2. Attribution Modelling

Integrating Business Intelligence and Social Activities

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Aligned with corporate objectives, integrating social media analysis and business intelligence gives you a competitive advantage in terms of immediate insight into online activities that have an impact on business.


Business Intelligence Integration

Social media channels and search have become the foundations of many companies’ digital marketing campaigns. Using data and business intelligence (BI) tools, companies can identify and then address critical business insights about such campaigns to achieve a more informed perspective. For instance, BI tools may visualize data and reveal trends, brand associations and fast-rising topics. Integrating data from social activities into business intelligence can optimize digital campaigns and position the brand for digital marketing success. Consider the following benefits:

Data trumps gut feel

With proper data, you can gain insight into which of your social media channels are getting the most attention. When it comes to search and SEO, raw data from keyword searches, keyword competition, click-through rates, etc. can be used to create visual reports and draw conclusions about the current state of online marketing efforts. As this data continues to accumulate, decisions become more data-driven, meaning that business intelligence has replaced ‘maybes’ with clear indicators of changes that need to be made.

Real-time insights

Facebook, Twitter and search engines provide recent news and updates on current events, which generate a constant stream of data. Seeing that timing is critical, companies can gain a competitive advantage by analysing real-time data. For instance, if certain keywords are not converting, business intelligence can help you quickly resolve these issues. Combined with social data, such data can explain why Product X sells so well in a certain geographic area and even help you spot early trends driving product development, delivery and marketing messaging.

Progress and environment monitoring

Once you have valid data, it becomes clear how much your marketing campaign has evolved. Business intelligence can pinpoint potential issues long before they escalate. If a particular medium or link-building tactic bring in little or no traffic, you can make an adjustment before wasting time and resources on an extensive campaign. Hence, it reduces the risk of repeating the same mistakes, as well as it enables you to focus your efforts and maximize impact. However, data generated from social media platforms tend to be vast and unstructured. This requires a BI system that can manage both structured and unstructured data. Hence, terms and phrases need to be categorised and adjusted to a common glossary so it allows for different types of analysis. For instance, companies may use an automated sentiment analysis tool that weigh keywords differently.

 

Context, Objectives and CRM

To ensure effectiveness, corporate objectives should guide business intelligence initiatives. Thus, deciding on the most meaningful metrics or KPIs upfront is important. Whereas brand awareness campaigns require metrics such as ‘share of voice’, other initiatives may focus on improving customer service, which call for analysis of customer sentiment, complaints, time to resolution, and post-support satisfaction scores. Mapping customer interactions on social media with CRM records, companies put customers into context of existing relationships. It is vital to analyse and distinguish among functional, emotional and behavioural sentiment. For instance, there is a huge difference between “I like my new Nexus phone!” and “I just told my friend to buy the new Nexus phone!” While both are positive, the later sentiment should be weighed much more heavily.

References:

Bookboon (2012). Social Media And Business Intelligence. Ventus Publishing.

DeMers, J. (2014). Here’s Why You Should Use Business Intelligence in Social Media, SEO Campaigns. Retrieved 15 October 2014 from: http://insights.wired.com/

Kapov (n.d.) Five Key Lessons for Converting Social Media Data into Business Intelligence. White Paper.

Zeng, D., Chen, H., Lusch, R., & Li, S. H. (2010). Social media analytics and intelligence. Intelligent Systems, IEEE, 25(6), 13-16.

Simon Raun Madsen

I am a marketing, business, and communications academic and practitioner with strong cross-disciplinary skills from courses and work experience within the fields of marketing and business intelligence. In particular, my passion for technology and several years of experience with online marketing have provided me with a flair for digital solutions, data analysis, and front-end development.
Simon Raun Madsen

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