4 Strategies for Making Your Product ‘Smarter’

Originally Published on Entrepreneur.com

“Smart” is the dominant trend in the area of entrepreneurship and innovation. In recent times, a plethora of new products have arrived that make an existing product “smarter” by incorporating sensors, connecting the product to their backend or adding intelligence in the product. Reimagining existing products to be smarter and better for the end user is a gold mine for innovation. Here are four ways to rethink your products and make them smarter.

1. Understand user intent and motivations.

Make your products smarter by making it listen and understand the intent of your user. What is the user trying to do at a given time or at a given location on a specific channel? By listening for signals that motivate the usage of your product, and accounting for how variations in these signals change how your product is used, you can predict and influence how your product should adjust to better serving the end user.

For example, a smart refrigerator can detect the contents, match it against the required ingredients for a decided dinner menu and remind the user to restock a certain missing ingredient.

2. Reach users at the right time.

You can make your products smarter by reaching the user at the right time with the right message, even if the user is not using the product at a given point in time. Making the product aware of the user’s environment offers the opportunity to craft a personalized message to enhance the user experience. You can then motivate and influence the user to use the product at the opportune time in the manner that is most beneficial for both the user and the product.

For example, a smart app can detect the user’s location in a particular grocery aisle and alert them an item they need to replace is on sale.

3. Enable good decisions.

Smart products help the user make the best decisions. By understanding the user’s context and their current environment, you can suggest alternatives, recommend choices or simply notify them of changes in their environment they might otherwise not have noticed. This capability enables the user to make informed choices and decisions, thus enhancing their experience and satisfaction from the product.

For example, by integrating traffic signals in a navigation system, the user can be notified of alternate routes when there are problems in their usual route.

4. Enhance user experience.

You can make your products smarter by enhancing the user’s experience, regardless of where they are in their journey with your product. If they are a new user, your product should help them onboard. If they are an active user, your product should make them more productive. If they are a dissatisfied user, your product should detect their dissatisfaction and offer the appropriate support and guidance to help them recover. In parallel, the product should learn from their situation and use this feedback in redesigning or refactoring the product.

For example, a product company that performs sentiment analysis on their twitter stream is able to swiftly detect user discontent and feed that into their support ticketing system for immediate response and follow up.

The ability to collect telemetry of how your product is being used, use sensors to detect the environment in which it is being used and use customer usage history in the backend to understand user intent has the potential to reinvigorate your existing products to be smarter and more beneficial for their users. Similarly, reimagining or innovating using the above principles offers entrepreneurs the opportunity to disrupt current products and markets and ride the “smart” wave to success.

The 2+2=5 Principle and the Perils of Analytics in a Vacuum

Published Originally on Wired

Strategic decision making in enterprises playing in a competitive field requires collaborative information seeking (CIS). Complex situations require analysis that spans multiple sessions with multiple participants (that collectively represent the entire context) who spend time jointly exploring, evaluating, and gathering relevant information to drive conclusions and decisions. This is the core of the 2+2=5 principle.

Analytics in a vacuum (i.e non collaborative analytics) due to missing or partial context is highly likely to be of low quality, lacking key and relevant information and fraught with incorrect assumptions. Other characteristics of non collaborative analytics is the usage of general purpose systems and tools like IM and email that are not designed for analytics. These tools lead to enterprises drowning in a sea of spreadsheets, context lost across thousands of IMs and email and an outcome that is guaranteed to be sub optimal.

A common but incorrect approach to collaborative analytics is to think of it as a post analysis activity. This is the approach to collaboration for most analytics and BI products. Post analysis publishing of results and insights is very important however, pre-publishing collaboration plays a key role in ensuring that the generated results are accurate, informative and relevant. Analysis that terminates at the publishing point has a very short half life.

Enterprises need to think of analysis as a living and breathing story that gets bigger over time as more people collaborate and lead to more data, new data, disparate data leads to the inclusion of more context negating incorrect assumptions, missing or low quality data issues and incorrect semantical understanding of data.

Here are the most common pitfalls that we have observed, of analytics carried out in a vacuum.

Wasted resources. If multiple teams or employees are seeking the same information or attempting to solve the same analytical problem, a non collaborative approach leads to wasted resources and suboptimal results.

Collaboration can help the enterprise streamline and divide and conquer the problem more efficiently and faster with lower time and manpower. Deconstructing an analytical hypothesis into smaller questions and distributing them across multiple employees leads to faster results.

