Learn about the importance of data in delivering personalized customer experiences and optimizing the customer experience with insights from Google and Albertsons. Discover the types of data collected, the significance of real-time data, and the use of data to provide real-time inventory information and personalized recommendations. Explore the future of retail and the need for a modern data platform to create exceptional customer experiences.
In this video, the combination of data and customer experience is discussed. The speakers, Danielle Crop and Bruno Aziza, talk about the importance of data in delivering personalized customer experiences. They mention the types of data that are collected, such as customer information, engagement data, and behavioral data. They also highlight the significance of real-time data in optimizing the customer experience. The speakers talk about the future of retail, where online and in-person shopping experiences are integrated, and the use of data to provide real-time inventory information and personalized recommendations. They emphasize the need for a modern data platform to bring together diverse types of data and create exceptional customer experiences. Finally, they mention the value of external data, such as weather conditions, in enhancing the customer experience.
The combination of data and customer experience has become essential. And so, today on CXOTalk,
we're speaking with two profound experts: Danielle Crop, Chief Data Officer of
Albertsons, together with Bruno Aziza, Head of Data and Analytics for Google Cloud.
My role and responsibility as chief data officer at Albertsons is data science,
data platforms (so data management and data governance), as well as data products (so all of
those wonderful things that enable the enterprise to deliver on customer experience with data).
Bruno, tell us about Google Cloud and tell us about your role.
I run advanced product management for Google Cloud data analytics portfolio.
That's products you might have heard like BigQuery and Dataproc and Dataflow. We have lots of news,
lots of new customers we can talk about today as it relates to the customer experience
challenges and opportunities. Danielle, maybe you can start
us off by giving us some insight into this relationship between data and customer experience.
Today, it's essential. Customers expect that you're going to know things about them that (30,
40 years ago) they would have never expected that you would know about them.
They expect you to have an understanding of who they are, what relationship they have with you
as a company, and what purchases they've made in the past. This is an expectation that they have
and that you're going to customize your experience with them based on the information that you know
about them. That's a fundamental difference. Data and CX are inextricably entwined at
this moment in time. I consider those probably going to get even more entwined in the future.
When we talk about customer experience, what kinds of data do you think about gathering, Danielle at
Albertsons? Then, Google, you're talking across a range of different organizations. What are the
kind of patterns of data collection that you see? To add to what Danielle said is this notion of
real-time, which is becoming really important for, I think, organizations but also customers who are
going through their shopping experience. Just like Danielle said, they expect that not only are you
going to understand them, but you're also going to understand cohort analysis, and there are some
people just like them, so you can create really compelling experiences, help them find products
that they need faster, and maybe consider products they didn't think about before.
I think, if you break down the types of data, there are at least three or four
data types you have to be able to collect, transform, and augment around the experience.
The first one is, of course, the data about your particular customers and visitors,
understanding who they are as individuals, but who they are as maybe groups of individuals,
and understanding what's going to augment (if you will) their experience.
The second type of data is everything around engagement. When you have a multichannel
experience online, a retail store, and maybe through partner stores, you need to understand
what are the click-through rates, what are some of the conversions that you're
experiencing, because that's a sign (if you will) that you're presenting
your customers with the right information. The other aspect is everything around behavior,
so implicit behavior around purchase history, just like Danielle was saying, or
abandonment of shopping carts. Why are people abandoning their cart? What does that tell us
about the experience? Is it about the products now being available? Is it about something wrong about
the site or maybe they're dropping subscriptions that you were hoping they would keep?
Then finally, is everything that's explicit – if you run surveys or if you ask them,
"Hey, how was the experience in the store?" or "How was the experience here?" – you can get a
sense of how well did you deliver on this promise of having a customer experience.
Maybe one last comment on real-time is if you think about non-retailers like Uber
or any of these organizations that are in the business of informing people of where are my
goods, and they are real-time platforms, I think retailers also need to be able to
provide this type of capability. We certainly see companies like Albertsons leading the way
here because they're able to not only optimize their inventory; they're able to optimize the
experience while shopping but also, optimize the experience while delivering the goods (if they are
being shipped to the customer). Danielle, I see you nodding
furiously as Bruno was talking. The real-time aspect in the way
that the cloud and more modern infrastructure allow us to gather signals from our customers
in real-time and then make decisions in real-time about what their needs are is really unprecedented
in human history. It's an exciting place for somebody in data, like myself, to be.
