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May 2013

Science of Analytics: Bringing Prepaid Top Ups & Revenue Maximization under the Microscope

The Science of Analytics: Bringing Prepaid Top Ups & Revenue Maximization under the Microscope

Having a mobile postpaid billing arrangement is a lot like marrying your service provider.

It’s usually a win/win for both parties as the operator and customer agree to give up some excitement and emotional roller coaster rides for a stable, predictable relationship.

By contrast, however, prepaid mobile puts the operator in a bit of an uncomfortable spot -- kind of like blind dating every night with people who don‘t have Facebook pages.  Will the customer be loyal and spend her money freely?  And what do you say to customers you don’t know very well?  Will your earnest pleas for monetary love work, or will the relationship end suddenly with a teary-eyed farewell?

Well, making a prepaid operator’s relationship with casual customers much less awkward and more financially rewarding is what Globys is all about.  The company has developed a powerful contextual marketing solution specifically for a worldwide prepaid market where Globys has established a great presence in places like Latin America and Asia.

And here to explain its business model is the company’s CEO, Derek Edwards, a big champion of scientific marketing.  My discussion with him provides a very interesting snapshot of the challenges prepaid operators face in maximizing their revenues.

Dan Baker: Derek, tackling the prepaid market is a tough assignment but I imagine the international globetrotting is a lot of fun at the same time.

Derek Edwards: Yes, Dan.  Mobile prepaid is a very interesting market indeed.  In fact, I would say, prepaid subscribers are the customers that carriers know the least about.

The operator is not interacting with prepaid customers on a monthly basis.  You’re not sending a bill, nor do you have detailed profiles on these customers, especially in the developing world where customers are buying SIMs very inexpensively at a grocery store.

But nonetheless, prepaid customers are constantly making revenue impacting decisions by the way they use their phones -- when they top-up, where they top-up, and how much of a balance they keep.

And in the prepaid world, churn is a major problem.  Some carriers have churn rates higher than 80% among their base.  It’s not like the U.S. market where you are worried about the hassle of porting your phone number.  In many markets, people don‘t care about keeping their phone number.  They are more interested in saving money.

So what we do is take the carrier’s data and then figure out exactly what the carrier should market to each individual customer at any given point in time based on where that actual customer is from a behavioral standpoint -- did they just top up, is their balance low, are they a new data user?

And by taking that information and sending simple offers -- typically via an SMS message -- you actually drive some pretty significant results.

Increasing the ROI on a carrier’s marketing campaigns is an important result of analyzing customer behaviors.  How do you accomplish that at Globys?

Yes, delivering effective -- and measurable -- campaigns is key to our success and this requires the right technologies and the right skill sets.  We have a large team of analytics folks, including PhD data scientists with disciplines ranging from computer-automated pathology diagnosis to pharmaceutical drug discovery to high frequency financial trading.  And just as important, we have a team that specializes in mobile carrier marketing — people who have run the loyalty and promotion programs for some of the world’s largest carriers.  This team possesses a unique expertise in quantitative marketing and helps our carrier clients connect the dots between the science and the marketing.

Top-Up Campaigns and the Mid-Cycle Sweet Spot

The whole concept of our campaign approach is to trial a lot of different offers, then see what works, and then rapidly scale.  This strategy is a 180 degree turn from the traditional process of running six-month trials where you dream up the campaign, gain approvals, roll it out, run it for a couple of months, and then wait a couple of more months to get the data to see if it worked.  We shorten that cycle into days or weeks and gather enough statistical evidence to justify rolling a campaign out to a wider audience.

For example, one campaign strategy we utilize is to deliver offers as customers enter the ‘mid cycle’ context.  If the objective is to shorten the customer’s recharge cycle, we engage the customer half-way to the point when they are predicted to top-up.

The offer itself could be something relatively simple, like free minutes.  Often, we take offers that the carrier already has in its portfolio and figure out how and when to use them more effectively.

Now “mid-cycle” sounds like a very easy thing to define, but in fact, it is a very subtle thing.  It’s highly individualized to how the customer uses that service.  In fact, the mid-cycle might be very soon after they’ve recharged.  It really depends on how the customer consumes his balance.  Some customers actually run out of balance very early in their cycle, so they tend to recharge often.  Others preserve their balance when it is high and then spend it all in one burst at the end of their cycle.  So writing algorithms to calculate the mid-cycle for individuals is a very complex thing.

We have found that it’s way more impactful to hit the customer at their mid-cycle point than, say, triggering an offer when the balance of the customer goes from $20 down to $10.  The point is to reach the customer when they actually think they are halfway through their timeframe.

