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hi I'm Dan I lead the auth optimization team at stripe and today we're gonna talk about driving higher revenue conversion first I want to start with a personal story of when I failed at hacking conversion so many moons ago I was an entrepreneur running my own business and as exciting in the early days you're getting our name out there we had some good early momentum in our business it was this incredibly novel idea for a start-up we were a food delivery platform ok so maybe it wasn't totally novel but we had our own spin on it we were focused on healthy food we were operating in New York at the time and we're working with a large network of local restaurants delivering healthy meals to our customers and it was a project of mine at the time of trying to go land our first corporate account so we're talking to this large law firm in New York about making us their primary delivery platform for all of their employees to use this is going to be an important early sale to help fuel some of our early growth and I was hoping this firm would become a reference customer to goal and more corporate accounts so when the benefits person that we're talking to it's time for her to test the platform and she goes to place her first order as you can see up here she got a decline on that first order so she calls me up she's kind of frustrated she wants to know why she got this error we scramble we get her food but I could tell the tone of the conversation at that point had changed and while we kept talking ultimately she and the firm decided that they couldn't trust a start-up and we lost the sale at a time when we really needed it so this moment of getting a decline it was frustrating for her it caused the loss of trust but as a business owner this was incredibly painful as an entrepreneur as many of you know you pour your blood sweat and tears into trying to build this great new product that doesn't exist and to trying to get new customers onto your website or app for the first time to try their product so you put in all of this hard work and that last mile step of taking purchase intent and turning it into actual red new it sometimes doesn't work and as an entrepreneur it's really painful when it doesn't so now it's stripe I get to sit on the other side of this challenge helping other businesses overcome this in drive revenue conversion so let's just take a step back for a second let's imagine in ideal world if the customer is who they say they are if they have money and they want to buy your product it should just work and here's what that looks like your customer would get to the checkout completion step and they would breeze right on through then your fraud protection systems would perfectly filter out illegitimate customers and let all the good ones through and then the issuing bank on the other side of this transaction they would approve a hundred percent of good transactions you'd be through these three steps in the conversion funnel done revenue in the bank okay here's the complication we don't live in this ideal world so let's take a look at what actually happens okay so imagine you got this great product you've got all these customers lined up who want to buy it you have this big pool of potential revenue just sitting right there all right your customers should check out 26% of u.s. online shoppers abandoned a shopping cart in the last three months alone simply because the checkout process was too long or complicated all right our potential revenue pool it just shrunk now we have to prevent fraud the average cost of fraud for online businesses today is 2% of total revenues and that number is growing more margin out of the funnel okay now we need the issuer to approve the transaction now in the physical world with card-present transactions businesses typically see very high approval rates but when you shift the digital transactions happening online we on average the approval rates that are 10% lower versus card-present so you can see we just went through these three steps of the conversion funnel and our potential revenue pool it just shrunk so you're probably thinking you know at this point in the story this isn't very uplifting so far so let's flip it around let's put some air back in the balloon because where there's loss there's opportunity and what we see is when online businesses invest in this conversion funnel and they really start optimizing the different pieces along the way there's significant revenue upside that can be achieved for large businesses that can mean full percentage points gains in revenue and we sometimes see with startups when they invest in optimizing this conversion funnel they can achieve revenue upside of up to 10% that's huge and not only that it all leads to a better customer experience this conversion funnel this is an area where businesses can make payments a strategic advantage so today in this talk we're gonna dive into each part of the funnel checkout fraud prevention Network acceptance and we're gonna show you what stripe is working on and we're gonna hopefully arm you with some practical ideas that you can take back to your business and put into practice in order to drive higher revenue conversion and ultimately to deliver a better experience to your customers so I actually want to start with a quick organizational hack something that's interesting is when you look at these three steps from the customer standpoint they happen almost simultaneously it's within milliseconds of each other and not only that but the variables they're interdependent if you make a change in one place it will impact what happens in others let me show you so let's just say you've got a very short checkout form right that's great for getting people through the checkout completion process but you've collected less information for your fraud prevention system and less information for the