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by Iver van de Zand, Business Analytics Lead BeLux 

The BI Restaurant Guest

The BI restaurant guest’s workflow is: 

The BI Analytics Home Cook

The Final Analysis


by Iver van de Zand,  Guest Blogger 

Enterprise End Users Require at Least: 

Using Background

Using Navigation

Example landing page A

SVG files

Dynamic Text


By Iver Van de Zand, SAP

A Dashboard

A Storyboard

A Dashboard versus a Storyboard

Dashboards and Storyboards: the “SAP way”

Financial storyboard


from Iver van de Zand

My book “Passionate On Analytics” is available now 

Passionate on Analytics (206p) has 4 sections:

Please find below some screenshots. Contributed by  Iver van de Zand, SAP

Watch the SAP Customer Activity repository video HERE

Basket Analyses

Shop Performance Customer Loyalty Weather Influences

Alternative Business Models

Follow me on Twitter – @IvervandeZand.

Thank you to Iver van de Zand, SAP

Online grocery shopping and personalized bonus cards – we all face these incentives every day. Each is strongly driven by the overwhelming power of the analytics that are behind them. This article will share my experiences on these topics providing examples of retailing and B2C customer journeys that I have been a part of. The below examples are not at all exhaustive; they are also not about the future but are what happens, and are in production, today!

One thing that makes the retail market segment so interesting is the extreme sensitivity to community influences. A small thing might happen in society that can immediately affect buying behavior: today people are connected everywhere and at any moment. A simple anecdote on social media is shared so quickly that it can influence consumer choices instantly. One simple bad review about, for example, a yogurt brand, can raise or lower the selling of this product the next day. If the retailer wants to act upon these influences, he needs state of art Insights and online operational analytics.

Retailers are Analyzing You

Your bonus card, combined with your social media credentials, tell the retailer a whole lot more about you than you might realize. Analytics, clustering, and predictive modeling inform the retailer about your family composition, your eating and clothing preferences, how many children and pets you probably have and even what kind of holidays you like. By smartly combining your information with reference groups, the amount of trustworthy information a retailer can predict is huge.

Now imagine that the retailer recognizes you based on your cellphone signal when you enter the store. This information is linked online to your bonus card and social media credentials: “the retailer knows exactly who is in the store”. Then based on the same cellphone signal, the retailer can follow (!) you through the store using GEO coordinates. It means the retailer knows you are in front of the vegetable section, and also knows – based on the bonus card info – that you like carrots a lot. The electronic banner automatically flips and messages about a special offer on “carrots that taste very good with a new white wine that you might want to try”. A message targeted at you.

Imagine?? Well, forget about “imagine” – this is done today and you are part of it.

Supply Chain Challenges

Imagine this scenario. The latest game controllers are very popular, so our retailer decides to order additional stock from one of his vendors. Using buying behavior and predictive algorithms, the retailer knows he will sell the controllers. Early in the morning the stock manager receives a message that the vendor’s truck driver is stuck at the border and will be very late. Order intake quickly searches for alternative vendors and places an online order. That order will influence consumer prices and using business analytics the retailer can immediately predict the effect this price change will have on today’s revenue. It also automatically adjusts the retailer’s forecast and rolling plan, even from its subsidiaries if they exist. Using basket analyses, the new type of game controller might be influential to the selling of USB cables too so the retailer decides to order additional USB sticks and the system automatically adjusts distributed forecasts and rolling planning. Imagine? Not at all!

Product Offers Apart from understanding the buying behavior of a customer (using bonus cards and others), retailers spend a huge amount of effort in understanding where the demand will be. Trend forecast algorithms combine social media posts, web browsing behavior, and ad-buying data to predict what will cause a trend or buzz. Social media discussions on the clothing habits of a popular band might cause specific trousers to become popular. These sentiment analyses get even more complex if you realize that there is a heavy demographic component embedded together with economic indicators. Offerings on detective books will increase significantly if two things occur – the weather gets colder and at the same time a significant crime is discussed on social media.

