Quantcast
Channel: OpTier Blog » David Hummel
Viewing all articles
Browse latest Browse all 2

Viewing a Detailed Customer Journey Using OpTier Big Data Analytics

0
0

In OpTier’s work with customers, it has become clear how difficult it is for companies to closely view specific customer transactional behavior. When these firms have issues with customer accounts, it is imperative they find out in a timely manner exactly what is going on and how to find resolution.  This is especially important for customer service representatives that are looking to help customers.  They need to understand what the customer’s journey has been to lead them to picking up the phone.

Traditionally, this has been nearly impossible.  The issue with putting together a complete view of the customer journey is the difficulty in creating a complete transactional record for each customer.  Transactions occur across multiple systems and platforms and are stored across distributed databases.  Creating a singular data set is often a long, and expensive, process that requires the integration of multiple data systems.  The complexity often leads the enterprise to abandon these projects.

With OpTier Big Data Analytics, it is no longer a problem.  What makes this functionality unique to OpTier is our Active Context Tracking technology.  Other analytics solutions that offer this type of customer view require expensive and time consuming data integration projects to pull data from multiple transaction systems.  OpTier is different.  We create a singular, real-time data set with each business transaction stored in a Cassandra database that is instantly available to customer service representatives.  This data goes beyond clicks on a web page, and includes how the end user is impacted as their business transaction flows throughout your enterprise.  Here’s how it works:

An important operation
A call from a customer inquiring about an issue is a vital flashpoint. For example, in financial services, trust is everything and speed is essential. Clients will not react well to institutions that they view as careless or incompetent with their funds, meaning every problem needs an effective solution within a very short window.

The main issue when engaging in this type of corrective action is very quickly ascertaining the details of the situation. A record proving exactly the events that just occurred is invaluable, and the faster it can be produced, the better. This is where OpTier big data analytics comes in. The customer will tell the service agent what they just did and what the result was – the agent can use analytics to verify that the information checks out with the company’s own summary of the situation – every click, and every element of the user experience.

For example: If a client calls the bank and reports that it took 20 minutes to log in, the representative can see exactly how the interaction looked on the company side of the equation. That can lead to questions about the end-user configuration and the effects that might have had on the transaction. If the bank’s system says the login time was actually four seconds, perhaps the client’s outdated hardware was to blame for the slowdown.

The process of finding the transaction in question is quite simple. The customer service rep simply executes a search for a time period (Screenshot 1 below).

Step 1 - Filtering transactions in OpTier Big Data Analytics

Step 1 – Filtering transactions in OpTier Big Data Analytics

Next, the rep selects the specific user they want to track.  (They could also do a search on customer name in advance if they know it.)

Step 2 - Select a User

Step 2 – Select a User

Next, the system will display everything that went on during that time, and which user accounts were involved. A click on a username yields up important stats about that client’s interactions, all the things the rep needs to know to deliver top quality help in a hurry.  In the screenshot below, we can view a specific user’s transactional record for the last hour. What we see is that:

  1. The customer logged in
  2. viewed her savings balance
  3. the balance was requested from the database and delivered successfully to the web page
  4. clicked on an offer but then returned back to checking
  5. made a withdrawal request from her checking account
  6. the withdrawal was for more than what was in her checking account
  7. received an error
  8. then hit live chat, which established a session titled “Savings account withdraw denied – WHY” which hung for 44 seconds
Step 3 - View Customer Behaviors and Transactions

Step 3 – View Customer Behaviors and Transactions

Based on this we can infer why the customer is calling, and verify the potential root cause of her issue – withdrawing from the wrong account.  We also know why she’s annoyed – it took too long to talk to someone on our site.  This type of detail would never be possible to determine with a web click stream monitoring because it could only tell what she clicked on, not what happened in the end-to-end transaction (including the actual requests made on her checking account)

Based on the information above, the CRS knows exactly what went wrong, and how to handle the customers’ complaint.

In addition, analytic users get a functional dashboard that answers the questions that are most important. The interface also has a few other helpful features to offer. For example, rather than viewing static data in rows and columns, employees can place the facts they uncover into helpful charts and graphs. These visual aids can help determine the exact details of the transactional environment and patterns within the client base to help guide improvements to applications.

Step 4 - View Aggregated Customer Behavior Data

Step 4 – View Aggregated Customer Behavior Data

Finance isn’t the only industry concerned with solving problems extremely promptly, but it serves as an instructive test case. Digging salient details out of labyrinthine transactional networks fast enough to act on them effectively is a widespread need, one that OpTier big data analytics can address.

No other analytics solutions can offer this type of customer view because no other solution comes with the data set that we can create in real-time.  The alternative is expensive and time consuming data integration projects.  OpTier makes it easy.

To learn more about OpTier’s Big Data Analytics solutions, check out our Big Data Solutions Brief or Contact Us.  If you would like to download a PDF of this case study, please click here.


Viewing all articles
Browse latest Browse all 2

Latest Images

Trending Articles





Latest Images