Silo’ed analysis and conclusions. If results of analysis, insights and decisions are not shared systematically across the organization, enterprises face a loss of productivity. This lack of context between employees tasked with the same goals causes organizational misalignment and lack of coherence in strategy.

Enterprises need to ensure that there is common understanding of key data driven insights that are driving organizational strategy. In addition, the process to arrive at these insights should be transparent and repeatable, assumptions made should be clearly documented and a process/mechanism to challenge or question interpretations should be defined and publicized.

Assumptions and biases. Analytics done in a vacuum is hostage the the personal beliefs, assumptions, biases, clarity of purpose and the comprehensiveness of the context in the analyzer’s mind. Without collaboration, such biases remain uncorrected and lead to flawed foundations for strategic decisions.

A process around and freedom to challenge, inspect and reference key interpretation and analytical decisions made en route to the insight is critical for enterprises to enable and proliferate high quality insights in the organization.

Drive-by analysis. When left unchecked with top down pressure to use analytics to drive strategic decision making, enterprises see an uptake in what we call “drive-by analysis.” In this case, employees jump in to their favorite analytical tool, run some analysis to support their argument and publish these results.

This behavior leads to another danger of analytics without collaboration. These can be instances where users, without full context and understanding of of the data, semantics etc perform analysis to make critical decisions. Without supervision, these analytics can lead the organization down the wrong path. Supervision, fact checking and corroboration are needed to ensure that correct decisions are made.

Arbitration. Collaboration without a process for challenge, arbitration and an arbitration authority is often found to be, almost always at a later point in time when it is too late, littered with misinterpretations and factually misaligned or deviated from strategic patterns identified in the past.

Subject matter experts or other employees with the bigger picture, knowledge and understanding of the various moving parts of the organization need to, at every step of the analysis, verify and arbitrate on assumptions and insights before these insights are disseminated across the enterprise and used to affect strategic change.

Collaboration theory has proven that information seeking in complex situations is better accomplished through active collaboration. There is a trend in the analytics industry to think of collaborative analytics as a vanity feature and simple sharing of results is being touted as collaborative analytics. However, collaboration in analytics requires a multi pronged strategy with key processes and a product that delivers those capabilities, namely an investment in processes to allow arbitration, fact checking, interrogation and corroboration of analytics; and an investment in analytical products that are designed and optimized for collaborative analytics.

Signals and Insights: Value, Reach, Demand

Published Originally on Apigee

The mobile and apps economy means that the interaction between businesses and their customers and partners happens in an ever broader context, meaning that the amount of data that enterprises gather is exploding. Business is being done on multiple devices, and through apps, social networks, and cloud services.

It is important to think about signals when thinking about the value that is hidden in your enterprises data. Signals point towards insights. The ability to uncover, identify, and enhance these signals is the only way to make your big data work for you and succeed in the app economy.

Types of Signals

There are three types of signals that an enterprise should track and utilize in its decision making and strategic planning.

Value Signals

When customers use an enterprise’s products or services, they generate value signals. The actions that are part of searching, discovering, deciding, and purchasing a product or service offer signals into the perceived value of the product or service. These signals examined through the lens of user context (such as their profile, demographics, interests, past transaction history, and locality in time and space to interesting events and locations) deliver insights into business critical customer segments and their preference, engagement, and perceived value.

Reach Signals

When developers invest in the enterprise’s API platform and choose the APIs to create apps, they create reach signals. They are the signals around the attractiveness and perceived value of the enterprise’s products and services. Developers take on dependencies on APIs because they believe that such dependencies will help them in creating value for the end users of their apps and ultimately themselves. Developer adoption and engagement is a signal that offers a leading indicator and insight into the value and delivery of an enterprise’s products and services.

Demand Signals

When end users request information and data from the enterprises’ core data store, they generate demand signals on the enterprises’ information. These demand signals, within the user context deliver insights into the perceived value of the enterprise’s information along with context around the information (such as the source, type, freshness, quality, comprehensiveness and cache-ability). These insights offers a deep understanding of the impact of information on end user completed transactions and engagement.

Apigee Insights offers the expertise, mechanisms, and capabilities to extract and understand these signals from the enterprise data that sits within, at the edge, and outside the edge of the enterprise. Apigee Insights is built from the ground up to identify, extract and accentuate the value, reach and demand signals that drive business critical insights for the enterprise.

