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.

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.

Protecting users, apps, and APIs from abuse

Published Originally on Apigee

Abusers or spammers are the bad guys looking to make money by getting unsuspecting end users or consumers of online services to interact with malicious content or spam that leads to one or more of the following scenarios:

  • Eyeballs on spam content that lead to clicks and purchases.
  • Gathering users’ private information through keyloggers (or other spyware) on the user’s machine or device which is then sold to the highest bidder.
  • Phishing for users’ private information such as SSN, credit card #, or passwords and selling those to the highest bidder.
  • Installing malicious software on users’ machines or devices, which in turn steals more of their information or uses their bandwidth or storage for carrying our further attacks.

Any workflow that creates or consumes content, shares or reshares content, sends or receives communications can be vulnerable to attack.

Online attacks almost always contain a “payload” that either delivers the attack or leads the user to another location where the attack is completed. Any time a piece of content is created or consumed or a communication is sent or received, there is an inherent “payload” that is delivered. In the wrong hands, this payload can be malicious.

Blogs can be spammed with comments which contain spam or malware, or which employ phishing techniques that redirect users to other malicious locations on the Internet.

In the same way, users can receive emails that contain spam or malware or other phishing techniques. Users can be redirected to malicious sites in their browser. Users’ machines can be attacked and malware (such as adware, spyware, key loggers and viruses) installed which can scrape users’ private information and send it to the abusers.

Thinking about apps and APIs as assets

Apps like APIs can be considered “assets” that can be used to carry out attacks against end users.

APIs (and especially free APIs) provide services that eventually reach other users. Even if an API has a usage cost associated with it, if the return on investment for an abuser for planning and carrying out an attack is higher than the cost of using the API (at scale), even paid APIs can be abused.

Both developers and enterprises have apps that are highly monetizable if abused. App-based attacks fall into the following general categories:

Malicious apps: These apps are written with the sole goal of abusing unsuspecting users who download and install them. With the increasing use of single sign on services and integration of social networks with apps through social network platforms, a user’s social network can be used to propagate these attacks.

Well-intentioned apps with vulnerabilities: These apps are not written to be malicious but have vulnerabilities either in their code (e.g. the apps’s functionality can be scripted or controlled through hooks provided by the app) or in their business logic (e.g. no throttling of calls from a single user to the back-end system). An abuser can exploit these vulnerabilities to carry out attacks on enterprise APIs or on the end users of the apps.

Well intentioned apps exploited by a malicious user: In this case, an abuser can use some legitimate capability offered by the app as a foundation to carry out attacks that propagate through the end user’s social network and spread through further usage.

Protecting your assets

You don’t want your APIs or apps used for abuse. It is crucial for both developers and enterprises to protect their “assets”. If an end user develops a perception that a certain app or service is “dangerous”, usage declines, growth can be reversed, and revenue and profit suffer.

Remember that abusers are most often out there to make money and if the cost to carry out an attack is less than the value derived from the attack, it will continue to be a sound business investment for the abusers.

Some things that developers can do to protect their apps and end users (and their friends)

  • Monitor usage of your app and its impact on the users. Build and maintain a proxy for user reputation and models of good and bad behavior in the content of the app.
  • Enable end users to report suspicious behavior of the app or of other users of the app.
  • Work with the enterprise or API provider to ensure that your app is not creating suspicious loads on the API.

Some things that enterprises can do to protect their APIs

  • Build traffic monitoring solutions and models to rate traffic as safe or abusive.
  • Ensure that any content or communication created or shared through the API is free of malicious payloads.
  • Invest in mechanisms to report and notify suspicious user and app behavior to the app developers.
  • Build reputation systems for users, content and IP addresses to be able to quickly classify traffic, users and apps as good or bad OR desirable or undesirable.

Next time we’ll look at some of the ways to push back against attackers, including use of quotas, throttling, and so on.

Your Big Data Needs Some TLC

Published Originally on Wired

In this customer-driven world, more and more businesses are relying on data to derive deep insights about the behavior and experience of end users with a business’ products. Yet end user logs, while interesting, often lack a 360-degree view of the “context” in which users consume a business’ products and services. The ability to analyze these logs in the relevant context is key to getting the maximum business value from big data analysis.

Basic contextual analysis requires a little TLC: Time, Location and Channel.

