8月 042017

In the spirit of my Forecasting Sharknadoes blog post, I now bring you Sunsquatch! In this blog post, I create a map that helps you find a location where you can see the total eclipse *and* have a chance of seeing Bigfoot (aka, Sasquatch)! But before we get into the nitty-gritty [...]

The post Sunsquatch - tracking the eclipse and Bigfoot ... at the same time! appeared first on SAS Learning Post.

8月 022017

There will be a total solar eclipse on August 21, 2017, and the umbra (total shadow) will pass right across the United States! As a data guy, a map guy, and an astronomy fan, this is an opportunity I just couldn't pass up! Follow along as I apply my computer skills [...]

The post Is your city in the path of the total eclipse? appeared first on SAS Learning Post.

8月 012017

Did you pay your taxes? From stiff penalties to even jail time, the federal government provides plenty of incentive for citizens to pay, but each year nearly one in five Americans do not pay on time. This leads to a more than $450 billion gap in unpaid taxes, creating a [...]

Where the federal government can find $450 billion was published on SAS Voices by Marie Lowman

8月 012017

A lot of tourists flock to North Carolina. We have beaches, wreck diving, and lighthouses. We have the Great Smoky Mountains with whitewater kayaking, colorful fall leaves, and snow skiing. We have hot air balloon festivals & Scottish highland games. Oh, and some of the best barbecue you've ever tasted! But [...]

The post Learning to speak like a local, in North Carolina appeared first on SAS Learning Post.

7月 282017

I recently saw an interesting PEW study showing the percent of each state's revenue that came from federal funds. They had some pretty nice graphs ... but just like jell-o, there's always room for more graphs, eh! Let's start with the map. Their map had an informative title, a reasonable gradient [...]

The post Which states rely the most on federal funds? appeared first on SAS Learning Post.

7月 282017

I recently saw an interesting PEW study showing the percent of each state's revenue that came from federal funds. They had some pretty nice graphs ... but just like jell-o, there's always room for more graphs, eh! Let's start with the map. Their map had an informative title, a reasonable gradient [...]

The post Which states rely the most on federal funds? appeared first on SAS Learning Post.

7月 282017

Think big, start small, take the analytics-driven approach

You want to be a customer-first organisation, but are the benefits worth it? Forrester reports that customer experience leaders enjoy 17 percent CAGR (compound annual growth rate) as opposed to laggards at 3 percent.[1]

Organisations of all shapes and sizes are embarking on digital transformation – a term that’s become synonymous with putting a slick digital front-end on traditional processes. In reality, true digital transformation is about adapting business culture and processes to work with new technology. This isn’t simple and presents many challenges that must be overcome in order to put the customer first, including:

  1. Functional silos: Beneath the glossy front-end of the customer experience machine sit functional and data silos created because many companies organise themselves around products or channels, not the customer.
  2. Legacy systems: Systems of record and channel-specific technologies, often with their own rules and logic, and little ability to talk to each other, fragment customer journeys.
  3. Cultural change: The various departments that contribute to creating a customer-first organisation have different objectives and key performance indicators. This undermines the collaboration and cultural change necessary to put the customer at the core.

Unfortunately, customers don’t care that your organisation is built on complex legacy structures in the back-end. When they interact with you they expect accurate and timely responses and decisions, regardless of the channel through which they engage.

What time is real time?

These days, organisations need to be able to respond to changing customer expectations and provide a seamless joined-up customer experience at every point of interaction, often in real time. The issue is that "real time" means different things to different organisations.

Many believe that a good real-time customer experience constitutes the ability to react immediately to what the customer is doing right now in a specific channel. Displaying a banner ad based on where a customer clicks on your website, or triggering an encouraging email when someone abandons their cart are nice tactics, but fall short of delivering a customer-first experience.

Excellent real-time customer experiences can only be delivered when you truly understand your customers: and their wants and needs; their price sensitivity and preferences; their propensity to buy; their lifetime value; and their service expectations.

Being a true customer-first organisation requires the capability to collect and analyse the data that customers make available to you, then use it (responsibly) to deliver value back to them. Today, these sources are expanding to include structured and unstructured data from social and multimedia feeds, streaming data from beacons and devices, voice calls, transactions and browsing histories.

Better faster, real-time decisioning

Once you’ve analysed the data to uncover valuable insights about your customers, you need a decisioning framework that allows analytical insights to be applied to both historical and real-time contextual data. It must encompass your organisational goals, all the potential offers and actions that a customer could be presented with, eligibility, budgetary and other constraints in order to infuse deep customer understanding into the decision-making process for each individual customer. Only then will you be empowered to make highly accurate decisions across your business about the right next action, next offer, next content or next recommendation and deliver that real time. Not having these capabilities could signal the loss of competitive ground.

Leading retailers, financial services, telco and media organisations have seen significant improvements in customer experience, profitability and reduced costs by using a customer decision hub.

Where do you start?

Choose a use case; a business challenge you would like to overcome. Once you have achieved your intended goals, replicate the model across other use cases or business problems. This is best illustrated with some of the work we have implemented with a leading European broadcaster and for a well-known insurer.

