The connection between social networks and purchasing behavior is becoming increasingly strong. Reports continue to show that social media engagement drives both online and in-store sales, making it more important than ever for retailers to derive actionable insight from social data.
A recent study from Vision Critical shows that social networks like Facebook, Twitter and Pinterest drive as much in-store purchasing as online purchasing. According to the paper: “the vast majority of individuals who purchase an item after sharing or liking it on Facebook or Twitter were already thinking of buying it. In contrast, one in three individuals who buy items they have pinned or liked on Pinterest had not thought of making that purchase until they found the item on Pinterest.”
Mined intelligently, social media data is a barometer for consumer influence and preferences; it holds the secrets to optimally communicating with and serving customers. However, considering that consumers share 500 billion impressions about brands, products and services annually, today’s retailers must contend with a staggering amount of data.
Traditional analytic tools are often not up to the job of interrogating the fast moving, highly diverse types of high-volume social data. As a result, analysis has become an expensive, resource-draining burden. But it doesn’t have to be. With analytic flexibility and scalability, retailers can perform rich data analysis with far fewer resources.
One effective approach is investigative analytics, which asks a series of quickly changing, iterative questions to figure out why something did or did not happen, or how to optimize any particular outcome in the future. Compared to traditional analytics, which may help retailers answer a question like “how many black dresses did we sell via Pinterest last week?” investigative analytics yields insight into questions that haven’t even been dreamed up yet.
For example, if you identify a spike in sales from Pinterest leads, it would serve you well to find out why it’s happening and identify patterns you can then capitalize on. Questions may include: “Are there 10 boards in particular giving the most leads for those black dresses?” or “Are we selling more black dresses to an older demographic when the dress is pictured on a real model instead of a mannequin?”
The first step for retailers to gain new insight and maximize ROI on multi-channel campaigns is to make sure they have the right social media analytic tools in place. Following are key factors to consider:
Speed – In a world where “trending” topics, social campaigns and consumer likes and dislikes change at lightning speed, retailers don’t have the luxury to wait hours or days for answers, as data is captured, loaded and prepped for analysis. This stale data just won’t do. Instead, retailers need to extract intelligence as soon as it’s generated, in real-time. Technologies that focus on ultra-fast query loading and performance against large volumes of data are critical for gleaning insight into your social channels.
Scope– While analyzing what customers say is always going to be critical, retailers trying to truly leverage social media insights need to go beyond just talk. This includes when and where a consumer said it, what else she was talking about, and what action she took. As a result, analytics solutions must be able to integrate and handle all types of data—from the unstructured text of posts to the structured content of what customers clicked on, forwarded, downloaded and re-pinned.
Simplicity – In a multi-device, multi-channel social media world, the questions that retailers need to ask of their data are constantly changing. Traditional databases are typically constrained by data schemas that limit the number and type of queries that may be performed, and require expert IT and database administrator help to mediate between information and end users. From customer service reps looking to resolve an issue to marketing staff trying to optimize campaigns, retailers require flexible, easy-to-use solutions specifically built for ad-hoc queries and complex data investigation.
Scalability – Scaling in today’s social media environment means that efficient data compression – reducing the footprint needed to store the data – should be a top consideration to capture exponentially growing volumes of data. Better compression reduces storage costs and allows companies to store more historical data to easily compare current data to what happened in the past.
In the hyper-connected, high-speed social media world, near instantaneous analytics can help retailers immediately capitalize on consumer interest and harvest insight for providing better products and service. Yet, the status quo of yesterday is not likely to be the answer, or at least, not the complete answer. While there’s not a one-size-fits all technology fix, the good news is that there are a growing number of solutions that can work together in combination to address retailers’ evolving analytics needs.