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.

Anatomy of a Retail API Program

Published Originally on Apigee.com

API programs have become commonplace at nearly all big retailers who offer multi-channel experiences to their customers through mobile apps, in-store kiosks, the Web, and personalized in-store services. Analyzing the anatomy of a typical retail API program uncovers some interesting patterns. The data here was gathered by analyzing several retail API programs that use the Apigee platform.

The No. 1 motivation for a retail API program

Providing a differentiated value proposition in the physical retail channel is almost always the largest motivation for retailers. The most popular functionalities in retailer apps are those that complement the end user’s experience in the physical retail channel.

The top functionalities were  “enabling mobile consumption” and “driving foot traffic,” followed by “personalization,” “product information,” and “driving customer engagement.”

Trends in retail API programs

Retailers are using their API programs to personalize user experiences and enhance customer service. A majority, for example, use their API programs to employ recommendation features. We also found that:

• 40% of retail API programs had personalized alerts and notifications
• 55% included recommendation features
• 30% offered shopping cart management features

Retail API programs also tend to exhibit high agility, an expansive breadth of API services, and heavy policy use.

Primary retail services exposed as APIs

Certain services exposed as retail APIs rise head-and-shoulders above the rest in popularity.  The most common retail services exposed in retail API programs are:

• Store locator services, which enable users to discover physical store locations using their current address

• Product catalogs, which enable users to search, discover, and learn about products and services

• Order services, which enable users to place and check the status of orders

“Identity”—the set of APIs that enable users to sign up, log in, or access account details—also ranked high, but we do not view it as a primary retail service that is core to this vertical.

Retail API programs: developers, apps, APIs, and policies

Our research also unearthed some interesting statistics about retail API programs: they tend to have large developer teams, a broad app portfolio with a diverse set of apps, and heavy usage of policies across a fairly significant array of APIs. Here are some average values for retail API programs, across a variety of categories (The most successful API programs posted much higher values in these areas):

• Average number of developers: 154

• Average number of apps: 87

• Average number of policies: 378

• Average number of APIs: 19

• Average number of policies per API: 16.8

App and API development

Another characteristic of successful retail API programs is the rapid development and improvement of retail APIs. Agility is defined as the number of API revisions divided by the API age in months, and it’s a key indicator of success in retail API programs. We found the average agility among the customers in our sample set to be 13.6, with a maximum of 36 and minimum of 2.

Focus on end user experience

Top retail API programs offer a much faster experience to app users. These retailers spend considerable effort on optimizing their backends and proxies using caches and other features. We found fastest average backend response time to be 20 milliseconds, with 563 milliseconds being the slowest. The average backend responsiveness in our sample was 224 milliseconds; this average is 209 million seconds at the top retail API programs.

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.

A Blueprint for Digital Partnerships: How Data Analysis Expands Your Customer Base

Originally Published on Wired

Everyone and everything seems to be vying for customers’ attention these days.

Attracting and keeping the interest of existing customers — let alone acquiring new ones — is tricky for most enterprises. Modern consumers have many choices in how they spend their time, attention, and money. This makes it particularly important for businesses to be constantly on the look out for new ways to engage and retain their customers.

One powerful way to aid enterprises in this search is cross-enterprise “customer sharing” via symbiotic digital partnerships. Enterprises have long tried to upsell and cross sell new products and services to existing customers. But they can now extend these tactics through digital partnerships with other enterprises. The key difference is that the cross selling and upselling happens in a meticulous, targeted manner to the partner’s customers, through the use of big data analysis and targeted and personalized advertising.

If approached correctly, such digital partnerships provide mutual, significant benefits for all parties: not only the digital partners, but the customers, too.

Customer Retention

For an enterprise’s existing users, this kind of partnership offers enhanced products and services that lead to a richer, more engaged customer experience. This, in turn, leads to an increased likelihood of customer retention, as the customer receives targeted and relevant offers and doesn’t need to look outside the partnership for required products and services.

Here’s an example: Users who receive targeted offers to purchase better health insurance while claiming health care cost deductions on their tax return are more likely to return to their online tax filing software. They can also expect a better offer from the insurance company, because it has access to a richer profile of the user.

This partnership model offers a cost-effective way to grow an enterprise’s user base by acquiring new customers that have been pre-validated with fully defined user models and a history of high value and sustained transactions.

