Hey everyone, welcome to the Data AI Productivity and Business with a Little Personality podcast.
I'm your host Mukundan Sankar and today I want to talk about using AI for customer lifetime value.
So, you know, you may be wondering what is customer lifetime value?
Well, to explain it in a more simpler and more relatable way, it's like figuring out,
you know, which of your friends you can count on to show up for like a free food night, right?
Like you got that friend who comes up once, he'll eat half the food and vanish until next
time, you know, you have free food. And then there's the other friend who will be there every time
and then they will pitch in for food, they'll pitch in for drinks and basically be a good friend.
And that's what customer lifetime value is. Like it helps you focus on
this other friend who is helping you with everything versus the friend who's just showing up for the
free food. So that's the easiest way to explain this. But like specifically, if I want to talk
about customer lifetime value in marketing, and that's the main focus of this, it is like helping
you identify which customers bring you more value, right? And that's what the example I mentioned
above was. Customer lifetime value is showing which customers will bring you lifetime value
over a period of time, how much revenue they will bring to you, right? And the old ways of traditional,
you know, customer lifetime value essentially were very, very outdated. The way we are doing it
is it's not, you know, completely efficient. I feel like we can always optimize this. And
that's why using AI, which we have available to us is the next step in this process.
So what could be some, you know, challenges? Why am I even talking about this today? In the old school
way for calculating, you know, customer lifetime value, it is like, you just think about it like
using your flip phone in 2024, right? Today's day and age, you're using a flip phone. You can definitely
use it, but you're not exactly going to be able to do anything with it in today's age, right? Like
you won't be able to use, you know, maps, not saying Apple maps, but Google maps, because everybody
uses that or Waze, right? For that matter. So you're typed in, let's say hello, and the group chat
is not going to be as effective on a flip phone. So basically that's what a traditional model
is indicating here. So what you need to be doing is like using what you have available to you,
which is now AI in the last year and year and some change, use that to, you know, go to the next step.
Again, with the traditional CLV of calculating CLV, which is customer lifetime value. It relies
on like old static models, like spreadsheets. I'm sure you're probably doing the same. You're
probably typing in these numbers of like how many customer orders have come through,
how many orders this customer has placed over time, and you're calculating the revenue that this
customer has given in that order, how frequently have they made a purchase,
and how many months has it been since they first joined. So those are usually the parameters that
you use for customer lifetime value. But here's the thing, right? It is just accounting for
something really static. Like, I mean, if you want to account for something which is like a change in
actual behavior, like see now the thing is if you're relying on old static rigid models using
spreadsheets, you're not going to be able to capture changes, like sudden changes in the
customer behavior. What I mean by sudden changes is like, like if customers start coming in three,
four months, and you know, they've just been active for... So you want to be... When I talk about
sudden changes in customer behavior, I'm specifically talking about these customers who come in,
you know, once in six months, and then maybe come in three months continuously after that,
and then again disappear after six months. So it's like a sudden change, right? Like, I mean,
so that that kind of stuff is hard to capture in a spreadsheet. So you are missing out on real time
insights, and that's something I'm hoping to tackle in this podcast, basically using AI, right? How AI
can help solve this problem of, you know, not capturing real time data, which I'll talk about.
So in using AI, what happens is you can do real time data processing, right? Now think about AI
specifically as your friend who always knows when to ghost the group without the session.
How would you think about AI? AI is like your friend who knows when you're about to ghost the
group. So they'll do some kind of an activity to make sure that you're not ghosting the group.
They'll maybe engage with you at the time, and that's what AI is. AI is your friend. So
AI for customer lifetime value, what it does is it will update based on your interactions
with the product or the company and like as a customer. So like let's say you're the customer
and you're interacting with the company, and AI will update your interactions with the
company, will update that model. So basically how the frequent interaction is, how many orders you
placed. So all of that is being tracked with AI. So you're really getting super swift answers,
right? Super quick. And I mean, you're seeing it real time. That's mainly the thing. And based on
that real time interaction, you will now see targeted offers specifically for you. I don't
think there's anything better than getting offers which is relevant to you as a customer.
I mean, versus getting recommended some random stuff, which you're not probably going to buy,
and it's a waste of your time, right? That's what I mean with AI, it can help. And the other piece I
want to talk about how AI can help is with smarter customer segments. So what that means
is like think of your AI power customer segments, which is like your Spotify playlist, right?
