16 Oct Top 15 tendencies, which will change interaction with customers – Insight technologies (part 2)
Recently Forrester has issued a new research report The Top Emerging Technologies to Watch: 2017 to 2021 which is looking at the tech trends that can help businesses in their relations with their customers. Here are the 15 trends in question. They are divided in 3 categories:
The 5 engagement technologies:
- Internet of Things (IoT) software and solutions;
- Intelligent agents, such as robotic process automation;
- Personal identify and data management, including personal data lockers, authorization management tools;
- Real-time interaction management, like customer recognition, offer arbitration and delivery, measurement/optimization; and
- Augmented Reality (AR)/Virtual Reality (VR).
The 5 insight technologies:
- AI/cognitive tech, such as deep learning and natural language processing;
- Customer journey analytics;
- Insight platforms, such as new business analytics tools;
- IoT analytics; and
- Spatial analytics, including in-store analytics, location analytics, sensors.
And the 5 supporting technologies:
- Security automation and orchestration, such as incident response and threat intelligence;
- Containers and container management. For example, Docker or cloud container management;
- Edge computing, where processing power resides throughout a network rather than in a central location;
- Cloud native application platforms, such Platform-as-a-Service and API management; and
- Hybrid wireless connectivity, where “chips and software […] translate between wireless protocols on same device.”
In this article we will have a look at the 5 insight technologies:
Insight technology 1 – AI / cognitive tech
The first of the top 5 insight technologies is AI. A good definition of AI is the following “AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.”
AI development started in the 50’s in leaps and bounds and different levels of interest from the business. By the late 2000s, a number of factors helped renew progress in AI, particularly in a few key technologies, which are:
Moore’s Law. The relentless increase in computing power available at a given price and size, sometimes known as Moore’s Law after Intel cofounder Gordon Moore, has benefited all forms of computing, including the types AI researchers use. A dramatic illustration: The current generation of microprocessors delivers 4 million times the performance of the first single-chip microprocessor introduced in 1971.
Big data. Thanks in part to the Internet, social media, mobile devices, and low-cost sensors, the volume of data in the world is increasing rapidly. Growing understanding of the potential value of this data has led to the development of new techniques for managing and analyzing very large data sets. Big data has been a boon to the development of AI. The reason is that some AI techniques use statistical models for reasoning probabilistically about data such as images, text, or speech. These models can be improved, or “trained,” by exposing them to large sets of data, which are now more readily available than ever.
The Internet and the cloud. Closely related to the big data phenomenon, the Internet and cloud computing can be credited with advances in AI for two reasons. First, they make available vast amounts of data and information to any Internet-connected computing device. This has helped propel work on AI approaches that require large data sets. Second, they have provided a way for humans to collaborate—sometimes explicitly and at other times implicitly—in helping to train AI systems.
New algorithms. An algorithm is a routine process for solving a program or performing a task. In recent years, new algorithms have been developed that dramatically improve the performance of machine learning, an important technology in its own right and an enabler of other technologies such as computer vision.
There are technologies that emanate from the field of AI – the so called cognitive technologies and here are some of them:
Computer vision refers to the ability of computers to identify objects, scenes, and activities in images. Computer vision has diverse applications, including analyzing medical imaging to improve prediction, diagnosis, and treatment of diseases; face recognition, used by Facebook to automatically identify people in photographs and in security and surveillance to spot suspects; and in shopping—consumers can now use smartphones to photograph products and be presented with options for purchasing them.
Machine learning refers to the ability of computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions. At its core, machine learning is the process of automatically discovering patterns in data. Once discovered, the pattern can be used to make predictions. Applications of machine learning are very broad, with the potential to improve performance in nearly any activity that generates large amounts of data. Besides fraud screening, these include sales forecasting, inventory management, oil and gas exploration, and public health.
Natural language processing refers to the ability of computers to work with text the way humans do, for instance, extracting meaning from text or even generating text that is readable, stylistically natural, and grammatically correct. A natural language processing system doesn’t understand text the way humans do, but it can manipulate text in sophisticated ways, such as automatically identifying all of the people and places mentioned in a document; identifying the main topic of a document; or extracting and tabulating the terms and conditions in a stack of human-readable contracts.