issuer to evaluate the transaction okay flip it around now you've got a much longer checkout form so you're collecting a lot of information bad for checkout good for fraud prevention and Network acceptance okay so between these two choices which is the right answer well as you know there's no one-size-fits-all answer the answer is going to be dependent on each individual business and their business goals but unless you're measuring the outcomes across this whole funnel you won't know what the right answer is okay so let's get back to the organizational point what's interesting is when we talk to businesses we sometimes see the responsibilities across the three steps here in the funnel split between three different teams that part's okay but what can be troubling is that there's not good coordination among teams because without good coordination you can end up with one team that's just locally optimizing for their specific area but not holistically optimizing for what's best for the business and when we talk to the most sophisticated companies out there they really bring close coordination to managing this conversion funnel and what that looks like is having shared metrics and goals across teams it means when they implement changes or they run experiments they're measuring outcomes across the full funnel not just in a particular area and ultimately it means that teams are aligned and trying to optimize the right business outcomes so today we're gonna talk to a company that really embodies this philosophy and spend a lot of time investing and developing a great payment experience for their customers we're gonna be talking to Matthew Neff he's the global head of product at skipped the dishes and just eat Matthew has personally spent a lot of time in helping create a great payment experience for customers on skip the dishes now skip the dishes is Canada's leading online delivery platform they work with over 20 thousand restaurants coast-to-coast across Canada and their technology platform powers millions of orders every month and yes for the next few minutes I get to live vicariously through what it looks like to run a successful food delivery business so let's please welcome Matthew to the stage hey thanks a lot for having me Dan I appreciate it thank you so much for coming in alright so as I learn Matthew as the food delivery business we face a lot of competition so tell me about your role let's skip the dishes and what you've done to really invest in creating great customer experience and a great payment experience yeah so my initial role was leading our customer application team which is responsible for our apps our webpage that we send out and really we have a very simple goal on that team number one we want to make it as easy as possible for a customer to get through the order experience and to find the food they want and number two we want to make it as easy as possible for good customers to pay on our network and when I look at how we apply this sometimes it's valuable to have an example of something where it's not maybe the most effective payment experience that I actually experienced quite recently so I went to a large department store because I wanted to buy a new pair of shoes I went in there I found the shoes I wanted I went to the checkout line to pay for them there were a few people ahead of me and I could see the person at the front of the till slowly folding up the clothes of the person ahead of them they were collecting a bunch of information of tell me what your email address is what is your phone number slowly entering that information and then when the person finally said cashier said how do you want to pay the customer said credit card and the cashier says well would you like to sign up for our credit card and the customer says no I don't want your credit card today and the cashier says well if you sign up for our credit card you'll get 15% off so then the customer says ok I'll sign up for it and they start filling out this paper form meanwhile there's a long lineup of people behind them trying to buy their products and eventually not being a very patient person I just gave up I set the shoes aside and I walked away so in the front row if you're wondering why my shoes look so old and raggedy I tried but when I look at that example it's it's such a shame because they did everything that they needed to in order to have the product that I wanted get me into the store get me as far as the checkout experience and then they just made that step too complicated so they lost me and when I look at that and I apply it to a business like skip the dishes I see that as just a huge shame because the hardest thing in business is to win a new customer and when our restaurant team does all of the work that it takes to sign restaurants up on our platform that customers want to order from or our courier team recruits couriers and brings them out onto the network our tech team builds a product that people want to use our marketing team creates awareness of it and everybody can add their job done correctly and once you get to that that final step or that payment moment we we can blow it all if we put too much in the way of the customer great thank you tell us as skip the dishes has grown from small start up to now a very large successful company how have you had to evolve your fraud prevention practices so all I'll answer this one in two parts so the first part I'll talk about is the principles of how we've looked at fraud prevention and how we make decisions on what we're going to implement and then the second part is I'll give you a little bit of our history because this is something that has changed of it's actually one of the largest mistakes we made as a company as we were growing was under investing in fraud and not being ready for it when it happened so the first part is how do we make decisions about what we invest in for fraud