The case for a Chief Digital Officer

When nobody in the corporation owns the strategy to exploit the consumer data the way the supply chain data is exploited for the benefit of the business, there might be a case for a Chief Digital Officer

Hagen Wenzek, 2013-06-26

In the early 2000s a major disruption swept through industrial companies and retailers: what started out as the Internet bubble became e-business and made all of their supply chains incredibly transparent. As a result, suppliers were forced to benchmark their prices against competition all over the world. They had to operate inventories at the client's site and open their manufacturing methods and show their costs. The benefits promised to a company that embraced that change were so great, that they took every effort to transform their operations, re-align their suppliers on a global level (including outsourcing to e.g. China) and innovated their products faster than ever before.

What has become mainstream on the supply side over the course of the last decade appeared also on the sell side. Here it is the transparency of the customer that provides tremendous opportunity. Consumers are handing their most private data to advertisers. Every one of their moves online or offline can be tracked and traced. And few clever advertisers provide most precisely targeted ads at every demography - since the tablet revolution even to most affluent consumers - and achieve up to 90% profit margin on their transactions.

However, that promise has not started a widespread transformation of operations, sales nor even marketing. Most of the time that 'Big Data' sits in big databases and might clumsily be harvested by individual data scientists using good old Excel. What is missing is a comprehensive strategy to exploit the sell side data in the same way as it was developed and executed on the supply side years ago. This lack is even more surprising given the advances in technology, especially thanks to cloud computing with its principles of scalability, self-service and drastically reduced costs. Developing such a strategy with the certainty that it can be executed successfully requires an understanding of marketing, technology, customer behavior, and the digital ecosystem paired with strategic thinking and execution capabilities.

The owner and driver of this strategic space is often referred to as the Chief Digital Officer, or, as visionary thinker and transformative doer Irving Wladawsky-Berger likes to say,"essentially the senior executive responsible for helping the organization transition into the 21st century digital economy and digital society".

Do you have somebody like this in your company?

Get to Know Your Customer

Get to know your customer:

How to use big data to improve decisions, when it is the CMO who owns it, the CIO who builds it and the innovator who disrupts it.

Hagen Wenzek, 2013-06-13

• Big Data makes the customer transparent, but nobody really knows how to use that knowledge 
• The CMO owns the relationship with the customer and wants to improve it using technology
• The CIO can make it happen by working with innovative startups

The emperor has lost his clothes, so how do you use what you see without getting blind?

Big Data promises to make the transparent consumer actionable with algorithms that can analyze all personal, contextual and operational data available about them. In the virtual world all of that data is collected by cookies, trackers and e.g. Facebook and one can easily be identified by an email address. That and credit or loyalty card transactions link the virtual world seamlessly to the physical world and via customer records to enterprise systems.

The big promise of Big Data is to process all that consumer data and facilitate better business decisions. In marketing and sales, that seems to be obvious, as such insight enables the right message to be sent at the right time and cheaper than ever before.

However, there are also better decisions to be made in product development and innovation, when instead of asking just a few focus groups what they want from a new product, one can continuously observe all customers and learn from their behavior.

And even in many other functions of the enterprise like supply chain management where data about the consumer originating in marketing has been notoriously suspicious for its doubtful quality, in procurement, manufacturing, and logistics, better decisions can be made based on really trustworthy consumer data. For example, a global chemical supply company is looking to leverage marketing data about consumers that is collected in the context of their clients, i.e. a consumer packaged goods (CPG) company like P&G or Unilever. Armed with that insight they can optimize their negotiations and supply chain as they sell ingredients for soap to that very CPG company, because now they can forecast what their client might need based on the marketing they do as well as what they see of the consumers’ responses.

 

Generally we can identify three big clusters of data:

• Transactional Data that gets collected when the consumer navigates or does transactions on the web. Those are the trackers and cookies that precisely identify a consumer as well as many more metrics

• Operational Data that comes from enterprise systems of records such as customer relationship management, sales, supply chain management etc.

• Research Data that aggregates consumer information and provides insights or presents audiences. Nielsen or Facebook fall in that category – though one might argue Facebook is moving into the first category

Today all of that data sits in individual siloes and is managed separately – if it is being collected at all – , but its real value comes from integration. Thus the question becomes obvious: who is responsible for the data itself and who for its management?

Traditionally the Chief Marketing Officer (CMO) and her/his department own the relationship with the consumer. Their expertise lies in the reduction of complexity to the simplest possible message and finding creative ways to transport that message to the consumer. The better they do that, the higher the brand value. On the other hand, neither managing a complex system that makes consumer data available is the core competency of a marketer, nor is in most cases the very analytical thinking that leverages detailed information. The latter leads to potentially risky behavior when oversimplification favors visualizing correlations over understanding causations.

Here is an example from a dashboard for a large consumer company where the media buyers wanted to know when to buy more ads on Facebook or what other events are related to certain spikes in reaching more friends. You can easily recognize the danger in that type of visualization, as it makes it very easy to ‘see’ a correlation. The dilemma is that dashboards that are any more complex are not being used at all.

