When probability becomes reality

When probability
becomes reality
10 data stories about how you can use predictive analytics
to drive digital change at your company
Turning
data into
value
A journey through a few data stories
directly from the daily business of
organizations
2
Contents
How to become a data-driven enterprise in 10 new ways 6
Data story 1
Causality in times of big data 8
Data story 2
Seafood in Italy
16
Data story 3
Towns see the light
24
Data story 4
Bagels in New York
32
Data story 5
East Coast versus West Coast
38
Data story 6
A receipt says more than a thousand words
48
Data story 7
Dynamic pricing with weather and data services
56
Data story 8
Replacement buying behavior for sushi
62
Data story 9
Transport planning between island and continent
68
Data story 10
Relieving a call center and improving customer service
74
Entering the digital future with Blue Yonder 82
How to become a
data-driven enterprise
in 10 new ways
This is a short e-book for people who are ready to do away with old ways of think-
With our data stories, we show you how you can forecast the future and master
ing in order to find a new way of dealing with data. In short, for people who are
it using predictive applications from Blue Yonder. Sounds like science fiction? No,
open to the digital future and who don’t just want to collect data, but also want to
it’s just straightforward science. Blue Yonder’s technology is based on scientific
gain knowledge from it and turn that knowledge and insight into tangible results.
methods and algorithms. These allow historical data and external factors to be
evaluated and used to create accurate forecasts. What was once just a probability
Data, data, everywhere
can now be calculated very exactly. Probability becomes reality.
Today, data shapes our lives: how we communicate, work, and do business. We
One other thing sets our data stories apart from science fiction. We are not describ-
constantly read about data now being one of the most important pillars of our
ing things far off in the future, but very tangible approaches, stories right out of
economy, the fourth production factor, as it were, the most important raw mate-
the daily lives of our data scientists and our customers. Some are obvious, some
rial, the new ‘oil’ of the future.
surprising. But all are exciting and taken directly from real life.
But how can you process this ‘oil’ to make it a useful commodity? Like many
other organizations out there, are you still searching for the formula that allows
you to profit from your data? Do you have mountains of data just waiting to be
harnessed and exploited? Would you like to use this data to help you predict
the future? If you answered yes to any of these questions… read on.
The key to success: scientifically based forecasts from a dynamic, young and
P.S. If you’re asking yourself why you should read the Blue Yonder data stories, then
innovative company
you should know that it’s not for nothing that we’re the leading SaaS provider for
predictive applications in Europe.
The world is moving at a rapid pace. Due to digitalization, organizations now need
to make decisions at the drop of a hat and take into account such a variety of
Would you like more information? www.blue-yonder.com
factors that it completely exceeds human capabilities. Organizations that want
to compete in the digital future must recognize the value of their data and the
plethora of information that exists within it, and be able to use it in an automated
way. To do so, they have turned to technology – such as the services that Blue
Yonder provides.
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7
Rethinking
relationships
Data story 1
Causality in times of big data
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9
“Half the money I spend on advertising is wasted;
the trouble is I don’t know which half.”
John Wanamaker (1838–1922)
“Half the money you spend on advertising is wasted;
the trouble is that you don’t know it. But we do.
And we even know which half.”
Prof. Dr. Michael Feindt, Blue Yonder (2015)
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You don’t have to dye
your hair purple to be
trendy.
Sometimes it just helps to rethink, reimagine and discard old traditions in favor of
new developments. That is especially true in marketing.
Blue Yonder’s Predictive Applications give you accurate forecasts about future customer behavior and identifying new causal relationships. This allows you to make
better recurring decisions than in the past and leads to huge savings in your marketing campaigns without endangering your sales. And you can even reduce your
marketing budget and still increase sales. Imagine a world with lower marketing
budgets and more effective campaigns.
Does this sound like squaring the circle? Not with advanced algorithms from
Blue Yonder. At one large fashion and lifestyle retailer, this led to a revolutionary
rethink in planning catalog sales.
Imagine this:
A fashion retailer has millions of customers in its database. It sends out an annual catalog, but as we already know, catalogs are expensive to print and ship.
For a company with a lot of customers, this soon adds up to huge costs, even
though not every customer receives a catalog, just those who are most likely to
make a purchase. The retailer’s goal is that customers will buy something within
four weeks of the catalog being sent.
In the past, only half of the customers received a catalog. And it only went to loyal
customers. Taking a look at historical data shows that only 6% of the customers
who didn’t receive a catalog visited the shop, while 30% of those who received
one did. This sounds like a huge marketing success... but it isn’t. There’s more to
this than meets the eye.
