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4 Ways to Ensure a Successful Analytics Tool Integration

Enterprises produce huge amounts of data from multiple sources each and every day. However, much of that data is unusable, unstructured forms and comes from either single or disparate sources (social media, surveys, websites, and customer service). To make use of this data, contact centers are now turning to data analytics tools.
 
Selecting the best tool for your enterprise is difficult, though. There are human elements to consider – how are contact center workers going to use this solution (will they use it at all?). Then, there are the bigger enterprise questions – what tool(s) makes sense for my organization and how do I implement them?
 
Below, I’ve highlighted some of these considerations and provide some tips on how enterprises should handle data analytic tools to ensure the best insight.
 
The Right Data Tools, the Right Users
Before selecting a tool, you need to know what insight you're looking to glean. Analytics tools cover a range of data techniques that fit different business requirements. Not every tool will meet your needs. Each tool uniquely generates business dashboards displays with selected key performance indicators (KPI) and provide other metrics such as graphics, dials, or progress bars. Analytics tools can also be used in conjunction with CRM to provide added insights for customer service interactions.
 
Enterprises also need to consider the software users. If technologists use the software, you must find software with easy-to-understand menu choices as well as graphs and charts that are easy to view and print. If non-technical staff uses the data-analysis software, it should quickly switch between various data displays.
 
Focus on Data Quality, Accuracy, Reliability
Data is the foundation of analytics and decision-making, and analytics is about making sense of the available data. A stable CRM platform is necessary before deploying advanced analytics. If the quality of the data inside the systems isn’t good, the results will be unpredictable. Data used in analytics tools have to be current, usable, and actionable. The CRM system data may be sales-focused, but it might not be collecting the data needed by other departments.
 
Striving for quick results may overshadow the need for higher data quality, accuracy, and reliability. Quality management and ethical data sourcing, entry, and retrieval should be combined with continual quality testing and improvement, which ultimately leads to increased value. Consider the appointment of chief data officers and chief analytics officers. Also, don’t overlook the demand for security, as privacy threats and public concern increase.
 
While data analytics tools are helpful, but they are nothing without a strong team and a big data management team with data scientists from different teams.
 
Use Cases that Deliver Value
When whittling down the options for analytic tools, an enterprise should identify their business requirements and select at most three of the most commonly requested use cases. These may include optimizing marketing promotions, sales leads, or improving customer service to reduce customer churn. Then, identify the types of data required for each, such as a customer’s interaction history (purchases and call center interactions) and profile data.
 
It’s More than Just the Right Tools
Here are four ways to ensure your data analytics project produces the insight that you want:
  1. Break down silos and take inventory. Evaluate your data assets. Do you know what you have and the ways you can use it? Inventory your data resources and the contained data, so you can determine the untapped data stores that can produce new value.
  2. Ensure various departments share responsibility for the same objectives. All enterprises want to retain customers and increase revenues. Different departments should all rally to the same goal. Data should inform sales, while customer service can use the insight to understand what makes customers happy. Marketing can also learn about which products customers like best, and what product enhancements customers are requesting.
  3. Develop algorithms and queries that deliver actionable insights. What if social media sentiments aren’t matching recent sales results? Are there product flaws or brand perceptions that the sales team doesn’t know about? Unless someone is analyzing the unstructured social media data with transactional sales history, then what conclusions can be delivered?
  4. Encourage co-creation. Three major barriers for creating a single source of customer intelligence are operational silos that don’t talk to each other, lack of systems or data standardization or integration in the organization, and data quality issues. Executives must promote the importance of data quality and collaborate with IT and other lines-of-business. This can improve the culture, producing shared visibility and opportunities for co-creation.
Insight Over Data
Sharing data isn’t enough. Turn data into information and then into deeper insights. Insights should go beyond simply re-sharing statistics and data displays. True insight is when knowledge can be applied to real problems and help foster future ideas. Also, don’t forget the user throughout the process. They’re usually the first to benefit from these insights.

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