Posted by Subramanya C
Through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage, according to Gartner. Appropriate use of this data requires filtration and analysis. As such, analytics has now emerged as an absolutely necessary business tool that delivers and enhances services.
Today, everything is connected. With people online, on different devices and with different capabilities, wherever they go. there is a definite need to track, record, and analyze all this engagement. But what does all this mean, in terms of deliverables?
Today’s analytics have moved from being descriptive and report-based to predictive and prescriptive. Predictive analytics typically involve analyzing vast sets of historical data to come up with a model to assess the likelihood of an event occurring. Prescriptive analytics would take this further by making the optimal decision in a given scenario.
Analyzing behavioral patterns of customer segments can help identify those customers most receptive to upsell and cross-sell offers, increasing revenue generation opportunities.
You can analyze past company performance to interconnect multiple aspects and generate projections of possible results in future. Doing this gives you dynamic data in real time, which can be a highly useful practice in crucial company meetings. Using analytics, you can cut across multiple dimensions and pull out data from dissimilar systems for comparison. This helps in making data-driven decisions. With prescriptive analytics (otherwise known as the final frontier of analytic capabilities), companies can synthesize data to make predictions and then suggests decision options to take advantage of the predictions
Big data comprises two main concepts: structured (such as, operational, transactional customer data) and unstructured data (e.g., enterprise dark data such as calls, emails, and social media) and using analytics makes it easier to sort through huge quantities of this data.
With this data, you can see the big picture of a certain segment or all your customers to show a beneficial emerging trend or you can narrow down your focus to individual customers, gain in-depth insights for each and deliver personalized services. Analyzing data on operations and customer transactions can give you better understanding of:
- Customers’ spending and transaction patterns
- Customers’ reactions about changes in your operational strategy and attitudes towards discounts/ special offers
Along with written data evaluation, you can also analyze recorded calls using speech analytics and:
- Detect emotions during interactions (whether customers are annoyed depending on the number of times they’ve called for one issue) and review customer choice of words (note the number of times a customer talks about “cancelling my service”).
- Understand situational context.
- Study previous call data and history of that customer.
- Make current decisions based on deductions from past data.
- Define problems.
Achieving a Data-Driven Culture
Organizing analytics will make data linking across departments easier. Inter-departmental collaboration is essential to ensure that the department data link to makes sense in the context of the big picture. By bringing data from multiple functional areas like HR, Finance and Operational performance – we will get a consolidated big picture and insights that are not otherwise possible. A data-driven culture from the top down helps departments understand the importance of the data and cooperation with each other.
Analytics can also be used to monitor your company’s social media presence. If a customer writes a Facebook post mentioning your company’s name, it can be tracked and recorded using social CRM tools. You can get valuable insights into customers, including product feedback.
Key challenges of introducing this level of analytics to your organization include:
- Finding use cases: Do you make, buy, or outsource?
- Hosting your analytics – cloud or on premise?
- Finding good data scientists
- Organizing a data science lab
- Making the organization data driven
- Creating big data architecture
HGS provides analytics in these categories:
- To categorize interaction types
- Watch trend & identify problem patterns
- Find root cause peaks and troughs
- On-going monitoring
- Structured to Unstructured text analytics
- Dissatisfaction Correlation to top call drivers
- Dissatisfaction Correlation to AHT
- Dissatisfaction Correlation to Agent behavior
- Dissatisfaction Correlation to product & services
Cross- channel Interaction analytics
- Speech – Keywords & Topics
- Talk over analysis, Emotion detection
- Text - Sentiment Analysis, Social Buzz
- Contextual - Desktop Analysis
- Customer demographics
- Customer & representative Voice separation
- Customer behavior analytics
- Representative behavior analytics
There are many analytics solutions to navigate, so choose wisely. Here are some tips on finding the right platform:
- Look for BPM assistance and analytics expertise, or providers that are market leaders on Gartner’s Magic Quadrant.
- Decide on a cloud-based or on-site model.
- Check the provider’s scalability in terms of bandwidth (Can they support your organization’s database size and continue scaling up?), skill set (Can they offer the skill set levels you need?).