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Marketing management based on Blue Yonder algorithms clearly shows that the
Catalogs were only shipped to those customers who, on the basis of histori-
selection criteria common to business sectors mostly reflect how loyal the cus-
cal data, could be expected to alter their purchasing behavior on receipt of
tomer is but say nothing about how the catalog affects their buying behavior. But
a catalog (causality!).
that is precisely the issue that we need to address. Which customers will be
moved to purchase by the catalog?
Advertising effect after new selection criteria (causality)
Causality structure
A loyal customer will in all probability buy from the company in the foreseeable
The result: With only half the budget (25% of all customers), the same advertis-
future, without having received a catalog. While a ‘bad customer,’ who buys less
ing effect and the same result were achieved as shipping to 50% of all customers
frequently or fewer items might be moved to buy if they receive a catalog. In this
based on the old selection criteria. In other words: with 75% of the budget, cus-
case, it is not enough to make correlations in order to decide which customers will
tomer activity can be increased by 5%. The circle can now be squared: more sales
receive a catalog. We must delve deeper into the data and discover new relation-
with fewer costs! This is because, when the causal effect is taken into account,
ships.
only half of the budget is necessary. This is not pie-in-the-sky thinking. This is the
power of big data, new technology, and scientifically based algorithms.
Big data and the algorithms from Blue Yonder make it possible to identify causali-
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ties that are not immediately evident. At the fashion retailer, Blue Yonder was able
Does this sound a bit too new and complicated? If you want to know exactly
to discover that a change in catalog shipping based on the causality criterion leads
how it works, we would love to give you a personal demo. We think you’ll be
to much better results:
surprised.
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Accurately
plan sales
Data story 2
Seafood in Italy
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17
Summer storm leaves
scallops abandoned on
the shelves, while heat
wave Helen sees heightened halibut sales.
Vacation in Bella Italia! Sun, sea and sand, la dolce vita. Some of Italy’s most
popular summer vacation regions are on the Adriatic coast, and for many tourists, enjoying regional Mediterranean cuisine with fresh fish and seafood hot
off the barbecue is a daily pleasure. It’s a good thing that an inexpensive supermarket with a fresh fish counter is just around the corner…
What does this have to do with data?
For a big supermarket chain in Italy, a whole lot. The right amount of fresh fish and
seafood has to be planned down to the last crayfish tail. And this is not just true
for the tourist coastal regions, which see millions of visitors each summer, but also
for the stores inland, which are mainly frequented by the locals. Low-stock situations mean money down the drain, and for seafood, which spoils rapidly, too much
stock leads to high write-offs. You can imagine that for a large supermarket chain
with over a thousand stores, imprecise planning can result in very high costs in this
fresh food product category.
The classical ‘manual’ goods planning, using an Excel spreadsheet is no longer sufficient. Professional, innovative data analysis that takes into account historical data
as well as complex relationships is what’s needed.
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The key question is:
Which factors influence the sale of
seafood?
Here are the results:
Factor 1 − Seasonality, or the ‘tourism effect’: Italian summers attract millions
of tourists, who in turn increase the demand for fresh seafood. This brings us to
factor 2 − the weather: it isn’t a secret and should come as no surprise that when
Ò
In the tourist season (July to September) considerably more
seafood is sold.
the sun is out and the weather is perfect for a barbecue , the demand for fresh
seafood is significantly higher than when the weather is bad. Factor 3 − location
is also interesting: in vacation regions on the coast, the demand for seafood rises
quickly, whereas at stores inland where the locals shop, the increase is only slight.
Factor 4 − prices and promotions − because, as everywhere, price plays a big
role in influencing our buying behavior. Customers just love sales promotions. And
last but not least, factor 5 − low stocks: it makes sense that when stocks are low,
you need to replenish them − at least if you know there is demand.
- In vacation regions there is a clear increase, while sales at the inland stores
only rise slightly.
- Weather and the barbecue and tourism effects overlap and can differ
according to the location: not just tourists on the coast but and also locals
living inland love to barbecue when the weather is nice.
- Specific store-product combinations differ distinctly from one another and
need to be viewed individually.
It all seems quite obvious, reasonable and simple. And it is. If you want to analyze
all these factors and their relationships for more than a thousand stores and hundreds of different products with daily offers, taking into account historical sales as
well as holidays, over a long period of time, you soon generate a huge volume of
data. A human decision-maker acting alone will very quickly lose track. As part of
a pilot project, Blue Yonder looked at a supermarket chain’s data. The predictive
applications used included all factors and dependencies in the analysis and in so
doing obtained accurate forecasts of future demand.
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Ò
Low-stock situations are money down the drain, particularly during
sales promotions.
Ò
Weather changes influence seafood sales considerably.
Weather service data is included in the analysis and the forecasts. When
the sun is out, demand rises sharply, while rain and clouds negatively affect
seafood sales.
