- Turkey Hits Its Shot
- Shades Of Turkey In China
- Let The Festivals Begin!
- Let The Roses Bloom
- Defterdarburnu As It Once Was
- Second Stop: Clay
- Art In Elazığ
- Classical Music On The Golden Horn
- Munich Loves You
- Forever Young, May 19
- Sultan Of Land And Sea
- The Work Of The Waqfs
- The World Is Speaking Turkish!
- Straddling Two Continents
- The Film Is About To Begin!
- Exhibitions Worth Seeing
- Hot Shopping In The North
- Redbud Time In Istanbul
- White Legacy In The Aegean
- The Conjunction Of Three Continents
- Romans Of Everyday Life
- A Master Remembered
- Shadow Of Istanbul Falls On Luxembourg
- Semih Sayginer’s Ho Chi Minh City
- A Legend That Came From The Sea
- Be A World Local
- Africa In Five Questions
3 Humans + 1 Computer = Best Prediction
Computers often beat people at predicting the future, as the political analyst Nate Silver convincingly demonstrated during the 2012 U.S. presidential election and shows in his book The Signal and the Noise. But studies have found that humans’ predictions are sometimes better than machines’. So let’s say you’re trying to forecast the success of a product at launch. Should you rely on a computer or trust an expert’s wisdom?
New research suggests that the best approach isn’t either-or; it’s both. But “both” doesn’t mean a simple 50/50 mix. In relatively unambiguous contexts, rely more on computer analysis. In highly uncertain ones, average the opinions of three experts and give greater weight to their combined judgment than to the machine’s results.
We discovered these guidelines by trying various combinations of human and computer predictions about hit songs on the pop charts in Germany and the UK. Over the course of 12 weeks we asked 180 people—half of them music-industry professionals, half of them graduate students with no particular knowledge of the music business—to predict the Top 100 positions of singles by established and new artists.
Making a prediction about a song by an established artist is a “well structured” problem—past performance data reduce the uncertainty. Here, we found that in a straight contest of man versus machine, the machine tended to win (by “machine” we mean software that relies on common statistical tools to analyze a presumed linear relationship). But the best results came from mixing human and computer predictions. The level of the humans’ expertise was irrelevant; we got the most-accurate results simply by giving the machine’s forecast slightly more weight than the humans’.
For unknown artists—a more uncertain context—the humans tended to beat the machine. But again, combining computer and human predictions produced the best results. And in this case expertise mattered a lot. When we looked at just the students, the optimal combination gave far more weight to the computer prediction. For the music-industry pros, it was the opposite. We then averaged the judgments of varying numbers of pros. The bigger the group, the better the forecast, but we got the largest gain when going from two experts to three.
Prior studies have been contradictory, with computers usually winning in lab experiments and people winning in natural settings. We believe there’s a reason for that. Lab environments tend to be well structured, which favors computers’ systematic processing. The ill-structured conditions in many natural settings favor the messy workings of the human brain.
That’s an important point when you’re deciding how much to rely on a computer prediction about a product launch. If it’s a groundbreaking product—something customers haven’t seen before—a computer can provide valuable insights, but you should lean more heavily on the judgment of experienced people.
Improving Your Forecast
In our experiment involving pop songs, the best predictions in a highly uncertain context came from mixing human and computer input in differing amounts, depending on the humans’ knowledge.
When the people were experts, the best mix was (1). When they weren’t, the best mix was (2). In a less uncertain context, regardless of the people’s know ledge, the best mix was (3).
(1) %65 Computer
(2) %38 Computer
(3) %48 Computer
It’s time to retool the 4 P’s of marketing for today’s B2B reality. As a framework for fine-tuning the marketing mix, the P’s—product, place, price, and promotion—have served consumer marketers well for half a century. But in the B2B world, they yield narrow, product-focused strategies that are increasingly at odds with the imperative to deliver solutions.
In a five-year study involving more than 500 managers and customers in multiple countries and across a wide range of B2B industries, we found that the 4 P’s model undercuts B2B marketers in three important ways: It leads their marketing and sales teams to stress product technology and quality even though these are no longer differentiators but are simply the cost of entry. It underemphasizes the need to build a robust case for the superior value of their solutions. And it distracts them from leveraging their advantage as a trusted source of diagnostics, advice, and problem solving.
It’s not that the 4 P’s are irrelevant, just that they need to be reinterpreted to serve B2B marketers. As the sidebar below shows, our model shifts the emphasis from products to solutions, place to access, price to value, and promotion to education—SAVE, for short.
Motorola Solutions, a pioneer of the new framework, used SAVE to guide the restructuring of its marketing organization and its go-to-market strategies in the government and enterprise sectors. Along the way the firm identified three requirements for successfully making the shift from 4 P’s thinking to SAVE.
First, management must encourage a solutions mind-set throughout the organization. Many B2B companies, particularly those with an engineering or a technology focus, find it difficult to move beyond thinking in terms of “technologically superior” products and services and take a customer-centric perspective instead.
Second, management needs to ensure that the design of the marketing organization reflects and reinforces the customer-centric focus. At Motorola Solutions, this led to the dramatic reorganization of the marketing function into complementary specialties, allowing focus on each element of the SAVE framework and alignment with the customer’s purchase journey.
And third, management must create collaboration between the marketing and sales organizations and with the development and delivery teams. Motorola Solutions required that specialist teams concentrate on solutions and coordinate their approaches to specific customer needs. This ensured that functional boundaries did not determine the firm’s solutions.
B2B marketers who continue to embrace the 4 P’s model and mind-set risk getting locked into a repetitive and increasingly unproductive technological arms race. The SAVE framework is the centerpiece of a new solution-selling strategy—and B2B firms ignore it at their peril.
Instead of Product
Focus on Solution
Define offerings by the needs they meet, not by their features, functions, or technological superiority.
Instead of Place
Focus on Access
Develop an integrated cross-channel presence that considers customers’ entire purchase journey instead of emphasizing individual purchase locations and channels.
Instead of Price
Focus on Value
Articulate the benefits relative to price, rather than stressing how price relates to production costs, profit margins, or competitors’ prices.
Instead of Promotion
Focus on Education
Provide information relevant to customers’ specific needs at each point in the purchase cycle, rather than relying on advertising, PR, and personal selling that covers the waterfront.
The Sweet Smell Of Success
University students completed 4% more levels in the Wii Fit Plus Perfect 10 game and scored 26% more hits during the Snowball Fight game when they were exposed to the scent of peppermint, according to a team led by Kristin McCombs, of Wheeling Jesuit University. The peppermint increased participants’ physiological arousal and kept them more engaged. Past research has shown that peppermint enhances attention, memory, alertness, and mood.