It’s critical for product marketers to get accurate feedback at every stage of the product lifecycle from both current and target customers. There are many methods for acquiring this feedback, but the reliability of traditional methods can be limited. Consider three examples:
- Surveys: A respondent may answer survey questions about your product long after they used it, relying on their memory (which may not always be accurate).
- Focus groups: Customers in a focus group may be asked to explain their motives, emotions, and behaviors in front of the group, which may inhibit honest answers.
- User groups: In real-life labs or user testing environments, researchers and product developers may peek over the respondent’s shoulder, distracting the customer. Or, because respondents are in an artificial lab environment, they may not use the product in the same way as they would if they were interacting with it more naturally.
This isn’t to say product marketers shouldn’t rely on these methods for feedback; they remain useful tools that can be critical to getting customer insights. However, there are new technologies that are less intrusive and can leverage product use in a natural situation. Let’s explore two of these methods.
Marketers have used many facial-recognition applications in the past several years. In-store, product marketers can track a person’s movements and facial expressions to understand which brands hold the greatest appeal. Brands with a service aspect (e.g., hotels) are deploying facial recognition to highlight areas of friction in their processes so they can identify how to improve and deliver a superior customer experience. For example, guests at a few Marriott hotels in China can use facial recognition technology at a kiosk to check into a room that has been reserved beforehand, circumventing what can be a time-consuming check-in process.
Product marketers also are leveraging facial-recognition tools on various devices. Because cameras are embedded with an algorithm that can read facial expressions, product marketers can, at a relatively low cost, evaluate emotional responsiveness and attentiveness to various stimuli. This provides a quick and easy understanding of customer reactions to content, advertising, messaging strategies, product placement, and product positioning. In turn, this data can empower marketers to deliver products that satisfy and potentially delight customers.
There also are other benefits to using facial recognition for product research:
- Improves the value of survey-based feedback: While surveys can ask customers to provide answers rooted in their emotional experience, facial recognition measures actual emotional reactions and attention levels, improving the ability to understand behavior and preferences at a deeper level. Research studies, for example, have detected changes in mood based on how a product was displayed in its advertising.
- Minimizes bias: Algorithms in devices are based on machine learning, so findings are more objective and reliable than feedback that’s subjected to human interpretation. Eye-tracking can demonstrate what users are looking at when performing tasks, rather than reporting this information. Eye-tracking movements can help discern, therefore, aspects of an app that might be confusing or distracting, allowing the designer to emphasize or change the elements to do a better job of leading a user to more efficiently complete a task.
- Eliminates the need for training: Because the data capture and analysis are preprogrammed, product marketers don’t need specific training to leverage this technology in a research environment. For example, event marketers can use cameras at events to learn which sessions attract the most users, determine how long attendees study event attractions or view certain marketing assets to find out what holds the greatest appeal for attendees. Such cameras often don’t require special hardware and can be easy to install and configure.
- Enhances other survey data: Facial recognition in research enables product marketers to understand other research findings at a deeper level. For example, respondents may be asked about purchase frequency or attitudes toward the product category in general in a questionnaire, whereas emotional data exposes more about frequent users or those who are experiencing barriers to purchase. This helps product marketers understand how to overcome these barriers or how to more deeply appeal to frequent purchasers.
- Measures dynamic sentiment: Emotional reactions to an advertisement often change many times over the course of 30- or 60-second spots. Facial recognition can identify in-the-moment changes, highlighting those aspects of the ad that hold the greatest appeal. It also provides direction about which tone has the greatest resonance at the beginning, middle, and end of an advertisement or video.
- Facilitates A/B testing and benchmarking: Product marketers are frequently tasked with A/B testing and comparing results with the competition. Facial recognition can be leveraged to test and compare advertising variables as well as gauge a respondent’s assessment of both your and your competitor’s content. If members of your target audience are exposed to two different ads, for example, facial recognition can be used to determine which ad does a better job of evoking a desired reaction.
There are still privacy concerns about the use of facial-recognition technology, and cost traditionally has been a barrier to entry. But the tide is turning. People are becoming increasingly accustomed to the many applications of facial recognition in society. Do you have an iPhone X or a higher version? If so, you’re carrying facial-recognition technology in your pocket, and you can use it to unlock your phone or authorize transactions. Similarly, banks are beginning to deploy facial recognition at ATMs to prevent fraud. And, because most computer webcams regularly come equipped with facial-recognition algorithms, the cost of using this technology is rapidly declining. There are still a few best practices product marketers should keep in mind:
- Ensure privacy and informed consent: It’s important that respondents opt-in to a disclosure that explains how you will capture and use the information you gather, and how long you will store the data. Complete transparency is a must.
- Be sensitive to geographies: When using a facial-recognition vendor as part of a study that covers different geographies, ensure that the vendor can address cultural and facial-structure differences. Ask them if they have been involved in the location you’re researching, how their technology has been used there before, and whether they have addressed cultural differences.
- Consider device options: If your research allows mobile device participation, facial recognition can be tricky because angles and lighting can change. Explore this issue with your vendor upfront—before the study begins—to see how it can be addressed.
Ethnography, or the practice of collecting data while observing customers in a natural environment as they interact with a product, used to be cost-prohibitive for all but very large companies. Technological changes have favorably disrupted the availability of this methodology, providing companies of all sizes with the ability to understand product use in real-time. Therefore, mobile ethnography has broadened the reach of this research method and offers an agile approach for product marketers to better understand how customers and prospects interact with their products in a native environment.
A variety of platforms can be used to prompt respondents to answer questions through exercises on their smartphones. For example, respondents can upload videos of themselves completing a task, and companies can observe various elements of product use (e.g., the context in which it is used, time of day, real-time respondent reactions, barriers to use, customer preferences). Other benefits of mobile ethnography include:
- Increases the opportunity for feedback: It is rare for someone to not have a smartphone; the ubiquity of these devices allows product marketers to get feedback from anyone in their target audience at a time that’s convenient for the customer.
- Leverages the reliability of spontaneity: While the developer decides which exercises respondents will complete, respondents are fully in charge of when and how they complete the exercises (unless specified). For this reason, it can be useful to keep instructions to a minimum so that you, the product marketer, can leverage the degree to which the data will be reliable and, therefore, actionable.
- Provides the opportunity for scale: Ethnographic studies were severely limited because of the costs inherent in an in-person study. Today, technology affords the ability to study more participants at a much lower cost.
- Leverages location: Companies can successfully use geofencing technology to choose when to ask respondents to complete exercises.
- Increases the ability to explore content: Product marketers often are surprised to learn about circumstances in which their product is used that they hadn’t previously considered. For example, customers may use products at certain times of the day, in the presence of others or when music is in the background. This data can be used to guide the development of marketing messages and advertising imagery.
- Increases the opportunity for a higher return on investment: The results of this research can be used in many situations. For example, videos that respondents take can be used to onboard employees and facilitate a greater connection between product developers, engineers, designers, and the end-user.
A New Door to New Opportunities
Again, facial recognition and mobile ethnography don’t replace other methods that researchers and product marketers have been using for decades. However, these options do offer the opportunity to gain insights into customer feedback that can lead to more successful product marketing efforts.
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