marketing insights

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The. Big Data. 22 MARKETING INSIGHTS. NOVEMBER/DECEMBER 2014 ... an increased potential for and use of advanced analytics. This is further fueled by ...
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What every marketer needs to know about advanced analytics By Marco Vriens and Patricia Kidd

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ith the increase of readily available data, the opportunities to develop predictive models for a wide variety of marketing metrics has not only led to an explosion of analytics, but also to an increased potential for and use of advanced analytics. This is further fueled by the increased pressure on marketers to do more with less and provide marketing-driven ROI. Hence, every bit of data needs to be mined so that all marketing insights can be found. This saves the firm money because the answer can be found in data that they already have, instead of having to pay for new research to get the answers. Also, there is increased interest in models that predict the outcomes of certain marketing activities. A recent survey by IBM and MIT in the Sloan Management Review showed that among 3,000 executive managers worldwide, improving information and analytics was a top priority for half of those surveyed. Furthermore, almost half of those surveyed indicated that a “lack of understanding of how to use analytics to improve business” was an obstacle to the widespread adoption of analytics in the organization. There is cumulative evidence that firms that successfully adopt advanced analytics are more successful than those that don’t. This means that marketers who “get” what’s possible with advanced analytics and how to properly deploy it will win in the workplace.

Certain firms have leveraged analytical insights to improve operational efficiency, grow revenues and improve market position. Firms often cited include Harrah’s, Netflix, Capital One, Google, Amazon, Dell, eBay and Intuit. Yet many firms have not been able to do this, and even fewer have been able to take full advantage of advanced analytics. The main reason is awareness. As found in the IBM/MIT survey, firms simply may not be aware

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of or familiar enough with what can be done with advanced analytics. Catching up on this area will be a significant challenge for many marketers and future marketers. To get started on the advanced analytical journey, marketers should know what advanced analytics entails, how to think about it and, at a high level, what types of marketing problems have been successfully addressed with advanced analytics.

What Does Advanced Analytics Entail? Advanced analytics is not a fully defined field. A variety of terms—analytics, predictive analytics, predictive modeling, advanced marketing research, advanced modeling, data mining, etc.—may all be used to essentially mean the same thing. Whatever the term used, the practice is more likely to be referred to as advanced when it:

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Considers data as a strategic component in the search for actionable insights. For

example, when designing surveys, advanced data collection approaches are selected that may go beyond the standard questioning approach. Examples include discrete conjoint choice tasks and laddering tasks. Advanced survey design also can connect the results of surveys to other data sources.

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Use more advanced statistical modeling or data mining tools. We define modeling

as any analysis that specifies a model and has a predictive element to it. To dig deeper into the data

and uncover new and better insights, an advanced approach would go beyond standard predictive modeling to include more realistic and rigorous modeling of consumer or market behavior. For example, a standard approach to analyzing the effectiveness of consumer promotions would use a standard time series regression model to determine if promotions are correlated with increased sales. Such a modeling approach would not capture some critical consumer behavior dynamics, such as: • A lead effect, which exists if consumers start to anticipate a promotion by buying more during a promotion than normal so as to bridge the time to the next promotion (stockpiling). • A threshold effect, in which a specific promotion has no impact unless it is at least a certain size, say 10%. If these effects are not captured via a more sophisticated model, the guidance that the model provides may be flawed and marketers could end up taking incorrect actions. There is a wide variety of effects that can capture specific behavioral consumer dynamics, but the key point is that there is value in looking beyond the standard analysis.

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Is executed iteratively, sometimes executed over multiple stages, using several different data sources. Frequently, the initial pass-through

of the data does not give the desired results. It is not uncommon to analyze one specific data set, with one set of variables, in many different ways before one discovers the insights. Sometimes there are various components to an advanced analysis plan that are executed as stand-alone analyses, but the results of different types of analyses are later integrated. For example, conjoint analysis can be integrated with brand perception data, concept testing can be integrated with forecasting, etc. Combining different data sources often can bring unique perspectives and uncover new insights. For example, we can combine attitudinal, financial and transactional data or we can combine micro and macro data. An example of the latter is when survey data is mixed with population and macroeconomic data. A common scenario is to use internal transaction data and match this with external survey data. In this way, we would pull a sample of customers and transactions, both internal data sources, and then fuse them with external satisfaction data. Next, driver models could be estimated based on the fused data set.

The foundational element of advanced analytics is that it aims to produce deeper and more valid insights into consumers, often aimed at understanding what factors are causing marketing success or what factors are predictive of marketing metrics.

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The foundational element of advanced analytics is that it aims to produce deeper and more valid insights into consumers, often aimed at understanding what factors are causing marketing success or what factors are predictive of marketing metrics.