Ò
Sales promotions increase sales considerably.
But the location of the store, the individual product and the existing stock
Weather changes influence the sale of shrimp
play a decisive role in sales.
All of this information goes into Blue Yonder’s predictive applications, which turn
them into accurate forecasts. This means the complex materials planning for seafood can be automated for the supermarket chain’s many stores. You can imagine
the cost savings that result.
By the way, the solution will still work even if you don’t have any seafood in your
product range. You can use it to accurately plan sales for meat, fruit and vegetables.
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Intelligent
communication between
machines
Data story 3
Towns see the light
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Golden Gate Park, San Francisco, California
11.27.2016, 6 PM
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Golden Gate Park, San Francisco, California
12.03.2016, 6 PM
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Feeling burned out?
We could see it coming
from a mile away…
Not just for humans, but for streetlights. Connected services based on predictive
applications make it possible. How? Blue Yonder and Capgemini have developed a
predictive application that accurately predicts the risk of streetlights burning out.
A light uses the most energy four weeks before it burns out completely. Logically,
this makes it the best time to replace it with a new bulb. Did you know that street
lighting accounts for 30 to 50 percent of the energy consumption of our towns
and cities? It goes without saying that this is a huge cost factor, but imagine being able to drastically reduce this by having the ability to predict the exact time a
streetlight will begin to use too much energy. Up until now, imagining this was all
we could do; predictive applications can make it a reality.
We have made streetlights ‘intelligent.’ We did so together with our technology
and consulting partner company Capgemini as part of a pilot project.
Equipped with sensors, the lights now provide information about their condition,
including vibration, brightness and energy consumption. The Blue Yonder algorithm can take the data from thousands of lights and tell the risk of a specific light
going out in a specified time period − in this case within 30 days. This allows the
operators to carry out the required maintenance proactively and to save on costs
− which, after all, is the main goal.
In the graphic below, you can see a simulation of the street lighting in a district of
San Francisco. The map entries represent the individual lights, and by clicking on
them you can see the individual risk profile. The Blue Yonder algorithm includes
historical sensor data as well as external influencing factors in its forecast.
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For example, it takes into account which locations have been more frequently van-
That’s not all: This is one of many illuminating examples of how connected ser-
dalized or when a power outage occurred. The time of year, weather, and other
vices from Blue Yonder and Capgemini can significantly increase the efficiency of
factors also go into the calculation. Based on this, the predictive application cre-
machine investment goods through intelligent automation. A streetlight is a sim-
ates a risk profile for each individual light.
ple object, but our algorithms really come into their own when things get complex: parking garages, industrial facilities, production lines, wind farms…
If there are big cost savings and a higher service level
for something as simple as streetlights, just think of
With a date slider, we can also filter based on the lights that have a high risk of outage due to overheating in the next 30 days. And we can use the risk slider to see
the level of the outage risk. The map immediately shows the ‘candidates.’ At these
locations, a fully automated, proactive maintenance mechanism can be triggered
the potential of connected services in industry and
in the consumer goods industry.
to prevent the light going out and thus save on energy costs.
The risk of light loss due to power failure can be determined for each individual
light. This allows the maintenance team to check the power supply before a problem occurs, and so prevent costly power outages. With a single click on Create
Issue, a sales force ticket can be immediately created and assigned to a service
team member.
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Do you want to shine light into the dark?
Then don’t hesitate to contact us.
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What makes
Blue Yonder
forecasts so
accurate…
Data story 4
Bagels in New York
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Data Story
How a humble bagel
can save an entire day
Data science sounds way too intellectual and complicated, doesn’t it? But the
right solution is very simple. With Blue Yonder, you are only a few clicks away from
easy-to-understand, accurate forecasts.
The best example of this: a 24/7 bakery in New York in a busy street near the city
center that is popular with office workers.
You know how it goes: You’d rather sleep in a bit later than eat breakfast at home;
you rush out of the house in a hurry to get to the office on time and drop by
the bakery on the way to buy a bagel to keep you going until lunch. You walk in
and wait in line checking your watch every few seconds and finally make it to
the counter… only to be told that bagels have just sold out. The owner is baking a new batch, but by the time they’re ready you’ll already be late for work.
Disheartened, you wave your dream of a bagel goodbye and make your way to
the office. On the way home in the afternoon you still haven’t had that bagel so
you decide to go back to the bakery to get your carb fix... And the same thing
happens again. The customer before you bought the last one, and the words
‘Sold out’ shatter your bagel dreams. If only you’d arrived a few minutes earlier.
Everyone has had days like this. It is frustrating for you as a customer and bad for
the bakery because the out-of-stock situation means it loses sales.
