Companies have increasingly realized the importance of social media to their business strategies. Data from social networks is like any other data collected by your company. It is unorganized, and is usually obtained from multiple different channels. Nonetheless, companies should consider social networks as another vital source of data for their business intelligence.
By gathering data from social media and analyzing it with business intelligence, companies can get an understanding of consumers, such as whether or not they have bought their products and services. This provides deeper insights, such as volume of sales for a particular product in a key market.
Social data can explain why a product sells so well in a particular market, and can even help detect early trends that drive product development or the right messages to market them. Applying business intelligence to this data will help to present it in an easily digestible manner, to enable you to easily identify trends.
However a lack of connection between social data and other data sets can lead to:
- An inaccurate view of key factors affecting the business
- A silo-ed, manual search for data across different systems
- Duplication of data
Only by mapping consumer data from social media to your customer relationship management (CRM) records, can you put these insights in the context of existing relationships, and thus develop a better understanding of your customers and prospects.
1. What are the Advantages of Integrating Social Media and Business Intelligence?
There is a myriad of consumer insights available on social networks, which you can gain with help of the right social monitoring tool. For example, knowing what customers are saying about your product can give you immediate access to powerful, actionable ideas on consumer confidence. Overlooking such sources of business intelligence could lead to a loss of market share to competitors.
Here are more reasons why incorporating social media into business intelligence is important.
Conversations generated on social media are spontaneous and comments, virtually instantaneous. In contrast, many of the commonly used data sources, such as point of sale, transaction reports, and customer surveys, are time consuming to aggregate
Real-time comments on social media, make it easy for brands to measure the success of their campaigns, services, and product launches. This immediacy allows marketers to get feedback on campaigns that are currently running and optimize them, or improve planning for future campaigns.
Customer Satisfaction: Responsiveness and Personalization
Access to accurate, real-time data allows agile and personalized decisions and communications to be made. Companies who respond promptly to the needs and concerns of customers, undoubtedly leave them satisfied, which increases brand loyalty.
Highly Targeted Messages and Data Accuracy
High responsiveness to customers also results in high relevance and accuracy of data. This is because customers tend to go on social networks to express their honest opinions about a brand or its products, or their dissatisfaction, in case of incidents. Such insights may not be obtained as quickly from a formal structured survey as compared to social media.
Monitor the Competition and Develop New Strategies
Social media analysis allows ease of monitoring competitors, to present opportunities and threats in the market segment.
Following consumers’ patterns and behaviors on social networks through analytics helps you to monitor their behaviour in various online environments. This will determine which platforms are the most profitable and therefore, guide the development of a winning social media marketing strategy.
2. Social Media Data to be Taken Into Account and Those that Should be Ignored
While there is a tendency to try to measure everything, there is no need to obsess over every metric available. Observe only the metrics that matter. There will be some vanity metrics such as: likes, shares, followers, retweets, views, re-pins, etc. Other back-end insights include those on the company’s website: page visits, time spent on page, keyword searches, conversion rates, etc.
There are advanced monitoring measurements offered by listening tools, such as: volume of mentions, sentiment, top media channels, top influencers, most used words, etc. These should be looked at alongside business metrics such as: total sales, new customers acquired, cost per transaction, number of qualified potential customers, number of customer service problems solved, etc.
The relevant data to include will depend on your objectives. Here are some examples:
- If the objective is to create brand awareness, metrics such as share of voice, are an indicator of the visibility of your brand against competitors. This can be looked at with an analysis of owned media communities in the various social channels.
- If you want to build engagement with customers or prospects, the audience engagement metric (calculated by the sum of comments, shares, and re-tweets, divided by total views) is a good indicator. This can show the performance of a campaign or follow up on sensitive issues generated by the audience.
- If you want to improve customer service, monitoring negative sentiment comments can inform the right solution to solve the problem. The data can also be integrated with indicators such as resolution time and the post-support satisfaction scores.
3. Putting Everything Together: From Views and Clicks to Sales
Social media data must be standardized for a common structure. The terms and phrases should conform to a common glossary, which is when a monitoring tool becomes useful. Storing this information over time, allows different types of analyses, either in real time or over certain periods.
As mentioned earlier, marketers choose social media KPIs aligned with business objectives so they can be analyzed with business KPIs such as return on investment (ROI) and profit margins. This allows marketing teams to associate social media activity to sales volume, income increases or decreases, and other relevant metrics.
We know that it may appear difficult to combine the qualitative data of social networks with quantitative data hosted in business intelligence systems. However, as social data becomes increasingly important for many organizations, it is possible to sync them, especially with BI systems that manage structured and unstructured data.