How do we take all of this infrastructure and this amazing amount of data that we
have and enable incredible experiences for our customers? One of the ones that
appeal to me the most, which we're still working on, is if you're shopping in the store.
You're in the store, holding onto your phone. If you're like me, you do that. I do that every
time I'm in the store. I've got my list in front of me. I've got my app in front of me.
What if we could push to people what they bought the last time they were in the produce
aisle while they're standing in the produce aisle? Wouldn't that be super helpful to our customers?
While that takes a lot of infrastructure and capability that we don't necessarily have at
this moment in time, it will be here soon, and I'm excited about those types of use cases.
We used to think there is an online shopping experience and there is an
in-person shopping experience. But, increasingly, it's a hybrid shopping
experience. You had this multichannel experience before, but what if you could bring them together
even beyond the experience inside the store? We have retailers that we work with who are able
to provide real-time inventory information with a specific aisle where you would find
what you're looking for while the person is traveling to the store to really make it efficient
for them to not have to waste a bunch of time to find what they're looking for. What Danielle is
describing here – I really do think is the future of retail – is understanding this multichannel,
multiformat relationship and bringing it together to the service of excellent customer experience.
In the end, they're going to buy from you because you have created an environment that is really
focused on getting them to what they're looking for, and sometimes even recommending things
that they might not have thought about, in very effective ways. There's a huge opportunity here
beyond just the experience itself. The challenge is, as there is tremendous growth in data,
there's tremendous diversity of the nature of data you'd have to bring in order to accomplish that.
We tend to think about searches, clicking on boxes, and so forth. But
images, audio files, what's consumed that really has been part of the universe of these consumers
that if you truly have a modern data platform, it allows you to bring it all together to really
create something that's different that maybe, frankly, we never thought would be
possible – just like what Danielle is describing. What I find to be fascinating about what you're
both describing is, very often, we think of customer experience as the screen looks nice,
attractive buttons, but you're really going to customer experience at the most foundational level
where you're deep into the operations, deep into the processes, and really reflecting
(at an intuitive basis) how the customer is behaving and what they actually want from you.
In addition to this is data that they might not have had access to before. If you think about the
context of the data that influences your decision as a buyer, of course, you've got information
about your preferences, and you've got information about the inventory (and if it's available), but
what about weather conditions? What about things that are outside the store and outside of your own
data repository (as an individual, if you will) that are going to make your experiences better?
What if we knew that, two days from now, it's really going to rain and you should
really consider that, or the conditions are going to change? There's also a lot
of opportunity in bringing external data to really create this experience that, up until now,
if you think about the world of retail 30 years ago, it simply just was not possible.
Danielle, as you're thinking through data and customer experience at Albertsons,
how is this all resonating with you, what Bruno was just describing – again, this
strong operational and infrastructure piece? The amount of data and the amounts of types
of data that are able to be brought together now, almost in the real world you can replicate
the same kind of data streams that you have online. Think about it in terms of if someone
wants, and they opt-in, you can know where they were shopping before they came to Albertsons.
Online, you can do that. You've got the signals of, we know what other sites you were at,
and we can use that information. It's almost like that's getting replicated in real life, and that
can be a really powerful driver of customer experience (as long as it's used for good).
With this kind of broad spectrum of what's possible,
how do you isolate, prioritize where to actually focus? You can do anything, so what do you choose
to do and how do you decide what to do? We decide what to do a lot based on
size of opportunity. The first thing that we try to ask ourselves is,
"Is this something that is essential to the business at this point in time, and how valuable
is it?" because we always have limited resources in every business to decide what we should do.
I really encourage my data science team to focus on,
okay, how much business value is there in this idea? Let's get an idea and size that before we
start going down into a possible rabbit hole that might be very interesting but not very valuable.
That is really how we do it at Albertsons. What we've observed with the organizations we work
with is it's highly correlated with the level of pain that people are experiencing in their
experience. I'll just give you a few examples. Searching a large catalog, for instance, is really
hard for customers (even if they know what they're looking for). Working with Cartier, for instance.
It's a company that was started a long time ago; 174-year history of Cartier watches.
