FULL ENJOY Call Girls In Mahipalpur Delhi Contact Us 8377877756
Modeling & Scoring A Match Made in Marketing Heaven
1.
2. Rick Erwin President of Data & Analytics, Experian Marketing Services Charlie Swift Vice President of Database Strategy & Marketing, Hearst Magazines Modeling & Scoring: A Match Made in Marketing Heaven
3. Today’s Topics Setting The Stage 1 A Proactive Approach 2 Under The Hood 3 Lessons Learned 4 Questions & Answers 5
23. Decision Tree: Upsells Thank You for your order Thank You for your order Name/Address Validated No Yes Initial Order Placed No Yes >80 “ Pay Now” for faster delivery Thank You for your order eMail Thank You w/ Pay now option Regular #copies served b/f payment Process Order to CDS Regular billing series Experian “Pay % Score Call No Thank You for your payment Upsells Process Order to CDS Thank You for your payment Upsells Process Order to CDS “ Pay Now” for $x off vary x by score 60+ 20+ Yes No eMail Confirmation w/ Pay Now option eMail Confirmation w/ must confirm note eMail Confirmation w/ must confirm note No Yes Yes Process Order to CDS Upsells Thank You for your payment Yes >20 Trial Copy Only Shipped Regular Billing Series 60+ 20+ 60+ 20+ >20 No copies shipped b/f payment eBill Series Only 1st email: ship on pymt Regular Billing Process Trial Copy only shipped Regular Billing Series Regular #copies served b/f payment No copies shipped b/f payment eBill Series Only 1st email: ship on pymt eBill Series Only 1st email: ship on pymt No copies shipped b/f payment No 80+ >20 Pay % Score? Bill Me Order? Good / Bad Pay Match? Paid Order? Paid Order? Confirmed Order? Pay % Score? Process Order to CDS with conditions met Paid Order?
24. Executed Simply Customer Real Time Good Marginal Bad Audience IQ Decision Tree: Predicted Pay Rate Bill Me Option Available Credit Card Now or Phone Confirm Option Experian Payment Model Score Hearst Web Site Customer Sub Order Pay Now Incentive Offer
35. OFFER Address Algorithm Model Score Propensity = LOW Call 1-800 to complete the trial offer 300 Milliseconds Sign-up for FREE Magazine! Name: Address: Bill Me Later!
Rick Erwin Introduction Our first speaker will be Rick Erwin, President of Data & Analytics here at Experian Marketing Services. Rick oversees the strategic direction and growth of Experian's data businesses. Rick has over 20 years of experience in the direct marketing industry. Prior to joining Experian, Rick was Vice President & General Manager at RR Donnelley. Rick serves on the board of directors of the Direct Marketing Association. Charlie Swift Introduction Our second speaker today will be Charlie Swift, Vice President of Database Strategy & Marketing at Hearst Magazines. Charlie is responsible for all marketing database related activities, marketing/subscription targeting and analytics, customer service and fulfillment, list management and execution. Previously, Swift served as VP, Consumer Marketing for LexisNexis, where he oversaw consumer, direct marketing and research efforts. Swift has an MBA from Columbia Business School and BSE from Princeton University.
Let’s start by reviewing today’s agenda: First, I’ll start things off by Setting the Stage I’ll then turn it over to Charlie to talk to us about the Proactive Approach they’re taking at Hearst For the fourth section both Charlie and I will be sharing Lessons Learned And then will close down today’s session with Q&A
Setting the stage….
Sir Isaac Newton famously said that “the only way he was able to see further than anyone else, was because he was standing on the shoulders of giants.” Today we feel just like this. This exciting new method of delivering the best of direct marketing knowledge with the speed of technology has enabled us to truly “stand on the shoulders of giants” and see farther opportunity than ever before...
The modern internet user is very reactive. There is no commitment to their browsing, things are only a “click” away. The Back Button is the 3rd most used feature on the internet How do we reach people more effectively in this medium
Because internet users are less involved, less impassioned about their activities, there is less commitment. A visitor presents very little discernible actions that show their commitment level. It is easy to click buttons and find yourself in an offer. It is just as easy to click “BACK” out.
This isn’t like the offline offers that people receive through the mail or actions of applying a sticker to a card to get a subscription - these require an affirmative purchase decision with an action. The online click does not carry this same weight. People are reactive, they explore, react, click, go back, click again, explore, find something that hits their interest for a moment, but they found it on their terms, at their speed… And there is still no proactive approach to provide a clear message or physical affirmation which carries enough significant weight to that individual user.
