analytics Archives - Amick Brown

GET MORE VALUE FROM OPERATIONAL ASSETS WITH PREDICTIVE ANALYTICS

Karen Gildea — Wed, 14 Dec 2016 01:29:21 +0000

By Pierre Leroux, Director, Predictive Analytics Product Marketing

Sharpening operational focus and squeezing more efficiencies out of production assets—these are just two objectives that have COOs and operations managers turning to new technologies. One of the best of these technologies is predictive analytics. Predictive analytics isn’t new, but a growing number of companies are using it in predictive maintenance, quality control, demand forecasting, and other manufacturing functions to deliver efficiencies and make improvements in real time. So what is it?

Predictive analytics is a blend of mathematics and technology learning from experience (the data companies are already collecting) to predict a future behavior or outcome within an acceptable level of reliability.

Predictive analytics can play a substantial role in redefining your operations. Today, let’s explore three additional cases of predictive analytics in action:

Predictive maintenance
Smart grids
Manufacturing

Predictive Maintenance

Predictive maintenance assesses equipment condition on a continuous basis and determines if and when maintenance should be performed. Instead of relying on routine or time-based scheduling, like having your oil changed every 3,000 miles, it promises to save money by calling for maintenance only when needed or to avoid imminent equipment failure.

While equipment is in use, sensors measure vibrations, temperature, high-frequency sound, air pressure, and more. In the case of predictive maintenance, predictive models allow you to make sense of the streaming data and score it on the likelihood of failure occurring. Coupled with in-memory technologies, it can detect machine failures hours in advance of it occurring and avoid unplanned downtime by scheduling maintenance sooner than planned.

This all means less downtime, decreased time to resolution, and optimal longevity and performance for equipment operators. For manufacturers, predictive maintenance can streamline inventory of spare parts and the ongoing monitoring services can become a source of new revenue. And as predictive maintenance becomes part of the equipment, it also has the potential to become a competitive advantage.

Smart Grids

Sensors and predictive analytics are also changing the way utilities manage highly distributed assets like electrical grids. From reliance on unconventional energy sources like solar and wind to the introduction of electric cars, the energy landscape is evolving. One of the biggest challenges facing energy companies today is keeping up with these rapid changes.

Smart grids emerge when sensor data is combined with other data sources such as temperature, humidity, and consumption forecasts at the meter level to predict demand and load. For example, combined with powerful in-memory technologies, predictive analytics can be used by electricity providers to improve load forecasting. That leads to frequent, less expensive adjustments that optimize the grid and maintain delivery of consistent and dependable power.

As more houses are equipped with smart meters, data scientists using predictive analytics can build advanced models and apply forecasting to groups of customers with similar load profiles. They can also present those customers with some ideas to reduce their energy bill.

Manufacturing

The manufacturing industry continues its relentless drive for customization and “Lot sizes of 1” with innovations such as the connected factory, the Internet of Things, next shoring, and 3D printing. It’s also hard at work making sure it extracts the maximum productivity from existing facilities, which traditionally has been accomplished by using automation and IT resources. According to Aberdeen, the need to reduce the cost of manufacturing operations is now the top reason companies seek more insight from data.

Quality control has always been an area where statistical methods have played a key role in whether to accept or reject a lot. Now manufacturers are expanding predictive analytics to the testing phase as well. For example, tests on components like high-end car engines can be stopped long before the end of the actual procedure thanks to predictive analytics. By analyzing test data from the component’s ongoing testing against the data from other engines, engineers can identify potential issues faster. That in turn, maximizes the capacity available for testing and reduces unproductive time. That is only one of the many applications manufacturers find for predictive analytics.

Innovations on the Shop Floor

Predictive analytics provides an excellent opportunity for COOs and operations managers to extract additional value from production assets. It can also be an opportunity to create critical differentiators in the way products are created and delivered to customers—by providing it as a paid service (predictive maintenance) or as insight (predicting future electricity consumption).

However a company chooses to use it, predictive analytics can be the key to beating the competition.

Discover and Follow

And join the predictive conversation by following me on Twitter @pileroux.