protection and one of the themes that you hear a lot of the time at these types of conferences or if you talk to traditional companies is the fraud team will say the marketing team won't let me implement anything every time I come up with a fraud control they block it and they they don't allow us to put it in because they just think that we're going to kill a conversion and if you have this tense relationship with your marketing team it really comes down to two things for me one as a tech team you haven't done your job of educating them on the value of the fraud controls or evaluating the value of those or number two your marketing team is probably right that you probably are putting too much in the way of the customer and when I look at this as it relates to our growth cycle so the second part of it is we started off skippa dishes in a bunch of small mid market cities in Canada so places like Saskatchewan Manitoba Alberta and when we looked at transactions there the odds of that transaction resulting in a chargeback was one in sixteen thousand six hundred so much more trustworthy and Americans it's amazing salt-of-the-earth people in the prairies but as we expanded as a company and as we grew we start going into markets like Ontario and Quebec and what we found is the chances of that same one transaction being fraudulent was one in 50 so all the Sun we had a country where it's we grew up where it was one in 16600 didn't have to invest a lot in fraud all the sudden we hit one and fifty and what that caused for us as a company is we ended up in the visa fraud monitoring program which I don't know is everybody here where what that is all stripe customers nobody's actually had to had to be involved in that one before this was prior to strike by the way I'll put that plug thank you you got it yeah that we ended up in the fraud monitoring program and when that happens the the issuers start to look at your transactions and say as skip the dishes we don't want to approve that one and your conversion and your transaction approval rates end up tanking which we experienced at a point when we were in a very high growth cycle and what that does is every time one of those transactions was lost we lost that customer and we lost the value of them so looking back had we actually invested in tools prior to expanding into those markets we would have grown much more rapidly and fortunately we were able to quickly recover we're now in good shape but but it was definitely one of our largest mistakes as a company so earlier this year you transferred all of your processing volume over to stripe enterprise level deployment I think he did in under three months how were you able to pull off such a large migration project like that successfully in such a short timeframe yeah so if I look at what we did on that project number one I am very fortunate that I get to go to work everyday and work with a brilliant team of payments and fraud people actually one of our product managers is here Carol hey Carol as well as a very talented development team so number one having a great team the second thing that we did is we brought the whole stripe team on-site right at the start of the project because I'm sure everybody here has worked on the project where you and your team lead scope it out you imagine how it's all going to work you write the tickets you send them out to all the developers and when it's time to merge the front end with the back end things just don't quite work how how you expect them to so we went we're gonna avoid that let's have this try team come on-site with us in Winnipeg engineers and everything and let's talk about how this flow is going to work and how this solutions going to work so Winnipeg beautiful place to visit visit in January for the guys -30 whether it was unseasonally warm for them while they were there visiting but that was a that was a critical part of the project getting a shared understanding at the start the second thing we did is we invited them into our slack channels so we use Jessica Yan as a deployment services we had Jamie as our Account Manager right in our slash channel so if we needed something between our team and the stripe team there was no hesitation of bouncing emails back and forth the other thing that we did was very very quick decision-making so anytime we needed a decision or a direction on something it was very very quick and responsive and that's really some of those things are what I've helped us deploy it so quickly okay great so since you've been on strike for the last few months you've seen some interesting results can you share with us what you've seen results learnings yeah definitely that we at our core at skip the dishes have always viewed ourselves as a tech company and we usually have a build at first mentality so whenever we're looking at partnering with another company and putting that on somebody else we're very picky about who we select as a partner and I can say for us stripe has been our most successful partner relationship in the software space because since going over to stripe we've seen a 6% uptick in our our payment processing approval rates which you increase your payment processing approval rates you're going to convert more customers and when I look at the impact of that on our business for every 1 million transactions we look at that 60,000 people who are getting a better experience and who are experienced earn our service so being a small-town guy from Moose Jaw Saskatchewan that's over double the population of the hometown that I'm from the other thing that we've seen is a further 30 percent so from our peak of charge backs were down 90 percent further 30 percent since moving to stripe it's at an all time and then some of the other features that we've been able to leverage and get a lot of value out of is things like radar so our old process for looking at putting new fraud controls in place you