All (Big Data) Roads Lead To Your Customers

Originally Published on DataFloq

A large number of enterprise report a high level of inertia around getting started with Big Data. Either they are not sure about the problems that they need to solve using Big Data or they get distracted by the question of which Big Data technology to invest in and less on the business value they should be focusing on. This is often due to a lack of understanding of what business problems need to be solved and can be solved through data analysis. This causes enterprises to focus their valuable initial time and resources on evaluating new Big Data technologies without a concrete plan to deliver customer or business value through such investments. For enterprises that might find themselves in this trap, here are some trends and ideas to keep in mind.

Commoditization and maturation of Big Data technologies

Big Data technologies are going to get commoditized in the next couple of years. New technologies like Hadoop, HBase etc will mature with both their skills and partner ecosystem getting more diverse and stable. Increasing number of vendors will offer very similar capabilities and we will see these vendors compete increasingly on operational efficiency on the pivots of speed and cost. Enterprises who are not competing on the “Data efficiency” i.e. their ability to extract exponentially greater value from their data as compared to their competitors (notably AMZN, GOOG, YHOO, MSFT, FB and Twitter) should be careful to not overinvest in an inhouse implementation of Big Data technologies. Enterprises whose core business runs on data analysis need to continuously invest in data technologies to extract the maximum possible business value from their data. However, for enterprises that are still beginning or in the infancy of their Big Data journey, investing in a cutting edge technological solution is almost always the wrong strategy. Enterprises should focus on small wins using as much off the shelf components as possible to quickly reach the point of Big Data ROI offered out of customization free, off the shelf tools. When possible, enterprises should offload infrastructure operation and management to third party vendors while experimenting with applications and solutions that utilize these Big Data technologies. This ensures that critical resources are spent on solving real customer problems while critical feedback is being collected to inform future technology investments.

Technology Choices Without Business Impetus Are Not Ideal

The Big Data technology your business needs can vary by the problem that you are trying to solve. The needs of your business and the type of problems that you need to solve to offer simple, trustworthy and efficient products and services to your customers should determine and lead you to the right Big Data technology and vendors that match your needs. Enterprises need to focus on the business questions that need to be answered as opposed to the technology choice. Enterprises who do not have the business focus will spend crucial resources on optimizing their technology investments as opposed to solving real business problems and end up with little ROI. Planning and implementing Big Data technology solutions in a vacuum without clear problems and intended solutions in mind not only can lead to incorrect choices but can lead to wasted effort spent prematurely optimizing for and commitment to a specific technology

Evangelize Analytics Internally To Better Understand Technology Requirements

Appropriate Big Data technology decisions can only be made by ensuring that the needs and requirements of the various parts of the organization are correctly understood and captured. Ensuring the that culture in the enterprise promotes the use of data to answer strategic questions and track progress can only happen if analytical thinking and problem solving are used by all functions in the organization ranging from support to marketing to operations to products and engineering. Having these constituents represented in the technology stack decision process is extremely critical to ensure that eventual technology is usable and useful for the entire organization and does not get relegated to use by a very small subset of employees. In addition, the specific needs of certain users such as data exploration, insights generation, data visualization, analytics and reporting, experimentation, integration or publishing often require a combination of one or more technologies. Defining and clarifying the decision making process in an enterprise is needed to identify the various sets of technologies that need to be put together to build a complete data pipeline that is designed to enable decisions and actions.