So tell us more about your main product, Mobile Occasions.  You call it a contextual marketing solution, but what does that actually mean?

Dan, contextual marketing is really about applying a scientific approach to marketing.  Now I’m not just throwing out the word “science”.  At Globys we actually assembled a diverse team of scientists and had them apply their principles to telecom data.

For instance, on our team we have experts in location, crowd sourcing, quantum computing, tweeting, computer vision, and computational chemistry.  A few months back our molecular biology expert was telling me about the parallels he’s found between telecom analytics and predicting how molecules interact.  I was surprised too at the similarities.

So let me walk you through our methodology here:

Contextual Marketing

When I say a contextual marketing solution, I mean taking all of the carrier’s data, turning it into actionable behavioral analysis and applying real-time decisioning to deliver the right marketing treatment to each customer at the right time.  And our approach to marketing is very personalized — down to the individual customer level.

The overall process we employ with our contextual marketing is shown in the diagram below and I’ll explain it in more detail as we go along.

Contextual Marketing

1: Data Exploration and Analysis

The first capability of our solution is data exploration and analysis.  We take financial transactions such as purchases, spending, balances, etc. and then combine that with call data records across voice, SMS, data, even video, and we truly get our hands on that data in as close to real-time as we can.

From that we create a “longitudinal” view of every customer.  Now in the telecom industry we are actually quite fortunate because in the online world, marketers face huge volumes of data, but they really struggle to connect one event with another and determine who the web surfer actually is.

In telecom, however, we not only touch huge volumes of data, but that volume is far more concentrated on a per customer basis.  And it’s that richness of customer-specific data that allows us to combine all of the different data sources we have about the customer into a single longitudinal view.

For example, we can determine that a customer purchased an international calling voucher, made the next five calls to Indonesia, and did a full recharge.  We can also detect subtleties — she sent a high quantity of SMSs to a very small social graph of individuals, which resulted in her receiving very few return text messages and several return voice calls.

So, each of these views is like plotting events on a timeline for a particular customer.  And that representation then allows for interesting sequential pattern analysis and pattern recognition, which allows us to model what we believe each customer will do next.

So it’s at that point that we identify cohorts, or groups of users that have these common patterns of behavior.  Our advanced marketing team then leverages those data insights to determine the best ways to act on those events and begins formulating a variety of treatment inputs.

2: Multi-Factor Experimental Designs

And that leads us to the second capability of our platform which is the ability to execute multi-factor experiment-like designs.

We don‘t just formulate messages, target individuals and then hammer them with messages to see which ones work.  Instead we model the different messages, timing, and other parameters in a more selective manner.  That way, you can ping the fewest number of customers possible — enough to get a significant statistical sampling — and still be able to understand and refine what’s working and what’s not.

Then we take the ‘what’s working’ and scale these treatments to the customer base while keeping the experimental designs ongoing so we can continue to test and tweak a large variety of multi-faceted offers.

When we first go into a carrier client, we find they often have some very successful pieces in their offer mix, but invariably they are also pitching offers that are dragging down their results.

Carriers tend to be very surprised to learn that some of the messages they send actually have a negative effect on revenue generation and may ultimately be driving customers away.

Again, the goal around experimental design is to eliminate that risk by determining — in a scientific manner — what works best and for whom, whether that requires ten different offers or hundreds.

3: Advanced Machine Learning

The third and final capability of our contextual marketing solution is advanced machine learning that automatically drives ongoing campaign optimization.

Because we have this longitudinal representation of each customer, we can apply machine learning techniques to continually discover patterns in the customer groups that exhibit similar behavior and determine the best way to influence desired behaviors.  This allows carriers to move beyond the traditional event-triggered approach to an approach where the right offer is delivered in the right context -- based on an ever-changing understanding of an individual customer’s behavioral profile and associated needs.

These advanced techniques also enable us to detect and act on the correlation among multiple subscribers.  By uncovering social graphs and then using the 80:20 rule, we can actually target a small number of individuals to influence their much wider social group.

Revenue Lift -- Recharge Stimulation

So how do we apply this contextual marketing approach to the operator’s customer base?  Let’s refer to the chart below which represents the revenue lift associated with recharge stimulation marketing activity.