The broadcaster wanted to use analytical-driven decisions to increase conversion rates. Within weeks its customer decision hub was up and running and over a 6-week period the organisation saw a significant increase in the uptake of online upsell recommendations.

A global insurer used a customer decision hub approach to automate complex claims decisions that were being handled in the call centre. They were able to cut average settlement decisions from 28 days to making decision in real time, and saw a 26 percent improvement in decision-making accuracy while also providing a superior real-time experience for customers.

Get started

We can help you brainstorm your first project and get started with less risk.

Find out how we can help you to become a customer-first enterprise - read Customer intelligence for the always-on economy.

[1] Customer Experience Drives Revenue Growth, Forrester Research, Inc., June 2016

So you want to be a customer-first organisation? was published on Customer Intelligence Blog.

7月 252017

When it comes to economic activity, a handful of the largest metropolitan areas in the US account for lion's share. In 2013, the top 23 Metropolitan Statistical Areas (MSAs) accounted for 50% of the total US Gross Domestic Product (GDP). I recently came across a map created by Alexandr Trubetskoy [...]

The post 23 MSAs account for 1/2 of US GDP! appeared first on SAS Learning Post.

7月 202017

Employment - that's been a hot topic here in the US lately. Many of the manufacturing jobs we had in past decades are gone now, and it would be great if there was a crystal ball to predict which jobs might be at risk of disappearing in the future. The [...]

The post Risks to US employment - automation and offshoring appeared first on SAS Learning Post.

7月 202017

For many industries, products and features are no longer the most crucial differentiators in the minds of customers.

Take mobile telecommunications, for example. The recent market shift from virtually no unlimited data plans to announcements of unlimited data offerings by every major US wireless carrier in a short span of time left many consumers wondering which plan had the best options for their lifestyle and budget.

As I read comments on a variety of blogs, it was apparent that consumers were confused, too. But by and large, they stated they were making choices based on how well (or how poorly) their current providers treated them, not necessarily the ideal plan.

Get this free report to see why SAS is a leader in real-time interaction management

And that’s why customer experience is the real differentiator today. For a sustained competitive advantage, you will want to improve customer experiences however and whenever you can.

Using real-time data for better customer offers

Take for example some recent work I did with a global telecommunications company.

This company wanted to explore the benefits of making more appropriate offers in real time for their customers. The definition of appropriate here being analytically driven with the use of real-time data. Targeted customers were those who reaching their monthly data cap before the end of their billing cycle. The decision point was the offer to top-up a customer’s data allowance.

The decision was based on:

  • Whether to make the offer or not. The company anticipated a low response rate. Sending an offer via email or SMS might be considered spam by the customer.
  • The size of the offer to be made (i.e., how many gigs of data).
  • The cost of the top-up when compared to the projected overage charges the customer would incur.

The way in which the decision could be made was either:

  1. In real time, trigger the same offer to all customers that reach certain percentage of their data allowance. This method was of limited value because customer data was not readily available to the system that executed this trigger. This led to option 2.
  2. Overnight, analyse the customer data to make offers to those more likely to respond, and ensure that the size and cost of the data top-ups were appropriate (e.g. smaller offers to customers closer to their monthly refresh of data allowance).

The result was an approximate 400 percent increase in response rate when compared to the company’s existing methods.

We showed the company how much better the response would be, if they combined the two approaches to make a customer-driven decision in real time.

Our suggested solution was to:

  • Create models based on using the real-time data.
  • Use SAS® Event Stream Processing to identify when customers were running out of data, and trigger an action.
  • Use SAS® Real-Time Decision Manager to run the models in real time and use the output of the models to decide on the most appropriate offer for the customer.

Real-time decisions need to be accurate and effective

The result was an approximate 400 percent increase in response rate when compared to the company’s existing methods. This improves customer satisfaction (more timely and appropriate offers, and not running out of data at an inconvenient time) reduced cost (fewer messages are sent) and increased profit (greater offer acceptance and improved margins).

This demonstrates that there can be huge value in real-time information in the analytics life cycle by creating or improving models and using those decision models to improve the customer experience.

The challenge that companies face is determining which decisions will be most affected by real-time data, and what type of real-time data will be most predictive. But it is not always the case that you need to test (or invest significant resources) to identify these two items because there may be natural tests in the data already.

Say a customer applies for financial services product such as an overdraft, a loan or a credit-limit increase. The customer application is the target variable, and you can use the data (transactions, balances, headroom, future payments, time to salary payment, etc.) as observation variables – taking care to ensure that you capture the timing of these, too – and then identify which of these is predictive of approval or denial. Some of this data will be easier to get in real time than others and this analysis can focus the effort onto the ones that are accurate and effective in real time.

Editor's note: This is the first in a series of posts that offer real-world examples of how to best use analytics to meet your marketing needs. The series will cover several industries including telecommunications, banking and retail.

Real-time decisioning offers unmatched customer experiences was published on Customer Intelligence Blog.