Spotify is helping you get recommended to the songs which you like, right? Like I mean,
it's obviously a recommendation engine. So they know which kind of music you would like. And
that kind of recommendations is what we're aiming for. So now if you think about like more in a
e-commerce base, so it knows which customers are in your big spending category or someone who's
like in your, you know, they recycle their Ziploc bag. So there's two different kinds, essentially
your one is a high budget customer and one is a moderate, I would say. But like that kind of
segment is identified using AI. So an AI, it will go beyond demographics. And I mean, obviously
demographics meaning their age, gender, location, and it will use behavior, the customer behavior
for better targeting, right? And you want as a company, you should be looking at the customer
behavior, but like AI would be helping you with that because doing it by yourself and looking at
a spreadsheet, that's not the future. So AI obviously means it is looking at predictive capabilities
because that's how it's learning about you. So it was predicting your next move. So yeah,
you want somebody to like actually learn about you if you're comfortable with that, that is.
So the traditional model won't allow you to do that. It will be giving you a weather report from
last week. It's like a weatherman which is giving you weather report from last week. Think about that
way, but AI would be giving you a future weather report for tomorrow. Like I mean, tomorrow's
weather report would be predicted more accurately. And that's something you want to be aiming for.
Basically you're forecasting what you're going to be doing tomorrow, what you're going to be wanting
right now. It should know about you to be able to be able to give like a proper lifetime value
prediction. Now, if I think about AI, right? Like, so think about when you're having like a quiet
customer, like customer who is, you know, buying every few months and then is quiet for a few months,
but they don't leave you any reviews, like a five-star review at least. So now with AI,
what happens is like even your quiet customer, the customer who is, you know, buying three,
buying a product like for three continuous months and then keeping quiet for six months and then
buying after three months. So even for those customers who are quiet in between,
what AI is doing is it is helping identify those customers in that segment.
So nobody is getting, you know, missed because those could be a valuable customer and AI is
helping you identify those. That's what we should be aiming for. Basically you're looking at
all kinds of data that they provide, this customer is giving to you, like any kind of review
information, if they're given social interactions. So that would be also helping you to identify the
customer's value. So do make sure that you're, you know, utilizing those channels to leverage AI
more correctly for customer lifetime value. Now, all this in theory might sound great, but it's
like you would be wondering, has this been practically implemented? There is, yes, obviously
there is a case that I want to talk about specifically today and this one was a Starbucks
example. So Starbucks, what it does is, and I'll link this study in the show notes, but Starbucks,
what it does is it will not just know your favorite drink, they will also ask you, you know,
what's up, basically like just to see if you're interested in some kind of a drink that your
barista made the last time. So because it has tracked your interactions, whenever you've gone
in and made a purchase, so AI knows maybe, right? Like, you know, around that time to ping you
on your, the Starbucks app and let you know that, Hey, do you want to get something today at
because it has looked at your past interactions that you've gone in at 3 PM every Thursday
to get your Starbucks pink drink and it would pink you, Hey, do you want to get a pink drink?
So that kind of stuff is being tracked in the Starbucks app. So it is using AI to track the
customer habits through their app loyalty program. So AI will basically flag the company
if there's any changes in the behavior. So you're getting more personalized offers based on
interactions and that's the world I would want to live in. Now, so like I wanted to also talk about
how do we, if we wanted to do it ourselves, right? If you want to do this process ourselves, how do we
go ahead and implement AI for customer lifetime value prediction? So think about it like this,
getting started with AI in general is like cleaning up your room before your guests arrive.
And what that means is your data needs to be clean, right? Like, I mean, AI needs to be clean,
your data needs to be clean, right? Like, I mean, AI can only be as good as your data. So if your
data is not clean and there's inconsistencies in the data and all that, AI is not going to work.
You would, yeah, I know you want to basically give all access to the data,
to the AI, but AI will definitely be more effective when you have cleaner data. So make sure data is
organized, you're choosing the right tools, like the ones I can recommend, I've seen, you know,
being used effectively, well, Google AutoML or Salesforce Einstein. So they have very
user-friendly AI integration. So make sure you're looking into that. And I would say start like
gradually, right? Like, I mean, start with a small focus group before scaling up with this AI process.
So sometimes AI may not be for you yet, but I would always suggest to start small,
definitely take your time. But like I would say, do look into this process of using AI for your
customer lifetime value. So that's the key takeaway really. AI driven customer lifetime value
prediction is essential if you want to progress ahead in a more modern marketing world.
So you can get more personalized campaigns, reduce your customer churn and build your
personalized campaigns, reduce your customer churn and build solid customer relationships
that last you maybe in your whole company lifespan. So that's something you want to be aiming for.
And just think about why, and I'm sure you know this, why AI is the future.
AI is everywhere around us. And if we are not adapting it, we're definitely lagging behind.
So I keep thinking about areas where we can use AI and customer lifetime value
is definitely a strong area to do that. So make sure you're using AI and
that's all for this podcast. Thank you and I will see you in the next episode.