Ever noticed how Google delivers relevant ads next to your search results? This personalized placement and contextual targeting of ad content is a well-known web content success story. Enter Google Now, which learns our habits and then serves back, in real-time, information that it thinks will be of use such as news, weather, traffic information, and so on. Based on previous searches and even movement, our devices are not just displaying ads but also serving information for fast decision-making even before we know we want it! We are rapidly shifting towards an economy driven by content thats relevant and useful to users right at the point of consumption on their mobile or wearable devices.
How is this possible? In this digital publishing era, smartphones and other devices such as tablets and others are part of our everyday lives. Picture, for instance, a young lady on her vacation, posting photos and updates on social networks while shes on the move. Based on her changing locations, intelligent tracking systems and travel applications can now serve relevant information, based on her areas of interest content on nearby restaurants, ATMs, stores and events and even deals and discounts from nearby stores. For the lady, the information is usable, because its contextually relevant. And when served right, content is not just of great help to the user, but also an excellent demand generator for businesses.
Converting Users to Brand Advocates with Contextually Relevant Content
No wonder businesses are investing in intelligent applications that can capture and learn user habits and preferences, and provide context rich, relevant information to their business users and customers. Google Now is a great example of such intelligent learning.
In this digital age of connected devices, business applications derive information like the users location, time zone, identity, activity, preferences, and several other parameters from mobile and wearable devices. The information gathered can then be used to enhance and improve content design, which in turn is used to serve relevant information sets to users, and drive contextually relevant user interactions.
Businesses can take customer experience to an altogether new level from simple personalization of content or ad campaigns, to contextually relevant interactions. Futuristic scenarios include IoT-enabled, smart home appliances communicating with each other, and pushing stock replenishment alerts to user mobile devices. Going a step further, mobile service providers could read these alerts, and push contextually relevant promotions through mCommerce apps on user mobile devices. Another interesting example is the use of content re-targeting by brands, to bring back bounced users of a website, and convert them into buyers. From the technical perspective, the retargeting code, popularly known as pixel by digital marketers, anonymously drops a cookie on users browsers. The cookie tracks user site visits, interactions and behavior, and drives re-targeted ad content, which facilitates brand recall, and has much higher click-through rates than traditional display ads.
To make the most of such user engagement opportunities, businesses need a nimble footed Quality Assurance (QA) & Testing strategy, which leaves nothing to chance. The first step would be a comprehensive, proactive assessment of the content relevancy of information disseminated to business users. Specifically, the QA strategy for context sensitiveness of applications and information is two-fold:
First, in the absence of trustworthy emulators, virtualization of the external context (location, temporal and network resources), to which the applications respond, helps mimic real life situations for testing, and avoids miss ups. This emulation enables testing for remote and busy traffic areas, hilly terrain, moving vehicles, and changing time zones. Although virtualization comes at a cost and learning curve, it limits the unforeseen contextual possibilities for testing. This cost benefit trade off clearly justifies the investment in virtualization.
Next is the need to steer clear of manual effort, and have a high automation quotient of QA systems. This really is a no brainer – a foolproof approach to assuring application algorithms, by testing their outputs and response against a multitude of contextual factors and varied data sets. QA must also play a role in shaping these intelligent application system algorithms that can accurately determine the users business or personal contexts, from a variety of parameters captured from their devices. As the civic madness increases, system input becomes voluminous and complex, making automation the only way to ensure adequate testing of all possible data combinations.
Times are changing, and so must QA. These context sensitive applications are questioning traditional test practices. While the design before you develop approach is popular and widely adopted for software applications, and quite rightly so, its time for the approach to be extended, and applied to application content as well. Simply because the usability of a software application depends on the usability of the content it disseminates to users.
QA clearly, has another task proactive information analysis and design – cut out for itself. As developers dabble with code and databases, and prepare them for testing, QA practitioners have one more item on their checklist assuring context relevance of the output generated by applications assured by them. Such a proactive, content design approach can result in unlimited possibilities for businesses from improving advertising strategies, to personalized promotions and high-impact content that keep customers connected and engaged. And when customers are engaged, they dont remain customers. They transform into brand advocates.