The post GET MORE VALUE FROM OPERATIONAL ASSETS WITH PREDICTIVE ANALYTICS appeared first on Amick Brown.

The Self-Service BI Application Dinner: Restaurant Guests and Home Cooks

amick.brown — Fri, 28 Oct 2016 11:07:21 +0000

by Iver van de Zand, Business Analytics Lead BeLux

In a recent thread on social media, there was an interesting discussion about just “how self-service-like” today’s self-service analytics components really are. Some of the thread contributors doubted whether self-service BI was really something one could hand over to a business end-user. They are concerned whether self-service really can exist in the day-to-day life of an end user. “Isn’t there always some ICT intervention needed?”someone asked. It’s an interesting discussion that hasn’t a black and white answer. So let’s take a closer look with the help of a restaurant analogy.

The doubters in the social media thread were talking about self-service for data analysts. But there is a small, but strict, difference between self-service for the end user or consumers, and self-service for data analysts. To explain this, I’ll need to use the analogy of an analytics dinner, and consider the differences between the home cook and the restaurant guest.

The BI Restaurant Guest

Our guests “equal” the business end users of analytics. A dinner can be seen as a collection of analytical insights. The insights are thoroughly selected as our guests pick either from a menu—and ordering à la carte—or they go to the buffet and pick the things presented to them already ready for consumption. Ordering à la carte refers to end users opening specific dashboards, reports, or storyboards from the business analytics portal.

The BI restaurant guest’s workflow is:

Screen the menu and roughly select the type and amount of items they want. Our analytics end user chooses whether he/she needs financial info or logistic info, and what kind of detail-level is needed.

Next our guest chooses a specific item from the menu. In analytics terms, the user decides which reports, dashboard and/or storyboards he/she needs to get the insights required. Our user also decides on prompts or variables needed to get the specific scope of the insights.
When dinner is served our guest just enjoys what he/she asked for, leaving leftovers if feeling like it.

The BI restaurant buffet guest’s workflow is similar, with the difference being that adding special requests (like steak well done) is not possible. However, the buffet allows the guest to digest multiple small plates according to their individual needs, just like an analytical end user could consume reports and dashboards in random order.

Our guest will typically be a user of existing SAP BusinessObjects Design Studio applications or SAP BusinessObjects Cloud storyboards.

The BI Analytics Home Cook

Our next ‘flavor’ of a self-service user is the home cook that has to cook for him/herself. This user is more like a data analyst. Somebody who may not have a clear view on what kind of insight is needed, or requires insight on non-corporate data that is not explored on a regularly basis.

Here the workflow differs. Imagine the workflow of the TV cooks we all see on tele every single day; it is the exact same workflow as our self-service end user.

1. Our home cook opens up the fridge and explores the ingredients needed; think of the data analysts that accesses the data sources he/she requires to start exploring data.

2. Next our home cook starts cleaning, cutting, seasoning, mixing and combining his/her ingredients. Only those pieces of the ingredients that are needed for the meal are used. This is where our data analyst starts filtering, enriching (hierarchies, formulas), blending (combining data sources) and cleaning his data.

3. When this is all done, we typically see the home cook putting his selected ingredient-mix in the pot on the stove. This is where the data analysts starts creating the visualizations, graphs, and maps and combines them to a final storyboard which might be shared with others later on.

4. Our home cook makes quite an important decision in the last step; either they serve the plate to their guests (his colleagues or management), or the final meal is just put on a buffet for guests/users to consume.

The Final Analysis

So in the end I believe self-service always needs to be seen in the context of the type of end user. Do we talk about a guest in our restaurant who wants to digest analytics, play with the data to any extent and conclude on the fly, or do we talk about a home cook who needs to create the insights from scratch?

In terms of the guest, self-service BI 100% exists today in the sense that they can use applications and reports and do anything (!) with the data as longs as this data is part of the menu. For home cooks, there is a bit more work to be done—they need to open the fridge and make choices. Maybe some of the ingredients are not in, and our cook needs to go to the shop to buy them. Also, the personal touch given to the meal is fully on the creativity and capability of our cook.

Oh, and You Mr. Restaurant-Owner, What Do You Think?