In-Memory Computing and Interactive Insights Make the Difference

Retailers and B2Cs in today’s market dynamically follow and influence customer buying behavior. They have to because the consumer is so well informed and has so many alternatives for buying. Retailers have to act instantly on changing behavior. To do so the amount and complexity of information that needs to be analyzed is so big, only in-memory computing can handle it. Bear in mind that an individual retailer is never on its own but part of a brand, meaning individual shop performance is rolled-up to the corporate level. This corporate level manages online shop performance indicators, compares the various stores, and delegates rolling budgets down to the shops on a daily basis. These budgets vary daily given the changing demand analyses we talked about above.

These dynamics also require online interactive analytical capabilities. Information on buying and demand behavior varies daily and is analyzed permanently. Ever changing sources, unknown structures of new information, or simulation models require the analyst to interact with the data all the time.

In a future article, we will deep dive into some of the other use cases for business analytics in Retailing and B2C market spaces. One of them is basket analysis. Using predictive modeling combined with business analytics, it’s possible online to utilize the buying behavior of the consumer. These are techniques that are used today! Looking forward to share with you

– See more at: http://blogs.sap.com/analytics/2015/12/09/the-overwhelming-power-of-analytics-in-retailing-and-b2c-part-one/#sthash.ozusXrxq.dpuf

The Closed Loop portfolio in Analytics

Authored by Iver van de Zand, SAP We talked about the overwhelming power of analytics in Retail and B2C market-segments earlier and one of the topics discussed there, was the integration of operational business activities with operational analytics. In the example we saw the stock manager using analytics to change his stock-buying-behavior. He adjusted his order system by choosing another vendor and placing the order. Immediately his analytics are updated and he now requires to adjust his rolling planning or run a predictive simulation how the price-adjustment of his new stock might affect buying behavior of his customers. He might even want to adjust the governance rules with his new supplier or run a risk-assessment. Below pictures visualizes the continuous integration of core business activities with business analytics, indicating examples of core processes with their accompanying analytical perspectives. These are just examples and not exhaustive at all.

Performance Management closed loop Basically what the stock manager in our example needs, is a full – real-time – integration of business analytics with his core business activities over all aspects of his performance management domain. A predictive simulation of changing buying behavior lead to new analytical insights on product mix which might influence the companies’ budget and causes a risk analysis for new vendors.

To do so, a closed loop is required of following core components driven by the continuous flow of Discover – Plan – Inform – Anticipate: This so-called closed loop of predictive analytics, planning and performance management, business analytics and GRC is NOT a sequential process at all. They interact randomly towards each other in real-time and at any moment needed. They are also dependent towards each other, since Digital Transformation requires us to be so agile, we have to constantly execute and collaborate on the interoperability of the components and monitor the outcome. Lastly, the closed loop platform interacts on core operational activities (real-time insights in operational data) and as such the analytics are defined as Operational Analytics.

Closed loop platforms more than anything else require business users to drive its content and purpose. They drive the agility to the platform that is so heavily needed in the Digital Transformation era. On the other hand the technical driven architects do make a difference too, since closed loop platforms are very sensitive to respect governance principles. A special role is allocated to the CFO or Office of Finance here; they will drive the bigger part of the Planning and Budgeting cycle.

One can imagine the calculation processes behind the closed loop platform are huge and therefor a business case for an in-memory system is a sine qua non.


Needless to say that the closed loop model applies to all industries and not only in the retail example that I used here. I can list plenty of examples here but just to name a few:

Apart from imagining the possibilities per market segment, we can also change perspectives and look at the possibilities per role within companies applying the closed loop platform. Below picture provides capabilities the closed loop components could offer to various user communities. The potential is huge and extremely powerful when used in an integrated platform. This is also the weaker point of the closed loop platform: the components must be integrated not to miss their leveraging effect on each other. A solution is available today

With its Cloud for Analytics offering, SAP is today the only provider with an integrated offering for the closed loop platform. Even more: SAP Cloud for Analytics is integrated in one tool offering analytics, planning, GRC and predictive capabilities. One tool?? …. Yes, one tool completely Cloud driven and utilizing the in-memory HANA Cloud Platform it is running on. One tool that seamlessly lets analytics and planning interact with each other. A tool where you can run your predictive models and analyses and visualize the outcome with the analytics section. A tool that allows access to both your on premise data, your Cloud data and/or Hadoop stored data. And lastly a tool with fully embedded collaboration techniques to share your insights with colleagues but also involve them with planning or others.  Our dream becomes reality. Data, Information, Intelligence and Insight

