Predictive Monthly: Sales Forecasting—How Algorithms Are Putting An End To Guessing

Pierre Leroux

Alan Greenspan, former head of America’s Federal Reserve, once famously confessed: “We really can’t forecast all that well, and yet we pretend that we can.

If the world’s top economists can’t get it right, then it’s hardly surprising that forecasting is a problem in business too. Forecasting is at its most challenging in sales, where month after month, sales reps churn out their revenue forecasts for management teams that have little or no faith in their accuracy. CSO Insight’s 2016 Sales Performance Optimization Study shows that the average win rate of forecast sales deals is only 45.8%. Flipping a coin would provide better odds!

The problem with sales forecasting lies in a combination of human bias in pipeline reporting and spreadsheet models that are not sufficiently sophisticated to take in all the possible factors that might be driving sales deals. Inaccurate revenue forecasts can potentially jeopardize a company’s share price, endanger its cash flow, and inflate inventory costs, so businesses are increasingly turning to a new technology—predictive analytics—for help. Predictive analytics is revolutionizing sales forecasting by replacing the limitations of human inference and bias with models based on machine-learning algorithms.

How predictive analytics improves sales forecasting

Sales forecasting with predictive analytics starts with the combining of internal customer data such as win/loss ratios, delay factors, close rates, and completeness of the sales process, with external data that indicate a customer’s propensity to buy (these data points could be as diverse as company revenue, executive changes, and social media activity). Forecasting algorithms then use machine learning to look for patterns in these large volumes of data, in ways and speeds not humanly possible. The relationships spotted in the data are then used to score each deal in the pipeline and predict its likely revenue with levels of accuracy reported to be as high as 82%.

Fresh insights, live and in the moment…Think, decide, and act in real-time. Learn more here!

This approach differs from using business intelligence tools and spreadsheets for forecasting: These traditional approaches rely on the human brain to infer correlations between the different factors (historical data) and the outcome (sales). However, the brain does not have sufficient power to spot links between thousands of variables and nonlinear relationships.

Sales forecasting with algorithms: in practice

While some have claimed that predictive analytics will replace sales reps altogether in the forecasting process, in practice this is not the case. At a global software company, sales managers compare forecasts by sales reps with those output from algorithms and discuss variances with their reps, who then take a closer look at their pipeline and fine-tune their estimates. When analyzing actual sales scored against those forecast, opportunities that didn’t close are also examined to find new factors driving closure that are then digitized and added to the forecasting algorithm for increased accuracy.

A Swiss chemical company uses predictive analytics to enhance the accuracy of its sales forecasting by complementing experience-based decisions with model-based forecasting. Through this process of forecast validation, the company has been able to reduce inventory levels and costs while increasing product availability, delivery capabilities, and customer satisfaction.

Predictive analytics is also becoming a key tool in sales enablement by letting salespeople know how and when to communicate with prospects based on algorithms that leverage every imaginable variable that impacts a customer’s decision to buy. A leading IT networking company is using predictive analytics in this way to help account mangers determine which customers to call on and which products to promote, based on deep insights into what their customers are likely to buy.

The bigger picture

In an earlier blog, Predictive Monthly: How A Little Bit Of Wizardry Can Transform Your Bottom Line, I explained that the key benefits of predictive analytics include not just objective and accurate predictions but also automated decision-making and the uncovering of new business opportunities. It’s for these reasons that predictive analytics is being adopted not just in sales but across many lines of business, including marketing, operations, HR, and finance. With the recent exponential increase in computer processing power and the rise of Big Data, predictive analytics is seen by many organizations as an ROI decision instead of a cost, because of the incredible value it can release from existing data and infrastructure.

Now that algorithms, machine learning and Big Data can support sales forecasting, excuses are running out for getting it wrong!

To learn more about adding predictive to your sales analytics strategy read my earlier blog, Predictive Monthly: The Role of Predictive in Your Analytics Strategy.

For 5 traits that lead to the successful use of algorithms, read the Digitalist blog, Algorithms: The New Means of Production.

 

Comments

Pierre Leroux

About Pierre Leroux

Pierre Leroux is the Director of Predictive Analytics Product Marketing at SAP. His areas of specialty include Data Discovery, Business Intelligence, Cloud applications, Customer Relationship Management (CRM), and ERP.

How Big Data Can Tell You Which Book To Read Next

JP George

If you enjoy reading, but still haven’t foundyour next book to cozy up with, your smartphone might be able to suggest one. Artificial intelligence (AI) is now able to rank literature to predict the next bestseller – a kind of recommendation system, not based on metadata, but on the patterns and themes found in books.

