Hello, im brian cardin im, the chief marketing officer at envision and its great to be with you all and uh very excited about our topic today, which is uh all about ai and marketing and uh im a marketing practitioner for a long time and uh weve. Of course, all seen how ai has touched so many things in our lives and today were going to be talking.
About specific examples of how artificial intelligence is being used in modern marketing organizations, first, a little bit about me, i began my career at the bottom of this chart. After business school, i joined a strategy. Consulting firm, called braxton, which is part of deloitte, became a partner there in their consumer marketing and strategy practice. My clients were companies like heinz and campbell soup and ralph lauren and merc and uh. That was a great uh, exciting time where we started to see.
Um lots of uh digital transformation going on um and from there uh being a a partner. I uh we had uh our first children were born twins and uh. The life of a consulting partner is traveling all the time. So i was thrilled that i got a job offer to be a cmo at reed elsevier about a 5 billion british dutch company. They own things
Like lexisnexis, the large online database elsevier science, which are subscription scientific journals, trade shows a whole bunch of things. I was there for about six years, i joined forester research as head of marketing and head of strategy. My first part of my career uh in software was with eloqua marketing, automation and that was very exciting. It was a small company,
When i joined about 15 million dollars a year, we got up to about 100 million dollars a year in about four years. We took it public and was acquired by oracle. Lattice engines is actually a company that does artificial intelligence for sales and marketing, and i was there for four years. It was subsequently bought by donna bradstreet to really marry the data heritage and legacy and assets have done
A bradstreet with predictive analytics and artificial intelligence and ive been at envision now for a little over a year, um just a little bit about what envision is, were a uh digital product design platform. It allows uh companies around the world to build digital experiences for the customers. We have about seven million people around the world using our platform uh. It includes a
Hundred percent of the fortune 100, so its very large companies, but also mid-size small. Some of our accounts that we work with are companies that were born digital uh like uber and netflix and others uh. There was a really compelling reason to become digital uh companies uh, like goldman sachs or bank of america. One of the uh very interesting parts about our company is. We were founded,
About 10 years ago and weve always been a fully distributed company, we dont call it remote, we call it fully distributed and we have zero offices. We have uh over 600 employees and weve never had offices anywhere in the world and so part of the idea was we help our customers build digital experiences and we wanted to see what its like to run a company through digital experiences and not face-to-face experiences.
And we have a lot of resources on our website that, if you’re thinking about in a covered world about being fully distributed forever or a hybrid model, there’s lots of things on our on our website. That can help help you through some of those issues. As i mentioned, we have 100 of the fortune 100 and lots of different kinds of customers. Customers like disney use us for all the applications for like fast track and fast lane and uh in-park experiences um. They also uh, you know, use it for
Um for different kinds of uh of uh in animation, customer experiences um in the case of some of these service providers like wpp or pwc. They use our platform on behalf of some of their customers to build digital experiences for their customers and as a platform that allows people to build digital experience. We have the design piece where you can design experience. You can share that prototype with other people. Then you can send it over to your front-end developers, who actually write code and build it out and you can scale it across the
Organization and get other people to comment and talk about it and eventually ship that digital experience so lets talk a little bit about ai. I very distinctly remember this moment. Do you remember the name of the player in the middle? That, of course, is watson um, who beat the player on the left. Ken jennings, who i believe to this day, is still the greatest human player, but uh for those of you who love jeopardy its a game that is very hard to program its not as simple as
Searching for uh, you know very objective information like how many humans are on the earth or um. You know, or or uh answers to equations or specific answers, its a very challenging problem that ibm through watson has been able to solve. When i went to business school, a lot of my colleagues wanted to be traders, and now we see that as a career that human beings dont do but bots do so. The algorithmic trading that
Happens now the algorithms dont just tell what trades to make they actually execute the trades as well, and so the human beings are very much employed in this process, but theyre writing the algorithms and theyre writing the code and using artificial intelligence to place optimal trades. Has anyone ever been in an autonomous car uh? These are a lot of fun to be in can be very frightening as well. Ive ive been in autonomous car, a
Couple times all in san francisco and its just uh really fun. We went on the the 405, so i remember leaving googles offices and getting on a ramp and getting on the freeway there and the car performed flawlessly. Curiously, when they brought the car to the east coast, i live in boston and they brought it to the east coast um in january.