All (Big Data) Roads Lead to Your Customers

For enterprises that are struggling to get started with Big Data analysis or have moved past the initial exploration stage in Big Data technology adoption, deciding what problems to tackle initial that will offer the highest ROI can be a daunting task. In addition, there is often pressure from management to showcase the value of the Big Data investment to the business, customers and users of the products and services. Almost always, focusing on improving customer/user satisfaction, increasing engagement with and use of your products and services mix and preventing customer churn is the most important problem that an enterprise can focus on and represents a class of problems that is 1. Universal 2. Perfect for Big Data analysis. As customers and end users interact with the enterprise’s products and services, they generate data or records of their usage. Because customer actions can be almost always divided into two sets: Transactional actions that represent a completed monetary or financially beneficial actions by the user for an enterprise. e..g purchasing a product or printing directions to a restaurant and Non Transactional, Leading Indicator Actions that by themselves are not monetarily beneficial to the enterprise but are leading indicators of upcoming transactions. e.g. searching for a product and adding it to a cart, reviewing a list of restaurants. Being able to tag the data generated by your users by the following metadata generates an extremely rich data set that is primed for Big Data analysis. Understanding the frequency of actions, time spent, when the actions occur, where they occur, on what channel and the environment and the demographic description of the user who carries out the action is critical. At the minimum, enterprises need to understand the actions of their users that correlate the highest with transactions, the attributes and behavior patterns of engaged and profitable users and the leading indicators of user dissatisfaction and abandonment, There are other very obvious applications of Big Data in the areas of security, fraud analysis, support operations, performance etc however each of these applications can be traced directly or indirectly to customer dissatisfaction or disengagement problems. Focusing your Big Data investments into a holistic solution to track and remedy customer dis-satisfaction to improve engagement and retention is a sure fire way to not only design the best possible Big Data solution to your needs but also to extract maximum value from these investments that impact your business’s bottom line.

It’s the End of the (Analytics and BI) World as We Know It

Published Originally on Wired

“That’s great, it starts with an earthquake, birds and snakes, an aeroplane, and Lenny Bruce is not afraid.” –REM, “It’s the End of the World as We Know It (and I Feel Fine)”

REM’s famous “It’s the End of the World…”song rode high on the college radio circuit back in the late 1980s. It was a catchy tune, but it also stands out because of its rapid-fire, stream-of-consciousness lyrics and — at least in my mind — it symbolizes a key aspect of the future of data analytics.

The stream-of-consciousness narrative is a tool used by writers to depict their characters’ thought processes. It also represents a change in approach that traditional analytics product builders have to embrace and understand in order to boost the agility and efficiency of the data analysis process.

Traditional analytics products were designed for data scientists and business intelligence specialists; these users were responsible for not only correctly interpreting the requests from the business users, but also delivering accurate information to these users. In this brave new world, the decision makers expect to be empowered themselves, with tools that deliver information needed to make decisions required for their roles and their day to day responsibilities. They need tools that enable agility through directed, specific answers to their questions.

Decision-Making Delays

Gone are the days when the user of analytics tools shouldered the burden of forming a question and framing it according to the parameters and interfaces of the analytical product. This would be followed by a response that would need to be interpreted, insights gleaned and shared. Users would have to repeat this process if they had any follow up questions.

The drive to make these analytics products more powerful also made them difficult to use to business users. This led to a vicious cycle: the tools appealed only to analysts and data scientists, leading to these products becoming even more adapted to their needs. Analytics became the responsibility of a select group of people. The limited population of these experts caused delays in data-driven decision making. Additionally, they were isolated from the business context to inform their analysis.

Precision Data Drill-Downs

In this new world, the business decision makers realize that they need access to information they can use to make decisions and course correct if needed. The distance between the analysis and the actor is shrinking, and employees now feel the need to be empowered and armed with data and analytics. This means that analytics products that are one size fits all do not make sense any more.

As the decision makers look for analytics that makes their day to day job successful, they will look towards these new analytics tools to offer the same capabilities and luxuries that having a separate analytics team provides, including the ability to ask questions repeatedly based on responses to a previous question.

This is why modern analytics products have to support the user’s “stream of consciousness” and offer the ability to repeatedly ask questions to drill down with precision and comprehensiveness. This enables users to arrive at the analysis that leads to a decision that leads to an action that generates business value.

Stream of conciousness support can only be offered through new lightweight mini analytics apps that are purpose-built for specific user roles and functions and deliver information and analytics for specific use cases that users in a particular role care about. Modern analytics products have to become combinations of apps to empower users and make their jobs decision and action-oriented.

Changes in People, Process, and Product

Closely related to the change in analytics tools is a change in the usage patterns of these tools. There are generally three types of employees involved in the usage of traditional analytics tools:

  • The analyzer, who collects, analyzes, interprets, and shares analyses of collected data
  • The decision maker, who generates and decides on the options for actions
  • The actor, who acts on the results

These employees act separately to lead an enterprise toward becoming data-driven, but it’s

a process fraught with inefficiencies, misinterpretations, and biases in data collection, analysis, and interpretation. The human latency and error potential makes the process slow and often inconsistent.

In the competitive new world, however, enterprises can’t afford such inefficiencies. Increasingly, we are seeing the need for the analyzer, decision maker, and actor to converge into one person, enabling faster data-driven actions and shorter time to value and growth.

