Category Archives: Analytics, Machine Learning & Data Science

Smart Machine Marketing and the Algorithmic Economy

The reason why Smart Machines are so much more powerful than conventional computer programs are the advanced AI algorithms and the data that they can absorb. Smart Machines can sense their own state and their environment, can communicate with other Smart Machines, they are self-learning and can solve very complex problems, and they can act, sometimes autonomously. There are many technologies behind the capabilities of Smart Machines. The most important enabler is the massive amount of computing power and storage that is available today for a relatively cheap price, which makes it finally possible to apply computational heavy artificial intelligence algorithms that would have not been possible some years ago.

Many characteristics distinguish traditional software applications from Smart Machines. Computers have always been pretty good in repetitive and clearly described tasks and in applying strict logic and complex mathematics. An abundance of tasks today are solved by computers much faster, cheaper and more reliable than by humans. Yet, in many ways, computers appear oftentimes annoyingly stupid. Have you tried to have a meaningful and interesting conversation with a computer? It can be a difficult and typically very frustrating endeavour. What computers are missing is the ability to understand the meaning of what we have to say. This is because language is very ambiguous. The very same sentence can mean something completely opposite if said in another situation or by a different person. “I love this computer” could mean either did I really like my computer a lot but it could also mean that I really hate my computer because it doesn’t do what I want it to do. It is very unlikely that love refers to romantic love in this context. The idea that computers can think like a human sounds stretched, it is, however, closer than you might think.

It is changing with the up-rise of Smart Machines. It makes machines being able to handle situations with ambiguity, sparse information and uncertainty, thus, be able to solve human kind of problems. Instead calculating the optimal solution using a predefined algorithm, smart machines evaluate different options and choose the best option out of the possibilities. Problems do not need to be provided in a specified machine readable format, they can be simply formulated in natural language or even normal speech. Looking at the context of the problem makes it possible to interpret the question correctly. When I ask a smart machine “what is the best restaurant?”, it should understand that I am probably looking for a good restaurant that is not too far away from my current location. Based on the outcomes of an action, smart machines can learn and improve their problem solving. Instead of being programmed, they can read PDF documentations to understand a business process and observe how humans perform a business process to build its own knowledge base and eventually be able to handle the business process on its own.

The key components of a Smart Machine are depicted in the figure and will be explained in detail in the following. An incentive and rule system needs to be set for a Smart Machine which provides a purpose for the Smart Machine to exist (e.g. as a self-driving car) and the rules it needs to obey (e.g. ethics, law, company procedures, business goals).

Smart Machine Marketing Artwork

In order for machines to see, feel, hear, smell, and taste like human beings, all aspects of the physical world need to be translated into “digestible” data for machines to process, reason, and act. The rise of low-cost sensor technologies and the Internet of Things with its connected devices enables the collection of data from the physical world without human interaction. All senses are needed to cover an entire customer journey from inspiration to usage. The augmented senses of machines allow a broader, deeper, and more personalized customer experience. Sensed information is fed, interpreted, filtered, interlinked and used to initiate further activities.

The most important ability of Smart Machines is to process the sensed information similar to the way us humans process information (i.e. empirical learning). Smart machines are able to think and solve problems by understanding and clarifying objectives (and sometimes coming up with their own objectives), by generating and evaluating hypotheses, and by providing answers and solutions like a human would do (and unlike a search machine which gives a list of results). Smart Machines are self-learning, they can adapt their own algorithms through observing, discovery and by doing.

Finally, Smart Machines can act, by visualizing and providing the responses to a human decision maker, by informing or even commanding a human to execute certain activities, or in the extreme case, by completely autonomously executing a business process or any other actions. Based on the results of the actions, Smart Machines are able to re-calibrate their goal setting.

The impact of Smart Machines will be observable in three domains for Marketing Professisonals. First of all, customers will get a more contextualized and personalized experience. Secondly, the marketing departments will be able to do more with less people building on automation and scale of intelligent algorithms that take over some of the human labor. Thirdly, there will be advances in the customer journey possible which are of disruptive nature.

The marketing profession will be impacted fast and significantly by Smart Machines and the Algorithmic Economy. Personalizing and contextualizing the customer experience is the aim of everyone. But, creating meaningful continuous 1:1 interactions can only be feasible on a large scale with thousands or millions of customers if Smart Machines take over a lot of the work. This means that smart machines take over work reserved for humans in the past, as, for example, generating new content, and supervising staff in retail stores to ensure high customer engagement. It also means that those companies that still struggle with data-driven marketing will be in deep trouble. Those who embrace Smart Machines will be able to drive productivity beyond the imaginable for marketing and sales within the next decades.

Like all things in life, Smart Machines are all a matter of perspective. For marketing divisions in traditional companies, they might be seen as the biggest threat in history. The way most marketing departments work today is very reliant on human labor and decision making. Shifting the work to Smart Machines will make a lot of the abilities needed for traditional marketing personnel redundant and will require new capabilities that the workforce does not necessary have. For others, Silicon Valley startups and companies, Smart Machines generate an once in a lifetime opportunity. Smart Machines enable them to scale their limited resources and, thus, be able to challenge even the largest established players in their own strongholds, irrespective if it is retail, consumer goods, banking, insurance, manufacturing, entertainment or any other type of industry which requires Smart Machine Marketing.

