integrated circuits and cryptography.

A credit card is nothing more than a data carrier used to present an account holder's bona fides to a merchant (within the context of an overarching scheme). Over time, the payment card industry has steadily adopted more robust forms of data carrier:

1. The original paper charge cards in the 1950s were transcribed by merchants by hand
2. embossed plastic cards were "read" by carbon paper click-clack machines
3. magnetic stripe cards were read automatically by electronic terminals which scanned data encoded in analogue magnetized patterns
4. now chip cards are also read automatically but using digital memory and mutual authentication between card and terminal
5. smart phones embody chips which can mimic smartcards, and bring added functionality, like a mobile wallet which can manage multiple accounts.

Magnetic stripe cards persisted for decades until criminal skimming and carding became unbearable. Magnetic stripe fraud is enabled because a card terminal cannot tell the difference between an original analogue stripe and a copy; the data encoded in the magnetic medium has no provenance.

The whole point of a chip or smart payment card is to protect the presentation of cardholder data, to prevent interception, tampering, illicit replay, cloning and/or counterfeiting. The cardholder data in a chip card is exactly the same as that in a magnetic stripe (or on the surface of either type of card for that matter) but the data transfer protocol from chip card to terminal is special.

A chip card holds the cardholder details within an embedded microprocessor, along with one or more private keys which are unique to each cardholder. Data is not passively transferred to the terminal as it is with a mag stripe card; instead, for each transaction, there is a handshake. First the terminal sends the purchase details into the card's microprocessor, which combines them with the cardholder data and digitally signs the combination before sending it back to the terminal. This operation renders each encoded transaction unique to the card and cardholder, and prevents substitution of stolen data or tampering with the transaction.

As the older technology is phased out, an overall systemic improvement is that raw data becomes useless, and valueless to thieves. Best practice is that no raw card details are relied upon but instead we expect transactions to employ chips and digital signatures, to ensure provenance.
The experience of progressively tackling plastic card fraud offers lessons for economy-scale digital identity, and data management in general. The core technique is digital signatures (applied and processed automatically by smart devices) underpinned by seamless key management (where cryptographic keys are registered to users for different applications). The provenance of all data could be safeguarded in the same way as credit card numbers are protected in the payments system.

In my two new reports I try to balance a positive view of classical privacy regulations, with a realistic, evidence-based vision of how standard cryptographic technology can protect the provenance of all data and systematise how it flows through the digital economy.

Research Unlimited and Executive Network members may access the reports here: 

Big Privacy Rises to the Challenges of Big Data and AI

How Data Supply Chains Must be Safeguarded in the Digital Economy

SAP Doubles-Down on Customer Experience and the Experience Management Category with $8 Billion Qualtrics Acquisition

On November 11, 2018, SAP announced its agreement to acquire Qualtrics, a provider of customer and employee experience measurement, for $8 billion in cash. Qualtrics was set to IPO this week at a valuation of $5 billion with plans to raise $500 million. The company turned its first profit of $2.6 million in 2017 on revenue of just under $290 million. Qualtrics anticipated surpassing $400 million in revenue this year. Its key customer sectors include B2B technology, auto, travel and hospitality, financial services, government, media, airlines, and retail.

Key facts:

• This is the second-largest purchase of a SaaS software company after Oracle’s acquisition of Netsuite for $9.3 billion in 2016.
• SAP will retain Qualtrics CEO Ryan Smith, as well as the company’s dual headquarters in Provo and Seattle.
• According to CEO Bill McDermott, SAP views experience management as “the groundbreaking new frontier for the technology industry.”

SAP Gains A Versatile Experience Management Platform

For SAP, Qualtrics solidifies its move into the customer experience management space. Qualtrics follows other recent acquisitions such as Callidus and Hybris, which added capabilities in sales effectiveness and customer experience, respectively.

Although the main focus in this acquisition is customer experience—including brand and product experience—it brings a broader set of capabilities. Qualtrics has expertise in comprehensive market research and data analysis. The same feedback-gathering abilities will also feed into HR with Success Factors and expenses with Concur.

Constellation POV:

SAP considers experience management to be a new market category and is prepared to pay a big premium to establish a position in it. The goal seems to be redefining the category, not just matching or bettering the offerings of other competitors.

Qualtrics Customers Gain Stability, SAP Customers Gain XM Capabilities

Current Qualtrics customers may well worry that acquisition by SAP will diminish the startup’s attitude thus far. Qualtrics has had a very customer-focused culture and an “anything goes, art of possible” approach to the world. By retaining the CEO and Qualtrics’ dual headquarters, the intent is to limit negative impact to customers.

Constellation POV:

SAP sees synergies with their sales and marketing suite C/4 HANA. Constellation sees opportunities in the SuccessFactors unit as well as the Concur unit given the dominance Qualtrics has in the travel sector. SAP customers who aren’t already working with Qualtrics can expect some heavy cross-selling for the next 12 to 18 months.

Upside and Downside Risks Enter The Calculus

SAP customers gain a new tool in the portfolio. Though customers will have to pay for Qualtrics, it won’t be coming free with maintenance.  SAP has made a clear—and big—bet on the importance of experience management going forward.

Constellation POV:

Despite the high valuation, Qualtrics technology is behind others in the space, notably Medallia.There has been good adoption and saturation in the market already in some industries such as travel and hospitality. What remains to be seen is how long it takes SAP to realize the value of this new market segment that’s implied by the purchase price.

The Normal Is Beyond a 9 to 10X Valuation for Hot Feature Companies

Over the past 10 years, VCs and investors have effectively chosen the winners and losers in each of the major software market categories. New startups have been funded based on features, not necessarily new market categories. The result is fewer major acquisition targets and greater competition to buy them. It seems that the days of a 9X or 10X revenue valuation are over. At 20X revenue, the Qualtrics acquisition may be a harbinger of things to come.

Constellation POV:

Competitors such as Survey Monkey will face a tougher road winning the B2B enterprise market.  Meanwhile, privately held startups such as Medallia and Clarabridge may benefit with higher valuations as they IPO.  It’s time for internal software R&D teams to up their game—they might just have become a much more competitive investment option.

Thanks to R. "Ray" Wang for his contributions to this piece.