The following article appeared in Pipeline Magazine on Feb 5th, The Future of Mobility Issue
By: Chris Checco, Charlie Thomas
As tens of thousands begin to gather for the 2014 Mobile World Congress, all are focused on the show’s central theme –“Creating What’s Next.”
This year Razorsight is taking innovation to a new level with the launch of the world’s first real time predictive analytics solution for Communications, Media and Entertainment companies. Just bring your smartphone and launch your browser and you’re ready to use Razorsight’s unique, cloud based predictive insights platform.
The best way to learn more about Real Time Predictive Analytics and why this intriguing development may be “creating what’s next” for mobile operators and, for that matter, all communications, media and entertainment companies — having Razorsight CEO Charlie Thomas and President and Chief Analytics Officer (CAO) Chris Checco directly address the most commonly asked questions about this breakthrough.
Razorsight recently launched real time analytics. Can we start by defining that term?
Charlie: We define real time analytics as the application of business rules, statistical algorithms and workflow in the moment to deliver a superior customer experience derived from proactive and predictive insights that position operators to maximize CLV (customer lifetime value) and revenue per subscriber (ARPU).
What’s unique about Razorsight’s real time analytics?
Chris: Every operator already has retention programs, churn models, CRM systems, and so on. They’re all doing increasingly good work with these systems today. Universally, churn has improved. We complement those systems and allow operators to move to a new dimension in customer experience by leveraging data science and predictive statistical algorithms.
Relative to “real time analytics,” operators presently utilize one component of the solution – real-time business rules. That’s not the same as real time analytics.
Razorsight applies both real-time scoring/categorization algorithms plus the absolute business rules. Our software looks at thousands of variables per customer, assesses a myriad of statistical scoring algorithms, and renders a decision or recommendation to address that customer’s unique situation in real time. This allows operators to act in a way that precisely supports the customer’s need at that moment, delivering a superlative experience that increases loyalty while extending lifetime value and creating incremental revenue opportunities.
You’ve described real time analytics as a “game changer” for communications service providers – why?
Charlie: Differentiate, innovate or go home is the predicament mobile operators find themselves in given the plethora of over-the top apps, WiFi options, and rapidly changing user consumption habits. Proactively addressing customers’ wants and needs is essential.
Mobile operators have the ability to offer mass customization in the way that Nike has, and to deliver unique “experiences” based on customer preferences, demographics and location. Layer onto that – the power of statistics with Razorsight’s pre-built predictive insight models and the Mobile Operator can truly WOW customers.
With multiple devices per person, providers are simply cannibalizing one another’s customer bases. With little to distinguish themselves beyond creative pricing and network quality, customer experience becomes the main fulcrum for controlling a positive outcome and optimal revenue result. Real time analytics enables service providers to proactively address various customer situations – from retention, to cross-sells and up-sells, and beyond.
For instance, with real time analytics the operator can ensure that a customer receives an offer for a free phone replacement after X dropped calls in Y hours – before the customer files a complaint. That kind of preemptive strike is a loyalty builder.
Real time analytics will also become important in other areas such as micro-targeted advertising and real-time offers, as location-based services become more prolific.
We’ve heard a lot of companies talk about real time analytics. What makes your approach different?
Charlie: I’ll provide the “color” and let Chris delve into the data science.
Consider this example of the difference between partial approaches that are strictly rules-based, and true real-time analytics. You’ve probably been at a store and attempted to make two consecutive purchases using the same credit card, with the second one getting denied. Why? Because a business rule identified the second charge attempt as a likely fraudulent transaction. Many organizations, especially financial institutions, have hundreds or thousands of these rules being applied in real time. Unfortunately, such a pure “by the rules” approach commonly ignores the context of a situation. And in the mobile environment, that can lead to arbitrary decisions that undermine the customer relationship and turn “thumbs down” on new revenue opportunities.
Chris: To avoid that kind of scenario, our solution draws data from multiple sources – heuristic or historic (the past), trending or predictive (the future), and real time (the present) using streaming data. The solution is highly extensible and scalable to handle 50,000 data points per customer and – overall – billions of transactions.
What is the specific value of real time analytics for mobile operators?
Charlie: Mobile operators stand to benefit in three areas: retention, sales and advertising.
The first, as mentioned, is through increased customer loyalty, longevity, and profitability – selling beyond the payback period and avoiding replacement costs, by which I mean replacing a churned customer with a new customer.
Second, real time analytics provides the mobile operator with up-to-the-moment data on what the customer wants or is most likely to purchase that very second – opening significant new up-sell and cross-sell opportunities.