What they did is they now enable people to take a photo of the watch and find that,
across their catalog, (within three seconds) with a high level of accuracy. That's a huge pain point
because the customer already knows what they want, and you want to make it as fast as possible for
them to find that product. It's a great example of just augmenting the customer experience in
ways that really removes a lot of friction. Another example is an organization like Loreal,
for instance. It's a French organization. Michael, you know I'm French, so here I'm giving
you two French examples. I apologize for that. Loreal has created this application. I think it's
called ModiFace where you can experience makeup virtually, so augmented reality.
I think, if I look at what retailers and organizations that are providing great service and
products like that to their consumers, how they're enhancing the experience is very much related to
what Danielle is talking about. They're coming to your online properties, or they're coming to
your physical locations to accomplish a job. That job might be finding something they already know
or experiencing something that they're very curious but would be really difficult to do.
Changing makeup is hard. It's a very physical experience. If you can use digital experiences
to suggest here – based on what you're wearing right now, what you should be
putting on as makeup – is really quite amazing and something that we just couldn't do before.
Would it be fair to say that this focus on data and customer experience ultimately comes down
to using data to help model digitally the things that customers care about that they want to do,
and that they want to engage in? Is that a fair way to put it, or not really?
I would say it's pretty fair. I would add in that the way I look at it is, "Is this
helpful? Is it useful to the customer?" because those are the features that they're going to use.
A lot of times, we'll focus on, "Oh, this is really cool." Right? But is it actually going
to be useful to the person and their life? You look at Uber, right? Why was Uber super
successful? It was incredibly useful and filled a need that people didn't even know they had.
Those are the types of things I think that are most powerful when you're saying CX meets data.
There's this theory called "jobs to be done," Michael. I'm a big
Chris Christensen fan. He's a professor at MIT and has written this book on jobs to be done.
I think people coign to your store or visiting your online storefronts (if you will), they're
not coming to just do that. They're coming to accomplish a job that sometimes is about
finding a specific item or sometimes even creating an experience for people they're hosting.
To the extent to which you can make that process a lot easier is when you're really
going to provide the best service. That's what's going to make them come back because
they're going to get a sense that you get them. You understand who they are. You understand
the job they're trying to accomplish, and the way they're hiring you (the retailer)
to accomplish that job and enable them to make this progress toward the end goal.
The end goal is not coming to visit your store. The end goal is coming to create
an experience for people they love or buying something for themselves so they can feel good,
things that are really around the human experience.
Now, it's a beautiful time to be in 2022 because we have tremendous technology that
allows you to get there a lot faster. Really, we couldn't do that before.
The cloud has really helped us accomplish a lot of that. Data sharing platforms have allowed us to
do this at a very large scale for lots of different data types
while the data is governed in a saleable manner. We're really in a good time now to create amazing
experiences for customers shopping, wanting to create experiences for themselves and others.
We have a question that just came up from Twitter from Arsalan Khan. Arsalan is a regular listener
who asks great questions. Thank you, Arsalan. He's asking about the ethical considerations.
What about that? How do you think about the ethical lines, as Arsalan mentioned in his tweet?
The key thing for me is that it has to be in service of the customer. If the usage is in
service of the customer (and you have to request consent), then I think you can say this is for
good, this is data for good. If it is not in service of the customer and it is in service
of just your shareholders, then you really need to think about whether or not you want to do it.
Fortunately, our use case is not one of those dopamine use cases at Albertsons, so we don't
need to worry about that so much. But I think that is a huge consideration for the industry at large,
which is, are we deliberately making people addicted to their devices? That is something that,
as a culture, I think we have to tackle. Fortunately, at Albertsons, that's not our
use case. We want people to be in and out of their app, and we want it to be
easy and useful and sticky, but not addictive. Bruno, thoughts on this issue of the ethics
and where you draw the line? The security of the data, this is
not a compromise. This is something you have to build in, design upfront from that.
An example is opt-in applications. Customers need to opt into that experience,
so you can be sure that the data is secure and it's only accessed by them.
It's their data. Ultimately, they own that data. The retailer really doesn't.