If you happen to be the restaurant owner—BICC or ICT manager—of course you decide on the quality of the overall meals presented by managing ingredients and menus, but you also monitor the experience your guests go through. We might call this governance and organization. Even in self-service environments, the restaurant owner is key to the success of the restaurant. If you fail, your guests will go somewhere else.

This blog is excerpted from Iver van de Zand’s article, “How ‘Self-Service Like’ Are BI Applications Really? Buffet or a la Carte.” Read the complete article at the Iver van de Zand blog.

AmickBrown.com

The post The Self-Service BI Application Dinner: Restaurant Guests and Home Cooks appeared first on Amick Brown.

Amick Brown Sponsors ASUG BI + Analytics Conference

Karen Gildea — Mon, 17 Oct 2016 23:53:40 +0000

Amick Brown Sponsors ASUG BI + Analytics Conference, October 17 – 20, New Orleans

Sep-15, 2016

When we get this excited about an event, we want our customers to be the first to know!

Americas’ SAP Users’ Group (ASUG) will be premiering a new, collaborative conference: ASUG BI + Analytics Conference, “IT and Business Users – Breaking New Ground Together,” in New Orleans, October 17 – 20, 2016 and Amick Brown will be there in full force as a Silver Sponsor in booth #106.

Why all the excitement? As new technologies emerge, the lines between IT and business are becoming blurred, demanding new cross-functional skills and collaboration to fuel enterprise-wide success. And Digital Transformation waits for no one. Just keeping pace means you may be falling behind. That’s why attending this conference is important to your future success. After all, amazing things happen when business and technology join forces!

Conference highlights…

As an attendee, expect non-stop learning and networking from peers, top thought leaders, authors, and product experts. Take home new skills, best practices, important connections, and innovative solutions that can enhance your career and your organization’s future success. 60 Customer Stories: Gain ‘from-the-trenches’ lessons learned from customers just like you who have been there and done that. Road Maps: SAP’s Vision for Tomorrow: Hear first-hand from SAP what’s on the horizon and how to be ready for whatever is coming your way. Keynote Speakers: See and hear the most authoritative voices in the field of BI and analytics.

Pre-Conference Seminars: Go deep on hot topics, strategies, tricks, and practical applications – there’s something for everyone. EXPANDED! Hands-On Sessions: Learn from ace instructors on the very latest in SAP Analytics and SAP® BusinessObjects solutions. SAP Influence & Usability: Give feedback to SAP experts, try out new products, and hear from SAP executives – and so much more. Networking: the knowledge and networking you acquire in just four days will last long after the conference is over. Your success is our success. And ASUG BI + Analytics Conference is where you can best prepare for the future today.

As an added incentive, we are offering our customers a $150 registration discount. ( Enter promo code EXH14)

It gets better! Amick Brown is sponsoring a half day Pre-Conference session by Wayne Eckerson: New Analytics Ecosystem: Organizational and Architectural Models for Data Driven Organizations.

Amick Brown is offering a big discount to attend it as well. (Enter promo code PREX100) and attend for only $295.

The post Amick Brown Sponsors ASUG BI + Analytics Conference appeared first on Amick Brown.

Amick Brown Hosts Wayne Eckerson Book Signing Event

Ashith Bolar — Mon, 17 Oct 2016 19:08:27 +0000

Amick Brown a Silver Sponsor at ASUG BI + Analytics Conference

October 17-20, 2016

Amick Brown was a Silver Sponsor at the ASUG BI + Analytics Conference in New Orleans October 17-20, 2016

Not only was Amick Brown a Silver Sponsor at the ASUG BI + Analytics conference in New Orleans this week, but we hosted a book signing with Wayne Eckerson, author of “The New Analytics Ecosystem: Organizational and Architectural Models for Data Driven Organizations” that turned out to be a great success!

The Amick Brown team would like to thank everyone who came by our booth #106 for the book signing. Lastly, a special thank you to Wayne Eckerson – couldn’t have done it without you.

The post Amick Brown Hosts Wayne Eckerson Book Signing Event appeared first on Amick Brown.

