Pauline E. Ranger The problem of capturing and analyzing “dark data,” especially for CMOs, who are increasingly responsible for improving business outcomes and generating revenue, is the most significant business challenge of our time.
What is dark data, and how can it be used? Dark data is the unstructured information that’s available on social networks. It contains valuable insights that can be used to improve your business and generate more revenue.
Likely, you haven’t used it yet.
IDC estimates that 90% of unstructured data never gets analyzed. Online reputation management is changing this by helping companies collect and make sense of dark data from Social Media to uncover trends and identify strengths and weaknesses of brand perception among targeted audiences.
Accessing dark data from social media could be a way to improve operations and optimize the customer experience. It is challenging to know how to use it and maximize your customer experience and operational efficiency.
What is dark data, and why does it matter
Dark data is unstructured digital data that lurks in the shadows. It’s not scary. It can give you an early warning of areas that require attention.
Gartner defines dark data as “information assets” collected, processed, and stored in the ordinary course of business but not used for other purposes.
Many organizations do not capture and store dark data on social media, making it challenging to analyze.
In a world where CMOs are increasingly responsible for improving business outcomes and increasing market share, gaining operational insights from dark data will be crucial to success. The ability to analyze data, apply learnings, and boost demand will be critical to differentiation.
Exploiting the value of unstructured data
Most of the marketing data we focus on is “structured”–information with a high degree of organization. These types of data are primarily stored in spreadsheets or relational databases.
Most structured data is collected from forms, for example, when website visitors fill out a form to download information from a site. This data is used to create targeted campaigns, segment lists, and inform our messaging.
Only 20% of your data is structured. 80% of the data is unstructured. Unstructured data can be generated by…
Call-details records from call centers or internal customer success teams
Consumer data on mobile devices
CRM and marketing automation systems, like Salesforce, may include notes from sales representatives or marketing teams.
Video, image, and voice data
The real value of unstructured data is found in free-text data, such as social posts, conversations, and online reviews, which can be found on sites such as Google, Facebook, and others.
What will happen when we learn to use the vast amounts of unstructured data in the cyber universe?
Dark Data’s Organic Nature
The majority of dark data comes from organic sources. Consumers use social networks to share their dining, shopping, and entertainment experiences. Tweet about it, share videos and photos and post to Facebook. Social shares are repeated, resulting in much content containing helpful feedback and data on public sentiment about your brand.
Imagine a Millennial buying a car from a large auto dealer. She posts a picture of herself standing in front of the vehicle on Facebook with the caption, “Finally got my new car, but the salesperson haggled for four hours!” Anyone who sees her post will now have several details about the dealership.
The company has a large inventory of certain types of cars.
The company sells vehicles that are aimed at younger car buyers.
They argue with you not because they rush to sell cars.
It may be a stressful and lengthy process to buy from that dealer.
Imagine that the reviewer’s best friend commented on the original post, saying, “I also bought my car at the same place, and the finance man was rude and condescending.” He said that I was too young for financing and laughed. This is more unstructured information that’s hard to capture. Combined with the previous post, it shows a recurring trend: Millennials buy cars at the dealership, but the finance department makes it difficult.
You can’t fix something you don’t know about. The corporate office person for the auto company could see a pattern if he were to collect the data of both Facebook comments about the dealership in question. He may notice that there is a problem at the location. He may not know if he doesn’t have access to the comments. He might see sales drop and overthink inventory had been allocated to the dealership. However, he may not know that the actions of a single employee caused terrible reviews.
Dark data from social media and the linking of it with existing CRM data can be used to refine marketing efforts, improve operational efficiency, and enhance customer satisfaction.
Sentiment Analysis: A Light in the Dark
Thematic analysis can filter free text input from online reviews and social media sources to identify trends in customer sentiment toward your business. Serious ORM platforms can interface directly with Enterprise CRM platforms. They compare new unstructured information to what you know about your target audience.
With this information, you can easily fine-tune your marketing efforts and change your day-to-day activities that will strengthen your brand.
ORM platforms provide unprecedented insights into dark data. Reputation.com, for example, uses word clouds to give a snapshot of the most common themes and words used in online reviews. The bigger the word, the more it appears in studies of a particular company. You can click on any word to go directly to studies that include it.
The sentiment analysis tools go beyond presenting a detailed analysis of the customer feedback and how your location compares to its competitors. Words associated with problematic keywords are flagged through sentiment analysis.
For example, a company that sells cars may track the word “finance.” ORM platforms that use sentiment analysis can also look for words that are related to finance, such as “pricey,” “interest rate,” and “interest rates,” to get a better reflection of customer experience. This data allows the auto company to determine sentiment in multiple locations or for individual ones, allowing them to improve their operational intelligence.
