Chief Digital Officers: Bridging the Innovation Gap Between the CIO and CMO

The chief digital officer (CDO) is no longer an exotic, quixotic, flash-in-the-pan role. In some of the world’s leading brands, the CDO is now the general manager of a large digital business unit with significant revenue targets reporting to the CEO. This is one of the fascinating conclusions from IDC’s latest report on the CDO role based on interviews with CDOs from: Caterpillar, CVS Health, The Metropolitan Museum of Art, Meredith Corp., SAP Digital, Travelex, the U.S. Department of Commerce, and Under Armour.
The title of this study should in no way insinuate any lack of innovation on the part of CIOs or CMOs. Both roles are managing digital transformations that are reshaping everything about their organizations. Those efforts can be all consuming, so some brands are establishing the CDO to lead strategic innovation. Free from the operational KPIs of the CMO and infrastructure demands of the CIO, the CDO is expected to invent the digital growth engines of the future.
Information and software-based companies are moving into services and support areas across industries. They are bringing new business models based on data services, sharing economies, and mobility much faster than established companies can. This is a huge threat as these areas are major revenue growth opportunities in industries that may be in low single-digit growth mode. Legacy brands typically don’t have the core competencies in software development or data and analytics needed to bring information-based products to market. In addition, cultures at many large enterprises are not used to the extreme cadence of digital business. As a result, leading companies are not only driving internal innovation and developing their own talent, they are investing and acquiring start-ups.
Based on our interviews, we have developed three archetypes for today’s CDO:
  • The digital GM: Reports to the CEO and leads the establishment and/or transformation of a significant business.
  • The digital Disrupter:Reports to the EVP or equivalent and leads a dynamic team charged with driving product and service innovation and cultural transformation.
  • The digital Evangelist:Reports a level or two down but is highly visible to the executive level. Leads a small team designed to raise digital IQ throughout the organization.

In practice, the CDO role spans a spectrum of overlapping responsibilities. The digital GM also drives innovation and raises the digital IQ of the entire enterprise. The digital disrupter is also in charge of raising digital and social adoption across the company. The digital evangelist is more of a support role that helps senior leaders drive digital transformation.
Two key questions every company should ask itself during the annual executive planning cycle are:
  1. If we wanted to completely disrupt our industry, what kind of company would we start?
  2. How do we become that company?

The executives running the companies profiled in this study have asked themselves these questions in one form or another. They may not have all the answers yet, but they have dedicated themselves to finding out before they get “Appled,” “Ubered,” or “Airbnbed.” New mantras for the digital era are:
  • The only way to control the pace of change is to set it — that’s the primary mission of the CDO
  • Always be disrupting
  • Follow the money: find out where the VC money is going in your industry and watch those companies closely, partner with them, and invest in them or buy them if you can

For more information about this report please contact me: gmurray(at)idc(dot)com.

Marketing to the Data Driven Customer

Customers with digital DNA expect data driven value
The digital native generation is bringing new expectations to brand relationships. They are mobile first, crowd sourced, and data savvy. Their first and most frequent interaction with your brand will be digital and mobile. They find out what’s cool, what’s trending, and what’s most likely to work best for them from their social networks. They don’t have emotional attachments to brands because the product is compelling or the advertising is cool. Their emotional engagement comes from unexpected insights that make them more successful. This is the new basis of customer loyalty, advocacy, and lifetime value.
Of course you still need a compelling product and cool ads (or messaging.) But once the prospect is a customer, continual engagement depends on over the top data driven insights. It’s no longer enough to just sell the hammers and saws and let the buyer go build their house. You need to monitor how they are using the hammer and saw. You need to deliver success by guiding their use of your product based on the behavior of your most successful customers. You need to leverage your position as the center of your customer universe to share best practices quickly and efficiently. The only way to do that at scale is through data.
Data Ownership vs Data Stewardship
In between the lines, you should be hearing a new philosophy with respect to customer data. Even though legally you “own” it, the data driven customer expects you to act as a data steward. You must treat their data as an asset to be used for their benefit, not just as the basis for driving revenue. Everything you provide to your customers should be designed to bring data back. Your customers should learn that the more data they provide, the more value they get in return – without negative side effects like having their data sold to an irrelevant ad network. Give to get and maintain the trust.
This has tremendous implications. Not only for marketers. Data marketing requires coordination with product development, IT, finance, fulfillment, point of sale, customer support, consulting services, sales. All these groups interact with customers and capture data on different aspects of their behavior – product usage, purchasing, problem resolution, planning, advocacy, etc. They all need to be understood to identify the most successful customers and the traits that drive their success. You can create tiers of services based on the level at which customer provide data. You can create cohorts of customers that exclude direct competitors. You can support exchanges within your customer ecosystem that enable strategic accounts to benefit from preferred peers. You can be extremely creative about how you structure your data marketing services.
The message is that in a world of shrinking product cycles, cheap knockoffs, and copycat services, data marketing is the new source of differentiation. No one else has the data you (should) have on how customers can be most successful with your products. Use it to attract and retain the best and leave the rest to your competitors.