And uh, despite having hundreds of sensors and cameras, the car did not know what snow was. So when it came to a little wall of snow, a couple of inches, the car would stop, assuming that it was uh, not a material that you could drive over and crush uh. So the car had to learn about different weather conditions, which i find really interesting. All of our lives have digital assistants, alexa and google and siri that are parts of our lives. Naturally, one of my favorite examples is there’s, a
Sundar from google and he wants to make a uh a haircut appointment and its not simply a matter of asking his digital assistant to put it on his calendar, but the digital assistant actually finds the hair salon goes through. His contacts finds a hair salon that he always goes to finds an available time places the call and uses a voice to actually
Interact with a human being at the hair salon and book, an appointment put it on his calendar and confirm it its quite striking there’s the url. If you want to watch the youtube, but its a great example of humans and ai working together in a very effective way – and we, of course we live in a world powered by a i in all sorts of areas like healthcare, insurance, credit cards, retail, it just Seems to permeate almost every field today were going to talk about marketing and sales. So let me give you a couple of examples:
That we believed five or ten years ago were going to be bright spots for artificial intelligence and marketing. The first area is smart, lead routing, so lead routing is when you have a lead and the idea was ill route it to the rep, who has demonstrated the highest ability to close a lead like that. So lets say its a
health care company that is a lead and you know that nancy has the highest close rate in the health care sector so the leads would be routed to the rep there except the second example would be content creation and uh can artificial intelligence identify the kinds of content that you should be creating on your website to drive engagement what are the topics of most interest and the third area that held a lot of promise was predictive email as you might expect we have lots and.
lots of history of when people open email when they click through is there an optimal day and time of day that you send email not to everyone but to a particular person based on their history can you build artificial intelligence that will yield higher open rates based on when people historically have opened emails and so in all three cases they have failed so im gonna in a couple of minutes give.
you some examples of things that have succeeded and these have failed for a number of reasons in most cases its lack of enough data to build a model that is robust enough that it would yield higher results that would happen happen randomly so these three examples while people believed eight or ten years ago were very promising have not proven to be very helpful so let me talk a little bit about what a lot of marketers say they say that were now modern marketers im particularly.
talking about b2b business to business marketers but the data would suggest otherwise because the results are quite poor so whats wrong here mql stands for marketing qualified leads so the marketing team spends a lot of time and gets a lead someone comes to the website engages with some content they pass that lead over to a bdr a business development rep or a sales development rep in sdr but in fact from all the data across thousands of companies we see that 94 of all quote marketing qualified.
leads will never close and so if i was on the receiving end of those leads i probably would not follow up very well because only six percent are going to close that doesnt seem like a very healthy rate and that rate has been pretty steady over the last eight years so we dont see a lot of proven despite all these marketers being modern.
marketers the second area in a related function is sales is that according to the data from gartner and serious decisions and foresters 52 of sales reps will not make quota so here we are i do a lot of planning and yet barely half of your reps hit the number that theyre assigned huge failure so both the mqls are not converting at a healthy rate most will never close and just about half the reps are not hitting quota so im going to suggest today that.
there’s a better way to do things sort of next gen marketing this era of modern marketing happened because of marketing automation and scaling things like eloqua where i worked or marketo but there were new marketing tools that are marketers to automate and scale and there are additional tools now with ai that allows marketers to move from poor performance to extraordinary performance im gonna go into some detail about these um william gibson the writer uh said famously that the future is already here its just not evenly distributed the point being that um were seeing.
some organizations applying ai very effectively in marketing and other organizations either not applying it or spending a lot of time and cycles and effort and money and its had disastrous results what were seeing is that if you are very clear about what job you want to get done there are very specific ai powered tools that will allow you to get there.
lets talk about them so the three examples today will be first identifying target accounts most marketing sales organizations have a huge universe of potential target accounts that they can go after what if you could apply ai to identify the accounts most likely to buy from you so it gets to use ai to disqualify tens of thousands of accounts you should never call because they have a low.
conversion rate so how would you do that the second example is engaging with prospects at scale so if you have lots of leads coming in and you only have lets say 20 or 30 humans to pick up the phone and follow up you may not be able to follow up in a timely manner so can you use bots just like sundar used a bot to make that.
haircut appointment can use a bot to engage with a prospect qualify disqualify and do a lot of the work that normally a human being would do and the real advantage here is speed uh of time uh very often with leads they dont come in an even way but there could be a spike of leads lets say you run a big webinar and you get 10 000 leads one day and you have a modest sized team they.
cant work through the leads in a timely manner and we know that the decay rate is quite fast and steep for leads if you dont respond right away and a third area is improving conversion rates um one area of marketing that’s been particularly impressive is believe it or not direct mail so everyone sort of leaned over to digital communications but were seeing that if you mail something to a prospect you have a much higher uh chance of having a meeting with them.
