Potential: Revealed

Strategic Thinking, Innovative Ideas, Growth Marketing, and Revealing of Potential

Good D

In a previous post I mentioned that D for Data was a critical component for revealing insights about your customers. In today’s age, that is as close to “goes without saying” as you might be able to get. So this post is intended to define a simple approach to improving your D … or in sports term, “playing good D”.

First, a “customer advocacy” mechanism is required. For some established companies customer-centric thinking (and ordering of data and systems to deliver insights) is second nature and a distinct “advocacy” role, department, program or such would be unnecessarily bureaucracy. For most companies though, such a formally designated entity is a key building block. A customer advocate can help both develop and drive the systematic approach for discovering and leveraging customer insights, and be a peer at the management table when priorities and on-going investments must be clearly proposed or vigorously defended.

Second, in the infancy stages of a customer insights journey, a world-class, competitively differentiating customer insights data set and deep analytics capabilities are too grand and far off to achieve in the near term. The risk and complexity in achieving this higher stage of customer enlightenment can overwhelm and obscure what are most likely low hanging fruit — or what I like to call “pumpkins” (pumpkins are actually a fruit, and they are not just low hanging but they can be found on the ground ready to pick up). An iterative-experimental approach can give both a good chance to take advantage of the pumpkins along the way and build momentum toward achieving the higher stages of enlightenment within a reasonable time frame. How to do this?

I’ve used a three-part, interconnected process that includes Research, Analytics and Experimentation. These ideally are set up to work together, under one leader. There’s no one best way to set this up and the choice depends upon the organization’s culture and management style.

Research: this is the “compass” function within the overall process. It helps to set the direction for where the keenest insights might be found. It develops an on-going portfolio of customer data and sources through qualitative and quantitative research.

Analytics*: this is the “targeting” function. Using analytics techniques and sound experimental design approaches, it generates testable hypotheses about the next best place to go, within an achievable distance, from the current understanding of customers.

Experimentation: manages the experiment through to implementation and measurement of results, working in partnership with other parts of the organization, external partners, vendors, etc. to get it done.

The key is setting up such a process and team to iterate quickly, gaining the benefit of pumpkin insights and the benefit of honing the skills of implementation of customer insights in the most rapid way possible.

* not to be confused with the general, pervasively useful capability of “analytics” (see Kyle McNamara’s blog for a clear view of analytics).

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4 Comments»

  marketingagent99 wrote @

Analytics, segmentation, customer insights and other buzzy words. I’ve struggled, as have companies and teams I’ve worked with to know the difference between these terms. I’m not sure but your post seems to use them interchangably? Are they? I would presume “no” mainly because, segmentation as a good example, some of these buzzwords have somewhat well known definitions and applications while others are truly broad (“customer insights” being a good example there). It would be helpful as a starting point to clarify what these things mean so you don’t lose the audience by creating a buzzword blizzard that tends to make them feel lost.

  potentialrevealed wrote @

Buzzwords — can’t live with them, can’t live without them. That said, you are right if I created a seeming blizzard with my post.

To try to cut through it and pick on just the three buzzwords you mention I’ll use the ends and means framework:

Analytics is an overarching approach and segmentation a specific tool, that are highly related and are a means to an end. Specifically, analytics can be thought of as a somewhat broad heading encompassing many tools, techniques, skillsets, etc. that are a fresh, and deeper, approach to addressing business opportunities and challenges. You can apply analytics to any point on the value chain, from Research to Production, to Customer Service and all points in between. Segmentation is typically a marketing tool for disaggregating a set of customers within a market into groups who share similar needs and who demonstrate similar buying behavior.

The end, at least from a marketing perspective is creation of a customer insight (a widget of differentiating knowledge about your customers). Customer Insights (captial C, capital I), to me, can also be thought of as an overarching process, with a set of integrated capabilities and skills, which includes analytics, and tools such as segmentation.

  adamgrizzly wrote @

I have read a lot about analytics in marketing to try to find if it would help my company. My boss is a major hater of buzzwords and jargon so I like to use examples for him. It helps if he can go visit a website, even get on the phone and try to talk with the people at companies supposedly doing this or that new thing. I seem to see Capital One, Visa, Amazon and a few others repeatedly mentioned. Is that because they are the only ones actually succeeding or they are PR hounds or what? Can you give other examples? Thank you. The Grizz.

  potentialrevealed wrote @

I don’t want to use my blog to cite anything more specific (company name) about my own clients (to protect confidentiality) although happy to discuss in a little more detail if you’d want to contact me directly (click the Latente Group link above). What I will say though is that you are thinking smartly regarding caution on analytics. CapOne, Amazon (others on the short list includes Harrahs, Wal-mart, Oakland A’s) get mentioned frequently because indeed “competiting” on analytics is very difficult and NOT achieved in a short time period. It has not been that long that the combination of data availability, analytical (software) tools, and well-known best practices and case studies have been around in a cost-effective manner. The firms most often mentioned had two key ingredients – -they were already, or were willing to change to, very analytical from a culture and business process perspective and they also got on the analytics bandwagon as pioneers and early adopters (taking those risks believing the rewards would be well worth it).

An interesting thing to note about most of these analytics pioneers: on balance they are relatively younger (or were smaller) firms who literally used analytics as a competitive dimension to break away from larger, incumbent market leaders. As is often the case in established industries (banking, logistics, retailing, lodging and travel, etc.), the market leaders get complacent while nimble upstarts and competitors figure out a new way to compete — and win.


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