Industrializing Data Science and Analytics

Gartner´s “Hype Cycle for Advanced Analytics and Data Science 2015″ has just been published. The trends indicated in the hype cycle show a rising maturity of this young organizational discipline.  It is interesting to see that the buzzword “Big Data” has finally disappeared from the hype cycle, while machine learning (a discipline that has been there for decades, at least in academia) has reached the peak of inflated expectations.  This underpins a tendency to  move from big data (the bigger the better) to smart data (the smarter the better). Simply said: “No matter if it is big or small data, it is still data and we aim to get more value out of it.”

A trend that is also visible at a second glance is the emerging industrialization of data science, which is underpinned by a number of developments. Vendors increasingly support the management of analytical models built by data scientist over their entire life cycle, when they are scaled from prototype to company-wide adoption. So far, the management of analytical models has been rather disorganized in most companies.  Data scientists would create new models on an use case by use case basis. Some of the models have been actually doing what they promised to do and would be deployed in operations.

An end to end management of the models and a reuse of solution patterns for analytical models across the enterprise has not been actively enforced or governed. In a new project, they would often start nearly from scratch although a similar model might have been already developed in a different business unit. From an organizational point of view, it makes sense to have a centralized data science unit that can support data scientists in decentralized business units. A central data science unit can ensure that learnings are incorporated and fed back to the organization and that analytical models are consistently governed even after they are handed over to IT.

Very connected to this is the concept of the model factory. The idea is to bring automation and scalability to the process of building and deploying predictive models.  To find the best models, a huge number of models are built and tested using software tools that provide a high  degree of automation during devleopment. At the end of the process, only the best few models are deployed.

Finally, a thrilling concept comes from Gartner´s Alexander Linden, which is the concept of the analytics market place. Some companies such as Microsoft, Rapidminer and FICO have created marketplaces, where data science services and additional functionality are provided by third parties which can be purchased by users of the analytics platforms. This can become a true game changer. Similar to the third party apps and services provided at Salesforce.com, analytics marketplaces could become a source of millions of very domain specific analytics micro-applications that drive innovation.

Today, we stand only at the beginning. I am convinced that in a few years time, data science and advanced analytics will be as industrialized as traditional IT. What has changed with the uprise of data science is the speed with which new applications are developed and deployed, the increased willingness to experiment and the direct way data innovates business models and business operations. Now, we only need to scale it to the rest of the enterprise to reap the full benefits.

 

 

Personalize Marketing with Digital Analytics

Once upon a time, everything was personal. When you went into your local bakery, you would hear something like “Nice to see you again, Mr. Richards. Since it is your son´s birthday in a few days time, would you like me to bake a birthday cake for him?” Today, every customer is one in a million of other customers. So, here is the trick question: how can you personalize marketing to thousands of your customers? The key is data. Digitize every touchpoint with your customer to collect data. Listen to the voice of your customer on social media. Give your customers rewards and incentives for sharing data with you. Look for data that explains the local context of your customer interactions, such as local news, events, economic situation, prices, culture, weather and political opinions. And integrate all that data to learn insights across all data silos.

Every single interaction with your customer matters. Use the data and insights to personalize every touch point that you share with your customer to improve the brand experience. Give your employees the power of analytics at their fingertips so they know what the customer has done online and on the mobile phone when the customer comes into the branch and telling the employee what the customer likes and what better to avoid. And use cognitive technologies that can fully personalize the automated interaction on mobile phones and online.

Then, you have to learn how to predict things that are relevant to personalize the customer experience. First of all, you have to creatively construct features as the raw data is not ready for analytics. For instance, calculate the age of your customer using the date of birth. Determine the product categories bought, the frequency of purchases and total revenue from transactional data. And turn the profession into an average salary for this type of profession, for example. Transactional data combined with customer and contextual data is extremely powerful. Predict the psychological profile of your customers. Predict where your customer are going and what they need at this very moment. And predict the next best marketing action for each individual customer.

Type of personalization Example
Personalize style for your communication “I am a fact driven person. So, please, just give me the facts.”
Personalize channel for your communication “We prefer personal interaction when it comes to our mortgage.”
Personalize content for your communication “You should know that I am a vegetarian.”
Personalize context for your communication (e.g. time, place) “I am on my way home and looking for a great place to eat.”
Personalize your service “I like action movies with Sylvester Stallone. No nonsense.”
Personalize your Product “My car can speak to me and knows my favorite restaurants.”

 

With the right digital tools, you can personalize nearly everything. The style of your marketing messages. For instance, your customer might be a fact driven person and expects you just to give him the facts. The preferred marketing channel. You might find out in your data that young couples that plan to buy a house might prefer personal interaction when it comes to choosing the right provider for their mortgage. The content of the marketing message. You better avoid selling meat to a vegetarian. And the context of the marketing message. An add for a restaurant works best, when I am hungry on my way home from work and looking for a great place to eat.

Besides your marketing, services and products can be personalized, too. In the old bakery times, a personalized service could be something like you coming into the bakery and the baker you´ve known for a long time says: “I noticed that every Wednesday you buy four pretzels. I have reserved some for you as we were nearly running out of stock.” Today, a personalized service is given to millions using customer data analytics, for example, by media streaming service provider Netflix that knows that you like action movies with Sylvester Stallone and maybe some movies with Bruce Willis, but certainly no romantic nonsense like Titanic or Shakespeare in Love. Personalizing your product in old time in the bakery could be your baker saying: “Last time you told me that you like bread with sesame sprinkles. I made some especially for you!” Today, it might be your BMW that calls you by name and knows your favorite restaurants

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