But if the bakery uses the cyclic-boosting algorithm from Blue Yonder, these
‘out-of-bagel’ scenarios could be a thing of the past. The model uses numerous
factors that increase the demand for bagels in its analysis and creates forecasts that
are very close to actual daily demand.
In the following chart, you can see the Blue Yonder forecast (the green line) and
the daily sales (blue line).
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Influencing factors on sales on 09.18.2014, 5 PM
If other competing products such as rolls are on special offer, this will also affect
the sale of bagels. The so-called ‘cannibalism effect’ occurs: more rolls and fewThe sale of bagels is highly dependent on the time of day. Considerably more ba-
er bagels are bought. Our model also takes this into consideration and shows it
gels are sold in the morning at breakfast or as a snack for work or school and at
under ‘Discount on other products.’ Because the factor is smaller than one, our
dinner, than at any time of day. It is clear that the time of day is the most important
forecast is corrected downwards.
factor for predicting bagel demand.
Influencing factors on sales on 09.16.2014, 8 AM
What is very simple and intuitive becomes much more complicated when other
influencing factors go into the forecast. If there are 60 or 70 factors, this quickly
gets to be too much for human planning capability alone. The data can only be
analyzed using computers and only when as many relationships as possible are
included in the planning.
Factors that the Blue Yonder algorithm takes into account include: the time of day,
the weather forecast, the day of the week, school holidays, upcoming holidays,
and also sales offers for bagels and other products.
For each forecast, the factors that influence the analysis are represented as bars.
The size of the bar represents the power of the influencing factor. It becomes clear
that both the time of day and a discount on the product positively influence the
What does this mean for a 24-hour bakery?
demand for bagels. The model ‘notices’ this and corrects the forecast upwards
The bakery knows ahead of time how many bagels are needed at what time and
(factor >1).
can offer exactly the right number − and that for each store. It thus avoids out-ofstock situations and write-offs due to too many products.
And the customer? Is happy and shops there again… and doesn’t begrudge the
customer ahead of him his bagel.
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Discovering
new and old
patterns
Data story 5
East Coast versus
West Coast
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Friday evening in San Francisco
40
Friday evening in New York
41
Saturday morning in San Francisco
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Saturday morning in New York
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And where do you spend
your Friday evening?
At home? On the beach? In a restaurant? In a supermarket? It probably depends on which state you come from.
Let’s face it, although the East Coast and West Coast are in the same country, they
may as well be worlds apart… or are they? Consumption and purchasing behavior
have become similar across state lines. Ultra-creamy New York cheesecake, a New
York specialty, is of course consumed in San Francisco too. Hot Dogs with all the
toppings, or Hershey’s Kisses, are enjoyed all over the USA. And which state doesn’t
love cola?
But is this completely true? Does East Coast versus West Coast actually mean
anything when it comes to buying patterns? Blue Yonder analyzed large data
quantities and found a clear relationship between locations, days of the week, and
sales.
An interesting result that confirms the experiences of the companies involved
in commerce in this area: While on the East Coast, Friday is the most purchaseintensive day of the week, people on the West Coast mostly shop for groceries on
Saturdays. In the East, people stop by the supermarket on Friday after work to do
their weekend shopping, and to have Saturday free for other things. In the West,
Saturday is the busiest day.
Blue Yonder visualized this in a cyclic-boosting model that includes a number of
characteristics in its analysis and the sales forecasts. Each attribute is given a factor
that is calculated on the basis of global sales and correlated with other model factors such as the sales area (for example East/West).
In the following graph, you can see clearly that there is a correlation between the
day of the week and store sales:
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Weekday mapping
To avoid making it overly complex, we have only displayed the relationship between the characteristics ‘day of the week,’ ‘sales area,’ and ‘buying behavior’
here. But our model includes many additional characteristics that are directly correlated to the day of the week, for example:
Ò Sales promotion offers (advertising or price reduction)
Ò S
ize of the product range/sales level in a store
*Learned influencing factor compared to the average sales level without differentiation based on the day of the week
Ò Time in days before or after a holiday
Due to the model being able to take into account correlations between different
characteristics, it is possible to learn region-dependent correction factors for the
Ò Relative price reduction for a sales promotion
average sales level without differentiation by day or region. The next graph shows
that the average sales level per weekday, which the model has learned from all
regions nationwide, has to be increased by a factor of 1.15 for Fridays and de-
Ò Store (to show differences in the weekly profile, including store-specifically)
creased by a factor of 0.9 for Saturdays in Eastern regions.
Weekday sales area mapping
To calculate complex dependencies of this kind, you need an effective solution
like the powerful predictive applications from Blue Yonder, which calculate highly
accurate sales forecasts from all these characteristics and factors.
