How to Leverage Big Data to Inform Product Content
The synergy between big data and product content is, to this point, largely unexplored. On one hand, big data consists of massive amounts of unstructured product, consumer and market information that, when mined, can yield valuable insights on industry trends. In a recent study by CA Technologies, 90 percent of the companies surveyed are benefitting from big data implementation.
On the other hand, the translation of such information to inform product content decisions is haphazard at best. Internal systems at retailers and brands were founded on the basis of structured approaches to data that in turn support standard processes and reports. But how does a merchant or marketer apply such interesting yet unstructured insights in a scalable, repeatable way to new product launches, seasonal brand campaigns, online and offline promotions, or any number of complex omnichannel offers and programs?
In recent years, big data has been a hot topic. Marketers gloat their ability to segment audiences and alter content accordingly using big data as their guide. But when it comes down to product content – from descriptions about features or attributes to images and videos – big data is too vast and complex to provide actionable insights for companies that don’t have the right strategy and supporting technologies in place. Brands and retailers need to consider the following approaches to truly gain the most out of big data’s enormous potential to operationalize the synergy of big data insights with product content development.
Segment and analyze data
While big data holds the answer to most of a brand’s questions about customer insights, it’s difficult to unlock these insights without a single view of all the data accumulated. Consumer data comes in many different forms and is collected in multiple different ways. Without the correct analysis and segmentation, data can lead brands to develop the wrong content for the wrong audiences.
To use big data in a productive way, brands need to implement analysis taxonomy that filters big data into more manageable segments. For example, rather than attempting to segment all consumers who have recently searched for shoes, brands should instead focus on the different attributes for which consumers are searching and the context of their inquiries. Are they looking for black sandals or a certain style of running shoes? Are they using a mobile device in-store or browsing on a full-screen device at home? A single view of content in one system that lets the merchant or marketer dial up the chosen content within context of customer segment, location, and device makes the convergence of big data and content a truly scalable capability. If they can align the results of internal product data searches with these external insights, it makes generating personalized and differentiated offers and messaging part of a their daily routine – not the realm of “Do Not Enter – Reserved for Brilliant Data Scientist.”
Additionally, brands should be able to integrate consumer data in their collaboration with vendors, making it easier for marketers to incorporate critical insights into both content and product development decisions. Rather than viewing the insights as hard numbers and statistics, leverage them for storylines, images and word choices that fit the customer personas and behaviors that are being observed. When done in this manner, external consumer and market data can be more easily integrated within campaign and content development processes.
Leverage user-generated content (UGC)
User-generated content is another form of big data that marketers need to learn how to leverage correctly. According to Kissmetrics, 25 percent of search results for the world’s 20 largest brands are links to UGC, which can uncover insights that other types of consumer data – like search history – cannot. It can help marketers understand the styles, features or attributes that are most important to consumers in real time, allowing them to personalize content accordingly.
For example, a brand should monitor social media for relevant trends. To a clothing brand this could mean certain colors, patterns or styles, and for a hardware brand it could be something like the type of finish or simplicity of the design. This also means they should mine what is often the bane of retailers – customer satisfaction complaints – since in fact such complaints can inform on recurring problems. If customers are constantly sharing one attribute over another, marketers can in turn reflect those trends in the print and digital product content that they product. Retailers can also incorporate UGC (with permission) directly into marketing campaigns to speak to consumers on an authentic, personal level.
Use adaptive business models
Another way for brands to take advantage of big data is by adapting infrastructures that make updating product content with big data accessible in real time. Most marketers understand the value of big data, but often lack an ability to integrate it within current systems.
Brands should consider more than just sales and revenue to determine marketing content. For example, if a brand is profiting more so on a certain product than others, it shouldn’t necessarily focus generically on pushing that product to all consumers across all channels. Instead, the brand should pay attention to how shoppers are responding to specific content about product descriptions, colors and images. A deeper analysis into the way consumers are interacting with a brand and their path to purchase can inform product content much more effectively than general data about product sales.
The use of such information is particularly important as companies expand into new markets, introduce new product lines, target new segments, or move more aggressively into new distribution channels. With a product information platform that supports dynamic data modeling, the system can follow business model not the other way around.
There are many ways in which retailers and marketers can use big data to better understand consumers and reach them through digital and traditional marketing channels. However, big data can sometimes be too broad for brands to uncover actionable insights. In fact, the 2015 Real-Time Big Data Report showed that only 36 percent of developers believe that real-time analysis can drive operational and customer experience improvements. But with the right strategies, marketers can use big data to its full potential. It’s important that brands not only take the time to break down the data into more manageable segments, but that they also leverage user-generated content and embrace more adaptive business models to inform product content. Only then will retailers truly reap the benefits that big data has to offer.