Cross-Product Preference Segmentation

Enterprises need to seek digital partners with similarly segmented customer bases, such that all users in a segment share preferences for the products of all the businesses in a digital partnership. This is where data analysis enters the picture.

Cross-product preference segments are identified by observing and segmenting users by products and services that are used in close proximity to each other, both spatially and temporally. To prepare for cross-product preference segmentation, enterprises need to:

• Collect product usage data about each customer, including demographic and interest-based attributes
• Standardize the format of product usage data; for example, user name, age, gender, interests, product used, and revenue generated
• Expose customer product usage to partners through a data API
• Anonymize personally identifiable information (PII) when needed
• Import product usage data from other partners and find intersections of product usage across different data sets
• Export “intersection outputs” to generate segments in which each user uses products from several enterprises
• Sort segments by average revenue generated per user

Caution: Don’t Upset Your Customers

Enterprises should enter digital partnerships to cross sell and upsell to each others’ customers, but this comes with a warning: they should be careful not to spam their most valuable and engaged users. To prevent this, enterprises should carefully segment their users by their stage in the customer lifecycle.

A company also needs to ensure that the sharing of users for up-sell and cross-sell opportunities is calibrated appropriately and leads to a partnership of equals. This also serves as a quality check on the process — enterprises need to ensure that they don’t create poor user experiences because of the actions of another enterprise in the digital partnership.

The goal is to enhance the experience of your most valuable users, not to diminish it.

A map of the customer lifecycle stages can be generated by first capturing all instances of user engagement with a product or service and then defining business rules that get applied by a big data processing engine.

The business rules define a set of activities and, more broadly, behaviors for every stage in the customer lifecycle such as new users, active users, engaged users, and abandoned users.

Complement Your Products

An enterprise needs to understand which products or services are complementary to its own. In other words, which products and services can enhance or even complete the experience of the user? This analysis is called the “product adjacency list,” and is defined for each user segment. This qualitative analysis returns useful information to inform potential partnership discussions.

Forge the Digital Partnership

The purpose of the digital partnership is to enable enterprises to share access to high-value, engaged users to solve their customer acquisition problem and offer such users highly targeted and enhanced products and services.

However, successful partnerships should follow several key guidelines:

1. Equitable exchange of access – A partnership of equals mandates access to each other’s users, where the “shared” users are of equally high quality, type, value, and lifecycle stage.
2. Stringent quality control – Access to a partner’s users needs to be tracked and verified to ensure there is no inappropriate usage
3. Data APIs and governance – Access to usage data should be exposed as an API to ensure easy experimentation and adoption. Access to the user’s views of the partner’s offers should also be API accessible.
4. Data-validated upsells and cross sells – Up-sells and cross-sells delivered to a partner’s customer base should be validated as being either on the user’s product adjacency list or in the supplementary or complementary product graph of the enterprise.
5. User feedback loop – Users should easily be able to opt out, suspend, or provide feedback on the quality of the up-sells and cross sells to enable the partnership to improve and become increasingly beneficial for both parties.

Symbiotic digital partnerships that offer users better products, services, and complete experiences and don’t leave users feeling spammed or violated offer a sure path to success for both participants in a digital partnership.

When driven by APIs, partners can experiment and innovate; they can create mash-up products and services, exchange user data securely and easily, and benefit from deep analysis of user behavior. This enables these partner companies to delight their customers while making their products and services sticky and driving mutual business value.

Read more: http://insights.wired.com/profiles/blogs/a-blueprint-for-symbiotic-digital-partnerships-how-big-data#ixzz3KbgcEvua
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The Importance of Making Your Big Data System Insightful

Originally Published on Wired

 

With all the emphasis these days that’s placed on combing through the piles of potentially invaluable data that resides within an enterprise, it’s possible for a business to lose sight of the need to turn the discoveries generated by data analysis into valuable actions.

Sure, insights and observations that arise from data analysis are interesting and compelling, but they really aren’t worth much unless they can be converted into some kind of business value, whether it’s, say, fine tuning the experience of customers who are considering abandoning your product or service, or modeling an abuse detection system to block traffic from malicious users.

Digging jewels like these out of piles of enterprise data might be viewed by some as a mysterious art, but it’s not. It’s a process of many steps, considerations, and potential pitfalls, but it’s important for business stakeholders to have a grip on how the process works and the strategy considerations that go into data analysis. You’ve got to know the right questions to ask. Otherwise, there’s a risk that data science stays isolated, instead of evolving into business science.