Publishers around the globe are mining all kinds of data, including what’s in the books themselves, in search of the magic formula for evaluating a book’s market potential. With more informed marketing, publishers hope to better target their customers.

Recommending the popular novel

So, how does AI determine what we want to read? It turns out that certain emotional patterns keep us engaged and interested while reading a novel. Kurt Vonnegut first described the curves of emotional plotlines in 1995. Now, with the help of AI sentiment and emotion analysis, such plotlines can be extracted quantitatively. By combining these plotline curves, researchers from the Stanford Literary Lab claim to be able to detect the next blockbuster novel.

Machines think from data

Under the hood of such an AI sits Big Data and machine learning (ML). The concept of Big Data doesn’t just mean lots of data, but also that the data comes from many different data sources and types (e.g., audio, video, images, text, etc.) that are often unstructured (unlike traditional databases with well-defined fields). ML involves statistical algorithms that utilize sets of multi-type, unstructured data to predict class membership. This is possible by either knowing ahead of time which classes exist and training the ML algorithm by example (supervised learning) or letting the algorithm discover the underlying patterns (unsupervised learning).

ML methods include embedded vector space techniques (principal component analysis, K-nearest neighbor, and support vector machine), decision-tree based techniques (classification and regression tree, random forest), gradient and Bayesian-based methods, artificial neural networks (ANN), and others. Many tutorials on machine learning methods can be found here.

ANNs were among the first algorithms to be applied to solve problems in AI, beginning as long ago as the 1940s. For many reasons, their use has waxed and waned over the years, yet interest has recently resurged along with the unprecedented advance of deep learning. This growth in deep learning has lead to what the New York Times calls the great awakening, given Google’s ability to translate text into more than 100 languages.

How AI uncovers sentiment and emotions from text

Imagine automatically extracting the sentiment or emotional impact of a literary work. For a computer to understand a text, what is called natural language processing (NLP), AI algorithms first find a mathematical representation that a machine can understand and that contains maximal information about the text. A simple representation called “bag-of-words” (as the name implies) is a collection of words that appear together, but with no other particular nexus, from which the frequency of word groups could be ascertained. This may provide enough information for classifying themes, but would fail miserably at understanding sentences if word order is important.

Two representations that can quantify information associated with sentence word order are Word2Vec and GloVe. More about NLP representations can be learned from this tutorial, while a tutorial from TensorFlow on Word2vec is found here.

Once sentences are converted to a meaningful representation, a language model is needed that discerns positive emotions from negative emotions. One method would be to use a supervised learning procedure with deep neural networks, as has been done to understand movie reviews. Another way is to allow the deep neural network to discover the emotional patterns by itself. This is the true power behind deep learning: its ability to teach itself, and with more Big Data, to learn more.

Through this process, the ML can understand at text’s major themes (from the word groupings) and emotion. These factors are the fundamental ingredients for an AI application that will recommend a novel.

From creating Animal Farm summaries to discovering who will be the next Danielle Steel, AI is revolutionizing what and how we will read in the future.

For more on using ML to upend the competition, see Why Machine Learning and Why Now?

Comments

JP George

About JP George

JP George grew up in a small town in Washington. After receiving a Master's degree in Public Relations, JP has worked in a variety of positions, from agencies to corporations all across the globe. Experience has made JP an expert in topics relating to leadership, talent management, and organizational business.

Could Governments Run By Artificial Intelligence Be A Good Thing?

Glen Sawyer

Put Skynet from The Terminator movies to the back of your mind for a minute, and stay with me on this one.

Certain political leaders are reminding us of their fragile humanity with increasing frequency these days. Prone to wild acts of emotion and unable to resist the urge to push their personal agenda at the expense of the greater good, it’s enough to make the concept of an AI-controlled government sound utopian by comparison.

I’m not quite naïve enough to think we’re already at a point where our human leaders could be replaced by an all-seeing, all-knowing, all-doing machine, but artificial intelligence and machine learning are becoming ever more tantalizing in their potential to simplify, accelerate, and improve many aspects of society and our lives.

Keeping reality in check

Governments are beginning to realize this. We’re already seeing small crumbs of evidence that they understand how AI can make public services more efficient and citizen-friendly. But these are very early days in discussing and figuring out how such technology could help us enforce laws, organize labor and welfare, and so on, in ways most people would be comfortable with.

And if the Facebook AI story is anything to go by, we’re still pretty spooked by the idea of an intelligence that can “think,” communicate, and potentially make decisions using methods we might not always understand, so a future in which we’re willingly ruled by a digital overlord remains very distant.

What’s more likely – dare I say, inevitable – is that governments will find ways to take advantage of AI in smaller increments, and this will eventually compound to form a political system in which machines are doing most of the “thinking” work.