This change will force analytics products to be designed for the decision maker/actor as opposed to the analyzer. They’ll be easy to master, simple to use, and tailored to cater to the needs of a specific use case or task.

Instant Insight

The process of analytics in the current world tends to be after-the-fact analysis of data that drives a product or marketing strategy and action.

However, in the new world, analytics products will need to provide insight into events as they happen, driven by user actions and behavior. Products will need the ability to change or impact the behavior of users, their transactions, and the workings of products and services in real time.

Analytics and BI Products and Platforms

In the traditional analytics world, analytics products tend to be bulky and broad in their flexibility and capabilities. These capabilities range from “data collection” to “analysis” to “visualization.” Traditional analytics products tend to offer different interfaces to the decision makers and the analyzers.

However, in the new world of analytics, products will need to be minimalistic. Analytics products will be tailored to the skills and needs of their particular users. They will directly provide recommendations for specific actions tied directly to a particular use case. They will provide, in real time, the impact of these actions and offer options and recommendations to the user to fine tune, if needed.

The Decision Maker’s Stream of Consciousness

In context of the changing people, process, and product constraints, analytics products will need to adapt to the needs of decision makers and their process of thinking, analyzing, and arriving at decisions. For every enterprise, a study of the decision maker’s job will reveal a certain set of decisions and actions that form the core of their responsibilities.

As we mentioned earlier, yesterday’s successful analytical products will morph into a set of mini analytics apps that deliver the analysis, recommendations, and actions that need to be carried out for each of these decisions/actions. Such mini apps will be tuned and optimized individually for each use case individually for each enterprise.

These apps will also empower the decision maker’s stream of consciousness. This will be achieved by emulating the decision maker’s thought process as a series of analytics layered to offer a decision path to the user. In addition, these mini apps will enable the exploration of tangential questions that arise in the user’s decision making process.

Analytics products will evolve to become more predictive, recommendation-based, and action oriented; the focus will be on driving action and reaction. This doesn’t mean that the process of data collection, cleansing, transformation, and preparation is obsolete. However, it does mean that the analysis is pre-determined and pre-defined to deliver information to drive value for specific use cases that form the core of the decision maker’s responsibility in an enterprise.

This way, users can spend more time reacting to their discoveries, tapping into their streams-of-consciousness, taking action, and reacting again to fine-tune the analysis

Virtual Sensors and the Butterfly Effect

Originally Published on Wired.

In the early 1960s, chaos theory pioneer Edward Lorenz famously asked, “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?” Lorenz theorized that small initial differences in an atmospheric system could result in large and unexpected future impacts.

Similar “butterfly effects” can surface in the increasingly interconnected and complex universe of enterprise partnerships and supply-chain and cross-product relationships. It’s a world where new or evolving products, services, partnerships, and changes in demand can have unexpected and surprising effects on users and other products, services, traffic, and transactions in a company’s ecosystem.

Monitoring these complex relationships and the potentially important changes that can reverberate through an enterprise’s network calls for an interconnected system of virtual “sensors,” which can be configured and tuned to help make sense of these unexpected changes. As enterprises increasingly interface with customers, partners, and employees via apps and application programming interfaces (APIs), setting up a monitoring network like this becomes a particularly important part of data analysis.

What are Sensors?

Traditional sensors are often defined as “converters” that transform a physically measured quantity into a signal that an observer can understand. Sensors are defined by their sensitivity and by their ability to have a minimal effect on what they measure.

Physical sensors can capture aspects of the external environment like light, motion, temperature, and moisture. They’re widely used in business, too. Retailers can employ them to measure foot traffic outside or inside their stores, in front of vending machines, or around product or brand categories. Airlines use physical sensors to measure how weather patterns affect boarding and take-off delays. Using a diversity of sensors enables the definition of an environment around the usage of a product or service in the physical world.

Besides investing in traditional data processing technologies, cutting-edge enterprises map their digital world by defining and building so-called virtual sensors. Virtual sensors collect information from the intersection of the physical and digital worlds to generate and measure events that define the usage of a digital product or service. A virtual sensor could be a data processing algorithm that can be tuned and configured to generate results that are relevant for the enterprise. The generated alert notifies the enterprise of a change in the environment or ecosystem in which the user is using a product or service.

