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IQ+EQ: Big Data and Primary Research in the Era of Decision Intelligence

At the Marketing Research Association’s Corporate Researcher Conference in St. Louis today, Greg Heist, the chief innovation officer of Gongos, discussed how primary data can be linked with hard data streams to inform smarter decision making.

The work we do to understand consumers as human beings is as important as ever. “Bad decisions are really, really expensive.” According to Pricewaterhouse Coopers, the top 1,000 publicly held corporations spent $647 billion on R&D in 2014. According to Booz and Company, 66% of new products fail within two years. So $427 billion, by Greg’s math, is lost every year – more than the combined revenue of Apple, Microsoft and General Motors. This is due to many factors: politics, culture, etc., but – for researchers – failures in innovations come from a fundamental inability to gain traction among consumers.

How do we move from failing to gain traction, to gaining attraction? Committed relationships need to blend the mind and the heart. Greg said, “At Gongos, we use the term consuman.”
What are they like as the consumer, what’s our IQ about their rational way of making decisions? But consumer is a reductionist label.
These are fundamentally human people with wishes and desires that have nothing to do with how they consume. What is the EQ, the Emotional Quotient?

The dimensions of human decision-making include the rational brain, the emotional heart, and the physical body. “We need to approach them as a unity.”

The challenge for researchers is to build the competency of Decision Intelligence, helping the organization making great, human-inspired decisions. To do this, organizations need to do three things differently:
1. Better take stock of the research they have.
2. Uncover the missing layers of knowledge.
3. Bring the consuman world into the office.

Within organizations, consumer wisdom is often underutilized, and the functions that understand IQ and EQ tend to work autonomously. Insights and Analytics are often different groups: Insights understands attitudes and uses self-reported data; Analytics analyzes behaviors with streams of activity data. IQ is the hard data of “who, what, where, and when.” EQ is the soft data of “why?”

These two functions must converge and diverge. As Camille Nicita said in Harvard Business Review, “Sophisticated decision analytics based on large datasets uncover new and important insights, but only people give the opportunity to go deeper.”

Organizations need to assess their skill set, mindset, structure and bandwidth to unify these domains. What data streams are available? What might be congruent or complement one another? How can the organization explore and optimize the linkages between these types of data? The final steps are validating and operationalizing the learnings.

As a fictionalized case study, “Kasserole King” has a loyalty program that every 8 purchases leads to a free 9th casserole but wants to also offer promotions targeted to individual buyers. Analytics knows what they buy, how often, and when they redeem. Insights has lifestyle data, attitudes and usage data, segmentation, and exploration of promotions. One approach would be to do a behavioral segmentation of the analytics database to better target promotions. For validation, calibrate research to actual behavior, then refine and re-test. These two disparate streams can converge and diverge.

For decision makers, the more we try to process, the more we multi-task, the more mistakes we make. Knowledge needs to be consumable and memorable. Too often wisdom is siloed, isolated on hard drives and forgotten, going unshared and unsocialized. To democratize wisdom, information needs to be promoted across siloes and across the organization. A researcher might use the lens of a psychiatrist, an accountant the lens of numbers, and any other number of “differently oriented compasses” that confuse the debate: “think of consumer empathy as the true north of the organization.” 

In the end, decision intelligence creates a reciprocal relationship between the organization and its customers.

 

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