For commercial organizations, this means that automated order decisions can be
made based on these accurate sales forecasts for different articles and stores. This
ensures that exactly the right quantities of products are available, write-offs and
out-of-stock situations are avoided, and the customers have a positive buying experience − every day and in every state.
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**Learned influencing factor compared to the sales level without differentiation based on day of the week/sales area
47
Discover
surprising
correlations
Data story 6
A receipt says more than a
thousand words
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Do you need your
receipt for that?
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Data Story
What is a receipt? A thin piece of paper, often screwed up and left at the bottom of
shopping carts. At least from the customer’s point of view. But from the company’s
point of view, this piece of paper is pure gold and is an almost unlimited source
What did Blue Yonder find out from the
customer receipt data?
of valuable information − but it’s a piece of gold that is often ignored or swept to
the side.
1. Some articles and article groups are more frequently bought than others.
With the help of Blue Yonder, retailers can gain important, detailed information on
customer buying behavior from their receipt data. This allows the sales, logistics,
Ò People buying pharmacy articles will also − with a high probability −
buy other pharmacy products (plausible).
and staff planning to be optimized. This data is also valuable for focused marketing: for example, if specific customers always buy the same things, you can use this
information to offer personalized discounts and coupons.
Ò People who buy pharmacy articles will also − with a high probability −
buy perfume articles (goes together).
Our data scientists analyzed about two billion cashier receipt items for a large
European retail chain. Each individual receipt contained the following data:
receipt number, store number, product number, price, date and time, and
total amount (gross/net). Of course the receipt data made available contained
Ò Interestingly, pharmacy articles are also often bought together with
stationery products (this is not so obvious).
no customer-specific information.
What interesting information can Blue Yonder filter
from this data?
Ò Confectionery is frequently purchased with other foods (not really
Today, many commercial organizations use the data from their systems and com-
Ò Battery purchases are not correlated with anything (i.e. they are often
surprising).
puters to forecast the daily demand at article and store level, and to do their de-
bought alone).
mand planning based on this. But by including receipt data in the analysis, we get
much more differentiated information. For example, customers who pay by credit
card have different buying behavior than cash payers. Out-of-stock situations also
And what do I, as a company manager, do with this information? Actually, these
become visible: is the article sold out five minutes before the store closes on Satur-
relationships are very useful, for example when it comes to organizing the store
day evening, or is it already sold out on Tuesday morning?
and creating sales offers.
2. In which order does the customer put the goods on the conveyor belt?
Even this information is useful, because it can provide information about customer
pathways through the store and so help optimize layout and product presentation.
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3.At what time of day and which article group is bought most?
Multi-media articles vs. household goods
Sales based on time of day over all product groups
Did you guess? The blue line shows household goods, the purple line shows
The graph shows that at specific times of day there are small sales spikes. Sales
multi-media products. It‘s up to you how you interpret this. Do more stay-at-home
rise a bit before lunch and shortly before dinner (or directly after most people fin-
moms buy in the morning (household goods)? And males and young people in
ish work). This information can be used for accurate sales planning, for improved
the afternoon (the ‘classic’ multi-media groups)?
planning of delivery times and staffing levels (customer density at the checkout
and thus in the store). For fresh products that only have a short shelf life, the time
of day plays a particularly important role. Using detailed information means the
demand planning forecasts become more precise, and reorders and logistics can
be planned much more accurately.
Would you like to find out more about what receipt data analysis from Blue Yonder
has to offer? There is a lot more hidden in those small receipts. We‘d love to
show you the possibilities.
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The profit
is in the
price
Data story 7
Dynamic pricing with
weather and data services
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Does too much sun
mean forgetting the
price tag?
We have good news for anyone interested in dynamic pricing influenced by
weather and data services. We’ll show you how you can use the weather to find
the optimal price at the right time.
For a fashion retailer, we adapt prices for diverse product groups. To find the optimal price for a specific time, we use external factors, such as the weather and
holidays in our calculation, and we do this automatically. We get excellent results,
which greatly improve the company figures.
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What should a swimsuit cost when it rains on
Pentecost Sunday? What should it cost when the
sun shines?
Data scientists at Blue Yonder wanted to answer exactly these questions − and
came up with intriguing results.
The optimal price for winter jackets on the last cold
days of the year
Weather, stock & sales promotions
Toward the end of a season, clearing inventory becomes a priority.
This generally involves that word that customers love to hear: SALE.
This means high stock levels − including those of winter jackets −
are reduced. And competitors are doing the same.