The strategic considerations include setting up an “insights pipeline,” which charts the path from hypothesis to insight and helps ensure agility in detecting trends, building new products, and adjusting business processes; ensuring that the analytical last mile, which spans the gap from analysis to a tangible business action, is covered quickly; building a “data first” strategy that lays the groundwork for new products to produce valuable data; and understanding how partnerships can help enterprises put insights to work to improve user experiences.

The Insights Pipeline

You can visualize an insights pipeline as a kind of flow chart that encompasses the journey from a broad business goal, question or hypothesis to a business insight.

The questions could look something like this: Why are we losing customers in the European market? Or, how can revenue from iOS users be increased? This kind of query is the first step in open-ended data exploration, which, as the name implies, doesn’t usually include deadlines or specific expectations, because they can suppress the serendipity that is a key part of the open-ended discovery process.

Data scientists engage in this kind of exploration to uncover business-critical insights, but they might not know what shapes these insights will take when they begin their research. These insights are then presented to business stakeholders, who interpret the results and put them to use in making strategic or tactical decisions.

The broad nature of open-ended exploration carries potential negatives. Because of the lack of refinement in the query, the insights generated might be unusable, not new, or even worthless, leading to low or no ROI. Without specific guidance, a data scientist could get lost in the weeds.

Closed-loop data exploration, on the other hand, is much more refined and focused on a very focused business function or question. For example, a data scientist might pursue this: Are there any customers who do more than $100 of business each day with an online business? If so, flag them as “very important customers” so they can receive special offers. There is very little ambiguity in the query.

In the insights pipeline, successful open-ended explorations can eventually be promoted to closed loop dashboards, once business stakeholders ratify the results.

Closed-loop analysis implements systems based on models or algorithms that slot into business processes and workflow systems. As the example above suggests, these kinds of questions enable fast, traffic-based decision-making and end-user servicing. They also don’t add development costs once they are put in place.

But the very specificity of the queries that define closed-loop data analysis can produce insights of limited value. And once the query is set up, the possibility of “insights staleness” arises. Revisiting the “very important customer” example, what if inflation makes the $100-per-day customer less valuable? The insight becomes outdated; this highlights the need to consistently renew and verify results.

This illustrates the importance of consistently retuning the model, and, sometimes, forming new questions or hypotheses to plug back into an open-ended exploration. For example, a system that filters incoming emails for spam can quickly become outdated as spammers change tactics or use new technologies. A closed-loop system like this often needs to be revamped entirely to reflect changes in smaller behavior.

The Analytical Last Mile

Making decisions is one of the most challenging parts of doing business. In IT, employees are very comfortable delivering reports or assembling dashboards. But deciding on an action plan based upon that information isn’t easy, and lots of insights but few decisions introduces a lag time that in turn erodes business value.

The analytical last mile represents the time and effort required to use analytics insights to actually improve the state of a businesses. You might have invested heavily in big data technologies and produced all kinds of dashboards and reports, but this adds up to very little if interesting observations aren’t converted into action.

The value of analytics and a data-driven culture is only realized when the analytical last mile is covered quickly and efficiently. The inability to do this often results in lost business efficiency and unrealized business value.

More often than not, human latency is to blame. It’s defined as the time it takes employees to collect the required information, perform analysis, and disseminate the resulting insight to decision makers, and, then, the time it takes decision makers to collaborate and decide on a course of action.

Covering the analytical last mile efficiently requires an investment in and emphasis on setting up streamlined data collection, analysis and decision-making processes.

A “Data First” Strategy

When you define, design, and introduce a new product or service, data generation, collection and analysis, and product optimization might be the last thing you’re thinking of. It should be the first.

A “data first” strategy ensures that the right kind of technology is in place to deliver insights that can improve the end user experience. Thinking through what kinds of user data might be collected ensures that the enterprise isn’t caught off guard when the new product or service begins to gain momentum.

Some of the data you should think about gathering includes:

• Data generated by user actions and interactions, such monetary transactions, information requests, and navigation
• Data that defines the profile attributes of the user, including information available from the user, the enterprise, or enterprise partners
• Contextual data about the user’s social network activity triggered by the product or service, the user’s location in relation to use of the product or service, or the channels through which the product or service is being used or accessed

Instead of losing critical time scrambling to set up methodologies to gather this data, you’ll be prepared to do some fine-tuning to the product to boost the end user’s experience.