Robotics, by integrating cognitive technologies such as computer vision and automated planning with tiny, high-performance sensors, actuators, and cleverly designed hardware, has given rise to a new generation of robots that can work alongside people and flexibly perform many different tasks in unpredictable environments. Examples include unmanned aerial vehicles, “cobots” that share jobs with humans on the factory floor, robotic vacuum cleaners, and a slew of consumer products, from toys to home helpers.
Speech recognition focuses on automatically and accurately transcribing human speech. The technology has to contend with some of the same challenges as natural language processing, in addition to the difficulties of coping with diverse accents, background noise, distinguishing between homophones (“buy” and “by” sound the same), and the need to work at the speed of natural speech. Applications include medical dictation, hands-free writing, voice control of computer systems, and telephone customer service applications. Domino’s Pizza recently introduced a mobile app that allows customers to use natural speech to order, for instance
Insight technology 2 – Customer journey analytics
The first of the top 5 insight technologies is Customer journey analytics. The objectives of customer journey analytics are as follows:
Specifically, the objectives are:
- Visualize and map the end-to-end customer journey by personas
- Optimizing on the right journey attributes to increase yields by >30% lift. Uncover the right combination of web, mobile and physical channels, content and experiences that best achieves the target goals
- Enable marketers to identify journey bottlenecks for individuals and aggregates
- Leverage actual behavior data to enhance and personalize the experience for each individual customer
To facilitate cross-channel marketing, sales, commerce or service, companies must be able to connect all the dots in order to see the ‘big picture’ of how customers interact with them across channels and touch points. Customer Journey Analytics helps organizations know where customers have been, what they’re trying to achieve, and why specific issues led them down a particular channel.
Many customers use an average of five different channels to contact companies and service providers. They switch quickly from web, to telephone, to social media, to chat, to interactive voice response, to visiting a retail location, depending on whichever is most convenient at the moment, all while expecting the company to remember what they said and did over several of their past interactions.
Yet companies as a whole, and divisions such as marketing, sales, and service, manage each of these channels as silos, optimizing the customer experience for each channel independently but losing track of the customer’s overall journey as they jump from channel to channel. A significant part of the problem is fragmented data… product, sales, campaign and customer data was managed by multiple divisions, agencies and vendors.
According to recent Mckinsey Research, “organizations able to understand and skillfully act on complete customer journeys can reap enormous rewards: increasing customer satisfaction by up to 20 percent and revenue growth by 10 to 15 percent, and lowering the cost to serve by 15 to 20 percent.
A new breed of customer journey analytics solutions are emerging that uses predictive and real-time analytics as well as machine learning (ML) technologies to identify customer behavior patterns and help determine customers’ next move, likeliness to churn, or interest in a particular product or offer. Organizations can use this information to personalize the customer experience in real time by deciding which offers or messages to present to a customer while an interaction is taking place.
Path analysis allows organizations not only to focus on the individual journey but also to understand the impact of the thousands of journeys that take place each day. By analyzing and monitoring cross-channel paths, companies can detect important insights about groups of people who exhibit a similar pattern of behavior. This could identify problems and opportunities that might once have gone undetected.
Every major vendor – IBM, Salesforce.com, Oracle – is pushing new products and platforms that enable Customer Journey and Path Analysis.
Why? Because there is a shift happening in many marketing organizations from focusing on moments of truth to customer journeys. Customer journeys are a discreet set of interactions a customer has with a brand around a need.
Understanding and addressing those journeys creates real value for managing customer experience and engagement
Insight technology 3 – Insight platforms
The third of the top 5 insight technologies is Insight Platforms. Forrester had published a report Vendor Landscape: Insights Platforms, Q3 2016, where it defines five insight platform classes, each with several segments, based on who the platform serves and what it does.
Insight technology 4 – IoT analytics
The fourth of the top 5 insight technologies is IoT analytics or AoT (Analytics of Things) . The Internet of Things (IoT) sounds like a consumer fantasy come true — who wouldn’t want to be able to turn off the lights at home from two towns away, or leave it to their refrigerator to make sure they know when milk, butter and other staples need to be replenished? But there’s more to the IoT than lifestyle enhancement. It also includes a corporate side, enabling organizations to collect and analyze data from sensors on manufacturing equipment, pipelines, weather stations, smart meters, delivery trucks and other types of machinery.