always have to ask yourself is this going to result in false positives so we would implement a rule in the background run it in the background evaluate that rule and make a decision if we were going to implement it in production with things like radar we just look at that rule against all of our historical data and can make quick decisions on does this new rule whether it's velocity rules or anything make sense to change in our business great Matthew thank you so much for joining us has been great really appreciate it yeah thanks a lot for having me all right okay so now we're going to dive back into the conversion funnel go into each step and take a look at some of the common pain points that we see and show you some ideas of what you can do to combat them so let's start out with checkout completion the name of the game here is removing as much unnecessary friction as possible so as you want to do a quick poll of the audience here raise your hand if you've been through your own company's checkout process in the last month okay this is great I see a lot of hands I was hoping to see a hundred percent of hands this is a simple hack but this is something that every single company should be doing on a regular basis I promise you every time you go through your own company's checkout flow you will find places where there's friction that you can pull out and I promise you this is not just a tip for startups this is something that established businesses should be doing all the time if you don't believe me our CEO Patrick recently went through our own onboarding process on stripe he created ten pages of notes with screenshots that he sent out to the company the places where we were introducing potential friction or confusion this is something that we can all get better on okay so it's stripe we're obsessive about studying payment forms and trying to find all the different ways big and small that we can reduce friction and there are ton of mini optimizations that one can do and I'm guessing folks here in the audience they do many of the things that you see here on screen but we wanted to stop in to a few examples of some common mistakes that we see companies make the first one is card number validation in real time as the customer is entering their card info if they enter wrong information or perhaps an incorrect character does the payment form alert then in real time that this is happening we've all been there when we hit pay we get some vague error message we have to scroll back through the payment form and try and figure out where we made a mistake the best payment forms will solve that problem before they've even hit that step but only 38% of checkouts online do this okay the next one autofill does your payment of some form support autofill have saved credit card details 21% of businesses don't we know browsers support it as you can see almost 80% of businesses support it don't be that company that makes your customer get up from the couch and have to go get their credit card all the way across the room to pay let's shift gears and talk about mobile checkout over 50% of e-commerce traffic is not happening on smartphones but we see cart abandonment rates that are 2x VAT on smartphones compared to desktop and the end result is that only 32% of online revenue is actually being transacted on mobile today so mobile checkout deserves attention to items to make sure that you check the box on is your mobile checkout form responsive and do you offer the ability to pay with mobile wallets which are oftentimes the least friction way to pay for something on your smartphone now one optimization on mobile checkout that a lot of businesses oftentimes overlooked is does your form bring up a numerical keypad when it's time to enter card info it's really annoying having to enter your 16 digits I'm just a normal keypad on your smartphone but 33 percent of businesses fail to include this feature now let's talk global for a second greater than 50 percent of global online payments do not happen directly on cards I know this that is hard to believe in the US but across the globe you see heavy use of digital wallets bank transfers and other local payment methods with businesses and customers crossing borders faster than ever these days it's really important for checkout forms to meet their customers where they want to pay so make sure that you have a checkout form that can accept the local currencies and local payment methods okay so we've hit on a few of these optimization examples this is the payments experience stuff that we have yes over at stripe and we channeled all of these learnings and optimizations into our check out products if you want to fully host it and optimize checkout form that allows you to be up and running in minutes we have stripe check out if you want to design and build your own checkout experience we have stripe elements which are a set of design UI building blocks that allow you to craft your own experience with all of these optimism optimism and for anyone here who's doing business in Europe we know it's September ese is top of mind all of these products come with our built in SCA exemption engine so that way you can reduce the impact that 3ds will have on checkout conversion onto fraud prevention to frame the challenge of fraud prevention it's actually helpful to understand the cost of fraud hits businesses in a few different places the first ones obvious having to pay for the cost of the fraudulent purchases themselves and the associated chargeback fees but the second one is one that businesses oftentimes overlooked and that's the cost of fraud false positives this is when your fraud prevention systems block legitimate transactions from going through on incorrect suspicion of fraud and we see for online businesses then on average they lose one to two percent of legitimate revenues because of these fraud false positives and then the last