Potential: Revealed

Strategic Thinking, Innovative Ideas, Growth Marketing, and Revealing of Potential

Archive for analytics

Big Data, Big Results

Recently a client of mine enthusiastically unveiled some fresh results from cross-sell marketing programs they were powering for a network of several dozen financial institutions (FIs).

Their enthusiasm was warranted: the results showed significantly superior improvements in impression-to-purchase ratios over traditional methods. The improvement in fact was over 3 times better!

Big Data Marketing Results Are Better

This is an excellent example of the implementation of Big Data in a direct marketing application.

Traditional methods that an FI uses for targeting (when they use targeting at all;  but that’s another story for another time) usually involve tabulating customer account data (e.g., who has a mortgage but doesn’t have a home equity line of credit?) and purchasing third party data for appending (e.g., demographic data based on zip code of the customer).

I like to call this “Who You Are” data. It is valuable but not rich and often out of date. It is often full of invalid indicators because all you can tell by looking at your account data for a customer is whether they have a home equity account with you … it doesn’t tell you anything about whether they have an account with another FI.

What’s needed is Big Data which is data that comes from many sources, is rich and voluminous (e.g., heavily sourced from transaction systems such as debit and credit card processing, bill payments, money transfers, ACH debits and credits, loan processing, etc.), and is handled by an analytic platform that can make sense of it and deliver it to a point of customer interaction or business decisioning -and if needed, in real time.

With Big Data you can add two key dimensions which I call “How We Behave” (which is a predictor of future behavior) and “What We Can Do” (which parses from the data what we can do based on our financial position and the trends in it). With these three dimensions a marketer trying to effeciently target customers with the right cross-sell offer has the insights needed to deliver superior results.

It is pretty cool! I’ll look forward to sharing more specific and detailed results when they are released soon.

 

 

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From Insight To Action In Under A Minute

One of the toughest challenges most businesses, including and perhaps especially the many small financial institutions in the U.S., face is having all three key ingredients for being a successful marketer:

1. strategy and plans to use data to target their customers with relevant offers (to get from talking about it to doing it)

2. technology to create the targeting analytics and deliver the offers (to be able to make sense of the data and take action)

3. investment in skills and resources to sustain marketing efforts (direct marketing is largely a numbers game – you have to get beyond the one-time and piecemeal marketing that is highly ineffective)

I’ve certainly seen this with clients of mine and in listening to the experts in the area of direct and digital marketing, and data analytics.

At FinovateFall 2012 this week there continues to be an emerging set of companies that are providing ways for financial institutions to breakout – particularly with the first two ingredients. But I believe there is just one company, Segmint, that is also tackling #3 – by taking the required skills and resources and putting them into a box – or more accurately behind OneButton. Making it ultra easy to take action on what the data is telling you and reach the right customer at the right time with the right offer, in real-time wherever a customer might be: online, mobile or social.

So easy that targeted FI marketing campaign can be selected and launched in less than a minute.

All the work is done for the marketer, as long as they have #1, #2 and especially #3 are taken care of automatically by Segmint’s platform and solution.

See the post on Finovate’s blog from the Fall 2012 conference this week. I’ll also update you with a post in a week or so with the video Finovate will make available of the presentation by Rob and Nate.

Let me know what you think too!

 

 

Big Data Drives ‘Loyalty Trifecta’ for Banks

A good panel at this week’s Payments Connect 2012, including a client of mine, Rob Heiser, CEO of Segmint.

Check out the transcript of the panel discussion which is very informative, here.

Let me know what you think too!

Randy

Payments Evolution Continues

Sorry for the delays between posts. Much has been going on for me. Recently I was working with a client on an assessment for where payments, especially online bill payment offered particularly by financial institutions. I turned part of it into a white and will share that here. Thanks for giving it a read and providing feedback!

The Digital Transformation – The Evolution Continues In Electronic Bill Payment

The digital transformation is changing everything – how we obtain and consume information, how we interact with one another and how we conduct business. The digital transformation has been evolving for several decades and it is easy to lose track of its impact in various parts of our personal lives and business dealings. Its impact is so pervasive that it requires stepping back and focusing to see the particular impact in any one area since pervasive does not equate to uniform. For example, in the area of bill payments the impact of the digital transformation has been profound but is still evolving and hardly complete.