Partnerships

A lot of skills and capabilities are required to take a data-driven effort to optimize the user experience and turn that into an actual, tangible improvement in your customer’s experience and, ultimately, boost the enterprise’s bottom line.
Many of these skills are not traditionally part of a business’ core competencies, so partnerships are a great way to bring in outside expertise to help polish the customer experience. Some areas where enterprises look to partners for help include: the ability to reach customers with content, offers, deals, and ads across multiple channels, devices or platforms; the ability to access user transaction history across multiple services and products; and the capability to know users’ locations at any point in time.

There’s a reason that big data analysis has become such a catchphrase. It’s an amazingly powerful tool that can improve user experiences and boost the bottom line.

But it’s critical that business stakeholders have an awareness of the process, think about the right strategic considerations, and realize the importance of moving quickly and decisively once insights are delivered. Otherwise, it’s all too easy for a business to get mired in data science, instead of transforming a valuable insight into an even more valuable action.

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.

When Your Product Design Makes Your Customers Feel Smart

Published Originally on Entrepreneur.com

Users love products and services that make them feel smarter. The more efficiently they can spend their valuable attention, time and money, the smarter they feel. The smarter that users feel when interacting with your product, the more they love it. We call this the smart-user theorem.

Strong examples of the smart-user theorem in action abound. Facebook and Instagram save users time by enabling them to connect and share with friends and family quickly and efficiently. Similarly, apps have become popular and ubiquitous, partly because of their availability to fulfill virtually any need or task.

The simplicity of the interface and the entire value chain on the iPad, the ease of planning a trip on Expedia via a mobile device or using Dropbox to store files — these are more examples that offer powerful guiding principles for enterprises as they engage customers with their products and services.

Taking this a step further, analyzing customers’ behavior can quantify the time, attention or effort required to engage with a business’ products and services and bring about a new understanding of the user experience. This awareness, in turn, arms businesses with strategies to fine-tune their products and services to be more efficient, streamlined and intuitive.

If enterprises carefully evaluate and optimize their products and services to make their users “smarter,” they will be rewarded with loyalty, engagement and a higher transactional value.

User investments: attention, time and money. There are three types of “capital” that customers invest in your products and services: attention, time and money. First, users turn their attention to your messages, advertisements and product communications. They interpret and internalize your message to inform their next steps.

Consumers also spend time thinking about, searching for, discovering, deciding to access, learning about and using your products and services; it’s safe to assume that they spend the same amount of time learning about your competition. Finally, there’s the money part. This one’s pretty obvious: Users pay you for your products and services.

The attention, time and money model provides a framework to optimize the design of the end-to-end user experience. Maximizing the value of the attention, time and money spent by users on your products and services can be achieved through a combination of baselining and experimentation.

Baselining involves breaking up the product-usage flow into logical stages and measuring the time and attempts it takes users to move through it. In addition, the consumer’s reliance on certain information and features should be analyzed to understand whether they encourage a person to move to the next stage in the flow.

Experimentation is the stage whereby, through the use of data analysis or customer interviews, product problems can be identified. Hypotheses are developed and then tested through changes in the product flow until the desired goals are met.

Big data’s role in smarter interactions and smarter users. Users save attention, time and money as a result of personalized and customized messages, which enable them to find the right tools to satisfy their needs quickly at the right cost. Creating these messages and products requires capabilities that the processing of Big Data can easily provide. This can involve the following types of analyses:

1. User-environment analysis, in which information is collected about the environment where users interact with the product or service.

2. User-profile analysis, whereby information is collected about consumers and their characteristics such as gender, age, likes and dislikes.

3. User-interaction analysis, in which data is collected about users’ activities and behaviors as they interact with a product along the customer journey.

4. User modeling, whereby data is collected and modeled to represent the behavior of a segment or cohort of users.

The analyses and subsequent correlations are used to optimize the messages delivered to users according to their environment, profile and behavior patterns, as well as their stage in the customer journey.

As users receive personalized messages and information that enable them to be smarter by helping them complete their tasks faster, more inexpensively and with less attention, the overall value realized from the product or service increases. This leads to higher productivity for the user, higher and sustained engagement with the product or service, a customer who feels smarter and, in the end, greater value for your enterprise.