one is the operational cost of actually having to manage charge backs we sometimes see with small businesses that they review and triage every single charge back it's a drain on resources so take it all together fraud's big it's a pain and it's hard to stay ahead of because online fraudsters continue to get more sophisticated and adapt their practices and it's why from very early days stripe invest in a building radar radar is our machine learning fraud prevention product that the text of transactions are legitimate or fraudulent and we look at thousands of signals across the stripe network and for each transaction to evaluate for an individual transaction whether or not we should let it through and as with anything with machine learning the power bit is really built on top of the data that we have and the size of the stripe network that we can utilize when a new customer comes to your business you may not have seen them before but for every single transaction on stripe there's an 89 percent chance that we will have seen that car before and this machine learning approach works we see on average that businesses use stripe radar able to reduce fraud by 32% and you heard in the keynote today how our team is obsessively shipping improvements to our fraud prevention system so we're working the hard behind the scenes but let's talk about some ways that you can take charge of your own fraud prevention fraud does not punish every business equally and here's what I mean let's say that you're a software company you have low cogs high margin that lost margin from one fraudulent purchase that can easily be made up for in just two or three legitimate transactions because of your margin profile but shift gears and let's look at an online retailer who's low margin high cogs having to front the cost of a single fraudulent purchase it might take 10 or more legitimate transactions to make up that loss margin and the reason why I bring up this point is that every business faces a trade-off around how strict they need to make their fraud filters the online retailer here they need strict fraud filters because it's most important of to them to block fraud while the software business what's most important is to mint is let as many legitimate transactions go through and so the right trade-off of where to sit on this curb it's gonna vary by business but it's an important thing for businesses to understand where they should sit and so a striped radar we now allow you to move and test your fraud blocking threshold directly in the dashboard and what's most useful here is we allow you to adjust your setting and see the impact it would have had on your business looking at your historical data all right think about what Matt talked about earlier where he's able to implement radar rules and get that immediate feedback loop to understand what impact it would have had on his business instead of having to wait weeks to understand did he make the right decision we've had that exact same mindset that we built into this new feature okay so even as you hone your fraud prevention practices chargebacks they're painful they're costly they're time-consuming and for some businesses that drain on resources it's just not worth it they don't want to touch it and this year stripe built charge back protection with charge back protection if the business has a fraudulent dispute stripe will cover the full amount as well as the charge back Phee it's a way for businesses to fully outsource the burden of chargebacks that they don't want to deal with it okay so customers through checkout there through fraud prevention now let's talk network acceptance you're looking at the climb responses a decline occurs when the issuing bank doesn't want to approve the transaction now sometimes the response makes sense insufficient funds the customer doesn't have funds but oftentimes the information you get back in these numerical response codes from networks and issuers is vague and unhelpful and understanding what's really going on behind the hood under the hood and the most commonly used catch-all is the dreaded oh five do not honor that you see on screen here so let's break it down why did the Kleins typically occur as I mentioned once fairly obvious the customer may not have funds this is a common source of declines on debit and prepaid transactions now a second common source of declines is due to outdated credentials card holders get issued new cards all the time but they may not have updated their payment information with your business and this can result in a pretty large source of declines for many businesses today and it comes back on a number of those different decline reasons that you looked at third major reasons suspicion of fraud so this is where something in the issuing banks risk model see something that it doesn't like and it blocks a transaction now sometimes these are correct decisions that the issuing bank is making but we oftentimes see that for every correct decision they're making on suspicion of fraud they're actually blocking five legitimate transactions from going through and we see this play out in our radar data so this is costly all right keeping these reasons in mind let's take a look at how stripe and our customers are driving down declines so as you heard in the keynote today a major new optimization feature that we're launching is our new machine learning powered real-time retries engine here's how it works so let's say we get a charge request we submit the charge to the network and the decline response comes back well before we return that decline charge responds to the strike customer in real time we have machine learning models that are evaluating should we retry this transaction immediately and what can we do to optimize the information were sending within the authorization request to increase the odds of acceptance and the results we're seeing our powerful over 5% of the