Detailed Cost Planning in SAP and Excel

amick.brown — Thu, 28 Jul 2016 16:00:24 +0000

Contributed by Gunnar Steindorrson , Kern Americas

In our previous Blog , Get a Reporting and Analytics Planning Edge with Allevo , we gave a short overview of our powerful product that give significant value when working with SAP and Excel for planning.

As you know, effective cost planning in SAP can present some challenges.

For one, standard SAP does not support driver-based costing, nor does the system provide an easy way to include non-SAP data (production or sales plan forecasts, for instance) into planning. Do you have side calculations – things like travel, consulting expenses, training costs, etc. – that you would like to include in your planning? Good luck with that.

Unless you’re using Allevo that is. Our solution tackles all of these challenges – and much more – with ease.

Better yet, with Allevo you work in your familiar, easy-to-use Excel environment, using your own templates, but are fully integrated with SAP.

So, if you’re tired of not having real-time access to your vital planning data, frustrated by cumbersome workarounds, and annoyed with working in a system that doesn’t support the way you want to work, give us a call. You’ll be glad you did.

The post Detailed Cost Planning in SAP and Excel appeared first on Amick Brown.

Context Awareness: Online or Real Time in Digital Transformation?

amick.brown — Tue, 08 Mar 2016 11:58:48 +0000

By Iver van de Zand , Business Analytics Leader, SAP

Excuse me? Online instead of real-time; isn’t that the same? Well, have I got news for you: It is not.

Driven by enterprise needs and technological capabilities, enterprises are massively going online. Why do they and how accurate is online? Why do I read about going online in the news sites all the time? Let me explain wearing my analytics glasses.

Definitions

First, let’s get some things straight: explaining the difference between real-time and online starts with a discussion on latency. Latency is a time interval between the stimulation and response, or, from a more general point of view, as a time delay between the cause and the effect of some physical change in the system being observed. Online means that there is some kind of interactivity involved, but doesn’t enforce limits in latency. Real time means that there are limits on latency. Pfff, need to re-read that a few times before it starts clearing for me.

Let me give an example: If you move your computer’s mouse, you expect the pointer to react immediately and precisely follow your actions. That’s real time. Another example is playing on a music keyboard controller and have some synthesizer program generating the sounds. Online, however, is when your actions show some response in some timely manner, but there’s no timely relationship enforced to it. For example, starting a video stream from a (remote controllable) webcam may show you the pictures with less than one-second latency, or even up to several minutes, yet be online.

My phrasing of “difference” should thus be adjusted—real-time and online don’t differ, they relate to each other.

The Market Out There: Context Awareness and New Business Models

According to Gartner’s recent Big Data Trends for Business Intelligence, by 2017 more than 30% of enterprise access to broadly based Big Data will be via intermediary data broker services, serving context to business decisions. These are massive amounts and it proves that digital businesses require real-time situation awareness. That covers full insight into the things going on inside and outside the organization. Retailers, for example, need to know in real time how weather patterns impact the buying behavior of their customers. The inventory manager requires real-time information when his supplier is in trouble delivering his goods, so he can immediately adjust and use analytics to find alternatives, re-plan and re-adjust for example his forecast.

The issue that occurs is that more and more the enterprises corporate data is insufficient to get the necessary context awareness required to adequately respond to digital business. Think of it; to compete in digital business, a combination of non-corporate data coming from outside the organization is required all the time. This—often unstructured—data could be about social behavior, environmental influences, and government or market trends, to name a few. Some of it is even from premium data brokers preparing data from specific industries or use cases.

We could say that the ability of enterprises to adopt digital transformation and digital business for a big part is influenced by their capabilities of curating, accessing, and interpreting their data to obtain context awareness.

Context awareness is crucial for any enterprise that wants to compete in digital business. Real-time availability of insights is the logical requirement to do so. We already recognized the need for real-time insights to corporate data, however, we also now recognize the need for real-time insights in contextual information:

Curating Insights

Digital business is about the agility to respond to market, customer and environmental influences and actors immediately when required. Digital business requires enterprises to act and respond almost real time to activities not registered in their corporate data.

Accessing data and insights

Digital business is about the agility to recognize and access information outside the enterprise that is necessary for curating insights immediately when it occurs.