The third value comes from additional advertising revenues – whether in a CPM, CPC, or CPA model. Real time analytics lets the mobile operator create micro-targets for their advertising group, which can then intelligently divide and sell digital advertising space, charging a premium for this precision targeting. Industry analysts forecast a ten-fold increase in revenue in mobile advertising over the next five years as a result.
What are some examples of apps you’re developing for Real Time Analtyics?
Charlie: We are focused on four areas where real time analytics will provide enormous value to our clients:
- Sales & Marketing – our apps are used for Proactive Customer Experience and personalized Ad Delivery.
- Finance – our apps improve Customer Lifetime Value by squeezing incremental value out of each customer interaction through Treatment Optimization.
- Operations – our app generates LOQs (lines of questioning) dynamically to improve customer service and vastly improve first-call resolution rates.
- Network – our apps focus on trigger-based events to proactively improve the customer experience, and address issues before the customer reaches a breaking point in satisfaction.
Can you walk through how real time analytics works?
Chris: At the moment of the interaction, the solution gathers new real time data attributes such as the answer to a live question, recent usage data, or interaction with an IVR. The solution proceeds to create thousands of analytic attributes by merging new data with historical data in real-time, and applies statistical algorithms. Then uses scoring algorithms and categorical models created to apply additional statistical or heuristic business logic. Finally, an arbitrage engine parses and prioritizes the results for use.
Typically, the best results come from a combination of absolute business rules and real time analytics. The solution then delivers the result to the end user in seconds. Ultimately, Razorsight will cut that delivery window to a sub-second response time.
For feedback that adds more intelligence on a customer, the system takes input from an end user, for example, if he or she declined the offer, or requested new information – and why.
How do you see real time analytics changing the competitive landscape for CSPs, particularly mobile operators?
Charlie: Three ways. It allows for more precise (custom-based on preference, demo and geography) and more timely (proactive) communications and creates new monetization opportunities based on such. It will expand the existing revenue “pie” by enabling exponentially greater value for the same digital media assets. Finally, real time analytics adds a new level of personalized touch, thereby improving the customer experience which increases Customer Lifetime Value while offering new incremental revenue opportunities – such as on-demand video content based on location and preference.
Will the Big Data analytics business change, too, as a result?
Charlie: Absolutely – I see business evolving in three tiers: Big Data analytics, Micro Data analytics and Real time analytics.
Big Data analytics will continue to be used to consolidate and perform some of the basis work, serving as a prime source for the analysis and creation of the heuristic and absolute rules, as well as for measuring and monitoring business success.
Micro Data analytics will be used to identify the longer-term trends in customer behavior upon which so many statistical algorithms are created to optimize the business – retention, cross-sell, up-sell, campaign optimization, ad impression forecasting, and so on. These elements rely on historical trends to identify pre-indicators of a significant change in customer behavior, but don’t require real time data. I believe Micro Data will remain a significant source of targeted offers. Many of these apps will be built on top of Big Data Analytics platforms. They both feed and are fed by the Big Data.
Real time analytics will be vital for the cases that require more time-sensitive information and can be used to override the Micro Data Analytics in certain cases.
For instance, if a customer has five dropped calls in an hour, Micro Data Analytics typically won’t capture that for a day – or longer in many cases. But real time analytics will catch this situation at the speed of the data.
So we’ll see analytics develop into three integrated realms?
Chris: Exactly. There is a very clean and clear intersection of between Big Data, Micro Data and Real time analytics.
Take this scenario. A customer experiences four dropped calls in an hour. The Big Data analytics platform gathers the streaming information for that customer with dropped calls in real time and triggers the Micro Data Analytics platform.
The Micro Data analytics platform has assessed the optimal set of campaigns for dropped calls based across a multitude of client profiles, profitability levels, channels and touch frequency.
Real time analytics statistically assesses the impact to the client’s loyalty in the moment, prioritizes the campaigns for the individual client, selects the optimal channel and message, and delivers the information to the execution channel.
Big Data Analytics continues to gather the effects of the Micro Data and Real time analytics treatments through the feedback and self-learning features.
Micro Data and Real Time will then use the feedback and self-learning features to continually improve the treatments and identify new micro- and macro-trends
What does real time analytics hold for the future?
Charlie: Real time analytics has only just begun to “create what’s next.” I believe the future will see real time analytics technologies embedded directly in devices, just as Google Glass has embedded technologies once spread across dozens of devices into a set of cutting-edge spectacles.