Crawling Data for Product Development
Look outwards with Big Data & analytics and you can see how most apply it to boost market shares or improve revenue streams. Yet, a significant use of crawling and collecting Big Data is inward looking. This is product development. This is of particular interest for start-ups who are more energized for innovation.
The race is not just about creating new products, but doing so quicker and before competition. Organizations need to leverage current and past marketing information, design data, manufacturing operations data, and testing & service data, to build a constantly evolving knowledge repository so as to create new products quickly.
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Thoman Redman stresses the need of “informationalization”, where existing product lines and services must be modified to bring more value to consumers. This is done by building a solid foundation of data and information.
How, you ask? Because it is the union of two of the most basic needs:
- The need to improve, and,
- The need to have accurate and reliable data
But this improvement of products and services hinges on how data crawled is read, interpreted, and importantly, applied.
Volumes of data generated internally from operations and research is only improved with the addition of large volume of data that comes from consumers. Data is an opportunity, and can be found all over the web in the form of blogs, social chatter, reviews, location-based updates, etc.
The Internet is, unequivocally, the largest source on product information vis-à-vis usage, experience, and behavior. Insights from analyzing this data yields valuable information on how existing solutions can be made better. This data also helps spawn newer solutions or product lines especially in industries that rely on experience—like entertainment and healthcare for example. This is pertinent since most products that directly cater to the end-user now rely on experience they provide. This rings true for services as well since user experience is an enhanced feature here.
Although user sentiment analysis, a low-hanging fruit, has been around for some time now; its traditional mold of manual observation, focus groups, and survey-based methodology is now undergoing a digital renovation. Social media postings, tweets and other online chatter now provides the edge to product managers and marketers in evaluating product performance.
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However, it is not just outside data that works best. There is internal data as well that must be looked at. This potentially untapped repository is a guide to innovation. Take for example, what Brad Holtz, Cyon Research’s Head of Technology had to add to illustrate: Brad suggested airlines can learn plenty by analyzing how passengers chose seats while booking tickets. Choices customers made gave an indication to what they were looking for while flying. If it’s more legroom, then it gave an indication of how interiors can be modified; thus effectively enhancing passenger experience.
Yet another data-driven product driver are Location-based services. When people check-in on Foursquare, marketers are privy to consumer data first-hand! Deals can be announced on the go, discounts can be offered to increase footfalls just by checking check-ins. While this isn’t product innovation, it helps driving the product by changign the marketing strategies. Social media becomes the crucial data mining target.
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Applying Data to the Product Life Cycle
Applying Big Data to product innovation isn’t as simple as checking a Twitter feed, though. First, companies have to focus on the right data. Big Data is not just about using massive data sets; it’s about being smart and finding the right data streams to make sense of data to derive insights from it.
Beyond this, applying Big Data to product innovation requires a high level of internal coordination. For example, the customer service and marketing may have data that, in tandem, will render deep insights on possible new product lines and services.
However, the different departments might have different metrics so they don’t know how to combine their knowledge easily.
That means, as companies expand their use of Big Data, they will often have to rethink their basic approach to product development. This also requires thinking about how the data is sourced. Do you want to crawl websites yourselves or outsource it.
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There was a time when data was used only to find a solution to be able to fix something. Today, in product life-cycle, data can help identify the pain points earlier and help change the way products are designed at the start!
Typically, product development entails strategic designing, validation and testing. During this time, even before it is made public, vast amount of data is generated. This data provides the feedback loop as reusable knowledge adding to deeper understanding of real-time data on products.
Entrepreneurial innovation relies on data too. Based on understanding the landscape of existing product lines and emerging trends.
Emerging Trends in Product Development
Traditional product development processes are now making way for newer and more robust ways to ensure customer acceptance of a product long before it is launched, that is, while it is being designed. Big Data analytics close-looping with knowledge base and telematics are building knowledge databases to glean info on product development.
The rising challenge for organizations is to derive meaningful insights from available data and use it intelligently.
Collection of data from direct and indirect sources, analysis and synthesis will lead to meaningful information and intelligence.