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SPEAKER: Is your Smart Analytics Platform built for the future?
SAP on Google Cloud is the digital transformation
your retail business needs.
Unlock insights faster.
Scale resources to meet demand, and leverage smart data
and machine learning to make decisions in real time.
This is Amy.
She's the CIO of a major retailer
with a global presence.
She's had a lot on her mind over the past 12 months.
The traditional consumer experience
is becoming even more digital, and COVID-19
accelerated e-commerce growth by four to six years.
Amy knows her customers expect a lot,
from just-in-time deliveries and real-time inventories
to a seamless experience, whether online or in store.
All of those expectations need to be met by her business
efficiently while still offering competitive prices
to their customers.
Understanding data for advertising, e-commerce,
inventory, supply chains, and customer loyalty
is critical to an effective digital engagement
strategy that can keep up with consumer behavior changes.
The ability to analyze all these forms of data
in a meaningful and scalable way has never been more critical
to her business's performance.
Last week, Amy received an email from her top merchant, Dante.
Dante has noticed that the company
is taking significant hits due to inefficiency
in managing markdowns and a lack of visibility across sales.
Amy knows it's time to take action
with a digital transformation across the entire
organization's value chain.
She and Dante turn to the Google Cloud
in order to get the most visibility out of the SAP data
and ensure their smart analytics platform is
built for the future.
By migrating her SAP workloads to Google Cloud
and leveraging the broad suite of Google's data
warehousing and intelligent technologies,
Amy will help her team capture data and break down silos.
SAP on Google Cloud democratizes data
with its integrated platform.
It works with an existing infrastructure that
relies on SAP to run ERP, logistics, and procurement,
while also incorporating non-SAP systems like CRM
or point of sales.
Merchants and category managers can
find actionable insights and more detailed
customer-centric information.
This will help Amy's retail business
make better evidence-based decisions
to improve their bottom line.
Now, with SAP on Google Cloud in place,
Amy's day is a lot more relaxed.
Easy visibility into her business's most essential
metrics means she feels more in control
of keeping up with the increasingly rapid consumer
behavior changes.
For her day-to-day, Amy has access
to dashboards through Looker specifically
configured to her CIO role.
While setting them up, she was able to aggregate data
from different sources and customize the way
she wanted it displayed.
From a security perspective, dashboards
can be set up based on roles so each user who is associated
with one or more roles can only see the data that they
are provisioned to see.
Now, she has real-time data at her fingertips
to quickly answer questions related to budgeting and risk
and make data-driven decisions on business performance.
Amy also feels at ease knowing that she
has an infrastructure in place that can scale flexibly
to meet business demands.
Best of all, Amy is confident she is giving her teams
the tools they need to succeed.
For retail merchant Dante, the Google Cloud and SAP
partnership allows him to create and adapt
sales plans confidently.
He can organize large amounts of SAP data
to predict local trends, prepare inventory, and optimize sales.
Leveraging these data insights reveal a customer's lifetime
value and purchasing patterns, enabling
Dante to ultimately work out ways to minimize and mitigate
product returns.
Today is Monday.
Dante wants to quickly check on overall retail sale
trends for the company.
First, he opens his Analytics folder,
where he has specific data access and dashboard
permissions.
Dante notices some positive trends,
but is alarmed that gross margins and inventory turnover
are declining.
The forecast for the upcoming winter doesn't look great.
He's familiar with the historical sales
and wants to understand which specific categories could
be heavily impacting the negative forecast.
Dante goes ahead and filters the dashboard by fashion hoodies
and sweatshirts to understand more
about a product category that should be a sales
driver in the upcoming season.
The sales target is underperforming,
but forecasts show an upwards trend.
Dante can see there is a substantial variance in brand
performance.
He also notices that there is poor performance coming
from the East Coast.
And as he scrolls down, he notices
Chicago, New York City, and Philadelphia are performing
much worse than last year.
Our analytics platform allows mass customization.
If Dante has filter options at the store level,
he can even get in touch with a specific store manager.
Today, Dante is less concerned about the specific store
and more so about the regional trends.
He knows the business will have to handle
the short-term problem of boosting this product category
while still maintaining margins.
Long-term, they will need to understand this seasonal trend
to better forecast next year's supply and pre-buy.
Before making any decision or contacting other teams,
Dante decides to review the e-commerce sales trends.
He goes back to his Analytics folder
and then dives into the real-time e-commerce sales.
He's pleased with the sales and trends he sees.
The business's online store is relatively new.
And, compared to the last company Dante worked at,
the amount of visibility he has into the website operations
is impressive.
He can see where the highest-spending users
are located with SAP transaction and geodata.
AutoML, Google's Machine Learning model,
discerns which social media platform users are coming from
and other data, like their gender.
Web clicks reveal which brands users are interested in.
And because Dante knows when conversion rates are highest,
he knows when to leverage resources
for targeted campaigns.