Sample Revenue Lift

The entire customer base is represented by the pie which totals 100%.  And the chart shows the various campaigns being applied.  For example, the Future Reward Strategy 1campaign that we devised is applied to 12.7% of the customer base.  And when we target that group -- with personalized, one-to-one treatments aligned to this specific campaign strategy -- we achieve a 13.5% revenue lift, which is considerably better than the 1.5% or less revenue lift achieved by the operator’s standard campaigns.

Over time, the goal is to identify offers that work across the entire base of customers, but during initial implementation, it’s helpful to demonstrate the transformational power of our contextual marketing approach by using control groups.  This enables us to measure our progress and show -- by the numbers -- how the results of our approach stack up to the carrier’s ‘business as usual’ marketing activity.

What is the Awaken Strategy all about?

Well, with prepaid customers, it’s common that inbound calling or messaging to a device is free and so it can be quite prevalent that customers purchase a very low cost SIM and once they activate their phone, they may never recharge.

So, the Awaken Strategy is actually designed to generate the customer’s first recharge and then stimulate their first outbound usage and activity with the objective of generating some revenue for the carrier.

The interesting thing about this strategy is that once you get someone to move from zero balance to carrying even a small balance, they tend to not churn as quickly because they have some unused value in their account.

Does your approach employ demographic data?

Surprisingly, no.  A lot of companies focus on identifying, say the 20-year old female demographic.  But we don‘t care about the 20-year-old female per se.  Our segmentation is based on customer behavior alone, so it may turn out that the 20-year-old female and the 70-year old grandmother both text their families all the time and share a similar profile.  And you would treat them from a marketing promotions perspective very similarly as opposed to assuming that the grandmother is maybe a low tech user because that fits the average senior user profile.

That’s the core of our analytics philosophy: it’s behavior that matters.  You can learn an awful lot by just looking at a customer’s inbound and outbound data usage for example.

Let’s talk a bit about Globys.  You’ve explained your analytic approach.  So how do you go to market?  What differentiates Globys there?

Well one thing that’s been very successful for us is offering customers a pay for performance model.  The majority of our clients work with us on a complete revenue share model.  It’s all about increasing their revenue lift, growing their active base, and/or reducing churn.  You succeed and we succeed.

For years, carriers have been hearing the great stories about the benefits of large data warehouses.  But very little of that data has actually made it down to the marketing departments at carriers in a way that is actionable.  So we’re capitalizing on the fact that carriers are skeptical about making large upfront investments in analytics platforms — proving to them the value of our solutions to then foster long-term, strategic partnerships.

We all know that prepaid is a big hit in countries like Brazil, Italy, India and Southeast Asia, but Apple’s iPhone kind of moved the needle towards wider postpaid adoption in developed countries especially.  Do you see that trend changing?

We are beginning to see some very interesting things -- and the postpaid versus prepaid market debate varies greatly depending on where you are in the world.  Mobile consumer demands are extremely diverse as you move from country to country or even region to region and one thing that we’ve learned is the importance of understanding the cultural nuisances which impact the perception of value among customers.  Trends such as prepaid smartphones and soft-SIM cards are very appealing for some consumers -- especially as the complexity and cost around data services continues to mount.  Although postpaid may come with a lower priced phone, consumers are realizing the value -- and freedom of choice — in not being tied to a carrier for an extended time.

Look at T-Mobile’s hybrid plan which is essentially 4G unlimited.  We can now have unlimited voice and data with prepaid and just go month to month.  Now the value is controlled based upon throttling the 4G limit -- how much data the user gets to transfer at 4G speeds.  You can bet that type of plan is cannibalizing to T-Mobile’s postpaid base but I think we are going to see more of these prepaid hybrid plans as carriers search for ways to expand their market share in unconventional ways.

While my evidence is anecdotal, among my circle of friends and family there’s a large base who are using prepaid with iPhones and such.  It is quite surprising for me and it is amazing how much they are saving.

Wow, if the market moves your way, your focus on prepaid could put you in a great position.  Good luck on that front.  And thanks for a very nice briefing.

Copyright 2013 Black Swan Telecom Journal

Derek Edwards

Derek Edwards

Derek Edwards is the Co-Founder and CEO of Globys.  Derek has spent the past 16 years building businesses based on data and analytics technology and as a founder, corporate executive, investor, industry leader and entrepreneur.  In 1996, Derek co-founded CallVision and as President and CEO successfully grew the business until he sold it to VeriSign, Inc. in 2005.  Derek continued to lead the business as a Vice President at VeriSign until 2008 when he led the spinout of Globys from VeriSign.  Derek is based in Seattle, Washington and graduated from the University of Washington, School of Business.   Contact Derek via

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