Latent customer needs can be discovered and knowable, says marketing expert
Companies can discover customer needs that are unknown to customers themselves through learning from their own market experiences and observing the market experiences of collaborators and competitors, says a research paper by a professor at The University of Alabama in Huntsville (UAH), a part of the University of Alabama System.
Doing so can increase the chances of creating a popular product that will avoid the scrapheap of failure that awaits some 40% of all newly introduced products, says Dr. Yongchuan "Kevin" Bao, an associate professor of marketing whose coauthored paper appeared in Strategic Entrepreneurship Journal.
Dr. Bao's work challenges the traditional viewpoint of entrepreneurial opportunity, which has been conceived from the supply side as either an introduction of new goods at prices higher than the cost of productions or exploitation of competitive imperfections in product or factor markets.
The challenge is that customers themselves don't perceive that they have these unrealized needs or find it difficult to express such needs, which Dr. Bao explains are called latent customer needs.
"Latent customer needs are tacitly embedded in customer experience and blend in with salient market events and changes," Dr. Bao says. "Such needs arise when customers encounter unexpected, anomalous problems in their daily lives or workplaces, but can easily escape the awareness of customers because they pay attention to those visible, prominent issues in their consumption routines or task performance."
Entrepreneurs and companies can detect and satisfy such latent needs by closely monitoring their own experiential market learning (EML) and the vicarious market learning (VML) of competitors and industry colleagues.
"To discover the latent needs, firms must notice and interpret unusual events to understand the causal pattern of customer problems," Dr. Bao says.
"From experiential learning based on direct market experience, firms develop a tacit, path-dependent knowledge script that enables them to notice the inconsistency of an unexpected event and to interpret the causal patterns of the anomaly," he says.
A latent need of farmers for a washing machine that could wash vegetables was identified by Haier, the world's leading home appliance maker, based on a farmer's complaint that the washing machine he bought from Haier frequently clogged up. When a technician found that the farmer's problem stemmed from using the washing machine to wash vegetables, that was inconsistent with the normal washing machine consumption routine.
Learning from that unexpected event prompted Haier to identify the unspoken, tacit desire of farmers for a new washing machine that could solve their pain point of manually washing vegetables.
Likewise, based on vicarious market learning observation of other firms' market experience, a firm learns from a wide distribution of marketing activities and lessons, says Dr. Bao.
"Because these vicarious experiences are rooted in idiosyncratic contexts, the learning stimulates multiple and even contradictory interpretations of market events encountered by firms in different circumstances," he says. "As a result, the vicarious learning acts to question the conventional thoughts about customer needs and helps to identify linkages among the diverse interpretations, which end up with surprise findings of unusual causal patterns that signal latent needs."
Apple inadvertently identified a latent customer need based on observations of the market experience of Napster, the firm that invented the software app allowing users to download music online. The controversial practice of free music downloads instigated lawsuits from music producers and recording companies, and invited debates in the music industry.
The controversy questioned the traditional way of listening to music and helped Apple discover the unspoken need of consumers to generate a music list of their favorites, which it fulfilled with iPod.
Dr. Bao says the pressures of active learning based on market experience appear to be immensely high in emerging economies, because rapid economic growth and unique market dynamics frequently bring forth unexpected events and surprises.
From a research survey of 238 firms in China, the largest emerging economy, Dr. Bao and his coauthors have modeled a portfolio of T-shaped corporate learning strategies formed by experiential learning based on EML and VML. In the model, EML refers to the vertical dimension of the T shape due to its path dependence on direct market experience and VML represents the horizontal dimension as it draws on the bilateral experiences of other firms.
"Specifically, firms and individual entrepreneurs should immerse themselves in the real lives of customers to uncover the pain points of customers and the anomalous events that contradict our expectation of customer behaviors or consumption routines," Dr. Bao says.
"Moreover, they should actively learn to observe the market activities of other firms and individual entrepreneurs and be vigilant of the contradictory interpretations of market events from different companies and entrepreneurs," he says. "These learning strategies can help firms stay ahead of rivals by identifying latent customer needs that are unknown to rivals."
In a fast-paced, turbulent market swirling with changes in customer preferences and segments, firms and entrepreneurs should rely more on vicarious learning than experiential learning in their efforts to identify latent customer needs.
"Because a firm's knowledge about customers accumulated from EML is path-dependent on prior market experience, the fast pace of market change diminishes the relevance of prior knowledge to the interpretation of unexpected market events that occur from the fast change, thus making it difficult to discern meaningful causal patterns that reveal latent customer needs," Dr. Bao says.
"In contrast, VML places a firm in a better position to identify latent customer needs in a turbulent market environment, because the diverse interpretations of the heterogeneous market experiences of other companies enable a firm to achieve a relatively accurate understanding of the murky causal patterns of unexpected market events that frequently take place in a market with a high pace of change."
Customer-driven innovation can help avoid alarmingly high market failure rates, he says.
"A primary factor that leads to product failure is that they do not meet a meaningful customer need. Many companies erroneously believe that a cutting-edge technology in itself would induce customers to buy, while neglecting efforts to figure out what customers truly want."
The research provides a portfolio of learning strategies to help firms overcome the challenge that customers do not consciously know what they want and increase the likelihood of new product success.
"Only when a firm identifies what customers truly want can it develop a win-win product solution that creates value to its customers on the one hand and propels the profit growth of the company on the other hand."
The study opens up new future possibilities to unify different theories on the origin of entrepreneurial opportunities.
"From the lens of latent customer needs, the real substance of entrepreneurial opportunities exists independent of entrepreneurs, but can be observable or discovered, without the requirement of profit materialization, and moreover, through the active learning process based on market experience, which ultimately leads to the creation of new knowledge about customer needs," Dr. Bao says.
"Despite these insights, we need a formal endeavor to establish a new theory to integrate these different views from the perspective of latent customer needs."
A second area of future research is to investigate the conditions under which identification of latent customer needs can sustain a firm's competitive advantage or market leadership.
Last, there may be a role for artificial intelligence (AI) in determining these needs, Dr. Bao says, since learning based on market experience is distinct from the traditional way of market learning based on abstract information like consumer statistics collected through systematic search.
"It would be very interesting to see if big data and AI technology can be advanced to the level at which the gigantic amount of information we collect online can be used to predict latent customer needs and outperform human or organizational learning based on market experience."