Google powers all this technology.
Dante can filter at the city level,
too, and understand local online trends.
All he has to do is click Run after the filters have
been created, and a real-time query is executed.
After browsing the filtered e-commerce Sales dashboard,
Dante notices two things.
First, there is more online volume and conversion
on the weekends.
Second, the top brands correlate almost entirely
with brands performing well in-store.
Dante can also drill into specific orders this year
and see the detail behind SAP transactions on Google Cloud.
Many BI tools may stop here, but Dante
can access a granular view of shipping and logistics data
at the transactional level.
This is where Apigee, our API management platform,
provides tremendous value.
Dante can pull order shipment status data
through SAP, IBP, and Blue Yonder
to get a 360-degree view of all logistics.
He notices that some orders aren't
processing as fast as usual.
The majority of inventory is sitting for 10 to 40 days.
This is not ideal.
Fashion hoodies and sweaters heavily
weight the negative sales forecast
for the upcoming winter.
Retail sales and inventory turnover
are low in the Northeast, while e-commerce sales
are picking up.
Dante is more concerned about the long-term seasonal trends
for next year, but there's clearly
a short-term problem at hand for the category manager to handle.
He decides to share his insights with his co-worker, Category
Manager, Eileen.
He selects the prefiltered dashboards
he just analyzed and sends them directly to Eileen's email.
This is Eileen.
She's a Category Manager who works with Amy and Dante.
Her ultimate goal is to increase customers' lifetime
value through techniques like affinity analysis.
SAP on Google Cloud helps her do this
by organizing data and offering very detailed customer views.
She can use dashboards to drill down on customer segments
and see who her top customers are.
Eileen leverages insights from this data
to know when to send out targeted campaigns
and effectively improve business revenue.
Eileen just received an email from Dante
that says there might be a problem in the Northeast.
Forecasted sales for hoodies and sweaters, this winter,
are expected to be down.
Can she boost sales for this category
without negatively impacting margins?
Dante's email also contains two prefiltered dashboards,
which is great because she doesn't
need to navigate to find the same data that he
is referencing.
Both dashboards zero in on the Fashion Hoodies and Sweaters
category.
The first shows a summary of Northeast in-store sales
over the past seven days, and the second
gives a 90-day overview of e-commerce sales
in Philadelphia.
Eileen opens the dashboard summarizing e-commerce sales.
She can immediately see the trends Dante has identified.
Next, she navigates back to her folder
to view the Retail Overview dashboard Dante shared.
Eileen knew that her category was not
meeting the forecasted targets through her dashboards,
but now with Google, she can see additional insights.
She decides to dive deeper into customer behavior
and see if she can determine what
is causing this negative trend.
Perhaps she can find insights to support a promotion or bundle
for the marketing team to consider before the winter
season.
All four customer segments seem to have positive trending sales
versus last year, especially Emerging Millennials, up 121%,
and one-off locals, up 341%.
Retaining emerging millennials seems to be a problem.
Why are they no longer purchasing
in-store in the Northeast?
Eileen drills down into the segment for a better look.
Eileen can check out the year-on-year performance
in the Customer Segment Deep Dive dashboard.
She notices that emerging Millennials' segment's basket
size is down 10%.
Eileen can also see a list of customers within the segment
who were loyal, but whose spend has
declined over the past year.
These are prime candidates to evaluate,
as their behavior might give clues
as to what is causing the overall downward trend.
Eileen looks at Alice Dorris's profile
to get an idea of emerging Millennials' behavior.
The customer dashboard for Alice is an SAP active repository.
The dashboard has data on Alice's loyalty, transaction
history, and even social media and ad exposure.
Eileen can see Alice's favorite categories,
which individual items she has previously ordered,
and the different channels she interacts with.
Eileen notices that Alice orders tops and tees, shorts,
and activewear.
Most of her brand exposure happens on Facebook and Google.
Eileen can see that Alice's sentiment towards their brand
is positive.
This is based on a data set of customer reviews
and social interactions labeled with emotions that AutoML
automatically trains, optimizes, and deploys as a visual model.
This machine learning feature gives Eileen insight
into Alice's Facebook posts, Twitter mentions,
and product reviews.
Eileen gets busy.
She plans to spend the next hour digging into Alice's behavior
so that she can provide some meaningful insights back
to Dante, and some recommendations
to the marketing team.
The infrastructure that Amy, Dante, and Eileen's business
now has in place gives them the power
to keep up with consumers.
As a category manager, Eileen can use custom dashboards
and drilldowns for visibility into segments that impact
revenue.
As a merchant, Dante can locate positive and negative trends
and keep an eye on forecasts so the business can
make adjustments proactively.
And as a CIO, Amy can make quicker decisions
that will drive her business to perform even as a customer
experience becomes more digital.
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Is your Smart Analytics Platform built for the future?
Unlock the potential of your SAP data with Google Cloud.