The Pentecost (Whitsun) holidays are between mid-May
Pentecost Sunday weather
and early June, depending on when Easter falls. As can
be seen from the fashion retailer’s historical data the sale
of swimsuits generally increases significantly on the first
Price-sales relationship
How does the weather affect sales? And how does it affect the competitors’ prices? Blue Yonder’s software calculates this using weather
warm days and on days off.
data. When the weather gets colder, sales increase. Sales advertisements fuel the sale of winter jackets. However, due to competitive
pressures, the price-sales relationship also gets steeper.
The price-sales relationship
People who use the warm weather for their first trip of
the year to the swimming pool, or who want to book a
last-minute trip for the Pentecost holidays are generally
also prepared to spend money on a swimsuit. This results
in changes in the price-sales relationship.
Optimal
price
This time, the strategic goal is reducing inventory while maximizing
sales. The Blue Yonder software automatically calculates the optimal
$59.99
$45.99
reduced price from all the existing data − taking into account the
weather, the inventory, and the sales promotions.
Optimal
price
$12.99
Sales & revenue
Assuming that the strategic goal of the company is maximizing sales and revenue, then the optimal price will shift
$15.99
upwards thanks to this changed pricing readiness around
Sales & inventory
Customers love reduced prices, aka sales. They buy. Sales rise fast.
Pentecost. Blue Yonder automatically determines exactly
What does this mean for the key figures? In this case, there is a con-
this optimized price.
siderable improvement thanks to dynamic pricing.
The fashion enterprise obtained excellent results by relying on Blue Yonder’s automated price recommendations.
Dynamic price optimization greatly increases the key figures.
Whatever your strategic goal is (increasing sales, clearing inventory, gaining market share…), with Blue Yonder dynamic pricing you always set the optimal price.
To find it, we include important factors such as weather and holidays in the calculation − and the entire process is dynamic and automated!
Do you want to optimize your prices, too?
We’ll show you how in a demo.
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Optimizing
products and
increasing
key figures
Data story 8
Replacement buying behavior
for sushi
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Chopsticks or forks? We
can’t influence how you
eat your sushi…
But we can ensure that
you get it.
Thanks to manufacturers of fresh products like Natsu Foods, sushi fans can now
get ‘sushi in a box’ at the supermarket. The company supplies more than 2,800
supermarkets in Germany with sushi, wraps, and salads − all fresh products with a
maximum shelf life of 3−5 days − which are sold in shops-in-shop.
The short lifespan of its products is a big challenge for the company. For that reason, for some time now, Natsu has relied on Blue Yonder to provide it with its precise daily demand forecasts to make its goods planning and production more efficient, to reduce its remaining unsold stocks and to optimize its logistics processes.
As sushi lovers know, sushi is not just sushi. There are several different types of
this delicious Japanese fish delicacy and Natsu has four different versions of the
popular nigiri sushi alone (small rice balls with a topping):
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At Natsu, nigiri sushi comes together with other sushi types in visually appealing
clear boxes of various sizes and with exotic Japanese names:
Certain boxes may already be sold out by the afternoon and people might then
Now it really gets interesting!
make alternative purchases. Because it is very popular with customers, the Blue
Yonder data scientists looked more closely at the replacement behavior for nigiri
Information from purchasing behavior helps improve sales planning. But it also
sushi. Can the data tell us how the customer acts when their favorite sushi
offers further insights. For example, conclusions can be drawn about which prod-
box is sold out and there are three different alternatives in the chiller?
uct range is best for which market. This means the product range can be better
planned at the market level.
Yes, it can. The data scientists discovered from the sales figures that certain sushi
variations elicit a stronger replacement behavior than others. People who normally
What can be learned from this?
prefer boxes with a high nigiri content will − more than average − either choose a
With the help of Blue Yonder Analytics, product-range planning at store level can
package of the same size or a box that has a similar number of nigiri.
be optimized, and not just for sushi.
The graph shows for which product packages there is an above-average replace-
Are you hungry now?
ment behavior. Fundamentally, this exists for all sushi, of course, just to differing
extents. The thicker the line in the graph, the stronger the replacement tendency.
To satisfy your hunger for sushi, we recommend you try the Natsu products in your
supermarket, or go to www.natsu.eu/en.
Our data science experts can satisfy your hunger for more information on the topic
of data-driven sushi (and other products). Let’s talk.
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Create eventtriggered
forecasts
Data story 9
Transport planning between island
and continent
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69
Somehow it all fits in,
but it is better with
Blue Yonder.
The 24-hour race at Le Mans is a popular long-distance sports car race. Since 1923,
the event has been held each year in mid-June on the outskirts of the French city
of Le Mans. Often described as the hardest automobile race in the world, today Le
Mans is as popular as ever.
Just like the Europeans on the Continent, the British are also big racing fans. This
isn’t really anything new. But the fact that we can gain this information from the
ticket sales data of a large transport company and can use it for the traffic and
logistics planning between Britain and continental Europe is new and innovative.