How to Build a Virtual Sensor Network

Building a network of virtual sensors for your business calls for requirements similar to those of a physical sensor system:

  • Sensitivity, or the ability to detect events and signals with configurable thresholds of severity
  • Speed, or the ability to speedily collect and process signals to generate business-critical events
  • Diversity, or the ability to collect, collate, and combine signals from multiple sensors with the goal of generating business-critical events

To begin charting the web of relationships that impacts the demand and usage of various enterprises’ products and services, businesses should determine which other products and services in the marketplace are complements, supplements, and substitutes to their own. Deep understanding of such evolving and complex relationships can help enterprises with planning partnerships.

  • Supplementary products and services enhance the experience of another product or service. For example, flat panel TVs are enhanced by wall mounts, stands, warranty services, cable services, and streaming movie services.
  • Complementary products and services work in concert with other products and services to complete the experience for the end user. Demand for car tires, for example, tends to generate demand for gasoline.
  • Substitute products and services have an inverse effect on each other’s demand. For example, two retailers offering the same selection of products targeted to the same consumer.

Understanding these relationships is the starting point of creating a network of sensors to monitor the impact of changes in traffic or transactions of an outside product or service on an enterprise’s own products and services. Detecting this change within the appropriate sensitivity can often be the difference between an enterprise’s failure or success.

Take for example, a web portal that aggregates content from several content providers. This portal uses APIs to connect to these third-parties. In many cases, these content providers are automatically queried by the aggregator, regardless of whether an end user is interested in the content. If for any reason there is a spike in usage of the portal on a particular day, this will automatically trigger spikes in the traffic for each of the content providers. Without understanding the complementary connection to the portal and the associated shifting demand properties of the connection, the content providers will find it difficult to interpret the traffic spike, which will eat up resources and leave legitimate traffic unserviced.

Here’s a similar example. Let’s say a service can support 100 API calls spread among 10 partners. If this service receives an unexpected and unwanted spike in traffic from one partner that eats up half of its capacity, then it will only have 50 API calls left to distribute among the other nine partners. This in turn can lead to lost transactions and dissatisfied users.

With an awareness of the network, however, the service would understand that this one partner routinely only sends 10 calls on a normal day, and would be able to put restrictions in place that wouldn’t let the extra 40 calls eat up the capacity of other partners.

In these kinds of situations, virtual sensors can provide the awareness and insights into this web of interdependency, and help make sense of traffic spikes that otherwise might seem incomprehensible.

Sensor-Aware Data Investments

Building a network of physical and virtual sensors entails collecting diverse signals from a complex map of data sources and processing them to generate events that can help enterprises understand the environments around their end users. Investing in these networks enables enterprises to track and monitor external signals generated from sources that have the ability to impact the enterprise’s traffic, transactions, and overall health.

This ability, in turn, helps digitally aware businesses negate potential troubles caused by the digital butterfly effect, and take advantage of the opportunities presented by a strong grasp of what’s happening in user and partner ecosystems.

How Data Analysis Drives the Customer Journey

Originally Published on Wired

Driving down Highway 1 on the Big Sur coastline in Northern California, it’s easy to miss the signs that dot the roadside. After all, the stunning views of the Pacific crashing against the rocks can be a major distraction. The signage along this windy, treacherous stretch of road, however, is pretty important — neglecting to slow down to 15 MPH for that upcoming hairpin turn could spell trouble.

Careful planning and even science goes into figuring out where to place signs, whether they are for safety, navigation, or convenience. It takes a detailed understanding of the conditions and the driving experience to determine this. To help drivers plan, manage, and correct their journey trajectories, interstate highway signs follow a strict pattern in shape, color, size, location, and height, depending on the type of information being displayed.

Like the traffic engineers and transportation departments that navigate this process, enterprises face a similar challenge when mapping, building, and optimizing digital customer journeys. To create innovative and information-rich digital experiences that provide customers with a satisfying journey, a business must understand the stages and channels that consumers travel through to reach their destination. Customer journeys are multi-stage and multi-channel, and users require information at each stage to make the right decisions as they move toward their destination.

Signposts on the Customer Journey

To understand what kind of information must be provided — and when it must be supplied — it’s important to understand the stages users travel through as they form decisions to purchase or consume products or services.

