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Progressive companies are engaging in sophisticated data collection and data-mining efforts to help uncover hidden insights about their customers' experiences with their products, services, and brands. Realizing that business intelligence is not an algorithm—looking at the same type of data the same way, hoping to see something new—they are adopting tools and techniques that help them gain broader insights, and to do so more quickly.

They are then converting this information into true business intelligence that will accelerate decisive action about product mix, marketing efforts, and service levels.

In large part, however, companies are using quantitative data-mining techniques to uncover trends in customer experience, determine the magnitude of problems or opportunities, and evaluate the relative performance of retail or service outlets.

For example, marketing leaders that want to dig deeper to better understand the real effectiveness of in-store merchandising on their customer experience—and ultimately on financial performance—are typically monitoring in-store merchandising execution through secret shops, tracking unit-level trial and usage statistics, and monitoring customer feedback.

These efforts can tell marketers how well merchandising is set up and identify which units are not seeing the expected sales lift. However, these techniques alone often fail to provide enough intelligence for leaders to know what the right corrective action is.

To enhance these traditional types of quantitative data-mining efforts, marketing innovators are implementing new tools and technologies to add a qualitative dimension to the analysis.

Unique approaches such as advanced video analytics and "day in the life" sketches bring a higher level of business intelligence that enables leader to more effectively forecast and prevent risk, challenge core business assumptions, accelerate preemptive product or service offerings, and find and eliminate constraints to delivering the right customer experience.

Here's how these advanced analytic tools work.

Video Analytics

To better understand the customer experience, video ethnographers have historically used video to help uncover the "unmet needs" of current and potential customers.

But recent advances in video technology and analytic practices allow innovators to unearth deeper and more systemic insights into the real experiences of their customers:

  • Video technology, including small, movable cameras, remote monitoring and downloads, and the ability to tap into existing security camera configurations, improves precision of data capture and reduces overall cost to the organization.

  • Advanced approaches to coding specific occurrences and conditions over thousands of customer observations improve quantification of results and the ability to dig deeper for cause-and-effect relationships.

  • Searchable, digital databases allow researches to find and report specific occurrences more efficiently.

With these advanced video analytic techniques, organizations can track specific travel paths and activities of customers or employees, identify bottlenecks in the service experience, and classify buying behavior to understand the where, when, and why of a specific issue.

For example, assume that your current marketing intelligence suggests that your in-store merchandising efforts are not translating into the financial return you expected. Enhanced video analytics provide the higher level of intelligence needed to pinpoint solutions:

  • In store merchandising can alter the way that customers travel through a store and what the subsequently purchase. Video analysis of hundreds of customers reveals the degree to which new in-store merchandising creates additional travel paths, increases the time spent engaging the merchandised product, and impacts time spent in adjacent areas in the store. It also isolates the factors that drive actual purchases of merchandized and related products, and uncovers unintended effects such as creating bottlenecks or low-traffic areas.

  • Employees play a central role in maximizing the impact of merchandising efforts. Video and audio can reveal that when employees engage customers, are visible and approachable during peak buying hours, and consistently reinforce marketing messages with customers... they have the power to convert more customers into buying customers. The video analytics help uncover the reasons for breakdowns in employee involvement—such as staffing shortfalls, positioning choices, and training level of staff.

"Day in the Life" Sketches

Another emerging approach to gathering deeper insights is to create detailed "day in the life" sketches to help articulate the "emotional experience" of customers and employees—providing a closer look at how they think and behave.

Techniques range from low-tech, such as written or voice-recorded journals, to more hi-tech, such as strategically placed video cameras and handheld devices for recording experiences and occurrences. They also range from high involvement of the "target," such as self-journals and recordings, to low involvement, such as observer journals and checkpoint interviews.

Like video analytics, data collected through sketches is coded and reported to bring to life the real experiences of employees or customers—from common experiences to unique and unexpected experiences.

In the example above, video analytics revealed that your staff is not engaging customers the way you expected to maximize the impact of your in-store merchandising efforts. What it does not reveal is why employees make the choices they do—and these insights are critical for developing solutions that will stick:

  • Managers perceive competing priorities that reduce their focus and time-spent on in-store merchandising. Written journals and interviews reveal that many managers believe their staff should spend more time engaging with customers, but that other activities are perceived to be more urgent. More effective managers, however, have developed a common approach to leading that allows them to make better choices, anticipate issues, and react more quickly. By isolating these effective approaches, manager training can be targeted to increase probabilities of the right interactions with customers.

  • Employees are uncomfortable approaching customers the way we've asked. Creating "day in the life" sketches across retail units reveals three primary causes of employee willingness to engage customers:
    1. A climate where it is the norm to go above and beyond to engage customers

    2. Rewards and recognition directly tied to financial and customer experience goals

    3. Good staff fit—individuals with the disposition to consistently engage customers

Savvy marketers use these root-cause insights to enhance near-term merchandising efforts and build the right foundation for impacting the customer experience.

* * *

Marketers can no longer rely on only quantitative data to address an issue. Qualitative tools, such as advanced video analytics and "day-in-the-life" sketches, are needed to unearth the deeper insights—the where, when, and how much—that are needed to make decisions that stick.

Once leaders get a taste of the breadth and depth of information these techniques provide, they start thinking about their organizations in a new way, and new doors begin to open.

Continue reading "Business Intelligence Is Not an Algorithm" ... Read the full article

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ABOUT THE AUTHOR

Ron Halverson is founder and principal of the Halverson Group, Inc. (www.halversongroup.com). Formerly a research psychologist in the US Army, he has over 15 years of expertise in applying advanced analytics in organizational settings.