Guest post from Burke Powers, Managing Predictive Analytics Consultant, IBM Business Analytics
Today, every company of appreciable size has some social media presence. Most companies I speak with are either just monitoring social media or are engaged in “spray-and-pray” tactics that are only loosely tied to corporate goals.
To realize the value in social media it is important to integrate social media into broader customer analytics programs and business decision making.
Below are best practices to getting value from social media analytics and integrating that analysis into the broader business analytics and decision management framework in a balanced and useful way:
1) Start with a Well-Defined Objective
Too often, companies ask, “What are customers saying about us?”
An objective like this is too vague to direct an analysis and identify actions. What we really need to be able to ask is, “Product XYZ will launch in two weeks. We have done A, B, and C campaigns to create awareness and to position the product.
· What kind of buzz (as measured by D, E, and F KPI’s) has this created around each of our message points?
· Are there other topics that we did not anticipate?
· Can we setup real-time reporting of the topics so that we can monitor the customer reaction to the product once they begin using it?
· Can we monitor any emerging, unanticipated topics after the launch?”
The objective should focus on an area of the business where you are confident additional insight can lead to quick improvements. The best opportunity might be related to a product, the service level of a critical customer touch point, competitor actions, a specific brand attribute, or a customer behavior.
This gives us the best opportunity to succeed when beginning social media analytics.
2) Collect the Data
The sheer volume of social data requires some planning. There are a limited number of data aggregators (major aggregators include BoardReader, Gnip, & DataSift) and each comes with its own benefits and trade-offs.
To choose an aggregator that best fits your needs, decide how important data history is, the cost of hosting the data, and the importance of access to all social media data (full fire-hose) versus sampling.
Secondly, decide whether to integrate additional data sources. Using the same filtering and reporting for social media and survey verbatims makes them more comparable for analysis and reporting. Also, determining whether to include internal social network data from Yammer or Lotus Connections may be a factor.
3) Plan and Execute the Analytics
By its nature, social media data is going to be different from what most business analysts are used to analyzing. It is unsolicited and unstructured and tends to be rich in attitudinal and usage information. It is frequently strongly positive or strongly negative.
But, it provides tremendous value because it has rich customer narratives of every product feature and customer touch-point that no other data source can offer. It brings traditionally dry analysis to life for business decision makers.
Most existing social media analytics tools offer only a limited ability to search and trend terms as well as view some sort of sentiment. Some allow filtering by the source metadata as well. These are necessary elements of any serious analysis, but stop short of offering the tools needed to take the data to an actionable level.
To be truly useful across many parts of the business, the free-text data needs to be understood in context and translated into an accessible format for reporting and analysis. This capability is one of the strongest differentiators for IBM Cognos Consumer Insight.
4) Motivate Actions
Once the analysis is ready, it is time to deliver the information to the decision maker at the right time, in the appropriate context to make a decision, and in a persuasive manner.
This may mean a PowerPoint presentation, but more often these days it also means integrating the information with the decision management and business intelligence systems.
Finally, be sure to include a rich narrative quote that illustrate the argument and provides an additional persuasive hook that augments the analysis and builds buy-in from the “gut” of business leaders.
For example, let's say your company recently launched the "Wonder Widget." As you prepare the first report on how the product has been received by customers, include a positive customer quote to support the data and drive the point home.
Ideally, the quote says exactly what your analysis leads to, "I love your new 'Wonder Widget!' It is already making a difference, except for one thing. The XYZ dial has got to be moved closer to the display so that I don't have to look away. Fix this and I can easily justify ordering more units."
5) Measure Results
There are many social metrics that could be used, from numbers of followers or tweets generated, to the ratio of issues resolved, to issues raised via social channels.
Additionally, you could track the results via click-throughs using IBM Coremetrics or email campaign response using IBM Unica.
You also might choose to experiment through customer support channels and monitor perceptions via both social media and surveys.
Finally, the metrics and actions need to be tied back to financial metrics either as revenue-generating or cost-reducing. This may require knowing the cost of resolving an issue via a social channel versus contact center or perhaps the cost of a response via one promotional channel versus another.
Identify a New Objective and Repeat
Now that we’ve gone through the process from beginning to end, it can now be repeated again with a new objective. A disciplined approach using these best practices will generate rapid returns on virtually any social media analytics endeavor.
For more information:
· Read the whitepaper on techniques for gaining valuable customer insight with social media analytics
· Watch a demo of IBM Cognos Consumer Insight