Aligning Business Model & Culture to Maximize the Analytics Opportunity

In a recent blog post Analytics in Services: Actions versus Talk, we reviewed how companies are applying big data and analytics for both internal and external uses. That review led to a survey and executive panel discussion at the November 2015 Arizona State University Center for Services Leadership (CSL) Annual Compete Through Service Symposium where we further explored adoption rates, challenges, and lessons learned.

Adoption rates

The survey of 42 CSL member-companies and Symposium attendees revealed that roughly 25% have actually deployed initiatives using this technology, 25% have not considered how they will utilize analytics, and approximately 50% are developing a plan or are in pilot. Interestingly, these percentages are consistent whether companies are trying to improve marketing effectiveness and operational efficiency, helping set service levels, or attempting to expand markets and build new sources of services and solutions revenue.

Intent

The survey also asked respondents to describe what they were doing in each of these areas, from which the panel discussed several case studies in some detail. What emerged was an interesting set of objectives that can be captured as:

• Efficiency – improve operational efficiency and reduce risk.
• Experience – enhance every aspect of the customer’s experience.
• Expansion – generate new services-based revenue streams.

As noted in our prior blog, the drive for efficiency has been well documented and the data reinforces that it is the most broadly adopted.

The second area, experience, generated a great deal of discussion and it became clear that this is where much of the energy in the market is focused. Experience encompasses all aspects of the customers’ journey: understanding each as an individual, marketing more effectively, setting and attaining appropriate service levels, providing support proactively, and anticipating future needs. It was evident that for a number of respondents this was the path to revenue growth both in terms of wallet share and market share.

Which leads to the third objective, expansion. A number of technology companies are aggressively pursuing the opportunity to be suppliers of technology, infrastructure, and consulting for analytics. However, a relative few organizations are also leveraging analytics to turn the data they own/access along with their expertise to generate new services revenue streams.

The executive panel was comprised of companies who fell into both of these categories: Siemens, IBM, DuPont Pioneer and Intel.

Challenges

A broad set of inhibitors were cited by the survey respondents and we subsequently discussed during the panel. The challenges fell into three major categories, with some unexpected challenges emerging:

• Data

There were two distinct sets of issues identified here. The first regarded the capture, integration, and filtering of data from a rapidly growing array of sources.
The second set of issues centered on data security/privacy/rights/integrity – and the potential financial and brand risks of getting it wrong.

• Resources & Infrastructure

Not surprisingly, skills in data science and analytics were frequently cited. Not only acquiring a skill set that is not traditionally found in many companies, but also nurturing and retaining those critical resources in a highly competitive market.
The infrastructure necessary to support new analytical workloads and the growing volume of data was something that many respondents cited as a ‘hidden’ cost—or at least one which was not always factored in up front.

• Business Model

The most frequently cited issues were associated with establishing a clear and compelling business model—particularly in regards to establishing new services revenue streams. The age-old challenge of competing priorities was compounded by the lack of effective means for calculating the ROI for the customer and the concerns over financial risk cited above. As one panelist pointed out, we are entering an age where data is the new currency—and yet there is no accepted methodology for measuring ‘return on data’.

Summary – Ideas to Consider

The executive panel shared their insights and made some compelling suggestions for companies considering leveraging big data and analytics to drive top line growth. Ideas that were discussed in the interactive session with the symposium attendees included:

• Integrating internal & multiple external data sources combined with your expertise for more value
• Identifying new markets and buyers for the services offerings based on data + analytics + expertise
• Developing a ‘skunk works’ first-of-a-kind team to launch and experiment—avoiding the culture trap
• Bringing on new skills and augment with university and industry programs
• Considering building a partner eco-system to fill gaps in your infrastructure and skills
• Establishing credible means for measuring the ROI for both the customer and the business