Post on 15-Apr-2017
transcript
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Evgeniya Burakova
Masters of E-Commerce
Business Analytics Portfolio: Exploration of Business Analytics Solution
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Abstract
Innovations in BI from various providers broaden analytic capabilities enabling almost every department of an
organization to participate in the extraction of relevant information. The data involved in data mining goes through
the process of extraction form a dataset, integration, transformation into an understandable structure for future use.
Due to the large number of sources together with the diverse nature of data different analytical tools are applied. It
is important to recognize that data coming from crowdsourcing, transactions, clicks (stream mining), and client data
is transformed into knowledge and therefore could be used in building future strategies and explain certain events.
Some analytical tools are lack of certain functions and represent certain type of data worse than others. This portfolio
comprises the screenshots of the examples how the software operates and what information could be derived.
Content
Sap Lumira
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IBM Cognos
BusinessObjects Analysisi
Business Objects Explorer
Business Objects Design Studio
Watson Analytics
Sap Infinite Insight
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1.1 SAP Lumira
The user is able to create customized dataset and include the attributes he is interested in by integrating several datasets.
The ability to acquire data from multiple excel spreadsheets, merge those files, prepare and start analysis can provide rich insight
for a business consultant. The reason for combining the dataset could be the missing values of one of the datasets and therefore
not enough “foundation for analysis”. On the other hand, once the data is combined Lumira smooth some picks and negative
instances because of the data scope. Before data is uploaded the system asks for the ambiguity to be resolved, so the user controls
the blending.
Figure 1-Two datasets combined
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Geographical hierarchy could be
displayed on the map of the USA
(Figure 2). The bubbles represent
the roaming vs non-roaming in the
different states. By clicking on
indexes on the right certain types of
revenue (that come from roaming
vs non-roaming) will be
highlighted while the opposed
faded. This will show the user the
state with the largest portion of the
revenue (the highest degree of the
circle).
Figure 2-Revenue by Regions
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This visualization shows PostpaidRev is
contributing most to revenue and Data plan is the
most popular. Understanding product
segmentation is important when developing value
proposition as marketing mix (product features,
price, place, and promotion) will differ between the
products. As we can see telecommunication
strategic focus should be on the product with the
highest revenue. Users acquire data plans as
oppose to messaging or talking plans as most of the
applications allow to do both talk and message but
those aps require internet.
When it comes to “place” component of marketing mix the strategic location should be considered based on the highest
consumption and revenue. The geographical location tool reflects the revenue by states (Figure 4). The next step is to find
minilmal valuable product , i.e. the startegic locations by ranking top 5 states (Figure 5).
Figure 3-Stacked chart for plan types by revenue
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The ranking could be applied to the types of the plan and presented as a score card. The canvas and the size of the blocks represent
the contribution to the revenue providing the manager informatio about the most popular plans.
Figure 4-Revenue by states Figure 5- Top 5 states by revenue
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Formation of clusters from a large number of instances enables a user to apply similar strategies. Having distinct groups make the
management easier. We can then explore different parametres of the cluster such as Cluster Density and Distance to understand
distribution of the clusters (Figure), Feature Distribution (Figure) or Cluster Center Representation to check on which attributes certain
cluster have high/low value (Figure)
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The scatter charts of store clusters plotted between various pairs of dimensions is useful for comparison purposes.
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The dataset on sales can be used to derive such information as material with no sales data. Total value for different dimensions or get
information on some unique value by applying according filtering options. The comparison between two values could be easily
visualized with either a line chart or column chart. The graph could be also helpful to show dramatic changes or build a scatter plot for
all the values achieved in order to see possible trends (by quarter). Sorting and ranking enabled to identify smallest ad highest results or
top 3 values, for example.
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1.2 IBM Cognos
Figures 14a and14b represent sales by customer types. The new crosstab cab be easily created by dragging and dropping the dimension
as column/rows depending on which aspect of the sales the user wants to concentrate. For this scenario business analytics can sort the
sales in descending order to see which customer type brings the highest revenue to the business. What makes this board even more
informative is the margin revenue vs cost and the margin score. This additional column with colored “signal” provides the user with the
quick overview of the areas that require more attention. As the revenue is sorted by descending order and the scorecard allows to
immediately notice negative values for retail channel (high costs vs sales), we can see that the biggest problem is with the “Retail”
customer type despite its highest revenue production as compared to another channels. We can use bar chart to visualize the findings
(Figure 14b).