To continue the conversation on data marketing and the data driven customer, contact me: gmurray (at) idc (dot) com.

80% of Your Customer Data Will be Wasted

Larger and richer collections of customer data are increasing available. That’s the good news. But most of that data is wasted. That’s the bad news. Poor data practices remain one of the biggest hurdles to marketing success.

Here are four ways that companies squander data and recommendations about how to stop the waste:

Data is Missing: A huge amount of customer data is available but is just not collected. Your ultimate goal should be to capture interaction and behavioral data at every touch point.
 
What to do: Acquire the data. Invest in marketing technology and services that capture data and in data management technology to store it for analysis. IDC finds that tech marketing leaders invest more than three times the amount of funds in marketing technology than their laggard cousins.  Big data is the marketer’s friend.  Providing lots of data to your analysts will enable them to predict the next best offer, discern buyer preferences, determine marketing program attribution, improve conversion rates, and much more.

Data is Unavailable: Some customer data is captured in company systems, but is trapped where marketing can’t access it. Marketing needs information on customers from a broad array of sources from both inside and outside the enterprise. Sales data, purchasing data, and customer service data, are examples of internally available data critical to seeing the full customer picture.

What to do: Aggregate the data. C-Suite executives must rush to the aid of marketing if they want to get full value from the function. To stop measurement at the MQL or even sales “closed loop” is insufficient for the full customer picture. Pay particular attention to converting unstructured data into structured data so it can help drive the content customization and delivery process.

Data is Junk: Sometimes customer data is captured, but is meaningless.

What to do: Analyze the data. You must be able to separate the signal from the noise. The first step is to gain a baseline understanding of the journeys taken by your best customers.  This point of view will give you a filter. CMOs need to invest in the tools and skills needed to gain insight from the data and tell a relevant business story.

Data is Late: Some meaningful data is captured, aggregated, analyzed – but the whole process takes too long for any relevant action to occur.

What to do: Act on the data. The point of data investment is to develop a rich understanding of the customer’s context so the most relevant response (typically content) can be delivered to them. In a digital dialog, a response is expected on the other side of every click.  Data needs to be made readily available to decision engines and content management systems so that they can take action.

If Content is Still King, Data is Heir to the Throne

Content marketing is becoming a primary strategy to solve the challenges of massively scaling and diversifying marketing channels. But content does not naturally support both scale and diversity at the same time. The only thing that scales as endlessly and cost effectively as the digital world is data. As a result, data marketing is on the rise and will ultimately inherent the throne as the core strategy for modern marketing. What is data marketing? It’s using interactive data to directly influence or add value to your prospects, customers, and partners. Think of it as content marketing without the editorial. Data marketing is already fueling the rapid growth of content marketing. The best pieces of content marketing are typically wrapped around a compelling piece of (static) data. The key is that stripped of editorial, data must become interactive and not only deliver personalized insights but capture and bring user input back. 
Modern business solutions are increasingly deployed in the cloud on SaaS platforms that capture every transaction of every user. SaaS vendors are finding huge value in these datasets. They provide empirical evidence of best practice, efficacy, and cost effectiveness. Marketing and sales automation vendors can show their customers and prospects what types of campaigns result in the greatest lead generation, the highest value and velocity through the pipeline and the greatest return. They can tell them what type of social media content and cadence is most effective on which social media channels. This insight represents enormous value-add over and above the operational efficiency the systems provide.
Consider the power of this model applied to channel marketing. A SaaS platform for channel enablement can offer partners a single point of access to content repositories, transaction systems, execution environments, (inbound and outbound marketing, sales process tools) and social networks. If it’s constructed properly it provides a place for partners to get work done, not just a library to read about how to get stuff done. For smaller partners that lack infrastructure and staffing resources this is an invaluable resource. As they use the platform it captures:
  • Engagement – who’s downloading what how often from the platform
  • Transactions – deal registration, order submission, billing update, MDF reconciliation.
  • Execution– the number of leads their marketing has produced, how leads are progressing through their pipeline
  • Social interactions – groups they join, how they participate, what SMEs they interact with.
  • Performance data – closed deals, order value

Access to this data can be offered from the platform through the development of a few simple forms and reports. The more data partners provide, the greater the level of analysis and insight they get in return. This information can be used to identify best practices of the top performers and shared (in aggregate) with other partners to help them run their businesses, resulting in better overall performance of all partners.
By utilizing pure data as collateral, companies can deliver highly targeted proprietary insights at scale much more efficiently than they can with content. While the role of content will in no way diminish, companies that master the art of data marketing will have greater levels of engagement, retention, and revenue with all their key constituents than those that rely exclusively on content marketing. 