As the Blue Yonder data scientists found out in a predictive analytics project for the
company, the race has a very strong influence on the volume of traffic heading
across the channel from Britain. Blue Yonder can accurately forecast events like this
that don’t always occur at the same time, and can predict the effects.
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Which events are included in the
Blue Yonder forecast?
The traffic specialist company has its own event calendar, showing holidays, Easter
and summer holidays and special events such as races like Le Mans. For the data
analysis, external event services did not even have to be used. The data scientists
were able to work exclusively with the company’s own historical data, using it to
create forecasts for tickets per day and vehicle category − cars, trucks and camper
vans. Because ticket prices vary according to vehicle type − large vehicles are more
expensive than smaller ones − this data was easy to obtain from the bookings.
The interesting result:
When a special event like the race at Le Mans takes place, ticket sales for large
vehicles increase.
If it is assumed that there are various events on both sides of the English Channel,
as well as different holidays and vacation days in different countries, the data and
the interdependencies increase very fast and quickly become enormous. A highly
modern algorithm such as that of Blue Yonder can manage these data volumes. It
continuously improves itself, and can provide numerous accurate forecasts on the
number of required tickets in each category on a daily level.
This not only makes traffic and logistics planning simpler, it can also be automated
– increasing efficiency and safety, while reducing costs.
So everyone gets a ‘flying start’ to Le Mans!
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Finding the
small
‘adjusting
screws’ for
optimization
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Data story 10
Relieving a call center and
improving customer service
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Here’s my number,
so call me maybe?
A typical scenario: You receive your broadband and cable bill and it’s much more
than you were expecting. You can’t seem to figure out why the charge this month
is so high. That’s because it’s complex and made up of a lots of different data, such
as per minute charges, extra online orders or pay-per-view. So what do you do?
You pick up the phone and call your provider. This is still the simplest and fastest
way to get help − assuming that you don’t get put into a call-waiting loop…
The latter does not mean that the call center team member is on break, is playing
solitaire or is ignoring you out of rudeness. It means that they are overburdened,
which isn’t good for either the team member or the customer.
In addition, each call to the call center costs the company money. For a large company, those costs add up fast. For this reason, a telecommunications company in
Britain made it its strategic goal to reduce the number of call center calls after the
invoices go out. This is where Blue Yonder’s data scientists come in.
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Determining the reasons for calls
Some of the exciting findings of this project include:
Blue Yonder was given the task of using the company’s historical data to find out
which factors primarily lead to customers picking up the phone, and calculating
1. Timing of the bill
the probability of a call. For this, two differing time windows were looked at:
Just before and during the festive period and vacations, the number of calls
to customer services is drastically reduced, and no wonder; just before Christ-
How high is the probability that a customer will call within a week of receiving
mas people have more important things to deal with than bill enquiries, and
the bill?
during the summer, people are often away on vacation.
Predictive power of the Blue Yonder model using historical data
2. Repeat offenders
Typically, people who have called customer services once, will be more likely
to do so again… perhaps the call center service was so good, they can’t resist
using it again!
3. Age before beauty
Older customers call more frequently than younger customers. The youthful
group is more likely to try and sort it out online and only call customer services as a last resort.
4. If it’s broken, fix it
Technical faults are the leading reason for calls to customer services. They can
be divided into two types of problems: those that can be sorted out remotely
over the phone, or the more expensive ones that require a technician to be
sent out.
The call center data was an open book for the data scientists, who were able to
extract plenty of information and relationships from it using the Blue Yonder algorithm.
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79
5.
Hello again…
Usually people who require a technician on-site will call more than once. This
can be for reasons varying from double-checking times to trying to ensure a
speedy resolution.
So what does a telecommunications company do
with these findings?
All of these reasons for customer calls, as well as the correlations and probabilities provide the telecommunications organization with precise predictions for the
future. If you know where and why call rates spike and when fewer calls can be
expected, the call-center staff schedule can be planned much more efficiently
6. TV and broadband?
thereby saving on costs and increasing customer satisfaction.
Customers who have a full service package including TV, broadband and landline are more likely to call than customers who are only subscribed to one ser-
Customer services online instead of calls
vice such as their landline or broadband − not really surprising as the more
services and products you subscribe to, the more complex the bill.
The telecommunications organization was also able to set-up a customer services
area which addresses the most typical questions about bills and charges online −
this will, of course, help prevent unnecessary calls regarding billing. What’s more,
individual products and tariffs are explained in detail, providing all the information
customers require, so there is no need for a phone call − online contact forms also
aid in filtering and pre-qualifying issues.
These measures have already relieved the call center significantly.
As you can see, sometimes the smallest change can have a significant impact
on making processes more efficient and reducing costs. One of the most
helpful ‘small changes’ is predictive analytics!
