Show me the numbers! Common Misperceptions about Analytics

As analytics begins a steady march into mainstream understanding, many banking executives still possess common misperceptions about how analytics can help their organizations. With all the talk about analytics, it shouldn’t be any surprise that the conversation tends to either be vastly simplified or overly complex. Many executives believe they already have analytics in place because their organizations are using data visualization tools, business intelligence reports, or they have already identified “the best” metric.

But the reality is there is no single solution or best metric. The bottom line is that the best metrics for a particular bank may not be the best metrics for another bank. It is not a “crystal ball” or nor does it come in “one size fits all.” Keep in mind that you will most likely be focusing on a handful of metrics that are gleaned from analytics, not just one best metric.

The core of usable metrics is understanding the entire data stream in analytics. Analytics is a multi-dimensional process:

  • Analytics requires a strategy and organizational understanding of what it can do and can’t do.
  • Analytics is a process of data accumulation and the source of that collection is critical to the viability and usability of information gained from that collection.
  • Analytics is a process that requires regular and programmatic data mining.
  • Analytics is a process that requires algorithms for testing and data verification.
  • Analytics is a process that includes modeling for predictions.
  • Analytics requires implementation into day-to-day operations.

So what can analytics really do? Forward thinking retail banks are making better informed decisions and implementing successful initiatives based on analytics. Here are some comments from Ignite’s customers on what analytics is helping them achieve.

  • We make personnel decisions including hiring and firing and timing of staff.
  • We make compensation decisions.
  • We make investments decisions by channel and even LOB.
  • We make product decisions, both thresholds, naming, and even what we can drop or invest more in.
  • We make marketing spend decisions.
  • We use the data to have coaching conversations.
  • We use the data to measure the value of mobile applications and determine the types of devices and entry points people are using to interact with us.

Stay tuned for more …

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