  • Search: The user starts on a path toward a transaction by searching for products or services that can deliver on his or her use case
  • Discover: The user narrows down the search results to a set of products or services that meet the use case requirements
  • Consider: The user evaluates the short-listed set of products and services
  • Decide: The user makes a decision on the product or service
  • Sign up/set up: The user completes the setup or sign up required to begin using the chosen product or service
  • Configure: The user configures and personalizes the product or service, to the extent possible, to best deliver on the user’s requirements
  • Act: The user uses the product or service regularly
  • Engage: The user’s usage peaks, collecting significant levels of activity, transaction value, time spent on the product, and the willingness to recommend the product or service to their professional or personal networks
  • Abandon: The user displays diminishing usage of the product or service compared to the configuring, active, and engaged levels
  • Exit: The user ceases use of the product or service entirely

Analyzing how a customer uses information as they navigate their journey is key to unlocking more transactions and higher usage, and also to understanding and delivering on the needs of the customer at each stage of their journey.

At the same time, it’s critical to instrument products and services to capture data about usage and behavior surrounding a product or service, and to build the processes to analyze the data to classify and detect where the user is on their journey. Finally, it’s important to figure out the information required by the user at each stage. This analysis determines the shape, form, channel, and content of the information that will be made available to users at each point of their transactional journey.

The highway system offers inspiration for designing an information architecture that guides the customer on a successful journey. In fact, there are close parallels between the various types of highway signs and the kind of information users need when moving along the transaction path.

  • Regulatory: Information that conveys the correct usage of the product or service, such as terms of use or credit card processing and storage features
  • Warning: Information that offers “guardrails” to customers to ensure that they do not go off track and use the product in an unintended, unexpected way; examples in a digital world include notifications to inform users on how to protect themselves from spammers
  • Guide: Information that enables customers to make decisions and move ahead efficiently; examples include first-run wizards to get the user up and running and productive with the product or service
  • Services: Information that enhances the customer experience, including FAQs, knowledge bases, product training, references, and documentation
  • Construction: Information about missing, incomplete, or work-in-progress experiences in a product that enable the user to adjust their expectations; this includes time-sensitive information designed to proactively notify the user of possible breakdowns or upcoming changes in their experience, including maintenance outages and new releases

Information Analytics

Information analytics is the class of analytics designed to derive insights from data produced by end users during their customer journey. Information analytics provides two key insights into the data and the value it creates.

First, it enables the identification of the subsets of data that drive maximum value to the business. Certain data sets in the enterprise’s data store are more valuable than others and, within a data set, certain records are more valuable than others. Value in this case is defined by how users employ the information to make decisions that eventually and consistently drive value to the business.

For example, Yelp can track the correlation between a certain subset of all restaurant reviews on their site and the likelihood of users reading them and going to the reviewed restaurants. Such reviews can then be automatically promoted and ranked higher to ensure that all users get the information that has a higher probability of driving a transaction—a restaurant visit, in this case.

Secondly, information analytics enables businesses to identify customer segments that use information to make decisions that drive the most business transactions. Understanding and identifying such segments is extremely important, as it enables the enterprise to not only adapt the information delivery for the specific needs of the customer segment but also price and package the information for maximum business value.

For example, information in a weather provider’s database in its raw form is usable by different consumers for different use cases. However, the usage of this information by someone planning a casual trip is very different than a commodities trader who is betting on future commodity prices. Understanding the value derived by a user from the enterprise’ information is key to appropriate pricing and value generation for the enterprise.

Information Delivery

Mining and analyzing how users access information is critical to identifying, tracking, and improving key performance indicators (KPIs) around user engagement and user retention. If the enterprise does not augment the product experience with accurate, timely, and relevant information (according to the user’s location, channel and time of usage), users will be left dissatisfied, disoriented, and disengaged.

At the same time, a user’s information access should be mined to determine the combination of information, channel, and journey stage that drives value to the enterprise. Enterprises need to identify such combinations and promote them to all users of the product and service and subsequently enable a larger portion of the user base to derive similar value.

Mining the information access patterns of users can enable enterprises to build a map of the various touch points on their customer’s journey, along with a guide to the right information required for each touchpoint (by the user or by the enterprise) in the appropriate form delivered through the optimal channel. Such a map, when built and actively managed, ends up capturing the use of information by customers in their journey and correlates this with their continued engagement with — or eventual abandonment of — the product.

Enabling successful journeys for customers as they find and use products and services is critical to both business success and continued customer satisfaction. Contextual information, provided at the right time through the right channel to enable user decisions, is almost always the difference between an engaged user and an unsatisfied one — and a transaction that drives business value.