One way to see both the impact and the evolution is to focus on the payments value chain. As a simple framework, think of it as a chain starting with payors (Originators), ending with payees (Receivers) and in the digital realm, passing through and facilitated by intermediaries such as Payment Networks. This payments value chain is changing at each stage. Players in this value chain, particularly financial institutions and payment network firms, will need to understand the current situation, clearly understand the developing changes across the value chain, and make wise choices in order to be relevant, competitive and win as the digital transformation plays out.

The Current Situation

The digital transformation is changing how consumers listen to music, how they shop, how they plan vacations, how they manage their money and how they make payments. As evidence, according to a recent Fiserv consumer research study, Internet Banking (36%) handily tops Branches (25%), ATMs (15%) and all other (24%) interaction channels as the preferred channel for U.S. financial services customers. The widespread adoption of mobile phones is further accelerating change. An emerging factor is the rapid growth in use of Smartphones. In 2010 there were nearly 60 million adults in the U.S. with a Smartphone and by 2014 this is expected to more than double to 129 million according to a recent Javelin Research study. Smartphones are a possible game changing device with their ability to provide an intelligent but easy to use user experience, always-on access and capabilities that facilitate several options for making payments while on the go. Altogether the digital transformation places increasing demands on financial institutions, businesses and their financial technology partners to both meet the expectations of consumers and to take advantage of the possibilities these changes may offer.

As expected these digital channels and devices are transforming payment options and choices. Overall they have driven the move from paper (and offline) models to electronic and online. Over the past two decades bills paid with checks, stamps and envelopes have been replaced by electronic bill payments made directly at biller sites or at consolidated sites such as a financial institution. Cash at the point of sale has been replaced by debit and credit cards and all sorts of pre-paid cards. Cash and checks used to pay friends, relatives and the babysitter is being replaced by electronic person-to-person payments. All of these developments, though, continue to march ahead, going mobile and making them ever more convenient and available on-demand. Correspondingly this further raises the bar on the level of intelligence, service reliability and security required. Clearly the move to electronic payments continues but is hardly complete. A recent study by a payments research and consulting firm estimated that from 2009 to 2014 over 11 billion paper (cash and checks) payments will move to electronic payments. Where those payments will go and who will benefit depends upon choices made by the players in the payments value chain.

Opportunities in the Payments Value Chain

Fleshing out the reference earlier to the payments value chain, a simple, traditional depiction begins with Originators, such as consumers, businesses and governments with an obligation to pay some other party. It also includes Receivers which are the “other party” and can also be consumers, businesses and government. The payments made by Originators are facilitated in some way by a Payment Network which connects the Originators and Receivers, moves information and money to handle settlement, and provides various levels of required security. Specifically in the electronic bill payment arena, value was initially created by giving Originators – largely consumers — more convenient options versus the routine of writing checks, purchasing a stamp and putting an envelope in the mail. The value to Receivers – also called Billers – came mainly from more timely payments, and cost savings due to reduced risk and more efficient processing with electronically received payments. The Payment Networks in the middle have been the drivers of these benefits enjoyed at the ends of the value chain. Through reach and scale they have offered low cost yet fast, reliable and highly secure payment services. The value provided however is limited and as electronic bill payment has emerged into the mainstream it has become commodity-like in growth and margins. 

Relative to potential value, the consumers who have adopted electronic bill payment have received a disproportionate benefit; saving substantial time, money and increasing their security. Despite this, still less than 50% of online banking users – those showing a clear interest in dealing with their bank electronically – use their financial institution’s online bill payment service. Over 60% of the electronic bills that could be presented to those who use online bill payment go unsubscribed and are never viewed each month.

The Billers have yet to receive, at least in significant proportion, a number of potential benefits such as richer and more standard information flows and as just noted with the lagging adoption of bill presentment, the elimination of paper from billing side of the payment cycle. The value potential, however, is real and substantial. A study done for a very large cable company identified over $100 million in annual savings from the application of intelligence available from the mining of bill payment data (e.g., understanding payment channel and type choices and precisely guiding subscribers’ choices), and an equally sizable amount of savings from elimination of monthly paper bills.