Where to Aim Advanced Analytics The domain of advanced analytics can be overwhelming and it is hard to figure out where to start. I propose a fairly simple framework that can help marketers quickly determine if advanced analytics could provide solutions for the marketing challenges that they face and could help them get an edge over the competition. Every marketer should know about the following key areas, which have been shown to successfully drive sales and profits:

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Market forecasting.

Statistical techniques are used to develop a predictive model of the market size for a given product or service, how the market likely will grow/shrink/remain stagnant, and what drives this dynamic. If a firm operates in multiple markets— some growing, some declining, all dependent on geographical region—then knowing these market trends can have a major impact on the ability to best utilize marketing resources. A number of firms have deployed forecasting analytics with great success, including Frito-Lay, Kraft, Procter & Gamble and Olive Garden. Olive Garden, for example, reduced unplanned staff hours by 40%.

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Quantifying customer needs and motivations.

With the advance of quantitative laddering and other approaches to capture consumers’ unmet needs, marketers should consider advanced analytics. Prudential Financial and UBS are two firms that successfully deployed advanced analytics and achieved tangible, measurable business results. Prudential captured quantitatively determined

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emotional states, leading to an improved ability to understand customer needs. The result: a net gain of $453 million in sales lift.

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Analyzing drivers of brand share.

Several successful case studies exist where firms captured market share by understanding which brand perceptions are most predictive of brand choice with the help of econometric brand choice models. Leveraging such insights, ABB Electric, a small manufacturer, went up against established firms and increased its market share significantly. More recently, Jetstar, a sub-brand of Qantas Airways, used this type of advanced analytics to aggressively grow market share. The airline gained four share points as a result of implementing the brand drivers’ insights.

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Product and pricing optimization.

This involves determining the best mix of attributes to optimize volume, share or profitability, often using advanced discrete choice conjoint techniques. The approach is among the advanced analytics approaches that marketers are familiar with because it is currently the most applied and popular technique used in marketing research. The degree to which these approaches have been adopted is evidence of their usefulness, and there are compelling case studies that demonstrate its value.

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Marketing mix and marketing efficiency modeling.

This is an area in which advanced analytics have more recently made steady inroads. Marketing mix modeling that leverages advanced econometric

techniques is now on this list of approaches to give marketers more insight into how well their marketing efforts are working. A recent example is how Inofec, a Dutch firm, increased its profitability by leveraging marketing mix insights to funnel a better allocation of its marketing budget to various marketing activities.

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Customer dynamics.

Every year at the predictive analytics world conferences, firms present cases in which they leveraged predictive models to gain insights into, 1) which customers are most likely to defect and when, 2) what the next logical product is to sell to their customers, and, 3) how to determine lifetime value of its customers and prioritize how customers should be managed. An example is IBM, which quantified customer lifetime value and used this to align marketing and sales. The result was an increased marketing ROI to 160%.

What’s Next? Going forward, marketers and marketing students should ensure that they are comfortable with the basic approaches and uses of advanced analytics. There is no need to become a technical expert, but some in-depth knowledge is needed. This means becoming familiar with the foundational aspects of predictive modeling, including how to assess whether or not a predictive insight justifies a particular marketing course of action. Another foundational aspect is validation. Marketers need to ensure not only that the insights are valid, but that they are being applied in a valid context to reach a better decision. Marketers also need to become data-smart. Most companies have a wealth of data, ranging from survey data to internal customer transaction data, quality or customer complaints data, secondary research reports about trends and markets, and online data (for example, Google trends, clickstream, online product reviews and social media data). It helps to sketch out which types of data could be used for the specific insights that are needed, and this data may reside in different

The sooner a marketer can map out, at a high level, the types of advanced analytical projects that might be feasible, the sooner the financial rewards of the analytical insights will be reaped.

parts of the firm, such as IT, sales regions, etc. I have been in situations where I had to tie together eight or more different data sources. The sooner a marketer can map out, at a high level, the types of advanced analytical projects that might be feasible, the sooner the financial rewards of the analytical insights will be reaped. Just clearly knowing the types of data needed can make them a voice for smart strategies, and they’ll become one of the winners in the Big Data shift. Advanced analytics is at a critical tipping point right now, and the more marketers are familiar with it, the more successful they will be. They need to know what marketing areas have been tackled successfully and what types of questions to ask. Is it vital for anybody who pursues an MBA to make analytical training a part of his curriculum? For those marketers already in the field, knowing which approach to use to get actionable insights that could lead to profitable decisions will be one of the professional challenges that they will need to tackle to stay relevant. MI MARCO VRIENS is the global chief research officer at Ipsos Market Quest and author of The Insights Advantage: Knowing How to Win. He can be reached at [email protected]. Patricia Kidd is the senior vice president of client service at The Modellers/Hall & Partners.



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