Before grabbing that streaming data so quickly, Joyce Norris-Montanari says you should ask a few questions.
When you hear someone refer to an ‘inside baseball’ move, it means they’re playing into the subtleties of the game. Inside baseball requires a high level of awareness, experience, and strategic thought. This typically results in a mix of strategies to get runners on base and manufacture runs rather than [...]
Three 'inside baseball' tactics for manufacturing leaders was published on SAS Voices by Roger Thomas
David Loshin provides an alternate take on streaming data in the context of legacy systems.
The post The streaming data paradigm shift for legacy systems appeared first on The Data Roundtable.
I have lived in the Town of Cary for more than twenty years; two of my three children were born at the local WakeMed Cary Hospital. I’m a big fan of my city, or town as it prefers to be called – even though the population is over 160,000. That’s [...]
A future of flying cars and Minority Report-styled predictive dashboards may still be some time away, but the possibilities of robotics and Artificial Intelligence (AI)-powered automation are a reality today. From connected cars to smart homes and offices, we see daily how big data and the Internet of Things (IoT) [...]
“Quick response forecasting (QRF) techniques are forecasting processes that can incorporate information quickly enough to act upon by agile supply chains” explained Dr. Larry Lapide, in a recent Journal of Business Forecasting column. The concept of QRF is based on updating demand forecasts to reflect real and rapid changes in demand, both [...]
Is quick response forecasting a reality or just another buzzword? was published on SAS Voices by Charlie Chase
If you’ve been waiting for the buzz to settle around the Internet of Things before deciding how to invest in this new technology space, now’s the time to stop waiting. I’ve been working in the technology sector for a few decades, and the innovation and excitement I’m seeing around IoT [...]
Will the IoT live up to the hype? Yes. A most resounding YES. In fact, it will exceed the hype, because we don't even know all the IoT possibilities yet. We don’t know what we don’t know, and that lack of imagination limits even our hype. Where we are with [...]
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.
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:
- 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.
- 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.