Thinking within a TLC framework will simplify the identification, collection, assimilation and analysis of context and make it more value driven. Enterprises can apply TLC for better attribution and explanation of end user behavior, to identify patterns and understand profiles that generate insights, and ultimately to enable the business to deliver better, customized, personalized products, services and experiences.

So what does it mean for business owners in the app economy to give their data and analytics a dose of TLC?


Does the time of day, the week, the month, or a particular event impact app usage?

The hypothesis is that certain events at a point in time and certain classes of events have a positive or negative impact on app usage.

Are there different patterns of app usage on weekends versus weekdays or on mornings versus afternoons versus evenings? Are you a retail business hurtling towards Black Friday (the biggest shopping day in the year in the USA)? What patterns have you observed in recent years? What can you expect from your store locator app, your catalog app, your gift card and coupons app… in the days before the event and on black Friday itself?

Are you running a Super Bowl ad? When it airs, will it drive traffic to your web and mobile apps? Will it cause a spike in API traffic?

Business executives need to understand how external events like these impact the use of their apps. Making the correlations and understanding the contexts in which the apps are used can then be used to promote or discourage certain usage of the app for maximum business impact.

  • What external events impact the use of my app?
  • Are there patterns? What types of external events impact the use of my app?
  • As users use an app over a time, do their usage patterns change? Does the how/why/what of app usage change?


Does app usage lead to cross-channel transactions such as store foot traffic or web based fulfillment?

Retailers deploy mobile apps to enable enhanced shopping experiences and sometimes with the purpose to drive foot traffic to their stores.

Where are users before, during and after they use an app? Business owners can use information about where their apps are being used and where they are being the most effective to tune the user experience and maximize impact.

Is your store locator app used most in the vicinity of your store, or most in the vicinity of your competitors’ stores? Do users follow through and walk into your store after using the store locator app, the catalog app…?

Is there a pattern to where users are when they access a gas station app? Are they in the vicinity of a gas station and trying to find the cheapest gas? Are they trying to find the gas vendor to whose rewards program they belong? Are they in a rural setting and looking for the closest gas station?

Location information provides the app developer and service provider with context to answer questions that help chart a customer’s journey of interacting with the service provider across multiple channels and across multiple locations, allowing the identification of patterns that signify and impact the customer’s search, discovery, decision and transaction.

Business owner should be asking questions like:

  • Where are the users before and after they use the app?
  • Are users using the apps in the vicinity of retail stores? How close are they to the stores?
  • Are the users using the apps in the vicinity of competitor stores? How close are they to the stores?
  • Do users use apps and then walk into the stores? Vice versa?
  • Do multiple users use the app in the vicinity of a single store?


Are online or mobile channels increasing? How do my business channels impact and improve transactions on neighboring channels?

The hypothesis is that the multiple channels of your business are symbiotic.

Does enabling one channel cannibalize, harm or improve business on other channels? Is your mobile app driving more traffic to your store… to your web site?

A powerful example of enabling business with apps, and the impact across their channels comes from Walgreens. The pharmacy chain made mobile technology a key part of its strategy and finds that half of the 12 million visits a week to its numerous online sites come from mobile devices. Additionally, Walgreens indicates that the customers who engage with Walgreens in person, online and via mobile apps spend six times more than those who only visit stores.

Some questions for business owners to ask about their channels include:

  • What is my strongest channel?
  • For multi channel transactions
    Do transactions transcend multiple channels – that is, do users channel hop?

    • Which channel is responsible for starting most transactions?
    • Which channel is responsible for successfully completing most transactions?
    • Which channel is responsible for most abandoned transactions?
  • How and what does each channel contribute to the users’ needs towards driving improved experience and transactions?

TLC for the User Experience

Consumers today are “always addressable”. We are increasingly surrounded by digital screens, which make us reachable at anytime, in anyplace and on any device. This leads to a new type of problem and opportunity that I like to call “screen optimization.”

Screen optimization is the opportunity and the ability of a service provider to optimize the message and content delivered according to the user’s context – time, location, channel, and position in their journey.

  • Adjust and adapt a user’s experience to their context and screen across the various digital touch points on the customer’s journey
  • Adapt the content delivered to a user’s surrounding screens (mobile device to highway billboards) according to the user’s context
  • Provide a personalized, mobile-centric experience that enables a user to orchestrate their multi-channel experience successfully
  • Enable a user to enter and experience the appropriate channel given their stage in the journey of interaction and transaction with your business

So, apply a little TLC to your data and analytics and create better, customized, personalized products, services and experiences for consumers.

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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