Potential: Revealed

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

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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 It is valuable but not rich and often out of date.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

Mobile Payments: Informative Hype

News and hype about all the things we can – and are supposed to one day be able to – do with our mobile phones is itself an industry. As with most emerging technologies with great potential this is a natural phemonema. Recently I saw this infographic published and found it to be both well done and informative (if not in and of itself also adding to the hype it hopes to cut through). I thought I’d share it here also and invite commments.

Personally I think the approach by Starbucks has the most promise in terms of generating real evidence about what level of interest and usage might exist with a mobile payments solution. Since it is Starbucks-only it is simple and not as subject to complex technology and adoption issues (e.g., point of sale technology updated or replaced to enable mobile payment acceptance, training and customer service issues of high-turnover retail sales personnel) that plague the other types below.

And it is not an insignificant fact that the ONLY mobile payment type that has any traction  today is the one used to buy relatively low importance, low priced things like ringtones and games. And outside the U.S., where supposedly adoption and usage has dwarfed the U.S., the by-far leading uses are parking and other incidental transportation purchases. This after a decade of hype that mobile payments were going to take over all manner of payments across the globe.  Seems like great potential is still yet to be fully and clearly revealed. Stay tuned (and wary).

The most important mobile payment infographic. Ever.

The most important mobile payment infographic. Ever.
Compliments of MobilePaymentsToday.com

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.