Financial institutions, which have been the allies of the Payment Networks have arguably benefited more so than the Billers but have also borne most of the cost of providing the service. Compounding the challenge, the benefits the Financial Institutions enjoy are indirect and soft since bill payment is typically provided for free and generates no direct revenue.

While the Biller benefits are lacking and the Financial Institutions find the benefits to be soft, the value to consumers is on a new, upward trend – driven largely by developments such as those described earlier with the widespread adoption of Smartphones. In turn these enhanced services are likely to further raise the costs of the Billers and the Financial Institutions and their Payment Network allies as they scramble to keep up with consumer demands, making the precise ROI even more elusive.

Winning Strategies

Returning then to the depiction of the payments value chain as a three part process, consumer and billers at the two ends and the payment network and financial institutions in the middle, going forward the opportunity for new value creation calls for a focus away from the middle and outward toward the two ends where emerging and unmet needs clearly exist. For consumers as the Originators there are still unmet needs in making basic electronic bill payment compelling for mainstream households. While a majority of households now pay at least one bill online at either the Biller or through their financial institution it remains a minority that have made the commitment to paying most or all of their bills this way. As mentioned earlier, a far larger number of electronic bills could be presented to online customers which would eliminate paper and further enhance security (with reduced identity theft).

The winners in the race to capture these potential consumer users and their transactions will recognize that existing choices must be expanded both in terms of where online bills can be received and paid, particularly on Smartphone devices and generally everything mobile, and choices about how the payment is made such as expanding to include as many payment options as possible and moving to even faster payment speeds. There are information-based opportunities as well, with examples including enhanced financial management tools and alerts, which can help consumers better manage and increase control over their household cash flow. The stepped up value proposition may in turn provide a firmer foundation for charging a fee for online bill payment and creating a more intense electronic relationship. A relationship which offers richer data that can be tapped for deeper insights financial institutions can leverage to improve cross-sell and enhance customer profitability.

On the Biller side, unlocking latent consumer demand for electronic bills is a clear opportunity. In addition, Billers today face a complex array of payment streams coming to them which are neither uniform in their quality nor the quantity and value of the information that accompanies them. Billing and accounts receivable systems while obscure are the life blood of consumer oriented companies such as energy and telecom, cable, insurance and financial services and any improvements made by payment networks that reduce errors and speed revenue collection cycles will be eagerly received. The winners will recognize this opportunity and work to streamline their information flows to Billers and provide easier ways to integrate and facilitate straight-through processing. They will also enrich the data that is transmitted including pre-processed analytics that Billers can act upon directly or integrate into their own data analytics engines and customer marketing systems for use in up-sell and cross-sell, risk and fraud management, and improving customer loyalty.

Together these types of improvements offer the promise of a more widely adopted and active value chain where richer payment and information flows move more quickly, nearing real time. In turn, latent and highly valuable demand at the ends of the payment value chain can be unlocked and monetized by those that compete in the race to provide the winning value proposition.

Uplift Marketing

Recently we’ve been working on a simple framework for data-driven marketing (i.e., integrated, cross-channel, analytics-based, closed-loop):

Accumulate: • Accumulate data (multiple sources) • Integrate • Cleanse • Aggregate • Store

Synthesize: • Normalize • Match • Common Data Model • Single Customer View

Crunch: •Segment • Score • Peer Compare • Recommendations • Alerts

Publish & Execute: • Publish analytic outputs • Integrate to execution apps

Feedback, Improve & Repeat

An example of results has been impressive. ROI is a mere few months based on what we’ve seen.

More work to do and much evangelizing to propagate and get everybody doing it. But as suspected when we started, there is much potential that has been hidden and shows great promise of being revealed and realized.

Predictive Analytics: How it Works (#2)

In the first post about predictive analtyics we learned about the essential building block of predictive analytics: the predictor. This is a value calculated for each entity (say, a customer) who’s actions or behaviors are to be predicted – for instance the recency, in months, since a customer’s last purchase.

