What I am Hearing Part 3

Ok, so here we are in part three of the series on what I am hearing in the communication service provider space. In the last week and a half, we have talked about how many companies are starting to focus on Offer Management and process enablers in order to make an impact in their service organizations. Today, we are going to tackle the issue of Intelligent Decision Making and it's impact on customer service.

The foundation around this particular post comes from a number of conversations I have been having lately about data and how using data or capturing data can be both a blessing and a curse. It can be a blessing because having boat loads of data can help people make better decisions, no real argument from most on this point. Not rocket science either. But data can also be a curse in the sense that many companies are swimming in data and can be bogged down in making decisions because of the enormous amounts of data in their Enterprise Data Warehouse.

There is also consideration made for data that is real time and data that is static. Companies are trying to understand how both types can be used to further the needs of the business and customers.

Again, after a number of different conversations, it is becoming more and more clear that companies are starting to depend on software to help make decisions for them. I know that it seems all to Tom Cruiseish from Minority Report, but companies are starting depend more and more on new types of software to help them not just make sense of data statically and make forward looking decisions. They are looking to software to help them make real time decisions about servicing their customers.

A great example of this is in the retention space for communications service providers. As we have been going through this downturn in the economy, there obviously becomes a focus on the existing customer base ensuring that churn rates are as low as possible. For years companies and specifically marketing departments have been using data that can be gathered about customers usage patterns, geography, competitive market etc, to create offers and present offers to customers to ensure they stay a customer. The challenge that companies have had in this regard is that they are using very static information to make offers or to try to save customers, they aren't using dynamic real time information to make the best offer to that customer. In the case of someone calling to say they are cancelling, their may be three reasons the customer gives the call center agent as to why they are cancelling: a new competitive offer, a service issue they are having currently and the first agent they spoke to was very rude to them. In a large majority of service organizations, that data is not used in an intelligent way to help agents make decisions about how best to care for and save this customer.

So what I see happening in the market is that companies are starting to use Real Time Decisioning engines to help make the most effective pitch or offer to save that customer that we described in the example above. These engines are able to take the static information or data that is available about the customer, combine it with some real time data input about what is happening in the moment with the customer on the phone and help pull together an offer that will be much more specific and thus much more likely for a customer to say yes to.

There seem to be a couple of keys in choosing these types of engines that are important to the people I have been talking with over the last several months:

1. Flexible- The Real Time Decisioning Engine that is selected needs to be flexible in a couple of different ways. First, it needs to be able to accept inputs from a variety of different points in order to make the best decision possible. Not only should it accept inputs from back end systems that will feed it static info about the customer and their history with the company, but it should also be able to accept input from people in real time so that the decision that is made is as accurate as possible. Second, the engine should be flexible in it's deployment model. It should not take you a year and buckets of money to deploy these engines. They are complex, but at the same time they should be flexible enough to start small and grow with the needs of the business. A SaaS solution or Cloud based solution may be a great option here.

2. Dynamic- The engine itself should be easy to use and dynamic enough so that if marketing makes changes to offers or programs, the decision engine can be changed within minutes to reflect the updated inputs from marketing. It should not be a situation where you need to call in the vendor in order to make big, expensive code changes in order to accommodate tweeks in data inputs.

3. Learning- Probably the most important criteria that needs to be considered is the ability for the engine to learn from decisions that are suggested and made. As the business changes, as the customers change, as the macro economic environment changes, you want the engine to be able to learn from what decisions were made and be able to make smarter or more effective decisions next time around. As an example, when a customer calls in and cancels and they happen to be from Chicago, are in a competitive area, have had 4 service calls in the last 2 months, are very upset, are referencing an offer from a competitor and have been loyal to you for 5 years, you want to ensure that the engine learns by making changes to the offer next time they see a customer with the same characteristics as the customer that was just lost. The idea is that the company should be learning every time they lose or win and thus really using the data they have in real time to make better decisions for the business.

There are only one or two companies that I know of right now that have successfully rolled out Real Time Decision Making software to their users. But, I have heard from a number of companies that they are starting to dig deeper and learn more about how it will be useful for them. Some companies see the value by helping to retain more customers. Some see it as a way to help agents make better decisions about who to give credits or adjustments to. Some see it as a way to enable sales agents to close more sales. In the end it is all about taking decision making into real time.

The take home message is that there is software starting to come into the market that can help companies make better real time decisions about their customers and their business. It can help them save more customers, differentiate in the treatment of customers, ensure credit or adjustment policies are adhered to and a variety of other specific customer service issues. The key to success for companies will be in taking baby steps to using this technology, ensuring success through small projects and then growing it to be more useful across the service organization.