So Digital Marketing has evolved to become much more intelligent, much more targeted in order to reach the right person. Contextually, we know a person may be interested in a sports ad, while visiting ESPN. If they are browsing football scores, we can serve up an ad for their local team. Behaviorally, if they purchase a team jersey, we can now advertise more to that person, we can show them ads based on what they’ve browsed or added to their cart
But still, traditional digital marketing, they don’t know who they are, they just profile a lot of look a likes. It’s evolved, certainly, and there are models built based on behavior, based on site visit profiles, but ultimately, there is a lot of information missing. But what if you could combine the shallow layers of information about customers with the depth of knowledge and modeling from Direct Marketing?
So here it is. How do we combine the best of the decades of direct marketing know-how with the immediacy of the internet environment and create something new? This is the question posed by Hearst to Experian. And this is where we stand on the shoulders of those giants of direct marketing:
The direct marketing model is built solidly on directing the right message to the right person at the right time. We all know this and it is our mantra. It focuses on the Marketers Delivering the right offer to the Consumer. [click] However, when it comes to Digital Marketing, the consumer is in charge of the transaction. It is on their terms, as they search for information click around, and ultimately find information on their terms.
This is where Hearst and Experian came together to develop a method of delivering the best of direct marketing knowledge with the speed of the Internet. It is now a cooperative approach where marketers can customize the message to the internet user, and become proactive.
Problem statement for me is very simple Internet is a new way of doing business, it’s a new place we go, it’s how we interact. Rick eluded to this earlier, but the problem with the internet is used don’t have to really make the commitment to engage. It’s easy, it’s a click. You haven’t really said I want this product or I’ve raised my hand. People have been condition to say, you know what, you can always hit the X at the top of your screen an abandon at any moment. So as users we say we’ll go a little further because although I’ve hit the button, I haven’t really committed. So commitment is lower. As a result, you get these lower pay rates if you look at just the top of the funnel. What’s happening is that we’re reacting and as soon as we start to see this beginning commitment we’re starting to service them like they had responded to a direct mail offer. And we put all this cost behind trying to get them to pay but they really, still have never committed. It’s not like trying to get someone to pay who has committed, you’re trying to get someone to pay who didn’t really say yes in the first place. We’re starting to send issues to them – we’re engaging them. But you have to think differently. You have to service them in a different way. Because right now we’re generating huge servicing costs with these people. So this is the problem.
Because the commitment is low, how do we either improve the pay rate – which is really hard to do – or more importantly, lower the servicing costs. And that’s the question. Can we lower the servicing costs, without losing those few people who have really committed?
So the answer is, in order to solve this we’ve got to proactively change how we treat customers based on who they are and when they’re interacting with us. The question for us, of course, is how do you do that?
There are really 4 steps that we need to take. One – we had to create these segments of who they are Two – then we had to set a decision tree up of how do we want to react, how do we want to address each of them when they came to us? But just because you understand a process and know who they are you still have to create offers to make it all work. Once you’ve tested all those offers, then you can go back in and look at the economics and say how can I say adjust all this to make the economics work for me?
Step 1 is the ID step. What we did was we partnered with Experian and said what information do we know at the moment. And can we proactively build a model right now that says just based on the information that we have when we’re in that reactive moment what can we do with that information to make a decision about the likelihood that they’re going to pay me? We built, as you can see on the slide, I fairly strong model that differentiates at a pretty good level. From that, we can begin to chop that model up into different segments based on their likelihood to pay.
Fundamentally, we get the really bad people, the marginal people, and the good people. Really Bad: The ones we know are really bad, that we don’t want, but we want to give them some opportunity so in case someone really wants the product, in case we’re wrong, we don’t ever want to say no. And the issue is we can’t necessarily just force you to a credit card. But what we can do is say it’s a 2-step process. We can incentive you more toward credit card.
Marginally Bad: That’s the next step up. People who are marginal we start investing in moving you to credit card. That’s why I’m trying to say lost order on the 2 step. Increase in credit card purchase on the middle step.
Good People: And then the good people, they’re going to pay me so I don’t necessarily need to give them the credit card incentive. Back to they’re going to pay me the first time out typically and the costs so I don’t want to give the $2 credit card incentive away right up front because they’ll just take it
I talked about, what do we really want to do with these people? We started by laying out this really complex strategy, but it’s easy to get caught up in the academics of this strategy. We said as great as this complex strategy is we can really dumb it down into a really simple strategy that makes it easy to go to market right now.
So think big, think complex, but start simple, start small and act now. That’s the strategy here. Let’s keep the decision to the very basics. Let’s go back to just break those 3 segments up and let’s do something different. For the good people, we’re going to bill them as they are. For the marginal people, let’s test some offer incentives. And for the bad people, we can treat them differently so we can avoid that servicing cost.
The key to all this though, is that it only works if you’re acting in real-time and doing offer presentations. You’ve got to differentiate them right then and there while you have them.
The other thought is that you’ve got to be proactive…you’ve got to push out to them. They are not going to tell you what they are, you have to know what they are and you’ve got to react to them in a proactive way.