that they feel somehow beholden to you because you sent them a gift and so i want to talk about ai gef gift giving is there a way to scale that rather than have all these sales reps trying to come up with a gift that’s either personalized or you just send everybody the same gift which would have a low conversion rate how do you personalize a gift at scale lets talk about these the first question is which accounts are most likely to buy from a given company and so this is the.
target account question and so uh what weve done is weve looked at um weve done a back test weve looked at two years of account history to identify accounts uh where weve won and lost and then weve appended a whole bunch of additional attributes to those accounts to build out a very robust data set and so basically its a multiple regression we run a model to identify the most predictive attributes and then we acquire accounts that have those attributes so for example if we find here that the tenure of the.
cio one thing we found in this example is that during the first six months of a new cio youll have much higher chance of having a meeting with them and a much higher conversion rate once a cio has been in their job for more than six months theyre sort of locked and loaded and theyre much less open to talking to new vendors so we would append that information to.
the data set and we would look for other accounts like that so in this particular case we found out that there was an ideal company size companies that were smaller or larger had a low conversion rate certain industries the existence of certain technology was predictive so you may ask the question well how could i possibly know what technologies companies are using.
there’s lots of good ways to do that one is scraping through their job descriptions so if you scrape through a lot of the job descriptions youll see that what technology theyre using in this particular case we found that multiple locations was much more predictive than a company that had one location global presence and some other on-premise systems we identified 20 almost 22 000 accounts we assigned accounts to rsms regional sales managers then we got contacts at those 22 000 accounts and we started running ads across all of those accounts and across.
all of those contacts whats important is its only 22 000 accounts in this particular case they started with an initial list of over two hundred thousand accounts so they paired it way back less than ten percent of the accounts that allows them to have much greater presence and more marketing there the bdrs uh a higher rate of calling target accounts it began when this program began only 59.
and then it went up to 84 and this are these are maps of where the target accounts are located one of the great benefits of understanding where your most likely accounts are located is you can put local events in those regions for example if i got a call from a rep in nashville that would say brian can you run a dinner or a special program here in nashville.
i would look at these data and id say i dont have enough target accounts in nashville why dont we do it in another city lets do it in houston or lets do it in new orleans or lets do it in miami that is a much greater density of target accounts and so what were the results of using artificial intelligence to identify target accounts well the first area is cycle time the non-target accounts were closing in 205 days the target accounts in 140 days.
a dramatic difference in uh in cycle time the deal sizes were also quite a bit larger and the win rate from opportunity to closed one was up quite a bit and so these are very dramatic results and the whole idea was to focus on fewer accounts that we knew based on the back test of looking at historical data were much more likely to close and so.
the win rate we could have expected the deal size is something that was a wonderful side benefit and the the sales cycle time was was fairly predictable so that was uh target accounts using predictive analytics lets talk about the second application of ai and marketing engaging with prospects at scale sometimes marketers and sales people have uh the curse of abundance and what that means is they just have too many darn leads and how do you decide which leads to.
follow up with the example that i gave earlier is very relevant lets say you go to a trade show and you get several thousand leads we know that responding to a lead properly really matters to conversion rates and very often human beings cant respond quite as quickly as as artificial means so our bdr team the business development rep this team is responsible for calling the leads following up on the leads the first challenge is lead surge so if you have a big spike a surge of leads.
how do they follow up with very promptly the consistency so the bdrs are human beings and we wanted them to have a cadence of seven touches two calls and five emails but we had no way to guarantee that and we wanted fast follow-up so heres some data that talks about um uh dials and uh and response and you can see that if you respond within the first five minutes of someone indicating interest you have.
a 10 times greater chances of engaging than after 10 minutes and you can see even after 30 minutes it falls and so how do you do this very quickly how do you respond to leads very quickly human beings cant do that necessarily and so can you automate this in some way and so we decided to use a bot and her name is natalie we called her natalie because our best bdrs uh at at our company are women and so we wanted to use a female name and so natalie uh really never needs time off.
natalie doesnt take vacations no sick days uh natalie also can respond at two in the morning or uh eleven oclock at night and uh natalie responds in a very consistent way and so um the bdrs were telling us they cant always respond as fast as they like to a prospect but a bot can and weve seen that bots uh the intelligence you can program into.
the kinds of conversations you have um really are as good as human interactions in email now natalie cannot do phone calls just yet only humans do that were working on that but natalie is extremely effective at sending out very effective emails personalized emails and even using things like calendly to schedule appointments with a rep so very high conversion rates and great engagement rates from natalie the bot the third area i want to talk about is gift giving which weve seen to be quite effective at converting.
