Quantitative data is great when you want to answer questions like how much, how far, how often, etc. How much did you sell this month? Was it more than last month? What features in your product get the most use? Where do users go most frequently on your website? How long do they stay?
Quantitative data is about how people behave. It’s typically structured data that lives in a traditional, relational database
What this kind of data won’t tell you much about is why. Did sales increase because of a promotion, an added feature, a price cut, or — in the case of physical locations — a change in the weather or a sudden increase in the availability of parking? Is that product feature popular because it’s easy to use or just easy to find?
Qualitative data is about why people behave the way they do. Unlike quantitative data, it’s usually unstructured information, often in the form of text. It used to be that qualitative data was just stored in file shares like Microsoft SharePoint. But now there are modern data frameworks like NoSQL databases that can house unstructured information and make it easier to analyze.
In this post, we’ll talk about how the most interesting and actionable insights come from combining qualitative and quantitative data, and how businesses can employ the unique advantages of both to answer their most important questions.
Social Data: Quality and Quantity
One of the most useful things about social media is that it can offer qualitative and quantitative data. When you’re measuring the volume of tweets about a certain topic and how they breakout into various subcategories, you’re looking at quantitative data.
But if you drill into those tweets and evaluate their content for sentiment, as an example, you’re looking at unstructured, qualitative information. When you use a sample of tweets to train an algorithm to analyze sentiment via machine learning and text analytics across a large volume of tweets, you’ve essentially added structure to unstructured information. The results are a combination of quantitative and qualitative data.
For example, banks have loads of quantitative data about their customers, especially when they have multiple products, such as a checking and savings account, a car loan, and a credit card, from the same institution. Banks can use this information to create profiles of their customers’ financial health. They also have access to economic reports such as the Census Bureau’s report on new home sales and The Conference Board’s report on consumer confidence.
Social media can augment these data sources by offering insight about how people feel regarding the economy locally and regionally. This kind of contextual information can help banks decide when to promote certain types of products such as mortgages and home equity lines of credit. It may reveal potential strengths or weaknesses in demand that aggregate, quantitative reports cannot.
Investment Professionals Turn to Social Media
Many of us, perhaps most, use social media, particularly Twitter and Facebook, to keep up with friends, follow and comment on the news, and post updates on what we’re doing during the day. Investment advisors, stock brokers, and hedge fund managers have also climbed on the social media bandwagon but with a very different agenda — making money.
Again, like retail and commercial bankers, investment professionals have plenty of quantitative data at their disposal. And, legendary investor Bill Miller has often said about the financial markets that, “If it’s in the news, it’s in the price.” But that was before social media.
Now, investment professionals can get an idea of how the general public feels about a company or how an adverse event like Volkswagen’s emissions fraud is affecting brand perception. Throwing this kind of information into the quantitative mix can give investors an edge — revealing an opportunity or an impending threat. Several companies have developed systems to serve this esoteric niche of the social media analytics market.
It’s all about speed and context. The sooner an investor knows about an event and what it means, the faster that information can be used to buy or sell securities.
Here’s just one example. In the summer of 2013, a plane caught fire on the tarmac at London’s Heathrow airport. Prior to the event, rumors had swirled about the manufacturers faulty batteries. A tweet about the fire preceded its coverage in the news and some investment funds quickly dumped the stock. By the end of the day, the stock had dropped by just over 4.5 percent.
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Data-Driven Decisions are Better Decisions
There’s mounting evidence that data-driven companies are more agile than companies that rely primarily on anecdotal experience and instinct. They make better, faster decisions Combining insights from social data with quantitative data is really a no-brainer. In fact, we do it all the time in “real life.” We might check the temperature before we go outside, but what we wear will also be governed by what we’re going to do — context. And, with the tools available now to analyze social media, you can also quantify social media data. It’s the best of both worlds.
To learn more about the combined power of data sources, download our Guide to Social Data Sources for Brands and Analysts.