All that being said, we still have to find offers that work. Even though you know they’re an unlikely payer if you don’t have something that changes their behavior you’ve got nothing.
That’s what we did, we started testing offers. Two examples of the offers we started testing and that began to work where… For the marginal people, if you give them just $3 off by paying now maybe we can move their ability to pay just a little bit more. And for the people who weren’t going to pay, we actually tried a 2 step process; where it’s either pay now or call us. With that one we started seeing some great results in it. By telling them to call us we created this barrier to them and what we’re trying to do is get that commitment. If we don’t see that commitment, then we don’t see that servicing cost. In the month of July only 4 people called for Cosmo – saved servicing costs right there!
Ultimately, when we start talking about the economic model. When we found offers that started to work, started to differentiate, what we said from an economics perspective is that you’ve got to screen everyone. You’ve got to evaluate everybody and the savings are only on those people who are bad. The challenge for us was to look at our titles and say this only economically makes sense if you actually have bad people. If you don’t have bad people, then there’s nothing to be saved and you’re spending a lot of money segmenting to say these are good people – do nothing. For us, what we’ve done is gone through all our titles and said let’s work with Experian in a batch off-line process to identify which titles have a high propensity to have non-payers in them. Those are the titles we’re going to funnel through this real-time process, and then we won’t funnel the good people. This is all about that trade-off in scale of you’ve got to have enough bad people to pay for the whole process.
So what was it worth to us? What we found was that on the net pay, we achieved exactly what we were looking for. When we did this process, we didn’t impact net pay – we may have actually slightly increased net pay if anything. But we ripped 20-30% of the servicing costs out, and that’s all driven by the percentage of people that were in that bad bucket. We’re essentially not servicing them anymore and as a result our net profits are up 9 to 10%.
Ok, so now you all are probably wondering HOW the data, technology and modeling all came together to make this happen. In this next section I’m going to talk about what’s under the hood.
What I’m really going to describe here is a real-time workflow conversation between Hearst and Experian that enables real-time marketing. Going forward, Hearst is going to be scoring roughly half of all of their sweepstakes sites with this technology.
What that real-time workflow conversation is doing is seeing a visitor on the Hearst site identifying who that person is validating that that person exists at that address enhancing that visitor’s identity with the variables required to predict their propensity to pay scoring in real-time that segment of paying propensity that that consumer belongs in, and based on that score, rendering a decision .
What does that look like specifically? When someone clicks on a sweepstakes offer that leads the prospect to an offer page to sign-up and get a free magazine If the prospect clicks the bill me later box, then their name, address, and bill me later request comes to Experian We first standardize the address and then run that name and address through our algorithm -- We run the model score in real-time We return the necessary data attributes to power the model score and return the score. This whole process takes less than a second – 300 milli-seconds Hearst has created 20 separate tiers based on the model scores Experian returns in real time. The Hearst process is to take those tiers and develop marketing treatment offers around them, specifically targeting those most likely to NOT pay.
Technology as the Connector – it’s about execution. The technology needs to facilitate the handshakes between systems and data sets The initial Hearst need was actually quite simple. They weren’t looking for a system out of the gate that could plug in 50 different data sources to help optimize a true multi-variant testing campaign or optimization program for his subscriber. We’ve solved this problem by building this connector, this handshake. The system is flexible , scalable enough where we can down the road bring in and test new data sources, new acquisition offers and price points, creative, landing pages, fulfillment treatments We developed a very basic connection point to solve a simple yet very impactful business problem for the client but we also have the capability in the technology and the system to run much more sophisticated marketing problems
We all know that every day we combine data and technology to make better decisions. But the real idea here is the notion of creating an addressable audience online and then treating that addressable audience to the appropriate offer at the point of interaction. It doesn’t have to be this example, it can be any such example in your business It could be a retail example like… Audience IQ is used to solve many client business problems The Hearst example is just one way to leverage the Audience IQ platform Direct Brands case study Problem – a membership club marketer wanted to combat the significant number of customers who were signing up for its clubs, receiving the trial offer and premium, but then refusing to pay past the trial period. The marketer needed a way to identify the quality and intent of each customer at the point of acquisition. Solution – In real time, Experian’s Audience IQ validated the lead contact information and scored the customer’s sincerity, capability, and intent, identifying potential customers with a high probability of being fraudulent before the trial offer was made. Results – the client was able to alter the marketing message and lead handling for the customer segment, reducing bad debt writeoffs 50% and saving millions of dollars in product, fulfillment, and service costs. Credit Card Marketer Problem – a global financial services firm wanted to increase credit card member acquisition by improving the product relevance and application rate of anonymous visitors to its credit card website. The problem was that the client had over 30 credit cards with no insight into incoming site visitors to be able to segment and target effectively. Solution - Experian first overlaid the client’s current cardmember file with Experian geographic, demographic, summarized credit, and lifestyle data, and mapped it to the client’s current product portfolio distribution. Experian then built a predictive model and deployed the algorithm on its Audience IQ real time IP-geo application. As anonymous visitors landed on the website, Audience IQ utilized the IP address to instantly segment visitors into the right product group and optimize the card offer and creative. Results – Because Experian was able to help the client segment their anonymous visitors and target their products more effectively in real time, the clickthrough rate increased 30% from the main credit card homepage to the application start page, and increased 25% through to application submission.
