Behavioral Science Based Market Research Our market research uses the most advanced implicit and explicit research techniques, coupled with a fundamentally more accurate model of decision making, to provide deeper insight and more valid forecasts of consumer behavior. Humans behave contrary to rational economic theory, using heuristics and emotion as guides to navigate the steady stream of choice options flowing non-consciously through our minds.
Without automated credit scoring techniques, the large-scale, modern day consumer credit industry as we know it would not exist.
By comparison, analytics in consumer retail marketing is still relatively undeveloped. Except in the area of direct marketing, where consumer response-based targeting models are widely used and well accepted, analytics applications in other areas of the business is less systematic and varies widely across companies and sophistication of executives in charge of marketing.
This article briefly reviews the successful evolution of analytics in the consumer credit business, while exploring four areas in consumer retail marketing that are not as well developed but hold great potential for substantial business impact.
Applications of analytics in consumer financial services The primary application of analytics in consumer financial services relates to credit risk scoring. Credit scoring techniques assess the credit risk of lending to a specific consumer.
Credit scores are used in deciding whether to grant credit to a consumer applying for credit, how much credit to grant and at what interest rate. It is also used in managing existing customer relationships.
Overall, despite the recent catastrophic failures in risk management related to consumer mortgage loans in the United States, the advancement of analytical applications in consumer credit businesses is no doubt one of most significant developments in modern finance.
It greatly increased productivity of the financial services industry, which in turn helped increase the overall living standard by Marketing research reveals consumer behavior consumer credit available and affordable to large sections of the population.
In that sense the wide spread application of analytics helped make consumer credit into a mass product from a privilege only available to the elite. Several reasons explain why analytics as exemplified by credit scoring achieved such tremendous success in consumer financial services and became so well accepted over the past 50 years: The emergence of small, evolving credit products meant the industry needed to replace expensive and labor-intensive manual evaluations by credit reviewers.
The advancement in statistical modeling techniques and computers made this feasible. Market competition also played a large part in popularizing the applications of automated credit evaluation based on statistically derived credit scoring models.
Once the success of such an approach was established by a consumer credit operator, the competitive edge it created quickly forced other industry players to adopt similar practices, thus making credit scoring quickly accepted and pervasive in consumer financial services.
As one innovative application led to another, credit scoring practices quickly spread from granting credit applications to management of the whole credit lifecycle. Credit performance of consumers is fairly predictable in a statistical sense.
This is why metrics such as percentage of times past loans have been satisfactorily paid back, number of historic delinquencies and severity of delinquency, length of credit history, as well as utilization rate of available credit, are important and effective predictors of future credit performance.
Credit bureaus made credit information widely available to consumer financial service providers. This factor cannot be emphasized enough.
Today, the application of analytics in consumer financial services extends well beyond assessing consumer credit quality at the time of acquisition to maximizing overall business profitability by optimizing a series of business decisions through the customer lifecycle.
Application of analytics in consumer retail marketing Analytics is certainly no stranger to consumer retail marketing. There are pockets of successful application of analytics in marketing. For example, in direct mail or telemarketing a standard practice is to apply statistical models to select consumers for targeting to maximize response rate or profit.
Market research is another area that relies extensively on analytics for complex sample design and hypothesis testing. The practice of consumer segmentation has also advanced significantly with the increased availability of sophisticated statistical analysis techniques.
Other applications in marketing that have seen considerable thought leadership and forward thinking in recent years include: The rest of this article briefly discusses analytic frameworks in these emerging areas, their potential to have significant business impact, and the challenges they have in being widely adopted and relied upon as a standard practice for ongoing business decision-making.
Sales forecasts and driver decomposition models. The most commonly used approaches to predict future sales and revenue in marketing rely on time series forecast or various smoothing methods. Smoothing methods include moving average, exponential and Holt-Winters triple parameter smoothing.
The major advantage of time series or smoothing techniques is that they only use past sales or revenue data to forecast. And they are relatively simple to do with many built-in procedures and routines to choose from specialized software.
However, these advantages have to be balanced against their shortcomings. Often marketing executives are challenged with explaining why the forecasted sales or revenue fell short of or performed better than the plan.
What factors, internal or external, contributed to the difference? And therefore how do we plan better and allocate resources smarter going forward? Time series or smoothing methods will not be able to answer these sorts of questions.Penn State Smeal research reveals that careful marketing can impact consumer eco-product purchasing habits responsible behavior in others.
Viral marketing or viral advertising is a business strategy that uses existing social networks to promote a product. Its name refers to how consumers spread information about a product with other people in their social networks, much in the same way that a virus spreads from one person to another.
It can be delivered by word of mouth or enhanced by the network effects of the Internet and. How Consumer-Identity Data Revolutionizes Automotive Marketing Multipoint predictive analytics allow you to deliver messaging that resonates based on consumers’ geography, lifestyle, life stage, and likelihood of being in the market for a new vehicle.
Consumer Behavior- Ethnographic Research on Victoria's Secret and Target Essay; Consumer Behavior- Ethnographic Research Marketing Research Reveals Consumer. MINTEL TRENDS. Trend tracking, analysis and interpretation of changes in culture markets, brands and consumer behavior.
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