prospects so trying to get a meeting with a c-level person is extremely hard these days we have all sorts of uh you know blockers on our on our phones we have ways to um put into junk mail email from people weve never heard from before so email has extremely low conversion rates cold calling is extremely difficult one area that weve seen work pretty well is direct mail weve seen.
all of us have probably received a bottle of wine or some sort of generic gift with a note what weve seen is that if the gift is highly personalized the conversion rate and the receptivity of the recipient to engage with the sales rep is very high so when i learned that personalized gifts when we sent them out were very effective i asked our bbr team to start going into facebook to start um looking at um prospects profiles to try to come up with a personalized gift now the downside was i remember a week.
after i talked to the bdr team about doing this i walked over to where the bdrs were sitting and theyre all on facebook all day like writing down things they saw in pictures and profiles and this person went to notre dame sent them a notre dame you know hat and a sweatshirt so theyre trying to personalize gifts so how do you do this at scale and there.
are tools to do this im going to talk about one this is one where if you give this tool the tools name is alice alyce if you give alice a um a business email address it will match that email address with the social profiles of that person so if you give it my email address there’s me brian cardin at envision app itll find my facebook instagram twitter linkedin accounts it will then crawl through my profiles and using its logic its ai it will make specific personalized gift recommendations and they will actually send out the gift.
or theyll send out a card that says a gift has been chosen for you click here with a perl a personalized url so heres what really happens is the bot goes out and this is an actual picture on my facebook account and it sees that i have a new dog and it sees it rated this picture very high because the number of likes that it had and also that im with my two children my sons and so the bot found images that would suggest that maybe there would be.
a gift related to a new dog and also found this photo im there with my son and my wife and were actually at an opera house and the software was smart enough to know that it was an opera house not a movie theater based on those balconies based on the architecture so it knew that this was an opera house in prague in the czech republic and so it recommended two things that were personalized one is a bark box which is a monthly uh box of uh of little treats and and little toys for your dog.
and the other was a coffee table book on the most beautiful opera houses of the world so highly personalized gifts and it was just wonderful and as i said alice this software actually will send out the gift automatically or at least a card that says the gift has been chosen for you and the conversion rates were quite high um.
so in our world nbm means new business meetings and that’s a stage in our process and we did a proof of concept where we had good conversion rates and then we tweaked along the way a personalized note and some other things we had amazing results so um we sent about uh 2 000 gifts in this test and uh we closed eight deals and uh the average deal size the asp was.
250 000 a year so about two million dollars in new bookings and the cost of this whole program was only twenty five thousand dollars so personalized gifts at scale if you try to send out two thousand two thousand personalized gifts and you let human beings pick out the gifts it could take thousands of hours for people to find all those gifts and personalize it so this was extremely effective so today we talked about three applications of ai and marketing the.
first is target accounts using predictive analytics the second is following up leads with a bot natalie and a third is choosing gifts at scale for a direct mail using ai so whats been the cumulative result ive masked a little bit of this data in the vertical axis but basically the sales rep productivity gains over this period that we applied all three they’ve more than doubled so the mrr was his monthly recurring.
revenue goal was eight hundred thousand dollars per rep uh the quote actually went up to 1 6 million it doubled based on the value of all of these tools i want to sort of conclude with some thoughts about ai and marketing and what were seeing across a range of organizations and i think one of the conclusions uh as william gibson said is that if you apply it the right way you can have extremely beneficial results but also for a lot of organizations has been a time sync so you have to do.
it in a way where you know precisely what you’re trying to get done but i think ai is becoming something that weve seen through these decades ill give you a few examples if you are old enough to remember the 1990s every company in the world wanted to be a quote com company and of course every company is now and so the word dot com sort of goes away and every company is a digital company in some way.
even companies making physical things think about ge making big engines they have sensors on their engines they have ways to track performance you know companies making drugs they become digital companies as well as people order prescriptions uh in the 2000s every company wanted to be a cloud company and so people were bragging that they were cloud first theyre a cloud company and now that’s pretty much gone away because every software company is a cloud company the third area and i remember this very clearly was the era of big data so obviously with social platforms and email and.
and sort of back-end tools were seeing the era of big data billions of bits of data are now coming out and every company was going to harness the power of big data but big data is no longer something special just like com cloud and big data its become in the background and its assumed i think the same is true of artificial intelligence that just like every company became a dot-com company and every company is putting their technology in the cloud.
and every company is using big data every company in some way is an ai company as well so i want to uh thank you all for being part of this presentation and uh and i hope to meet you soon in person once kovits gone thank you very much.
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