Keeping humanity in check

Unless you believe the singularity is possible, that “thinking” will remain under the control of a far more streamlined government made up of regular, everyday humans. Our greatest hope is that the AI-run aspects of governance are powerful and transparent enough that those humans can’t get away with the deceit, selfishness, and emotion-based political decisions that plague us today.

That said, it would likely be a very different group of people to today running an AI government. If governments do come to rely heavily on technology, it could be a few technologists at the top of the tree – the ones who understand how it all works – who find themselves wielding immense power. With the likes of Mark Zuckerberg already accruing vast political influence to do with as they please, that’s a worrying prospect.

Keeping AI programmers in check

Thankfully, it won’t happen in the way some are fearing it might. Government decision-making is so complex, with so many interlinked aspects, that no one or small group of technological minds could comprehend and control it entirely. I also don’t believe people generally hold the Silicon Valley view that technology alone can solve everything. What I’m saying is, let’s embrace AI safe in the knowledge that collectively we’ll be able to keep it and its programmers in check.

If we do, we’re opening a whole new world of possibilities in efficient, logical, and honest governance. It’s essential we don’t let the same thing happen with a tech-run government that we’re letting happen with the internet – where power is consolidating into too few hands. That will take a combination of remembering the democratic principles that got us to this point and educating enough people to understand the technology overseeing us. I, for one, am optimistic we can get there. Please tell me I’m not alone.

This story originally appeared on the SAP Community.

Comments

Glen Sawyer

About Glen Sawyer

Glen Sawyer is National Director of IoT Digital Transformation at SAP. 

Diving Deep Into Digital Experiences

Kai Goerlich

 

Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

Link to Sources


From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.


What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”


Download the executive brief Diving Deep Into Digital Experiences.


Read the full article Swimming in the Immersive Digital Experience.

Comments

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

Tags:

Blockchain: Much Ado About Nothing? How Very Wrong!

Juergen Roehricht

Let me start with a quote from McKinsey, that in my view hits the nail right on the head:

“No matter what the context, there’s a strong possibility that blockchain will affect your business. The very big question is when.”

Now, in the industries that I cover in my role as general manager and innovation lead for travel and transportation/cargo, engineering, construction and operations, professional services, and media, I engage with many different digital leaders on a regular basis. We are having visionary conversations about the impact of digital technologies and digital transformation on business models and business processes and the way companies address them. Many topics are at different stages of the hype cycle, but the one that definitely stands out is blockchain as a new enabling technology in the enterprise space.

Just a few weeks ago, a customer said to me: “My board is all about blockchain, but I don’t get what the excitement is about – isn’t this just about Bitcoin and a cryptocurrency?”

I can totally understand his confusion. I’ve been talking to many blockchain experts who know that it will have a big impact on many industries and the related business communities. But even they are uncertain about the where, how, and when, and about the strategy on how to deal with it. The reason is that we often look at it from a technology point of view. This is a common mistake, as the starting point should be the business problem and the business issue or process that you want to solve or create.

In my many interactions with Torsten Zube, vice president and blockchain lead at the SAP Innovation Center Network (ICN) in Potsdam, Germany, he has made it very clear that it’s mandatory to “start by identifying the real business problem and then … figure out how blockchain can add value.” This is the right approach.

What we really need to do is provide guidance for our customers to enable them to bring this into the context of their business in order to understand and define valuable use cases for blockchain. We need to use design thinking or other creative strategies to identify the relevant fields for a particular company. We must work with our customers and review their processes and business models to determine which key blockchain aspects, such as provenance and trust, are crucial elements in their industry. This way, we can identify use cases in which blockchain will benefit their business and make their company more successful.

My highly regarded colleague Ulrich Scholl, who is responsible for externalizing the latest industry innovations, especially blockchain, in our SAP Industries organization, recently said: “These kinds of use cases are often not evident, as blockchain capabilities sometimes provide minor but crucial elements when used in combination with other enabling technologies such as IoT and machine learning.” In one recent and very interesting customer case from the autonomous province of South Tyrol, Italy, blockchain was one of various cloud platform services required to make this scenario happen.

How to identify “blockchainable” processes and business topics (value drivers)

To understand the true value and impact of blockchain, we need to keep in mind that a verified transaction can involve any kind of digital asset such as cryptocurrency, contracts, and records (for instance, assets can be tangible equipment or digital media). While blockchain can be used for many different scenarios, some don’t need blockchain technology because they could be handled by a simple ledger, managed and owned by the company, or have such a large volume of data that a distributed ledger cannot support it. Blockchain would not the right solution for these scenarios.