You first have to have that contract (that ethical contract, if you will) where it's very clear
that you're never going to compromise on that. Secondly, the way we see retailers work with data,
in general, is the aggregation of the data, so you never really look at individual
information. You really look at trends, and that's how you maximize the relationship between the data
you have and the experiences with your customers. What Danielle was saying here is that
you have to align to what the job is and how you're creating the experience to the service
of the people who are buying that from you. You have to focus on where do we align here.
That's why I was connecting earlier in the buyer's journey, if you will. That's where
you're going to be able to provide the best value for your organization and the customers is when
they're aligning. Any time they're not aligning, I would say, is probably a red flag.
Let's shift gears a moment and talk about the organizational aspects. Building a data
machine, so to speak, is complex. Danielle, can you tell us about the composition of your team?
A team of product managers and data scientists that own the data lakes (so the data platforms,
data management, data governance teams), as well as data product leaders,
and then the data scientists that drive all of those algorithms. That is the composition of the
data office at Albertsons. They're responsible for just really driving this change.
We're 11 months in, at this point, of the data office at Albertsons, and so we're still building,
growing, and changing. But really exciting opportunities to build some platforms at
scale for Albertsons to drive really omnichannel. I would say that omnichannel has been a buzzword
for over a decade, and everybody has been like, "Oh, this is..." I think we're actually at the
point now, between data and infrastructure, that omnichannel is going to be a reality.
That's really exciting for us to be at the center of building those products and capabilities
and the data science that underlies it to drive true omnichannel experiences.
Bruno, the notion of a data culture, where does that fit in?
First of all, I'd say that Danielle is an exceptional chief data officer.
What's amazing in our industry is that we have now graduated and matured to having a lot of folks
like Danielle driving data strategies, and that's where data culture starts. It starts at the top.
I think, 10 years ago, if we look at the latest data, only 12% of companies had hired a chief
data officer. Now, in 2022, according to the latest data, I think it's 74% of organizations.
We're making a lot of progress. Culture, as vague as it might sound, also needs to be supported
by an organization that has a team, that has a charter, that is recognized and brought to the
executive table to drive the data strategy. It has now been recognized just like your
CFO. You have a finance organization. Well, now you have a data organization.
I think that's where it's starting. We have a long way to go because,
if you look at surveys in the market and we ask organizations, "What is the number one problem
to being successful with data?" 91%, almost 92% of people that have been surveyed will
tell you that culture is their greatest challenge. I think Danielle has a perspective on why that is,
so I don't want to steal the thunder from her on that. But I think it starts at the top,
and then there are, of course, some principles that you've got to go and apply.
Danielle, tell us about data culture and your thoughts on this?
It's really interesting to have been in the world of data since 2001, in the corporate environment,
and see the change that has occurred. I think, when I first started, the data culture challenge
was more of, how do we get people to use data to make decisions? Now, the problem is more,
what data do you use to make decisions, because there's so much of it?
It's almost like an analysis paralysis. You can get into so many different metrics and looking
at metrics. I think this is where big data and data science is going to have to come in,
in this "Fourth Industrial Revolution," is in making sense of all of this data and making
it actionable, which is why I think data science is at the center of all of this.
You could look at data all day long, metrics all day long. Are you really
making the right decisions? This is where models become very important.
Then going back to our data for good conversation, they have to be ethical models. So, it's an
interesting moment in time for data, but I think that that's where the culture needs to shift.
I think we have a lot of very traditional leadership
at this moment in time, and they have a tendency to want to look at their reports.
But I think, ten years down the road, what we're going to be doing is they won't have
to look at those reports necessarily, except for at the highest P&L level. It will be automated.
The decisions in the business will be far more automated through data science and algorithms.
I think that that will free up so much capacity within our organization to focus on higher-order
strategy. I think that will be fantastic, but I think we're at that moment of, like,
a lot of leaders don't really know what this new Fourth Industrial Revolution means. As data folks,
we have to move them forward in that direction. Or even trusting that we'll get there.
There's still a lot of education to occur. The encouraging piece is the rise of the chief
data officer and the rise of really good best practices around: How many data people should you
hire? What is the makeup of the data team? What is the role of the engineering and data science?
There are a lot of best practices there that just go way beyond what we used to talk about
ten years ago on, "Hey, let's have your data culture principles, print them on the wall,
and then hope people will respect them." Of course, you need to have that, but you also
need to have an organization, and you need to have practices that allow you to remind people of that.