Using Data as a Service for Scalable Channel Enablement

The magic ingredient for successful channel enablement at scale is data. Imagine having the financial, operational, and behavioral data you need on partners to optimize new product launches, coverage models, and channel programs. Imagine being able to show partners — no matter how new or small or niche their focus — how other partners like them have achieved high return on investment (ROI) on their business with you. IDC’s Channel Enablement Maturity Model provides a stage-by-stage guide for advancing the organizational, process, technology, and data infrastructure necessary to transform your channel marketing and sales enablement operations. The journey along IDC’s Channel Enablement Maturity Model is one of evolving from a publishing/transactional framework to a process-driven one.

IDC’s Channel Enablement Maturity Model – Summary View
Stage 1:
Ad Hoc
Stage 2: Opportunistic
Stage 3: Repeatable
Stage 4: Managed
Stage 5:
Optimized for Scale
Key characteristic
“Every product for itself”
“Portals grow like weeds”
“Consolidation but still stuck in publishing mode”
“Central control over process-driven approach”
“It’s all about analytics (Data as a Service)”

Source: IDC 2013

The DNA for Success is in the Data 
IDC defines channel enablement as “developing the right competencies in the right partners to deliver the right solutions to the most profitable customers.” Ultimately, the goal is to provide a scalable model to identify high ROI best practices and propagate them throughout the partner population at a very granular level. There are three ways in which manufacturers can capture the partner data needed to support the analysis:

  • Contractual obligation: Requires significant time and effort from partner account management, is limited to the largest partners, and is periodic at best. 
  • Operationalized data capture: The partner platform should be thought of as a SaaS offering that provides a wide range of functionality but also collects data on every partner interaction. The ideal platform will consolidate all of the interactions with partners by offering personalized access to content and transactional systems, as well as execution platforms for marketing, sales, and support. By virtue of this consolidation, it captures an increasingly large portion of partner interactions and thus provides a great deal of valuable data to inform channel marketing and management. 
  • Data as a service: Externalize partner performance data and make it available to partners in a way that captures even more data from more partners. The level of detail they get depends on the level of detail they provide. As a result, they can get actionable insights on how to better manage their businesses and market, sell, and support specific solutions. The database is in a virtuous cycle of enrichment. They should be able to get insight into a wide variety of strategic and tactical questions such as: 
    • How many people do I need in marketing, sales, technical, and support roles? 
    • What level of skills and training should they have? 
    • What marketing activities are most effective? 
    • What sales methodologies and plays are most effective at what stage? 
    • What manufacturer resources and networks should staff be utilizing most frequently? 

While data is the crown jewel, there are significant people, process, and technology prerequisites for success. To find out more please see IDC’s Channel Enablement Maturity Model or contact me at gmurray (at) idc (dot) com.

Data Analytics wins 2012 US Presidential Election

Data analytics was the big winner in the 2012 US Presidential race. In fact, 11:17 PM (US ET) November 6th was the moment data analytics went mainstream. This was when Ohio was officially projected to go to Obama. It was the ultimate validation for Nate Silver and his data analytics approach to election forecasting. To much fanfare he accurately predicted the results of the election in all 50 states without doing any of his own polling. He used sophisticated analytic models based on data from as many third party polls he could find. To this he added the secret sauce of data analytics – a keen understanding of how different types of data from different sources relate to one another in context.

