How Big Data Can Help Your Product Content

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Rick Chavie HeadshotIn this special guest feature, Rick Chavie of EnterWorks discusses several areas where all businesses can make marked improvements and avoid the big data pitfall of having great insights that can’t be used in a tactical way. Rick was appointed CEO of EnterWorks in May 2015. He came to EnterWorks after serving as SVP, Global Solution Management with hybris and SAP’s Customer Engagement and Commerce group, where he brought together digital and physical commerce and CRM assets for seamless customer experiences.

Big data has been a big topic in 2015. As 2016 budgets are finalized, retailers, manufacturers and wholesalers are feeling the pressure to deliver top line results from a big data strategy. Unfortunately, many companies are struggling to develop investment road maps to operationalize such capabilities for business managers. Sure, some companies have opted to bring in a team of data scientists to analyze the numbers and come up with insights. But how many businesses put their line managers in a position to do it themselves as a part of their daily routine?

There is a major area where big data can be operationalized without embedding scientists in a company’s business departments: the domain of product content.

Big data is often unstructured content, and while it can identify trending behaviors regarding consumers, geographies and target audience segments, it does not offer marketers and merchants concrete, actionable insights that are integrated into their daily workflows. Marketers are responsible for channeling how users act online into powerful content and campaigns, but the challenge is mapping such data to internal data structures that enable you to transform insights into action. This is troublesome as consumers continue to rely on friends and family and robust digital product information to inform their purchasing decisions.

Rather than improving business productivities, big data is overwhelming marketers. But with the right technology, companies can amend big data shortcomings and adhere to emerging best practices. In the following areas, all businesses can make marked improvements and avoid the big data pitfall of having great insights that can’t be used in a tactical way.

Analyzing Big Data

The goal of big data is to make more informed and tactile marketing and category management decisions. However, without a single view of content for all of the data accumulated, most retailers underperform when using these insights to generate personalized content at scale and measure how well their content resonates with customer segments.

In order to tackle big data in a profitable way, brands have to match external data to internal taxonomies in a way that puts a “sense and respond” capability in their business leaders’ hands without requiring a team of data scientists. With the right technology, retailers can ensure that they are linking external content with internal content at the time that the merchant or marketer generates product narratives and promotions.

Marketers can orchestrate insights at their exact point of need by filtering big data into more manageable segments. For example, when an online shoppers research shoes, they are much more likely to input a query like ‘blue running shoes’ than a specific style of Nike. Recognizing this, retailers can track particular product attributes from within big data and develop content that matches. As marketers pick up on the big-data content trends that resonate with consumers, they can ensure their product content reflects these trends.

Embracing User-Generated Content (UGC)

In addition to analysis, marketers need to embrace UGC and tie it into their overall product content practices. This will help them personalize big data insights by aligning future outreach with what consumers themselves have deemed worth sharing. Today’s marketers have to focus on the content they use to promote their products, and this includes UGC and competitor content.

When targeting specific audience segments, UGC can help marketers identify if their content is engaging consumers. Brands must be able to monitor social media, competitors’ websites and catalogues to see what content is being shared so they can track popular product attributes. If users are consistently sharing one attribute over another (e.g. bronze finishes over brushed nickel), marketers should reflect those trends in print and digital. In order to supplement missing words and images, retailers can also incorporate existing UGC into their marketing campaigns. This creates on-target outreach that connects directly with consumers and increases brand loyalty.

Developing Adaptive Business Models

To take advantage of big data, marketers must bring content improvements back into their most common operational policies. To do so, businesses need adaptive infrastructures that make updating product content with big data accessible in real time. These systems must be flexible to changes in consumer preference and adaptable to industry and market trends in a meaningful, sales-driven way.

Big data information needs to assist day-to-day workflow and promote collaboration with vendors, making it easier for marketers to map critical insights back onto their next campaign decisions. As a first step, companies have to alter how they approach big data. Rather than thinking about data as numbers and statistics, companies have to start appealing to the ideal customer shopping experience by translating that data into images, words and narratives.

Historically, attribution of the drivers of product sales, beyond pure price cutting, has been elusive. Even with big data and a team of scientists, it can be challenging to determine attribution and base core marketing and merchandising tactics on those insights, especially in the face of constantly shifting consumer preferences, new product cycles and competitive forces. But a test and learn approach that doesn’t presume to “optimize” from historical data can turn the tide.

With a large enough team and the right expertise, most retailers can develop usable insights. The challenge is developing those insights consistently by mapping a single view of content across all channels with the big data being produced in those channels. Then, that information must be filtered and sent to key business leaders – merchants and marketers – who can take advantage of those insights.


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