Here are some common factors that can help identify potential blockchain use cases:

  • Multiparty collaboration: Are many different parties, and not just one, involved in the process or scenario, but one party dominates everything? For example, a company with many parties in the ecosystem that are all connected to it but not in a network or more decentralized structure.
  • Process optimization: Will blockchain massively improve a process that today is performed manually, involves multiple parties, needs to be digitized, and is very cumbersome to manage or be part of?
  • Transparency and auditability: Is it important to offer each party transparency (e.g., on the origin, delivery, geolocation, and hand-overs) and auditable steps? (e.g., How can I be sure that the wine in my bottle really is from Bordeaux?)
  • Risk and fraud minimization: Does it help (or is there a need) to minimize risk and fraud for each party, or at least for most of them in the chain? (e.g., A company might want to know if its goods have suffered any shocks in transit or whether the predefined route was not followed.)

Connecting blockchain with the Internet of Things

This is where blockchain’s value can be increased and automated. Just think about a blockchain that is not just maintained or simply added by a human, but automatically acquires different signals from sensors, such as geolocation, temperature, shock, usage hours, alerts, etc. One that knows when a payment or any kind of money transfer has been made, a delivery has been received or arrived at its destination, or a digital asset has been downloaded from the Internet. The relevant automated actions or signals are then recorded in the distributed ledger/blockchain.

Of course, given the massive amount of data that is created by those sensors, automated signals, and data streams, it is imperative that only the very few pieces of data coming from a signal that are relevant for a specific business process or transaction be stored in a blockchain. By recording non-relevant data in a blockchain, we would soon hit data size and performance issues.

Ideas to ignite thinking in specific industries

  • The digital, “blockchained” physical asset (asset lifecycle management): No matter whether you build, use, or maintain an asset, such as a machine, a piece of equipment, a turbine, or a whole aircraft, a blockchain transaction (genesis block) can be created when the asset is created. The blockchain will contain all the contracts and information for the asset as a whole and its parts. In this scenario, an entry is made in the blockchain every time an asset is: sold; maintained by the producer or owner’s maintenance team; audited by a third-party auditor; has malfunctioning parts; sends or receives information from sensors; meets specific thresholds; has spare parts built in; requires a change to the purpose or the capability of the assets due to age or usage duration; receives (or doesn’t receive) payments; etc.
  • The delivery chain, bill of lading: In today’s world, shipping freight from A to B involves lots of manual steps. For example, a carrier receives a booking from a shipper or forwarder, confirms it, and, before the document cut-off time, receives the shipping instructions describing the content and how the master bill of lading should be created. The carrier creates the original bill of lading and hands it over to the ordering party (the current owner of the cargo). Today, that original paper-based bill of lading is required for the freight (the container) to be picked up at the destination (the port of discharge). Imagine if we could do this as a blockchain transaction and by forwarding a PDF by email. There would be one transaction at the beginning, when the shipping carrier creates the bill of lading. Then there would be look-ups, e.g., by the import and release processing clerk of the shipper at the port of discharge and the new owner of the cargo at the destination. Then another transaction could document that the container had been handed over.

The future

I personally believe in the massive transformative power of blockchain, even though we are just at the very beginning. This transformation will be achieved by looking at larger networks with many participants that all have a nearly equal part in a process. Today, many blockchain ideas still have a more centralistic approach, in which one company has a more prominent role than the (many) others and often is “managing” this blockchain/distributed ledger-supported process/approach.

But think about the delivery scenario today, where goods are shipped from one door or company to another door or company, across many parties in the delivery chain: from the shipper/producer via the third-party logistics service provider and/or freight forwarder; to the companies doing the actual transport, like vessels, trucks, aircraft, trains, cars, ferries, and so on; to the final destination/receiver. And all of this happens across many countries, many borders, many handovers, customs, etc., and involves a lot of paperwork, across all constituents.

“Blockchaining” this will be truly transformational. But it will need all constituents in the process or network to participate, even if they have different interests, and to agree on basic principles and an approach.

As Torsten Zube put it, I am not a “blockchain extremist” nor a denier that believes this is just a hype, but a realist open to embracing a new technology in order to change our processes for our collective benefit.

Turn insight into action, make better decisions, and transform your business. Learn how.

Comments

Juergen Roehricht

About Juergen Roehricht

Juergen Roehricht is General Manager of Services Industries and Innovation Lead of the Middle and Eastern Europe region for SAP. The industries he covers include travel and transportation; professional services; media; and engineering, construction and operations. Besides managing the business in those segments, Juergen is focused on supporting innovation and digital transformation strategies of SAP customers. With more than 20 years of experience in IT, he stays up to date on the leading edge of innovation, pioneering and bringing new technologies to market and providing thought leadership. He has published several articles and books, including Collaborative Business and The Multi-Channel Company.