What we see organizations do is data literacy programs. They do office hours, and they train
people on what does it look like to understand and work with this data, this dashboard, this insight.
You'll be surprised that people are really interested in learning that. Not the tooling,
not the dashboard in themselves, but they want and they recognize that they're sitting on a gold mine
of information. Up until now, until you really bring the chief data officer in the organization,
they really have not been able to use them beyond just the standard reporting
tactics that Danielle is talking about. I think all of us are consumers and we're
experiencing the tremendous benefits that occur when you have intelligence that surrounds you
and helps you throughout your day. But I think if we take an honest look at most organizations,
they're not there. In fact,
there's research that shows that only a third of organizations are able to get value out of
the data that they have. I'm encouraged by the progress we're making, but there's still a lot
of work to do. The number one barrier is the culture and the application, the deployment,
if you will, the execution on that culture strategy, which is still in progress right now.
We have a really interesting point from Robin on Twitter who says, "It's analysis paralysis,
but it's also the data story and value that should be connecting with the data products," so company,
visions, and value for the customer. Any thoughts on this? I think it's a really interesting point.
Your data program and data strategy has to connect to value, and it has to be quantifiable.
That's part of the role that I have, which is to develop this organization,
strategy, and then make sure that it's very clear
that this is tied to business value. Increase in sales, whatever the core metric might be,
and that the program is driven towards that. I am curious, as to Bruno's comment earlier
about people, like how few organizations are getting value out of their data.
That doesn't surprise me, and yet it concerns me because, to me, data is so valuable that
it's hard for me to even imagine a scenario in which you don't get value out of your data.
Everyone should learn from the work that you're doing. It's just still really complex, I think,
for most organizations to get full visibility on their data because the data that they work with is
often highly distributed, like we said. It's in all types of shapes and formats, and it's really
challenging if you are evolving on a platform that might not be modern.
It's really challenging, first of all, to even just get basic visibility. Then building data
products on top of that is certainly really complex for organizations to get there.
Beyond the technical aspects, what we are seeing is the organizations that succeed really connect
to this point that the listener is making on the value of the storytelling. The business
of making decisions is a very emotional one. We're not logical machines that also
have emotions. We're emotional machines that also use logic (sometimes) to make decisions.
What we see is people using internal marketing vehicles to make sure that
people are reminded of the culture. Do you have a brand around your initiative?
Is your brand memorable? Are people recognizing this brand as they're going through their reports?
A lot of organizations I work with will brand their reports or their dashboard saying,
"This is certified data based on the brand that we're using." And so,
there's definitely a logical component to it, but a big component is also how you're connecting at
the human level, not just with your customers but also with your employees who are trying to
make the right decisions with their data and sometimes they just can't connect with that.
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Okay. We have a great question. This is from Suman Kumar Chandra on LinkedIn.
He says, "How do you align the data strategy of your organization with the business strategy
when the business is changing very rapidly?" It's a really good question.
I like to simplify this. How you do it is you say, "Okay, your business strategy is always tied
to your P&L (in one way or another)." And so, if you keep your objectives very clear
(and three-year in nature), then you can tie your data strategy to your objectives quite clearly.
I don't see a difference between the business strategy and the data strategy. They are one
strategy. And so, to me, it's about my role and my team's role is to serve the organization.
What is the business strategy? What do we need to
build in order to be able to support that business strategy?
It's about making sure you're connected in and not in a silo (as a data team). That
you're connected into what the business is doing and how they're doing it.
How do you connect data and business strategy when things are changing really fast?
The data strategy and business strategy are the same. What we see organizations do to make sure
they are connected is shared OKR, so a shared goal, a shared goal between someone in the data
team and someone in the business team, and that they can't achieve unless they partner.
You just have to be able to advance together. I will say one more thing is that
data strategy has the potential of actually advancing your business strategy. There is
another French retailer for you. Carrefour just launched an initiative called Carrefour Links.
It's the result of the maturity of their data abilities that now enable them to create a data
platform where they can share their best practices on customer behaviors
with the ecosystem, a business that they were not in before. But it's the result of
connecting data strategy and business strategy. Now, because you have become such a mature
data organization, you can advance the business itself. Again, not many organizations are there,
but that's where the opportunity is. Arsalan Khan comes back, and he says,
"If culture is the biggest challenge for data, then where should the CDO report: CFO,
CIO, CEO, board? To whom should the chief data officer report?"