declines that we retry in real time come back with an approval so for stripe customers without having to lift a finger and make any changes to their stripe integration this is just pure revenue upside turning declines into approvals the next feature I want to hit on that we're excited to make available is access to the real-time card account updater this means that we can check for potential potential credential updates directly in the authorization flow you remember I mentioned that outdated credentials are common source of decline well here when we submit the charge request we can check against the network's card account updater systems and if an update is available it will get directly attached to the auth message and when we get the approval response back we will also get those updated credentials that we can save on file for you for future payments so think about that scenario where you have a customer got reissued a new card yesterday or last week they haven't received in the mail they haven't updated up with your business this is a way that we can get those updates in real time during the auth message real time retries card account updater these are optimizations that are applicable for all business types but I want to take a minute and just talk about the special needs that recurring revenue business models have the Kleins are a big deal for subscription businesses if you think about a subscription it's not just a single transaction it's really a contract for a series of future payments and if you get a decline at one point that can cut off that full revenue stream so this is a big deal for subscription businesses to manage and you can see it's a challenge 9% of subscription invoices fail on the first attempt for payment that's a big number so we've put a lot of investment into building advanced revenue recovery tools for the subscription businesses and the results are powerful we see that users of stripe billing or a subscription product they're able to save over 40% of that initially declined subscription revenue that's huge revenue uplift now there's a lot of work and optimization features that go into achieving that 40% result but I want to highlight just one feature because I think it illustrates some interesting learnings so with recurring revenue businesses if you get a decline the good news is is that you still have time left in the billing cycle to try and get an approval at a later date so many businesses will retry at later times this is common the industry it's known as Dunning now I want to clarify these time-based retries that we're talking about here and this is different than the real-time retries that I mentioned okay so with Dunning an issue in the industry is that a lot of the solutions on the market they use very basic time-based logic get a decline wait seven days try again seven days later try again seven days later there's not a lot of nuance going on so we've built a much more sophisticated approach it uses machine learning and the data that we see across the stripe network for example will time our retries based on specific issuer level behaviors when they're most likely to accept a transaction this is especially important for cross-border businesses where that first attempt let's say the issuing bank sits halfway across the world that first attempt could have occurred in the middle of the night when issuer risk models are most risk-averse so we can time it based on where the issuer sits to get an approval and we'll look across the stripe network if we see that payment method that was initially declined being used elsewhere successfully we know it's a good time to retry it with high odds of approval and so for businesses operating on the type of monthly billing cycle that you see up on the slide this more nuanced approach is 14% better than just basic time-based retries and not only that we're saving stripe customers money because we're not wasting retries when they're not gonna work now coming up after this talk there's a technical talk called tackling the conversion challenge with machine learning it's taking place on the other stage during this talk you're going to hear from other members of the auth optimization team we're gonna go into a lot more depth around the experimentation a machine learning approach that our team uses it's really fascinating stuff if you're interested in payments or you just want to learn about what a multi-armed bandit is I really encourage you to check it out but I did want to provide just a sneak preview into some of the experiments that we run on our team ok so it's helpful to remember that on the other side of this transaction is an issuing bank and every issuing Bank has their own unique black box model it's a mishmash of different third party systems and ml models and custom risk rules that are constantly changing oftentimes issuers themselves can't even explain why certain decisions were made by this black box risk model so we do a lot of experimentation because in essence we're trying to reverse engineer and understanding what preferences are being expressed by each individual risk model let's look at one example dealing with new network features networks regularly released new features or configurations the example you're looking at on the slide is the new credential on file framework that first came out in late 2017 I'm not gonna go into details but anyone here who works closely in payments you know this is a significant change and the way you're supposed to format transactions for repeat customers now when new network features come out a lot of processors will just kind of blindly adopt them and move on but when you're looking at trying to optimize all three it's what matters most is have issuers adopted the new framework and how will they adjusted their own risk models in response right so this new framework that came out of late 2017 let's fast forward a few months