Where the CMO's team is strong in simplification and creativity, it is the CIO who manages complexity and is process centric. Therefore, it should naturally be the IT department that builds and maintains the Big Data solution. However, to be successful, there are certain competencies that need to be strengthened at IT. Three of those appear to be most critical:

1.      The relationship with the CMO

2.      Partnering with service providers for scale and

3.      Working with start-ups for innovation

(1) Forging the CMO – CIO Relationship

The first important aspect is forging a good relationship with the CMO. Just like in large infrastructure projects, bridging a gap is often more a political challenge than the actual engineering problem. CIOs have over the past decade broadly succeeded in gaining trust by most functions in an enterprise, such as sales, manufacturing, finance as well as the office of the CEO. Therefore, they should be able to achieve the same with the CMO. There are distinct aspects that stand out to be considered: Outcome, small steps and storytelling.

• As mentioned above, reducing complexity is marketing’s core competency. Thus CIOs needs to avoid talking about how the system works, as that is necessarily a very complex undertaking. The risk to be misunderstood and seen as miscommunicating is high. What counts is explaining the outcome, talking about the ‘What’, not the ‘How’. Succeeding with that communication style immediately frees the IT team up to get the engineering work done.

• Small steps can already be big. What a seasoned IT executive might think is just a small step, can already be a very big one for somebody else. While having a vision and architecture for an end-to-end automated infrastructure that provides detailed consumer data synchronized with the supply chain management system to accelerate sales and product innovation is absolutely doable these days, just indicating for what product category advertisement dollars are being spent on a weekly basis can be a great improvement. Therefore, don’t be too ambitious.

• Learn to tell stories. Great marketing is all about telling stories, as people naturally understand and remember stories. Therefore, wrapping the value proposition of a big data solution into a story e.g. about how a client, competitor or already somebody within the company used this newly found consumer insight and was successful with it can be much more powerful than the usual formulation learned in engineering class or even business school.

(2) Partner for Scalability

The second aspect of creating a big data platform is already well known to an IT department: Picking a partner that provides robust scalability for the main components of the infrastructure. By definition, big data is BIG, therefore a trusted partner needs to be able to deliver at scale and do so globally if your business has global exposure. All the major players in IT are also offering strong big data solutions and choosing the right one is no different than choosing the right partner for most other major IT enterprise solutions. Those companies that lead also in cloud computing and have consumer domain expertise occupy the sweet spot and are the best candidates for a successful consumer transparency solution.

(3) Working with Start-ups

Lastly, real differentiation against competition where practically everybody uses the same data and large-scale infrastructure happens through distinct innovation. The most disruptive innovation in the digital domains can be found at nimble start-ups. However, finding and working with one poses a real challenge as neither party is well equipped to do so. The methods to select an enterprise IT partner like a Gartner ‘magic quadrant’ and other frameworks typically do not exist for niche players or they do not apply. And some frameworks that are available, such as the LUMAscapes from LUMA partners do offer a very comprehensive overview of solutions and companies, but are hard to navigate without help.

Let’s examine three less obvious aspects to this challenge: Choosing the right perspective, making a decision and looking at the right place.

• Through creative eyes, start-ups can be seen as an outsourced R&D department: A dedicated, highly ambitious team that comes up with an idea and has funding to take an invention to the next level – just that the enterprise does not have to hire and manage the people and their infrastructure nor cope with much of the risk of an innovation portfolio. Thinking of a start-up as R&D provides a perspective of openness and trust, that is different than when viewing a traditional, established supplier.

• Actually selecting a partner and leveraging an innovative solution in a critical business infrastructure will drag on for far too long when the paradigm of ‘best of breed’ is overstretched. ‘Innovation’ and ‘disruption’ do not lend themselves to the usual assessment of risk and leadership. Most of the time, the difference between different solutions by similar start-ups is small, while the gap to the existing capability is large. Thus, choosing one of the ‘good’ players in a certain domain and quickly deploying their solution will quite certainly trump spending a lot of additional time trying to select ‘the best’.

• Just like in the early 2000’s when practically every company went to China to search for a manufacturing partner or to India for call centers, Silicon Valley is praised as the silver bullet for technology innovation and for their start-ups. However, the culture in that closed ecosystem is quite unique and might often not be a good fit for an enterprise on the US East Coast, Europe or let alone Asia. And if you follow the advice to view innovators like an outsourced R&D function, cultural fit especially with their management team is critical for success. So not to repeat the disappointment of those companies that had to detract from China and India, one might rather take a close look at the start-up scene in New York, Barcelona, Berlin or Tel Aviv.

Conclusion

Big Data holds a big promise when insight from any consumer can be translated into superior business decisions. However, to make that promise real requires flexibility and openness to new approaches, especially by the CIO. Establishing a strong relationship with the CMO is critical. Picking an enterprise IT partner for scale makes for solid business. And using a niche innovator leads to competitive differentiation. If these components are all brought together, the transparent consumer can be spotted through technical lenses that make for better business decisions, and don’t make you blind.