IoT analytics applications can help companies understand the Internet of Things data at their disposal, with an eye toward reducing maintenance costs, avoiding equipment failures and improving business operations. In addition, retailers, restaurant chains and makers of consumer goods can use data from smartphones, wearable technologies and in-home devices to do targeted marketing and promotions — the business side of the IoT’s futuristic world of connected consumer gear.
There are several challenges which the industries will face:
- The amount of data. IDCsays there will be 28 billion sensors in use by 2020, with $1.7 trillion in economic value. Imagine a few billion sensors sending messages 20 times a second or even once a minute. The scale of the data is astonishing. Possible solutions to manage these vast amounts of data are the following:
- Filter and send the data only when needed.
- Send data only when it crosses a threshold.
- Compress the data using lossless algorithms.
- Dispersion of sensors. Data comes from devices with attached sensors. Some things are stationary (wind turbines), others are mobile (cars). While 70 percent of sensors areinside the intranet, 30 percent are “in the wild.” In many implementations, sensor data will be massively dispersed around the planet. That’s vastly different from getting ERP or CRM data extracts.
- Interruptions in data flow – Mobile sensor data arrives from thousands of airplanes, cars, patients, tools, and inventory pallets. These sensors disappear when in a tunnel or 10 kilometers up in the sky. Hence, sensors can be disconnected from the network. This means that data is sometimes lost, and also that developers must plan for “data catch-up mode” when the device is back online.
- Time series data – A majority of sensor data is time-series data. It arrives as a sensor ID, date-time stamp, and measurement. Typically it’s a continuous stream of data per sensor. Often, the granularity of sensor data is more than is needed. Imagine 1,000 sensor measures per minute when we need only 20. This leads to a sliding window of intervals applied to the data with only 20 measurements output. But these interval results are much more complex than averages. Sliding interval windows leads to curve-fitting techniques. Sensor data integration requires advanced analytics algorithms to simplify the data. Now, mathematicians and algorithm suppliers are needed for data preparation. Add deep math skills to the data integration team.
Some of the solutions and useful tips to overcome these challenges are:
- Design an architecture for massively dispersed or disconnected sensors.
- Never use lossy algorithms to compress sensor data.
- Add strong math skills to the data-integration team.
- Keep raw sensor data in a data lake for cost savings and archival purposes.
- Exploit sensor data in the data warehouse for exponential value creation.
Insight technology 5 – Spatial / Location analytics
The fifth of the top 5 insight technologies is Spatial and Location Analytics.
Location analytics is the process or the ability to gain insight from the location or geographic component of business data. Data, especially transactional data generated by businesses, often contains a geographical component that, when laid out in a geographical information system, allows for new dimensions of analysis and insights, in this case through a more visual approach.
Retail, real estate, energy, insurance, manufacturing, healthcare, government, planning, public safety — every industry under the sun benefits from location data and analysis.
Retail sites can compare sales territory revenue and assess marketing campaign effectiveness. Businesses can see exactly where customers live to determine where to open stores and distribute products. Hospitals determine demand for new vaccines or make sense of sudden disease outbreaks. This is possible because every information system, desktop solution, or mobile app can take advantage of location.
Location data may improve customer engagement by targeted engagement based on location and improving Ad effectiveness. Another business case is improvement of operational effectiveness – instead of waiting for queues to build retail managers can proactively staff based on traffic. Businesses can use location data to implement workspace optimization – to identify “hot zones” or lightly used spaces in order to cut costs.
You have to bear in mind that most “real world” implementations of Location Analytics require integration with other data sources (Sensors, Loyalty databases, POS, etc.) to create more meaningful data.
Spatial analysis is more than simply understanding the physical location of key assets on a map. It’s about gaining a richer perspective on service offerings, consumer interaction, transactional information, and how location and demographics play into an organization’s performance. Systematically analyzing relationships between the spatial environment and organizational performance offers a wealth of information; yet, many analysts struggle with spatial analytics because it requires them to blend geospatial data with traditional data sets. Most existing business intelligence and spatial analytics tools simply display spatial data on a map, leaving the analysis between operational data and location to the user.
In order to use location analytics and spatial analytics one need to do some geocoding or use specialized software and services, in order to transform consumers’ data (addresses, ZIP codes etc) into geographic coordinates (like latitude and longitude), which to be later used in the analyses. This allows for creation of heatmaps, color coding and other convenient ways of representing different types of information onto a map or the blueprint of a building, store, etc.