to april 2018 we're comparing auth rate performance here of the new credential on file framework against the old regular e-commerce framework and what you can see here is this wide variation this is a shuffle list of top issuers in the u.s. you see wide variation among which framework is going to give you better off performance as you can see by the green bar some if yours have adopted this new framework weekly giving preferential treatment but you can also see that a lot of issuers are providing worse author aid performance on this new framework so this type of nuance experimentation and testing approach it really allows us to optimize your behaviors issue or by issuer all right fast forward 12 months April 2019 we see consistent higher performance among this new credential on file framework against the old framework and so it's this continuous testing approach that really allows us to adapt to what the data is telling us from issuers hopefully this gives you just a small flavor of why experimentation and rigorous testing really matters in this space and I really encourage you check out the technical talk after this to learn more homestretch I promise we spoke about checkout we spoke fraud prevention Network acceptance I just want to take a couple minutes and spend a few minutes looking to the future of Revenue conversion what are the topics that we'll be talking about more in the months and years to come historically conversion it's just measured statically on a transaction-by-transaction basis in the industry and I think this is a legacy mindset because we have to remember that a transaction is just one outcome but behind every transaction there's a customer and a customer represents a lifetime of outcomes for example if there is a decline what's the real impact of that decline on your business let's pretend you're a ride-sharing startup in San Francisco maybe that decline just came from a tourist visiting from out of town and that single the client only cost you one or two future transactions but let's say that customer is someone who's going to be commuting to work every single day that decline could have cost this business 10 or more future transactions it's really important to understand your customers behind this data now there are a lot of different insights that one can draw from this but I just want to dive into one area looking at high-value customers because of their importance to every business let's look at some data this is a random set of top stripe customers and what we've done is we've isolated high value customers for them so these are high value customers people place 10 or more purchases and we've taken them and we've now split them into two different cohorts those who always received approvals and those who experienced just a single decline back in q3 2018 so between these two cohorts we wanted to measure what's the difference in return purchase rate how much is getting that one single decline matter and as expected you know there's a return purchase rate drop-off for those experiences decline what's really interesting is looking at the variation by merchants if you look at merchants 2 & 3 up here there's over a 50% drop off rate from high-value customers just from one single decline well for merchants 6 & 7 you know the reaction is more muted right it's a less than 20 percent drop off rate in return purchases now the important thing here is that for businesses to understand their data and to really pay attention to their customers and their high value customers for example it's so important for merchants 2 & 3 here to know that these high-value customers had a negative experience and do everything they can to bring them back to the business in a positive way now this is something we're working on it's stripe we want to give strike customers more insight into both the historical and future predicted value of their customers this is in the works now let's say that we understand our customers really well we have all this real-time intelligence coming in we have a nuanced understanding of our business goals the next step where this is headed is what I like to call dynamic conversion being able to take each unique interaction and optimize the correct behaviors across the funnel and do it in real-time this could look like dynamically changing checkout forms that expand and contract to collect more information as necessary or maybe radar wrist rules that are constantly adapting to different customer profiles or dynamic offers that get triggered to your high-value customers when they're bought to drop off from the funnel imagine the same checkout form for car rental business to returning customers but something's off about that first customer coming back in right some of the wrist signals we've collected makes us suspicious of this well the checkout form should just dynamically expand to collect more information on the other hand the second customer fully trusted the checkout form should contract to get them through the checkout process as fast as possible and all of this should just be happening automatically so at the beginning of this I shared a story of how my business was the victim of a costly decline and it's really great for me to be able to sit on the other side now at stripe helping other businesses combat this challenge and drive higher revenues I hope you will take away some lessons from today that you can take back to your business and put into practice test and experiment and figure out ways to increase revenue conversion and really deliver a better experience for your customers if you want to talk about conversion optimization experiments please come find me afterwards I love to chat if you want to learn more about what it takes to run a successful food delivery business I'm afraid for that you better go talk to Matthew thank you so much everybody you

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