Interpreting and act upon insights

Digital business is about the ability to interpret insights and act upon them instantly. This is not only about interacting with the insights, but especially about applying the closed loop portfolio of analytics: insights often generate follow-up actions that affect business planning, finance budget allocation, or require new predictive models to argument on influencing variables of the contextual information.

An interesting side effect of contextual awareness is the new business models that come with it. A new category of business-centric cloud services enters the market space that delivers data to be used as context in business decisions, whether human or automated. These information services (or data/decision brokers) will become an essential part of intelligent business operations and smart business decisions.

The Case for Online Analytics

Online analytics is primarily about cloud-based analytics. If we narrow down to business intelligence (BI), the cloud BI market will be worth $4 billion by 2017 whereas the current full BI market (software and services) is estimated at $86 billion.

But how important is the aspect of “being online” for context awareness? Well, it’s quite important:

Contextual information is very often residing on websites. Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself and the world of exogenous data now available from syndicated and open data sources.
Products like SAP Cloud for Analytics connect to various cloud-based solutions like SAP S/4HANA, and others. It is obvious that online analytical tools integrate more easily with other cloud-based applications.
Reduced or eliminated capital costs. Because BI systems are managed on the cloud service provider’s hosted architecture, a user company has no up-front capital investments or multi-year equipment leases with depreciating value. It also stands to benefit from improved cash flow. The subscription fees charged by cloud vendors are treated as operational expenses and don’t incur additional interest charges, which can lead to better cash management and debt avoidance.
The simplicity of the online, cloud-based analytical applications is key when it comes to user adoption. If we realize that the people creating insights on contextual awareness are business users, you’d agree with me that simplicity makes the difference when it comes to adopting the applications and leverage their power.
Streamlined system design and increased elasticity. In the cloud, companies can rely on a provider to architect the BI environment, select the technologies that will power it, assemble systems and manage the hardware and software stacks. That frees them to focus their attention on running BI and analytics applications and interpreting the results.
Fully-integrated business analytics components into the so-called closed-loop portfolio. Analytical environments hosted in the cloud comprise a complete end-to-end architecture. SAP Cloud for Analytics for example, spans the ETL, data management, analytics, planning, predictive and risk spectrums, simplifying and speeding the deployment process for users. Cloud BI systems should be ready to use out of the box, so to speak, and the standard setups can quickly be augmented with templates that vendors have developed over the course of different customer engagements.

The Case for Real-Time and Online Analytics: Context Awareness

Man, does Digital Transformation bring us interesting times! There are so many aspects of it, with contextual awareness being just one of them. For me, it’s crystal clear that real-time contextual awareness is key to any enterprise that wants to be competitive in digital business. Given the flavor and behavior of the contextual information, online analytical applications can make a significant difference.

Follow me on Twitter @ivervandezand.

The post Context Awareness: Online or Real Time in Digital Transformation? appeared first on Amick Brown.

Amick Brown Partners with Kern to Resell Allevo Solution

Karen Gildea — Wed, 03 Feb 2016 00:00:59 +0000

Amick Brown Partners with Kern to Resell Allevo Solution

Feb-06, 2016

San Ramon, California – February 3, 2016 – Amick Brown today announced a partnership with Kern, to resell their Budgeting and Forecasting solution for SAP, Allevo. As a valuable addition to Amick Brown’s solution mix, Allevo targets Excel and SAP users for planning, budgeting and forecasting.

Allevo builds a bridge between the existing MS Excel world and SAP, enabling real time bidirectional reading and writing. This will allow our clients to choose the strategic option that works for their business case. Targeted at SAP ERP users for budgeting, planning, forecasting and reporting, Kern has resolved a real need from the market to combine the #1 data tool, Excel, with the #1 ERP power of SAP.

Sameer Gandhi, Managing Partner for Amick Brown’s SAP practice says, “Allevo empowers business users by offering a flexible and powerful planning, forecasting and budgeting tool that seamlessly integrates in SAP ERP. It creates efficiencies by eliminating use of manual, time consuming data gathering, consolidation processes required to generate and monitor various planning processes. Our users will be able to use this solution for various planning scenarios such as Sales planning, Headcount planning and much more. We are excited to offer this solution to all of our customers.”