Prediction power is enhanced if you use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. In this post I’ll discuss how you can find the “best” predictive model. I put “best” in quotes because from a practical standpoint, unless you assume unlimited time and resources you may be best off finding a model that improves your results (e.g., reduction in customer churn) over previous experience. Today there is available very powerful modeling software and well-trained and talented statisticians, but the number of variables to consider in any predictive model (across demographics, transactions, behaviors) can be extremely large making determination of the “best” model cost prohibitive.

Fortunately, taking an incremental, continuous improvement approach can yield solid results for most any business and the promise that results will improve over time. A common tool is to develop a yield curve. For example, plotting the results of a predictive model for churn with amount of churn on the Y axis and percentage of customers contacted in a retention campaign on the X axis will show a curve the decreases to a point — i.e., up to a certain percentage of a universe of customers contacted, attrition rates will fall — but will bottom out and then move upward. Meaning that not all customers will respond to a retention campaign and you are best off contacting only those predicted to respond well. After that point, you are best leaving the balance of the universe of customers alone – either because they are not likely to churn anyway or because the predictive models say campaigns to retain them will be unsuccessful (and possibly other methods are needed – along with models that might predict how these approaches can be equally tuned to expend effort on just those predicted to be successful).

Now, although the model does not work perfectly, the socring and ranking of customers according to their likelihood to be retained provides clear guidance on how to invest in retention programs to yield the best results. It will prevent campaigns to retain customers that are too aggressive (trying to retain those that are not likely to respond positively, or wasting effort on those that are likely to stay).

There is a great deal more to predictive analytics than I’ve covered in the past two posts. But I hope one message is clear: you can gain practical improvements in marketing results or other customer touch points through the use of analytics that don’t need to be complex (at least to start) nor perfect. Commitment, willingness to experiment and continuous improvement are what’s really required.

Thanks for reading and I’ll look forward to comments.

Predictive Analytics: How it Works (1)

Sorry for the delay between posts. For past month or so we’ve been working on a very interesting project dealing with product ideas based on financial transaction data and powered by predictive analytics. While we are working to develop some early prototypes we have also been talking about challenges that need to be addressed when taking such products to market.

One issue over and over has been risk of market launch failure due to lack understanding of how analytics work (often lacking even rudimentary let alone deep understanding). A majority of key stakeholders – potential customers and internal business unit and functional area team – have heard of and are relatively convinced of the potential for analytics to optimize decision making. Whether that be to improve marketing effectiveness or precision of sales forecasts. Yet the basis for belief is often what they’ve read about or been led to believe by others. Analytics are not perfect and an important approach to achieving long term benefits from analytics is experimentation, challenging current results, and continual tuning of analytical models. We can foresee a gap forming where confidence in what is being developed and sold to clients falters due to lack of basic understanding of predictive analytics.

So, I thought I’d put together a brief series of posts (sort of like I did on “Practical Strategy” a little while ago) to explain predictive analytics.

The essential building block of predictive analytics is the predictor. It is a value calculated for each entity to be predicted – for instance the recency, in months, since a customer’s last purchase. Typically, the higher the calculated recency the more recent was the last purchase. As you’d expect, a good predictor is usually a reliable variable that consistently improves accuracy of some decision or action. Such as “customers with a high recency value typically have a higher response rate to marketing programs.”

There are other predictors that might work better with certain actions or decisions. For example, if you have an online subscription-based service, customers who spend less time logged on are less likely to renew annually. Tuning attrition or churn reduction campaigns by targeting customers who have low usage predictor values can boost effectiveness.

To make prediction even more precise you can use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. Some simple models that might predict likelihood of a customer to renew their subscription:

– Linear – adding predictors together. For example: Recency + Household Income.

– Behavioral Rules – joining two or more behaviors with rules defining predictions of another behavior. For example: Usage (high or low) and Responded to Offer in Past 3 Months.

The best predictors will be predictive models that combine multiple aspects of a customer (e.g., demographics) and their behavior. A predictive model characteristically must be deeper and more complex than the above examples – uniting sometimes dozens of predictors. More on determining the best predictive model and harnessing rich sources of data to create powerfully predictive analytics in the next post. Thanks for reading and let me know if you have comments or can share your own experiences.