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And finally,
your story
can begin…
Entering the digital future with
Blue Yonder
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83
The future is now!
To summarize: we have presented you with 10 excellent approaches for using Blue
ÒAs a solution provider, we optimize your business processes and ensure that
Yonder predictive applications to turn your data into value.
they function smoothly and securely. And our certified, highly secure computer centers are protected against power outages.
Don’t put off your data project. Let’s talk. Why? Here are a couple of good reasons:
ÒWe bring data science competence to your company. Our experts will
ÒWith the Blue Yonder Platform, we make available to you a cloud-based
share their knowledge with you and your team in our Data Science Acad-
scalable platform for predictive applications that uses the most cutting-
emy. We train decision-makers and management as well as specialty and IT
edge modern machine learning algorithms. The predictive applications can be
departments.
very easily integrated and run with your existing systems (ERP, CRM, HR, SCM,
etc.) using application-programming interfaces (APIs).
ÒBecause we offer predictive applications as software as a service (SaaS), you
don’t even have to invest in hardware.
Are you a bit skeptical? Would you rather have concrete estimates and figures for
your company?
ÒYou receive predictive applications for the most diverse business require-
We would be happy to provide you with them. With Blue Vantage, we provide
ments and business sectors: sales planning, automated goods planning, dy-
you with a consulting service that shows the concrete use possibilities of predic-
namic pricing, returns optimization, customer analysis, risk analysis, predictive
tive applications at your company. You will also receive a quantitative estimate of
maintenance.
the value and a proposal for the next implementation steps.
ÒAs Europe’s leading SaaS provider for predictive applications, in us you
have found a very competent partner.
ÒFor numerous international customers, we provide successful predictive
Did you find our data stores interesting and want to know more about the use
possibilities of predictive applications for your enterprise?
Let’s talk. We would be glad to give you a personal presentation.
analytics solutions for automated, fast, and optimally managed decision-making.
www.blue-yonder.com
ÒWe combine future-oriented software development with the world’s
best data science and a unique cloud-based platform for predictive applications. And we have received numerous prizes and awards for this.
ÒWe support your entire team with our knowledge, our experience and
our ideas: from the evaluation phase through the common development of
your individual solution to the implementation with subsequent training and
support.
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Karlsruhe
Blue Yonder GmbH
Ohiostraße 8
76149 Karlsruhe, Germany
Phone +49 (0)721 383 117 77
Fax +49 (0)721 383 117 69
E-mail info@blue-yonder.com
Hamburg
Blue Yonder GmbH
Heidenkampsweg 45
20097 Hamburg, Germany
Phone +49 (0)40 180 47 64 20
Fax +49 (0)40 180 47 64 64
E-mail info@blue-yonder.com
United Kingdom
Blue Yonder UK Limited
6−9 The Square
Stockley Park
Uxbridge UB11 1FW
Phone +44 (0)203 008 717 0
Fax +44 (0)208 610 606 0
E-mail infouk@blue-yonder.com
This e-book and any parts of it may not be duplicated or otherwise
disseminated without the express written permission of Blue Yonder.
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Photo credits
Page 9 © bestdesigns – iStock
Page 10 © Alamy – mauritius images
Page 11 © Galina Peshkova – 123RF
Page 12 © Massonstock – iStock
Page 17 © eddyfish – iStock
Page 18 © habari1 – iStock
Page 25 © Zocha_K – iStock
Page 26 © Beerlogoff – Dreamstime.com
Page 28 © Yeko Photo Studio – Shutterstock
Page 33 © IBushuev – iStock
Page 34 © kcline – Shutterstock
Page 39 © Ron Chapple – Dreamstime.com
Page 40 © Rubberball – Fotosearch
Page 41 © Ilya Terentyev – iStock
Page 42 © 4FR – iStock
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Page 44 © weseetheworld – Fotolia
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Page 50 © RapidEye – iStock
Page 57 © sharply_done – iStock
Page 58 © olly – Fotolia
Page 63 © Volt Collection – Shutterstock Page 64 © Alekc79 – Dreamstime.com
Page 65 © Natsu Foods GmbH & Co. KG
Page 66 © Natsu Foods GmbH & Co. KG
Page 69 © gyn9037 – Shutterstock
Page 70 © paul prescott – Shutterstock
Page 73 © mevans – iStock
Page 75 © londoneye – iStock
Page 76 © SpellbindMe – iStock
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Blue Yonder UK Limited
6−9 The Square
Stockley Park
Uxbridge UB11 1FW
Phone +44 (0)203 008 717 0
Fax +44 (0)208 610 606 0
E-mail infouk@blue-yonder.com
www.blue-yonder.com