If you’ve been reading any online or print media, then you know that social media volume has been growing exponentially. This explosive growth has introduced several challenges, to name just a few:
If you’ve been reading any online or print media, then you know that social media volume has been growing exponentially. This explosive growth has introduced several challenges, to name just a few:
- volume of noise obscures true consumer signals
- conversation shifts
- outliers become dominant issues quickly
- traditional channels sometimes corroborate other times contradict social trends
Each of these challenges alone would make leveraging social media analytics as part of an overall business strategy a risky approach, which is why CI has actively encouraged integrating social media analytics into other more traditional business metrics. But getting to the point, where your business can begin using social media insights requires that the consumer intelligence you’ve surfaced be accurate. There’s no point in integrating poor social metrics into your social business just to have a unified view of your market or brand.
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There are really two fundamental issues that need to be addressed when analyzing large volumes of text:
- extracting pure data
- surfacing verifiable and actionable insights
Yes, your analytics tools maybe able to report on mentions and RTs with stunning accuracy but how can your social business leverage that information to make better business decisions? Emphasizing volume activity is simply not enough to evolve towards a social business. Surfacing inaccurate or misleading actionable social insights can expose your organization to very public missteps. In other words, the data better be accurate if your organization is going to engage on the social landscape.
Clean Data = Accurate Social Consumer Insights
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Recently, we partnered with CNBC to analyze social media conversations from Black Friday through Cyber Monday for 20 retailers for their Real Time Read segment. Leveraging our semantic technology, we were able to process and categorize 11 millions posts daily to provide insights not only on volume activity, like which retailer was generating the most conversations but we then took it a step further to extract information from consumers expressing an intention or issue, like ‘intent to buy’ or ‘having a problem’. By leveraging our analytics technology, CNBC was able to predict that one of the 20 retailers was not having a strong holiday shopping season. What’s interesting about this particular case was that the retailer (Kohl’s) released their actual November sales data and they drastically missed the number they had projected to Wall Street – consequently, their stock declined nearly 10% on that day.
Of course, the effectiveness of insights and intelligence can only be fully realized if the information is shared and acted upon. If as the weekend unfolded and Kohl’s had been monitoring their social customer would they have had the course correction strategies in place to react appropriately? I think many companies are still adapting to a landscape that is largely driven by a small but growing and critical segment of their audience and the window in which companies can effectively respond is almost real-time in some respects. Having reliable and timely insights on your social customer can give your organization an early alert to potential issues or emerging trends.
If you’d like more information about how the other retailers ranked according to volume and more importantly in the intent to buy” category, check out the full video: