Platt Perspective on Business and Technology

Understanding the crowd and how it can be misused as a source of insight – 2: crowd sourcing and business intelligence

Posted in business and convergent technologies, social networking and business by Timothy Platt on July 10, 2012

Several weeks ago I posted a thought piece on search engine filter bubbles, and with a focus on how misconstruing the crowd and skewing how it is sampled and what is assumed from that can lead to problems (see Part 1: in search engines and their underlying algorithms. I said at that time that I would write a follow-up piece examining the way that businesses sometimes fall into similar traps as they gather and analyze crowd sourced business intelligence.

• That, I add here would include marketing intelligence.
• But it should also be seen as including crowd-based insight that would inform product and service design, customer services and support, and essentially any and every other aspect of the business that interacts with the customer or with potential customers
• And with this interaction originating on either the business or marketplace side of those relationships.

The question here is one of what constitutes the meaningfully connected and relevant crowd for any given business, and for any given situation.

When I wrote in Part 1 of this short series about search engine bubble searches, I was writing about a circumstance in which the only meaningful answer should be all of humanity and all of human experience. Unless someone is explicitly seeking to do a local search when they conduct an online fact finding search they generally want to have access to as wide a knowledge base as possible, and with any search engine filtering simply helping them to find the most relevant resources by content area – and even when they already have firm opinions on what they are searching for. Even the most ardent partisans would object if told they were being limited in their online searches, by some corporate search engine algorithm acting on its own and outside of their control, to only show them results that it has calculated to be acceptable for them, and regardless of its selection criteria.

Businesses seeking crowd sourced business intelligence would not necessarily start out looking to the entire global online community – the universal set of all possible crowd sourced discourse and insight when seeking to develop what is for them, meaningful and relevant business insight. But they should seek to capture input and insight drawn from and representative of their customer base and of the larger pool of potential customers that represents their marketplace as a whole. And for many purposes they should be reaching out for insight to their supply chain and to potential supply chain partners in like manner.

So this posting is about blind spots and about the assumptions that can be and are made when they exist but are not understood or acknowledged – which is in fact exactly the situation discussed in Part 1.

• How many businesses fail to pick up on feedback and potential feedback and insight from unexpected directions or sources?
• Even when they are listening or at least supporting communications channels for doing so, how many set aside and lose negative feedback, criticism or problem reporting, and if nothing else from a failure of customer facing employees to pass along those crowd sourced messages to the right people – or for them to even have capability to do so?

Think of this as the business world’s version of search engine bubble filtering, and the more important interactive online connectivity and the crowd and its insights become as defining and differentiating sources of competitive advantage, the more limiting and damaging this bubble filtering of message becomes – and the more common.

Listening costs money – it created ongoing operational process requirements and expenses to cover them. The return from any given message received is likely to be slight if any. But collectively, and from the patterns of understanding that these messages collectively could convey if nothing else, this feedback brings value too – if it is picked up upon, analyzed and utilized. And listening removes some of the skew and misunderstanding as to where a business is in its community and its context.

As a final thought here, the input I write of here is all at least potentially grist for what has come to be called big data. But I find myself thinking of phone center customer support systems, and particularly outsourced ones disconnected from the organizations they represent for the level of data collection needed here. There are gaps and their presence, their unacknowledged and unaddressed existence creates skew. This represents a significant and growing problem for many businesses and organizations and even for entire industries.

You can find this and similar postings at Social Networking and Business and also at Ubiquitous Computing and Communications – everywhere all the time.

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