His FiveThirtyEight blog drove as much as 20% of the web traffic to the New York Times website – the 6th most visited US news site on the net – leading up to the election. As a result, data analytics is officially mainstream. Any business leader at any level that does not immediately embrace its power is putting his or her career and company in jeopardy.
Data analytics works. It does not produce miracles, but it does produce results that far outperform human judgment on its own. The Obama campaign employed an army of retail data analytics wonks to beat the Romney campaign in every battleground state. They did it by applying analytic techniques proven in the supermarket industry:
  • Standardizing records: Unifying the customer (voter) database
  • Widening perspective: Combining diverse data types: demographics; buying/voting history; response by media; donation/activity by trigger (celebrity dinner), model (contest) and method (mobile); group/church  membership, social networking activity (Reddit), etc.
  • Judicious targeting: Carefully identifying the potential for influencing voters that could influence the election. Not worth targeting easily influenced voters if they don’t live in a county that can help swing a state. Not worth targeting difficult to influence voters even if they live in a critical county. This is essential for achieving impact and ROI.
  • Media mix modeling: which media channels have the greatest impact on which kinds of voters?
  • Action oriented outreach: Understanding the specifics of why and how certain people act and designing multiple outreach experiments (progressive offers, channel mix, social references, etc.) based on that.
  • Openness to innovation: data driven models may point to approaches that are counter intuitive for some decision makers. They can seem risky and mysterious. They will not be right all the time. Controlled risk is part of the evolutionary process to effectiveness. Without a tolerance for experimentation however, you will not develop a data driven culture, you will in fact kill it.

Marketers in the world’s largest high tech companies are finally acquiring the enterprise data services needed to apply data analytics to long cycle B2B customer creation processes. We are already seeing signs of how significant the impact of these new approaches to marketing and sales can be:

  • $200M EU lift based on a sophisticated solutions recommendation engine
  • 45% more subscription revenue with no increase in a multi-million dollar marketing budget
  • Tens of millions of dollars in revenue uplift from simple web behavioral changes

Embracing data driven decision making is now a matter of survival. You simply cannot win against competitors that have faster, deeper market insight. They will beat you in every stage of the customer creation process. Your marketing will be months behind, your inside sales reps will be calling customers already committed to alternatives, your field sales reps will miss opportunity after opportunity to get more revenue from existing customers. Your funnel will collapse, your pipeline will dry up, your renewable revenue will shrink, and at that point it will be hard to recover. Hyperbole, you say? In the great A/B test of who uses data analytics and who does not, stay in the B group at your peril.
IDC EAG group has done extensive research on the key ingredients needed to create the enterprise data services that are a prerequisite for data driven customer creation and has ongoing research into how to create a data driven culture. To find out more please contact Gerry Murray – gmurray(at)idc(dot)com. 

Big Data Comes to Marketing?

What’s the most commonly heard word in marketing organizations today? It is “Transformation.”

Dramatic transformational change is sweeping through marketing functions in most industries. And the main “change agent” is the customer. Or what we at the IDC Executive Advisory like to call the “New Buyer” . Our customers and prospects today are crafting their own routes to learning about products and services. They are motivated and skilled at educating themselves and learning from peers. They travel through numerous digital pathways in their exploration process. And by the time they come to a meeting with the vendor sales person, they are smart and savvy. They are empowered.

Marketers need to ask and answer these questions: Where did our Buyers come from? What do they know already? And above all: How do we  add new value to where they are in the process of discovery about our product or service? The New Buyer dynamic creates volumes of new data and customer intelligence analysis opportunities for vendors.

In turn, Those in the marketing job function must be able to bring better data into any planning meeting, including discussions on budgets and investments; programs and campaigns; or performance measurement. Hard data needs to complement the “softer side,” or the “art,” of marketing.

The tools for accessing and mining data, and turning data into insights, are now plentiful for today’s marketers. And, The marketing job function might be the last of all an organization’s major functions to become automated.

The marketing winners of tomorrow will be masters of rapid data management — able to turn data into intelligence, intelligence into analysis, and analysis into decision support and execution. Achieving this will be the first step in the rudiments of sales-to-marketing cost control.

For the CMO, there are three critical, inter-departmental, data-driven intersections that need to be created and nurtured. Marketing is now too important to run in isolation, so here are the three key intersections:

1. The Marketing and CIO intersection. New IDC research shows that the investment in marketing automation technologies in 2012 will be at three to four times the rate of 2011 levels. Automation technology development is going to sweep through sales and marketing over the next 10 years.

2. The Marketing and Sales (CSO, or Chief Sales Officer)  intersection. Today, the CMO needs to be able to connect sales technologies, such as CRM, with new marketing automation technologies.

3. The Marketing and CFO intersection. The CMO needs to deliver a return on investment in measurable terms in order to have meaningful budgeting and planning discussions with the CFO. Measuring impact of push programs in terms of conversion to leads, opportunities, and revenue is the game today.

I like to say that there will be more change in Marketing in the next five years, than we have seen in the past 25 years combined. These Marketing data and IT automation issues will be at the forefront for the next generation of successful CMOs.