-Rick Chavie, CEO of EnterWorks
On the other hand, the translation of such information to inform product content decisions is haphazard at best. Internal systems at retailers and brands were founded on the basis of structured approaches to data that in turn support standard processes and reports. But how does a merchant or marketer apply such interesting yet unstructured insights in a scalable, repeatable way to new product launches, seasonal brand campaigns, online and offline promotions, or any number of complex omnichannel offers and programs?
In recent years, big data has been a hot topic. Marketers gloat their ability to segment audiences and alter content accordingly using big data as their guide. But when it comes down to product content – from descriptions about features or attributes to images and videos – big data is too vast and complex to provide actionable insights for companies that don’t have the right strategy and supporting technologies in place. Brands and retailers need to consider the following approaches to truly gain the most out of big data’s enormous potential to operationalize the synergy of big data insights with product content development.
Segment and analyze data
While big data holds the answer to most of a brand’s questions about customer insights, it’s difficult to unlock these insights without a single view of all the data accumulated. Consumer data comes in many different forms and is collected in multiple different ways. Without the correct analysis and segmentation, data can lead brands to develop the wrong content for the wrong audiences.
To use big data in a productive way, brands need to implement analysis taxonomy that filters big data into more manageable segments. For example, rather than attempting to segment all consumers who have recently searched for shoes, brands should instead focus on the different attributes for which consumers are searching and the context of their inquiries. Are they looking for black sandals or a certain style of running shoes? Are they using a mobile device in-store or browsing on a full-screen device at home? A single view of content in one system that lets the merchant or marketer dial up the chosen content within context of customer segment, location, and device makes the convergence of big data and content a truly scalable capability. If they can align the results of internal product data searches with these external insights, it makes generating personalized and differentiated offers and messaging part of a their daily routine – not the realm of “Do Not Enter – Reserved for Brilliant Data Scientist.”
Additionally, brands should be able to integrate consumer data in their collaboration with vendors, making it easier for marketers to incorporate critical insights into both content and product development decisions. Rather than viewing the insights as hard numbers and statistics, leverage them for storylines, images and word choices that fit the customer personas and behaviors that are being observed. When done in this manner, external consumer and market data can be more easily integrated within campaign and content development processes.
Leverage user-generated content (UGC)
User-generated content is another form of big data that marketers need to learn how to leverage correctly. According to Kissmetrics, 25 percent of search results for the world’s 20 largest brands are links to UGC, which can uncover insights that other types of consumer data – like search history – cannot. It can help marketers understand the styles, features or attributes that are most important to consumers in real time, allowing them to personalize content accordingly.
For example, a brand should monitor social media for relevant trends. To a clothing brand this could mean certain colors, patterns or styles, and for a hardware brand it could be something like the type of finish or simplicity of the design. This also means they should mine what is often the bane of retailers – customer satisfaction complaints – since in fact such complaints can inform on recurring problems. If customers are constantly sharing one attribute over another, marketers can in turn reflect those trends in the print and digital product content that they product. Retailers can also incorporate UGC (with permission) directly into marketing campaigns to speak to consumers on an authentic, personal level.
Use adaptive business models
Another way for brands to take advantage of big data is by adapting infrastructures that make updating product content with big data accessible in real time. Most marketers understand the value of big data, but often lack an ability to integrate it within current systems.
Brands should consider more than just sales and revenue to determine marketing content. For example, if a brand is profiting more so on a certain product than others, it shouldn’t necessarily focus generically on pushing that product to all consumers across all channels. Instead, the brand should pay attention to how shoppers are responding to specific content about product descriptions, colors and images. A deeper analysis into the way consumers are interacting with a brand and their path to purchase can inform product content much more effectively than general data about product sales.
The use of such information is particularly important as companies expand into new markets, introduce new product lines, target new segments, or move more aggressively into new distribution channels. With a product information platform that supports dynamic data modeling, the system can follow business model not the other way around.
There are many ways in which retailers and marketers can use big data to better understand consumers and reach them through digital and traditional marketing channels. However, big data can sometimes be too broad for brands to uncover actionable insights. In fact, the 2015 Real-Time Big Data Report showed that only 36 percent of developers believe that real-time analysis can drive operational and customer experience improvements. But with the right strategies, marketers can use big data to its full potential. It’s important that brands not only take the time to break down the data into more manageable segments, but that they also leverage user-generated content and embrace more adaptive business models to inform product content. Only then will retailers truly reap the benefits that big data has to offer.
-Rick Chavie, CEO of EnterWorks