Amick Brown has worked in the SAP ecosystem for over 20 years. The knowledge and skill gained serves as a foundation for understanding the challenges that our clients face. Kern, a global SAP partner, has been providing SAP software solutions in the FI/CO world for over 20 years. The combined experience is a win for our prospects and clients. We continue to improve our offerings so that our clients can be certain of meeting their business goals through Amick Brown Consulting and Technology Service.

“We are excited to have Amick Brown as our partner and are looking forward to deliver customers a unique value added experience through Allevo, our SAP Planning solution, together with the expertise of the Amick Brown team”, states Adrian Rochofski, Managing Director, Kern Americas.

The post Amick Brown Partners with Kern to Resell Allevo Solution appeared first on Amick Brown.

The Overwhelming Power of Analytics in Retailing and B2C: Part 2

amick.brown — Wed, 06 Jan 2016 16:13:12 +0000

Contributed by Iver van de Zand, SAP

Retailing and business-to-consumer (B2C) market requirements for online insights are relying heavily on the closed-loop portfolio. The permanent and online interaction of analytics towards rolling planning or predictive models applies all the time. As a follow-up to Part One, today I’ll discuss the various ways analytics is applied in retailing and B2C. The situations below are far from exhaustive, but at least they provide insights into what I’ve experienced in various engagements with the retailing sector.

Retailing and B2C segments rely on real-time availability of data insights. Customer behavior, societal influences, the distribution column—they all fluctuate drastically and affect commercial behavior so intensely, that only real-time insights empower the retailer to monitor and adjust the closed-loop portfolio. Needless to say, retailing and B2C require in-memory platforms, which provide the calculation and data handling power, plus the scalability, that are needed so badly.

At the base of getting insights are in-memory systems that track every single transaction done in the shops or online. An often-seen solution for this is called SAP Customer Activity Repository (CAR). SAP Customer Activity Repository is a foundation that collects transactional data that was previously spread over multiple independent applications in diverse formats.

Watch the SAP Customer Activity repository video HERE

The repository provides a common foundation and a harmonized, multichannel-transaction data model for all-consuming applications. Retailers can use SAP Customer Activity Repository to gradually transform their system landscapes from traditional database technology to revolutionary, in-memory database technology.

Assuming the real-time platform and SAP Customer Activity Repository are available, what is the typical scope for this market segment’s insights using analytical components from the closed loop? Let’s have a look.

Basket Analyses

Basket analyses are the core insights that provide information about what people buy at what moment and at what location. We get insights into what is in their “basket.” The mix of products consumers buy is especially interesting. For example, a retailer can use real-time predictive models to predict whether a young female teenager buying red trousers might also be interested in purchasing accompanying earrings.

Sensor techniques can really help the shop employees to focus their advice specifically to the customer’s needs. Consider this shopping scenario:

Sensors inform an employee that a customer is picking a blue shirt, sized XXL from the rack.
Online analytics and predictive models immediately tell the employee on a mobile device that the customer took the wrong size (based on his buying history) from the rack.
The buying history then indicates that the customer typically buys three pieces at once.
The employee is also informed that the customer’s profile indicates he might be interested in buying jeans to go with the shirt he’s looking at (based on predictive models).
Finally, loyalty card information indicates that if the customer today buys four pieces, an extra bonus will be provided to his savings card.

All this information helps the employee interact with and sell to the customer.

Shop Performance

With shop performance, I mean the ability to use real-time, closed-loop analytics on an
overseeing shop level. Sentiment analysis based on, for example, an impacting television show last evening where a popular boy’s band showed their new, hip-colored sneakers, might trigger the retailing group to discount a second article when customers buy similar sneakers. The agility here is crucial—sentiments are notified from social media analyses and action needs to be taken immediately.

Local influences could mean specific sizes of a product are sold very well in one place, but less well in other places. This might trigger shop management to re-allocate stock to other shops. The same thing applies to ranking capabilities—permanently monitoring top-bottom rankings per article, color-item or size is valuable, since the slightest social change (a big event in one specific city) might cause immediate changes in buying behavior locally.

