Customer Experience and Data Strategy with Google and Albertsons

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. 
I just want to invite everybody to subscribe  to our newsletter, so we can keep you up to  
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watching on CXOTalk.com, subscribe to our  YouTube channel too, and tell a friend. 
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  
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Everybody, thank you so much. I hope you have a  great day, and we will see you next time. Bye-bye.