Actionable Insight = Strategy Analytics + Big Data

“People commonly use statistics like a drunk uses a lamppost: for support rather than illumination.” - Mark Twain

Data and statistics without strategy are like ingredients without a recipe: A wasted opportunity.

And what you are about to read here has been written about by me and others numerous times over several years. So why am I writing about this again? Mainly because I continue to see far too many companies not heeding the advice that without a well defined and internally bought into analytic strategy and resulting roadmap, analytics and Big Data and all related technology will likely not realize their latent value.

Strategy alignment – the first of the “6 Steps to Analytics Success” (links available Here, Here and Here) -- is a decision science approach that arms a business with the capabilities for using analytics to unearth useful data. Remember – I said “useful” data. This is data that managers can use to inform and then measure the impact of business decisions.

With analytics linked directly to business strategy, a “Small Data” or “Big Data” initiative can more easily attain senior leadership support, garner appropriate prioritization, and generate a measurable return on investment that can fund subsequent data projects. This is what I call “strategy analytics.”

Strategy analytics simply makes sense – by conceptualizing and constructing a roadmap of short, medium and long-term analytic based initiatives, a business can transform information into insight and foresight with resulting return on investment (ROI) along the way. The roadmap should mesh tightly with the business’ holistic strategy. In this way, the roadmap can generate short-term ROI that helps fund the cost of the next downstream effort, which can then generate its own ROI to help fund subsequent efforts. This self-funding mechanism, if planned and executed well, can result in a much lower cost sequence of projects than the usual project docket planning approach of discontinuous or tactical efforts.

Embedding analytics into strategy and planning helps build a flexible business with an ongoing ability to anticipate rather than react.

Know Your Customer – A Strategic Analytics Example

A key example of the potential confluence of strategic roadmaps with strategy analytics is found in customer insights. To fully assess their readiness for strategy analytics, business leaders should consider how well they know their customers. They should have an intimate understanding of the customer experience. It’s also critical to understand what a failure of customer intelligence costs in real terms. These insights can be found by asking probing questions on structured and unstructured data sets. For example:

  • Does the company display a deep, or even a general knowledge of its customers and the people its customers influence through their societal or virtual social networks?
  • Does the business touch its customers in meaningful ways and make next-best-offers to them?
  • Why do customers stay loyal (or not) amid a sea of competitive choices?

Consumers intuitively know what they want. They send signals that companies must interpret.

For example, in the telecommunications sector, problems can occur during moments of customer-facing interactions or service experiences. Also, failure to understand subscriber preferences can spell disaster. When the provider misses functionally or with plan bundles that do not meet the subscriber’s needs, frustrated customers often turn elsewhere for service.

Today, telecom companies have an opportunity to jointly leverage diverse data sources to find deeper insights that can be applied to improve the customer experience. Where little data falls short, big data steps in. For example, a service provider may know only a few details about a subscriber purchasing a handset and some accessories. But that is often where the understanding ends. There is typically no larger, holistic view – such as the customer’s total disposable wallet-share, the linked correlation to future offers or purchase options, or device usage information. Little data simply doesn’t provide a basis for more sophisticated insights. Big data is another story. For a wireless network, big data is the abundance of call and detail records, network activity, subscriber application usage, and more. By leveraging diverse data in a strategic way, telecom companies can re-examine important problems requiring foresight with their customers, such as:

  • Handset performance expectations: Problems with defective or malfunctioning handsets could be handled with integrity by carriers that know of these problems. The carrier could anticipate the customer’s issue before the customer experiences it himself, providing proactive customer service that helps to virtually eliminate tensions before they occur.
  • Revenue leakage in shared plans: When providers fail to anticipate how the customer and their families use their services and plans, they are leaving revenue untapped and loyalty denied. Connecting usage patterns and shifting revenue elsewhere with credits and rewards can foster an innovative customer connection. But then there is also the risk of ill will when customers believe the company should have anticipated such issues, especially in cases where the company is perceived to be netting more revenue from the confusion than it otherwise would have.
  • Complexity associated with billing and customer care: If customer-facing personnel fail to grasp customer issues, telecom subscriber confidence erodes. Alternately, providers that can resolve an issue can build loyalty.
  • Minimal optimization of utilized minutes and features: When customers aren’t using what they are paying for, they can feel cheated. The backlash from such negative feelings is often subscriber attrition to another carrier. Deciding how to tailor an offering of customer minutes and features requires insight to customer data and an understanding of customer behavior (both rational and irrational).
  • Failure to understand the true customer wallet: Most customers know, or at least viscerally feel, what amounts they are willing to spend for their service relative to their perceived product usage. By critically examining usage patterns in a more sophisticated way, providers can pinpoint the intersection of the customer, his expectations, his wallet, and his actual use of services.

Customers want the companies they do business with to understand and appeal to them in a meaningful, insightful, and timely way. Strategy analytics, coupled with an analytics roadmap, can serve to correct—and connect—customer knowledge, understanding, and tactical performance.

Are you trying to improve how you align business strategy with analytic techniques via a roadmap to improved decision-making? I hope to hear from you.

Follow me on Twitter (@johnlucker), email me at JLucker@deloitte.com and read the latest insights from Deloitte Analytics.

Sal Guerrero

Fraud Prevention/Investigation | FinCrime Investigation | AML/CTF and KYC Due Diligence | Transaction Monitoring & Screening | Ex-PayPal | #FinCrime | #FinTech

9y

Thank you for contributing this article, John - very practical and insightful. And thank you too, Richard, for adding your own comments. I agree thoroughly.

Richard Lee

Management Thinker & Advisor: Data Leadership (infomgmtexec.me/2014/09/16/recap-the-data-leadership-nexus/)

9y

John, I appreciate the passion with which you write about Data & Analytics and your 6-point approach, but I believe that you are missing two key elements in order to make Data & Analytics truly pervasive (and thus delivering tangible strategic benefits); 1.- The need to establish Data Leadership (and Accountability from the Top Down and not leave it to IT, their Proxies or Champions. 2.- The need for the entire Organization to embrace (much less use) Data & Analytics in every daily activity that they are responsible for. To achieve these Outcomes requires a Long-term Business Strategy that has Data & Analytics at its core, along with a Culture that is "evidence-based" (as demonstrated from the Top Down). I could drone on about this for hours, but I imagine you get my drift. RL

Data without strategy is an empty well

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