Customer Loyalty

Customer loyalty cards provide the retailer with a wealth of information if used well. The loyalty cards “identify” the person buying. We can see the consumers’ buying behavior, what they buy, and when. Tracking techniques (picking up mobile devices’ signals when customers enter the store) show us in real-time exactly where each customer spends their time in our shop, what is the route that customer typically follows, and what is their average visiting time.

Retailers can go a step further by combining loyalty card information with the customer’s buying history and social media information. This further completes one profile, allowing the retailer to tailor make marketing initiatives on an individual level.

For example, I—as customer X—might receive a special offer for a new external hard drive from a retailer, since combining data shows that I like audiophile equipment, buy music magazines (basket analyses) and spend quite some time at the electronics department when visiting that shop. The data insight is that I might need (or want) a storage device to store my music.

Customer loyalty cards might also bring great value to the customer retention program. Customers nowadays really quickly change their providers of goods because they are enormously well informed.

Weather Influences

Weather conditions greatly impact the buying behavior of customers. In general, windy weather has proven to have a highly negative impact on retail sales revenue. Making other general statements is difficult since a specific weather condition can have a positive effect on one type of retail, and a negative on another. Think about how cold weather might improve sales of books but negatively affect sales of handbags, for example. (A nice article focusing on the impact of temperature on shopping can be found at the Summit Blog.)

Likewise, a retail shop’s location—inbound or outbound—and its availability of underground parking are very important in rainy conditions. For retailers, it’s important to realize that weather conditions have so much impact that they can’t be excluded from operational insights on shop performance. Thus, they must make them part of the closed loop portfolio.

Alternative Business Models

Retail and B2C markets are probably one of the most highly interesting market segments to follow. Why? Well, they’ll be under great change. The “Digital Transformation” age and the availability of information to both the retailer and the consumer are changing everything.

Consumers are not only wanting to know “everything” about their product, they are also shifting to buying (or should I say “renting”) a product experience rather than the product itself. For the latter, think about the accompanying services to a product that the retailer might want to offer. Have a look at this article from Forbes, which describes three trends for retail in the future—instant gratification, borrowing, and customization.

For me, there is enough food for thought to write a Part Three of the series on the overwhelming power of analytics within retailing and B2C. Stay tuned.

Follow me on Twitter – @IvervandeZand.

– See more at: http://blogs.sap.com/analytics/2016/01/06/the-overwhelming-power-of-analytics-in-retailing-and-b2c-part-two/#sthash.YIdYhjL9.dpuf

The post The Overwhelming Power of Analytics in Retailing and B2C: Part 2 appeared first on Amick Brown.

The Overwhelming Power of Analytics in Retailing and B2C: Part One

amick.brown — Mon, 28 Dec 2015 14:47:24 +0000

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.

The post The Overwhelming Power of Analytics in Retailing and B2C: Part One appeared first on Amick Brown.

The Closed Loop portfolio in Analytics

amick.brown — Fri, 04 Dec 2015 14:09:36 +0000

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:

online Analytics on big data with interactive user involvement
ability to adjust and monitor a rolling Planning for budgets, forecasts. A planning that that allows for delegation and distribution from the corporate level into lower levels
GRC software to perform risk analyses on for example vendors or suppliers
online Predictive analyses components to apply predictive models like decision trees, forecasting models or other R algorithms. Predictive analyses allow to look for patterns in the data that “regular” analytics is not able to discover. The scope of predictive analytics is gigantic: think not only sentiment analyses for social media, but also basket analyses in retail markets, attrition rates in HR and many, many more.

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 therefore a business case for an in-memory system is a sine qua non.

Imagine the possibilities

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:

HR: attrition rates of employees
Banking & Insurance: customer segmentation, product basket analyses
Telco & Communications: churn and market segmentation nut also network utilization
Public Government: Fraud detection and Risk-Mitigation
Hospital: personalized healthcare

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 is completely Cloud-driven and utilizes 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.

The post The Closed Loop portfolio in Analytics appeared first on Amick Brown.