Depending on the business strategy at the time and what agenda you're trying to move forward,
the CDO should sit probably closer to whatever you're trying to move forward at that point in
time. Lots of times, the CDO will report to a CTO or a CIO. I think that if you're at the stage in
which you need to move your platforms from very old infrastructure to modern infrastructure,
that's probably a good place for the CDO to sit for that period of time.
At Albertsons, I report to the chief customer and digital officer
because that's the agenda we're moving forward at this moment in time. But I do think that where the
CDO sits is different in every organization (in a lot of ways), and so that's not inappropriate,
honestly. It is very appropriate, and I think that where the CDO should sit is dependent upon the
business objections of the company at that time. We're seeing the same thing. Certainly,
we asked (through surveys) where do CDOs report to. People had the choice:
the chief product officer, the chief technology officer, the CIO, the CFO. It was interesting
because we got about the same percentage across these, and I think that's probably because
of what Danielle is saying is it really depends on who is leading the charge on the most important
business imperative inside your organization. At Albertsons, your goal is to provide the most
compelling digital experiences or hybrid experiences for customers, and it makes a
lot of sense that data is going to be the fuel that's going to create those experiences. You
find organizations where they run under the CFO because they have the CFO that is very mindful.
We focus, just like what Danielle was saying earlier, on the initiatives that drive the
best value for customers. Therefore, the way to assess that value is through the conversion into
revenue and the ability to sell more and then sell different things that
customers come back to us with and so forth, and really optimize our processes.
Then you have organizations where, interestingly enough, they work under the chief product
officer because their business is to build data products. And so, they have data product managers
who will take the assets they have and then create experiences (like recommendations
and others) that are creating those products that really are creating
value for your organization and your customers. There is not one answer. It really depends on
the culture and the makeup of your organization and its business goals.
The role of the CDO kind of matures over time because I think the first CDO was actually
appointed in, I want to say, 2006. It's still a relatively new area and discipline.
I think that if it's driving really core business objectives, there are places in
which the CDO reports to the CEO, and maybe that will become more common over time.
How do you measure? How does one measure data initiatives, and especially as it relates to
customer experience? Robin, on Twitter, comes back. He just wants to be very clear that data
should not just sit in a dashboard. It needs to be informative, insightful, personalized,
actionable, and ultimately lead to accountability. Metrics, how do we measure these types of
initiatives, and especially with an emphasis on customer experience?
Data, if it's in a dashboard, isn't data. That's a metric that you track. Metrics are informed
by data but they are not data. Data is what's underneath and behind, and so that's another kind
of tweak to what he said – I would provide. I can sense Robin, in the ether on
Twitter, is smiling now, with you saying that. Again, that's part of the data culture concept
as well, going back to that. A lot of people think metrics or reporting are data. No.
Bruno, how do we measure? How do we decide? What are we measuring here?
There are many ways to decide it, and I'll probably just take
one and the opposite. The first one is, how do you measure value inside your organization?
I think one of the issues in the success of the chief data officer today is the inability
to connect the data with its value. I think your listener here is hitting on a very specific one.
There are many ways to look at the ROI, if you will, of data analytics. The first one is,
of course, just the simple level of adoption. Are your employees engaged with the data? Are
they making decisions based on data? Are they ignoring your initiatives? That's clearly the
first thing you want to look at it because if nobody is looking at what you've built, well,
if you don't have data-driven changes inside your organization, that's probably a red flag.
The other piece is customer satisfaction because, ultimately, if you think about what you're trying
to do here, you're trying to use data so you're more informed about what your customers want,
so you can provide the best experiences for them, so survey your customers.
Just like you see on the back of these trucks that say, "How is my driving?"
you probably should have the same level of interest on how are we delivering on
the promise. You came to our store. You came to our site. Do we really get you? Do we know you?
Then probably the other area that I would look at is how are your products evolving as
a result of the information that you have. Is the inventory you have today the same inventory that
you had last year? If the answer is it's a 99% overlap, then you have to ask yourself, did you
really not learn anything or you were just right on when you started?