General (Rick): Earlier we talked about standing on the shoulders of giants. The giants we’re talking about here are the direct marketing principles that you’ve been successful with your whole career. Are you thinking about your proactive and reactive marketing strategies online using all of what you know about offline direct marketing? The notion is bring everything you’re doing in your current contact strategy and customer and prospect work and bring that online Apply all the same principles… bring offline to online Specific (Charlie): This idea of commitment is important. In direct marketing you know the stage they’re at in the lifecycle process… where they’re going to be in the process may be slightly different on the digital side because they can go deeper before they make the commitment. Treat them the same way but recognize where the decision points may be slightly different in a digital environment. You still want to do what we’ve done in direct marketing but it’s just this idea of commitment has changed the decision points digitally. Recognize how that consumer may present differently online because of the online experience
General (Rick): How should you be thinking about your addressable audience? In offline marketing it’s easy to identify your addressable audience. It’s a name and address and then automatically a whole bunch of other information becomes available To have an online addressable audience you have to be prepared to deal with any fragmentary form of information connected to an online ID That could be starting with something as simple as an unidentified visitor who presents with cookie information, a cookie ID, all the way to a consumer who engages enough to tell them something about themselves. You need to be prepared to seize upon any level of information along that spectrum and then present your offer or treatment accordingly. Specific (Charlie): If we wanted to be traditional we have name and address, cookie, and e-mail, and all this information that makes me know 100% who they are online for only about 5% of my population . But I can expand this to about half of my population if I start going with cookies, e-mails, previous visit information, etc. I can get to 90% of the population if I go broad and tap into the outside world of cookies and bring in all this information. Addressable Audience The question most marketers have, which is what Hearst had, was about addressable audience at the time of interaction. The question is…how much do I know about the person and can I use what I know about the person as that moment to inform my decision? It doesn’t have to be the traditional – I need to know everything about them? The Hearst example with the paying score, we don’t need to know everything about them. You’re limited with cookies because you know very little. What you’re trying to do is take that step from addressable audience at a cookie level to saying as soon as I know more information can I bring more information to the moment of decision? Right now our addressable audience is all around I’m being so forward thinking and I’m being proactive but not reactive.
Don’t need to solve for everyone General (Rick): You need to be crystal clear on the business problem you’re solving for so that you can be crystal clear on the programs you’re going to execute We all face these big, hairy problems but you don’t solve big hairy things in one fell swoop, you’ve got to be deliberate and specific about attacking a portion of the problem. Or a portion of your customer or prospect base. Specific (Charlie): For example, in our case, we were very tight about a very specific problem we were having with our online offer uptake. The problem was that marketers were all saying what can you do to improve pay rates. Get these people to pay! The answer is to not spend a lot of money on people that weren’t going to pay You’ve got a customer…keep billing them until they pay – it doesn’t work that way! This just increases my servicing costs. Malcolm Gladwell – we’re moving from puzzle solving to mystery solving. Fascinating concept. In puzzle solving you have a question and you can go get data to solve it and get the answer. In mystery solving all the data in the world isn’t going to tell you the answer. That means you have to test and probe to try and figure it out. With digital, we have the ability to solve mysteries by testing more. Don’t get caught up in trying to solve the puzzle, solve the mystery. Examples: Cuban Missile crisis (puzzle solved). In 9/11 we had all the information in disparate parts, we had the pieces, but we couldn’t solve the mystery. We couldn’t solve the puzzle because there was too much data.
[Rick Slide] Is this you? If you can identify an economic problem specific to dealing with your customers or your prospects online… on your web properties… then this type of solution can solve for your problem. But it requires the discipline of being very clear about what you’re trying to solve for and being very focused on… We’ve shown you A way to start thinking about it earlier in your process, at the more critical moment. The key for Hearst was the Who and the When The When can change now because of the technology So much of the energy around the analytics has been around the Who for so long; for 50 years. We haven’t been able to do when. What we’ve enabled is the ability to differentiate on WHEN. The problem has always been the WHO but are you having that problem of WHEN? Charlie gave an example of the WHEN problem If you are you have to think about the world differently now. Bring the direct marketing approach. Hopefully this presentation has shown you A way, A solution to solving this question.