The introduced elements of IBM Cognos BI are used to understand the comprehensive corporate performance management solutions.
One of the prerequisites for working with the software is to understand organization's reporting needs: whether it is about visualizing,
predicting. The color serve as alerts for the senior managers to quickly identify the areas that need more consideration. The
storyboards could be used to present monthly sales of materials. With column charts, pie, crosstabs, area, point charts, tree maps a
user can discover which distribution channel has the weakest performance in terms of the margins (cost vs revenue) for example.
Dataset for exploring the tool contains information about material, customers’ types, year, territory, sales channels which serve as
dimensions of the dataset and revenue/sales/quantity is set as measures.
Figure 14 a- Sales by customer types Figure 14b- Sales by customer type (bar chart)
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The next storyboard reminds of the matrix where product types are reviewed customer wise. The crosstab contains many figures
therefore again margin score are good alerts of the performance. Below the crosstab are the independent graphs for each customer types.
Each product`s performance is reflected by the color which is assigned and indexed on the right of the graph (computers & tablets-navy,
mobile-yellow etc). User can select measure by which the sales could be compared and by changing to score, ie margins, the sales
performance could be shown.
The filters showed that mobile sold online was more successful compared to retail marketing channel as it, first, requires no labor costs
and second, due to the nature of the product (as customer is more inclined to purchase devices online since extra research in terms of
the alternatives, prices might be needed). This might be the one of the rational a marketing analyst will provide to complement the
information derived from Cognos. The graph helps to analyses which product were the most/least successful for which channels.
Surprisingly, portable electronics that were successful for all distribution channels did not have successful sales figures for retail. The
Figure 15a- Sales by customer and product type
Figure 15b- Sales by customer and product
types measured in scores
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managers might continue to track the performance and the case the situation does not change the expanses spent for portable electronics
should be transferred to computer and tablets segments which has substantial revenue losses. Marketing campaigns might be included.
What makes Cognos even more powerful is the capability to predict future trends/ sales. Based on the values of what is going on in the
business one year to predict how the business would perform considered adjustment in sales/cost a manager can develop marketing
tactics to achieve forecasts.
Right allocation of budget and achievement of the revenues that we set as the predictions will change the performance of not only retail
but other channels as well (we can see that the revenue has increased above 6 m among all channels).
Figure 16-Sales prediction
Figure 17-Sales
prediction
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1.3 Business Objects Analysis
Figure 18 provides an overview of
the performance for each year.
Having sorted Sales Quantity in
descending order, gives a user an
information about the year with the
highest sales quantity (2007 in this
case) as compared to the lowest in
2009.
SAP Business Objects Analysis, a plug-in for Microsoft Excel and PowerPoint, makes the data analysis more profound and pretty
easy due to Excel`s familiar to an average business user design. It connects to query designer to directly extract data from data
warehouse so the BI content is pre-defined in Microsoft Excel or Microsoft PowerPoint. Under design panel a user can find
analysis, information or components. Business Analyst can define conditional formatting or conditions by measures. OLAP
techniques such as filtering, slicing and dicing are used to perform data analysis on:
• The city/department with the highest revenue and visualization
• The year with the highest sales quantity
• Department with the lowest sales
All was done by applying filters and sorting by measures.
Figure 18-Financial information by years
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The next figure presents the
table that contains info on
year, material code and
material name as
Dimensions and financial
data as Measures. Sorting by
measures allowed to identify
not only the year but also the
material with the poorest
performance in 2009.