I think there are so many factors that are going to affect your inventory. Hopefully,
with the knowledge you have from your customers, your industry has to evolve
in places maybe you didn't expect. Look at these three things. It's
probably a good way to get started: adoption, customer satisfaction, and the nature of the
inventory you propose, the type of company that you've become because of your knowledge of data.
What advice do you have for folks who want to use data to deepen their customer relationships?
I'll ask you to answer pretty quickly. Connect with the community. There are many ways
to learn from leaders. I'm excited that they're watching you today but start a conversation
with people you admire in the industry. It really starts with the dos and don'ts.
You want to learn from best practices. You want to learn from the worst practices.
People are really good at sharing that. We have our own video program we call Data
Journeys (every Tuesday) where I interview customers. It's really designed to do that.
I would say the best practice is connect to the humans, the human beings that are
behind these best practices, and reach out to them. Use LinkedIn. Use platforms
like that to ask your questions. It's going to be the best way that you learn.
Danielle, you started us off and you're going to get the final word here.
What advice do you have for folks who want to use data to deepen the customer relationships?
Know thy data and know where it is. You can get creative and inspired by data,
but you have to know what it is and where it is. Start there. Start with the simple stuff of, like,
"Okay, where does this data reside? How do we pull it? How do we know it? How do we understand it?"
Then you can come up with some really great ideas about how you can use it.
You can always come up with data, the things of how you're going to use it, but maybe that falls
apart when you actually go get it because it's not there. So, I always say, "Start with what's there,
and then grow from there," because if you try to abstract back from an idea,
you may be disappointed. If you start with the data, you can come up with
some really interesting and creative things. With that, we are out of time. I want to say
a huge thank you to Danielle Crop. She is the chief data officer of Albertsons. And to
Bruno Aziza, who is the head of data and analytics for Google Cloud. Thank you both so much for
sharing your valuable time with us today. I really, really appreciate it.
Thanks. Thank you so much for having us.
Everybody, thank you for watching, especially the folks who asked such awesome questions today.
Before you go, please subscribe to our YouTube channel, hit the subscribe button at the top
of our website so we can keep you up to date on our shows. We have just amazing shows coming up.
Everybody, thank you so much. I hope you have a great day, and we will see you next time. Bye-bye.
In today’s highly competitive business landscape, the combination of data and customer experience has become essential. Customers now expect companies to have a deep understanding of who they are and to customize their experience based on that information. Data and customer experience are now inextricably intertwined.
For companies like Albertsons, gathering and utilizing customer data is crucial for delivering personalized experiences. They collect various types of data, including information about individual customers as well as groups of customers. Understanding customer engagement and behavior, such as click-through rates, conversions, and shopping cart abandonment, is also important in optimizing the customer experience.
Real-time data is increasingly becoming a priority, as customers expect businesses to provide up-to-date information and tailored recommendations. Companies like Albertsons are at the forefront of leveraging data to optimize the customer experience, both in-store and during delivery. This requires advanced infrastructure and capabilities, but the potential for enhancing customer satisfaction is immense.
Retail is undergoing a significant transformation, with the merging of online and in-person shopping experiences. The goal is to create a seamless, hybrid shopping experience that incorporates real-time inventory information and personalized recommendations. By bringing together data from various channels, retailers can provide customers with a more efficient and satisfying experience.
The challenge lies in the diversity and volume of data that needs to be managed. Data platforms need to accommodate not just traditional data types like searches and clicks, but also images, audio files, and other forms of customer data. The ability to leverage diverse data sources is what enables retailers to create truly innovative and personalized experiences that were previously unimaginable.
At the heart of effective customer experience strategies is the ability to make data-driven decisions. By analyzing and understanding customer behavior, preferences, and external factors like weather conditions, companies can enhance the customer experience. Both Albertsons and Google recognize the value of leveraging data to optimize operations, processes, and ultimately, customer satisfaction.
However, it's crucial that this wealth of data is used responsibly and for the benefit of the customer. Personalization should not compromise privacy or cross ethical boundaries. Prioritization of data initiatives should be based on the size of the opportunity and the potential business value it brings.
As technology continues to advance and data becomes even more valuable, companies must adapt and harness the power of data to deliver exceptional customer experiences. The future of retail lies in leveraging data to create personalized, seamless, and efficient interactions that delight customers and drive business growth.
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