Social media platforms are rapidly becoming the new force behind the global 2000 companies, allowing companies to reach out and understand their valuable customers like never before. Social media networks analysis is the study of patterns of social relations. Through network analysis the behavior of social relations structure can be analyzed. Social media networks and peer groups are creating large data sets that are now enabling organizations to gain a competitive advantage and improve performance. These data sets provide vital insights into customer behavior, brand reputation and the overall customer experience. Intelligent early adopter are beginning to monitor and collect this data from the proprietary and open social media networks.
There are many ways of exploring social media networks through which, companies can succeed. Social media networks have many factors that contribute to the increase of data volume. Using DWBI tools can help properly analyze social media to create a significant value to the relevant data. Following six ways can be highlighted how the business intelligence tool can leveraged to best analyze social medial networks:
- Capture and research new product ideas and dissatisfied clients. Customers are becoming smarter and the conventional wisdom that consumers can’t innovate is being turned on its head. Smart companies are harnessing the creativity of their customers. BMW posted a toolkit on its website that let customers develop ideas in telematics and in-car online services. In 2004, 120,000 people around the world signed up to join Boeing’s World Design Team to design their dream airplane. Conventional marketing wisdom long held that a dissatisfied customer tells ten people about their dissatisfaction. But, in the new age of social media, customers have the tools to tell over ten million.
- Advertise, promote, optimize marketing and create customized campaigns. If your company ignores the benefits of marketing on social media, you are automatically allowing your competitors to adopt it. Mercedes Benz and Coors Light invited consumers to co-create advertising campaigns, with Mercedes encouraging proud owners to submit snapshots of themselves with their vehicle.
- Monitor trends, be aware of situations and predict customer behavior. Ethnographic approaches can achieve deeper levels of insight into a customers’ emerging and unmet needs, than other techniques. This way, it is more effective and easier to analyze, interpret, and describe the culture, tastes and preferences of customers to understand their requirements. It can still be hard to define value when we are still trying to measure it.
- Follow up with customer user groups and interact with your customers. Without monitoring conversations on the web, it is not possible to know who’s talking about your brand or your products and services or you will not know the positive and negative sentiments associated with your product or brand, let alone how influential a particular praising or criticizing customer can be. Additionally, you will have a tough time comparing different brand messages, commercial videos, etc., and monitoring customers’ reactions to them. In short, one could say your business is largely missing out on marketing opportunities.
- Monitor online focus groups. When it comes to ground-breaking ways of reaching people, social media still has so many more legs to go. We are just at the beginning of this. The networked market knows more than companies do about their own products, and whether the news is good or bad, they tell everyone.
- Identify the primary influencers and sponsor the interactive content. We know that people are out in social media channels seeking information and researching. This is a fruitful opportunity to influence their buying behavior. Influencers have the potential to spread their word to their audience in multiple ways. They cover your events like a journalist, become a spokesperson for your brand, introduce your brand on a new social platform, produce creative content for your brand, help create new products/services for your brand, spark and facilitate conversations in your brand’s online community, and support your brand in times of crisis.
It may not be possible for every global giant to follow these golden rules successfully even if they were to employ thousands of digital marketing executives, and spend millions of dollars. This is where DWBI tools come in to help you at an extremely negligible cost, compared to the benefits they return. Today, Natural Language Processing (NLP) techniques are powerful enough to automatically gather data from social media networks around the world. Most DWBI tools are empowered with intelligent algorithms, which could easily be applied on top of any kind of data mining problem. It takes a fraction of a second for these DWBI tools to analyze customer feedback posted on a social media network and categorize the customer by if they were satisfied or not. Also, you can capture new product ideas and identify the primary influencers of your product. By combining the power of NLP with data mining algorithms available with DWBI tools, you can easily monitor online focus groups and customer user groups. Most importantly, DWBI’s time series analysis techniques tool, helps facilitate the analysis of this data, identify current trends and predict future customer behavior. Clustering techniques in DWBI tools can help you identify market segments and optimize your marketing strategies by customizing it to better suit your particular customer base.
We have highlighted a very positive outlook for the social media networks analysis using DWBI tools from a business perspective and have given an outline of the areas that we consider to be the most relevant to the future business impact. The field is still in its infancy and there are many challenges, technical on one hand and social on the other. The amount of big data volume flows, created by social media networks, could be highly unpredictable, with peaks in the periodic intervals. Such heavy big data volume loading from what is trending in social media networks, mixed up with unstructured data are even more challenging to handle, yet very interesting to explore. Huge data volumes generated in social media networks come from a wide variety of sources. It is a great challenge to go through different processes like linking, matching, connecting, correlating relationships, hierarchies and multiple data linkages. This is how complex data from social media networks can be and if not handled properly, they can go beyond our control. None of these challenges are impossible to overcome for a strong global innovative technical giant that provides high value services.