Figure 19-Sorting the products by sales
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Excel Analytics helps to understand the overall revenue of
each products during 6 year period. Again by sorting in
ascending order we find that water bottles had the lowest
revenue. Year and total revenue by the countries are presented
in a line chart (Figure 21) and we can clearly see that though
starting almost with the same revenue 2007 Germany
improved its overall results over 4 years
Figure 21-Overall results by country and year (line chart)
Figure 20-Product by revenue
Figure 22-Overall results by country and year
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1.4 Business objects explorer
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1.5 SAP Business Objects Design Studio
The software provides an environment to create apps and dashboards in Design Studio is a powerful yet easy to use platform. In the
business world where mobile manager requires immediate response business object design is useful as it supports both desktop and
mobile theming. Since no additional download or installations but an HTML5 capable browser are required to open a dashboard
either on smartphone or tablet, companies find Business object design cost effective and hassle free tool. Once the properties are set
up for the future application, sales dataset is uploaded in object design
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We have to determine the layout of
the future app and include objects
such as title, graph, switching
between currencies and filtering
options. The following is possible by
dragging and dropping components
from the left-hand menu. Having
created this interactive app a
manager now can make it accessible
in the web. Design studio allows a
user generate the QR code for the
link
Figure
Figure 5- App design development
Figure 6-QR code generation
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The user friendly interface is created considering CRAP
design principles. We used the default black theme for
mobile app. We can enable the user to apply the filters on
customer/material/sales organization or check the key
figures. In the scenario when comparison of revenue
between customers in Germany North is requested, key
figures is selected as revenue and sales organization as
Germany North to check the most successfully sold material
with the most frequent customer
Figure 7-Global Bikes App
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1.6 InfiniteInsight
We used InfiniteInsight to understand the contribution of variables on the propensity to have an accident. Since the demand for the
insurance service is high it is worth identifying those triggers and perhaps customize the service according to the demographics of the
customers or other attributes. As we can see on Figure there is a negative correlation between the number of children in the family and
The software allows to understand and predict a phenomenon as well as describe a data set, by breaking it down into homogeneous
data groups and clusters. Association rule is used in order to determine basket analysis which could enhance customer relationship
as a vendor will be able to offer better deals or launch a marketing campaign based on the information derived. InfiniteInsight can
also support future decision making by matching the attributes of a new instance with the past data in order to see the probability
of the vent happening.
The examples below illustrate the application of the tool on insurance industry, retail sector
Figure 9-Probability of an accident (children as an indep variable) Figure 8-Probability of an accident by different variables
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the chance of the accident occurrence (as the number of children increases the risk of accident decreases). Therefore, parents in larger
families tend to be more cautious drivers. Figure explicitly reflects that the number of children is the most determining factor.
The third most influential factor is gender. We can then understand that
influence in more detail. The well know myth that woman cause traffic mess
could be destroyed with this graph. Female are actually safe drivers as
compared top male.
The software allows to construct the decision tree using the
attributes and see the probability of accident occurrence. We used
the factors analyzed above (children and gender) and now can
make a statement that a woman with 1-3 children is in a lower
risk group (6.16%) as compared to a man (12.19%).
Unfortunately, we cannot drill down using more than two
attributes. The simulation function allows to make quick decision
based on the known statistics and the personal info of a client.
The probability for a claim of a student of 22 year old man driving
a Sedan without any children is 19.05 % (the driver belongs to
the group “Claim=Yes” is 19%).
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The software allows to upload a new dataset of customers and using the
prediction algorithm calculate the probability of claim for those
customers.
The examination could be applied to other industries as well. For
example a bank analyst can find the factors that contributed to customer
retention. We can explore how a region influence on the propensity to
leave a bank
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The recommendation based on the algorithm
function determibes the best option for the
customer.
In the predictioN InfiniteInsight shows the
accuracy of the model.
The results from the analysis could be used to generate an
executive summary in the form of PPT.
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With the confusion matrix function we can predict the correct outcome by setting the desired values Imagine you have budget to contact
10% of your contacts. If you contact your customers randomly, you will only reach 10% of the customers that purchase. However,
with a good predictive model you will be able to increase the success rate.
Infinite Insight allows to conduct Link Analysis on
the Members and Products. We can either suggest
what might appear in the shopper basket by setting
customer Id or find the most complimentary
products. We can see the shared community
(Figure). People are likely to buy panini with coke
(Figure)