By 2019, there will be an estimated 2.77 billion social media users, meaning social media is a key source of consumer insights for brands. Social media is being used by brands across the globe for communications, reputation management, and more often: to identify and capitalize on new opportunities.
With an absolutely massive scope of data available online, marketers need to filter out irrelevant information in order to effectively create data-driven initiatives. The lack of quality data integration strategies is a major obstacle for the modern digital marketer. According to the following graphic, low-quality data is the most common issue for data-driven marketers.
Effective data filtration enables organizations to focus their efforts on relevant data sets so that they can efficiently refine their decision-making process and overcome business challenges. Smart data practices are integral to enabling marketers to interpret campaign metrics. To ensure that data-driven strategies provide value to their company, marketers should carefully follow the following four-step process:
- Define campaign objectives
- Use customized reporting dashboards
- Leverage proprietary consumer insights
- Create transparency across departments
1. Define Campaign Objectives
Prior to the launch of any marketing campaign, marketers clearly define their strategy and general objectives. The initial definition of campaign objectives is crucial for selecting the most effective KPIs for measuring ROI. After identifying a campaign’s objectives, it’s important to consider the relevance of individual informational channels (social media, TV, news, etc.) to the final goals of the project, and thereby identify which data sets are most appropriate in helping to achieve these goals.
For campaigns on a larger scale, segment and share the data that supports the specific roles of each department, partners or suppliers involved in the project, so that they can adjust their activities and boost overall performance.
2. Use Customized Dashboards
A competent social intelligence tool will not only allow users to collect and analyze data in real-time but also to create personalized data visualization dashboards useful for highlighting the most relevant insights.
Visualizing data in easily-digestible formats enables teams to:
- Understand key concepts about your company and surrounding industry
- Visualize brand associations with other brands or verticals
- Monitor emerging trends in your market and related sectors
- React to consumer insights in real time
3. Leverage Proprietary Consumer Insights
Just as the success of a marketing campaign depends on the context of the campaign’s objectives, the process of data analysis should be contextualized as well. A data analysis strategy should take the following into account:
- Time frame of the data collection period
- Relevant company activities including campaigns, product launches, events, etc.
- Social, political, or economic factors
- Competitor strategies
At the same time, it’s important to avoid information overload. Ensure that all project collaborators are on the same page, but keep briefings as relevant and concise as possible so as not to confuse anyone. Tools like Slack, Asana, and Google Drive are excellent for keeping business processes organized.
4. Create Transparency Across Departments
Operating within data silos is an outdated business practice. Companies must use the wealth of data available to them in order to strengthen their daily strategies across all departments. Transparent and fluid communication across departments is key to delivering consistent messaging and equipping each department with as many resources as possible. Use internal message boards to distribute relevant information to those who need it. Managers and directors should meet regularly in order to ensure consistent communication across departments, while dashboards and command centers can illustrate relevant data to employees in real time.
When well-implemented, data-driven marketing strategies create value across all levels of an organization, from product development to post-sale service. As data technologies continue to evolve, brands must be prepared to integrate these innovations into their day-to-day operations.