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 ‘Adjacent Possible’ of Big Data: What Evolution Teaches About Insights Generation

Originally published on WIRED

brunkfordbraun/Flickr

Stuart Kauffman in 2002 introduced the the “adjacent possible” theory. This theory proposes that biological systems are able to morph into more complex systems by making incremental, relatively less energy consuming changes in their make up. Steven Johnson uses this concept in his book “Where Good Ideas Come From” to describe how new insights can be generated in previously unexplored areas.

The theory of “adjacent possible extends to the insights generation process. In fact, it offers a highly predictable and deterministic path of generating business value through insights from data analysis. For enterprises struggling to get started with data analysis of their big data, the theory of “adjacent possible” offers an easy to adopt and implement framework of incremental data analysis.

Why Is the Theory of Adjacent Possible Relevant to Insights Generation

Enterprises often embark on their big data journeys with the hope and expectation that business critical insights will be revealed almost immediately just from the virtue of being on a big data journey and they building out their data infrastructure. The expectation is that insights can be generated often within the same quarter as when the infrastructure and data pipelines have been setup. In addition, typically the insights generation process is driven by analysts who report up through the typical management chain. This puts undue pressure on the analysts and managers to show predictable, regular delivery of value and this forces the process of insights generation to fit into project scope and delivery. However, the insights generation process is too ambiguous, too experimental that it rarely fits into the bounds of a committed project.

Deterministic delivery of insights is not what enterprises find on the other side of their initial big data investment. What enterprises almost always find is that data sources are in a disarray, multiple data sets need to be combined while not primed for blending, data quality is low, analytics generation is slow, derived insights are not trustworthy, the enterprise lacks the agility to implement the insights or the enterprise lacks the feedback loop to verify the value of the insights. Even when everything goes right, the value of the insights is simply miniscule and insignificant to the bottom line.

This is the time when the enterprise has to adjust its expectations and its analytics modus operandi. If pipeline problems exist, they need to be fixed. If quality problems exist, they need to be diagnosed (data source quality vs. data analysis quality). In addition, an adjacent possible approach to insights needs to be considered and adopted.

The Adjacent Possible for Discovering Interesting Data

Looking adjacently from the data set that is the main target of analysis can uncover other related data sets that offer more context, signals and potential insights through their blending with the main data set. Enterprises can introspect the attributes of the records in their main data sets and look for other data sets whose attributes are adjacent to them. These datasets can be found within the walls of the enterprise or outside. Enterprises that are looking for adjacent data sets can look at both public and premium data set sources. These data sets should be imported and harmonized with existing data sets to create new data sets that contain a broader and crisper set of observations with a higher probability of generating higher quality insights.

The Adjacent Possible for Exploratory Data Analysis

In the process of data analysis, one can apply the principle of adjacent possible to uncovering hidden patterns in data. An iterative approach towards segmentation analysis with a focus on attribution through micro segmentation, root cause analysis change and predictive analysis and anomaly detection through outlier analysis can lead to a wider set of insights and conclusions to drive business strategy and tactics.

Experimentation with different attributes such as time, location and other categorical dimensions can and should be the initial analytical approach. An iterative approach to incremental segmentation analysis to identify segments where changes in key KPIs or measures can be attributed to, is a good starting point. The application of adjacent possible requires the iterative inclusion of additional attributes to fine tune the segmentation scheme can lead to insights into significant segments and cohorts. In addition, adjacent possible theory can also help in identifying systemic problems in the business process workflow. This can be achieved by walking upstream or downstream in the business workflow and by diagnosing the point of process workflow breakdown or slowdown through the identification of attributes that correlate highly with the breakdown/slowdown.

The Adjacent Possible for Business Context

The process of data analysis is often fraught with silo’d context i.e. the analyst often does not have the full business context to understand the data or understand the motivation for a business driven question or understand the implications of their insights. Applying the theory of adjacent possible here implies that by introducing the idea of collaboration to the insights generation process by inviting and including team members who each might have a slice of the business context from their point of view can lead to higher valued conclusions and insights. Combining the context from each of these team members to design, verify, authenticate and validate the insights generation process and its results is the key to generating high quality insights swiftly and deterministically.

Making incremental progress in the enterprise’s insights discovery efforts is a significant and valuable method to uncover insights with massive business implications. The insights generation process should be treated as an exercise in adjacent possible and incremental insights identification should be encouraged and valued. As this theory is put in practice, enterprises will find themselves with a steady churn of incrementally valuable insights with incrementally higher business impact.

Pragmatic Big Data for the App Economy

Meeting key business objectives (in the context of a platform strategy) such as increasing market share or profitability in the enterprise’s niche can be achieved by increasing the enterprise’s user base, the user engagement and improving the product mix.

Strategic Objectives

Big data analysis of the large volumes of data being generated in the app economy is key to designing and executing strategies that can help enterprises meet their strategic objectives.

Entities in the API Ecosystem such as end users, apps, developers, API and backend systems are continuously generating streams of data within the API value chain and outside the API value chain. For example, App Users are not only using their apps and APIs but sharing opinions on social media, looking for content on the internet and interacting with products andservices in the physical world. These continuously growing streams of data contain hidden signals that hold the key to meeting the enterprise’s strategic objectives.

Enterprises looking to gain a competitive edge in the app economy have the opportunity to harness the power of big data and contextual analytics to solve three key problems that directly offer increased profitability & market share through higher usage of their products and services and higher user satisfaction.

Understanding End Users

End users drive value in the API value chain. It is critical that the enterprise understand the behavior and actions of their end users and why users act and behave the way they do. It is critical that the enterprise is able to segment their end users by product usage, by value they drive to the bottom line and by how engaged these users are with their products and services.

Offering sticky products and services requires that the enterprise understand the value that end users are looking for in the products and services. Enterprises need to understand their desired profile of the end user. The desired profile is an intersection of the end users who find value in the enterprise’s products and services and the end users that are profitable for the enterprise.

Attracting the Best Developers

Developers are key to building great apps across a diverse use case set. A diverse app set attracts a broader set of end users directly translating to more end users, higher usage and higher profitability for the enterprise. Enterprises do not retain control over the end user experience on apps written by third party developers. This makes it critical for the enterprise to attract, detect, nurture and promote developers and apps that offer the best user experience and the best value for the enterprise.

Attracting the best developers requires enterprises to understand their desired developer profile, their current developer profile and emerging trends in the developer world being promoted and embraced by the “early adopter” developers. The ability to adapt and embrace these emerging developer trends can increase the attractiveness of an enterprise’s platform.

Enterprises need to understand how their developers communicate with them and how and where developers hang out or seek support. The ability to keep a tab on all developer communication avenues, quickly gather areas of dissatisfaction and move to address and quelch any unhappiness can be the difference between retaining the best developers who build the best, most profitable and desirable apps.

Monetizing Data

Understanding the value of an enterprise in eyes of end users, developers, partners and other enterprises is critical for building new and innovative products and services, improving existing products and services and building new business models around data monetization. Every time a request is made for an enterprise’s data, the metadata generated around the context of the request event can offer deep strategic insights into where value is concentrated in an enterprise’s data set.

Understanding and monetizing data requires enterprises to extract and process to build comprehensive accounting mechanisms across all data access mechanisms enabled through APIs, Apps and other data transfer mechanisms. Enterprises need to be able to understand the end user intent and request type metadata to determine the highest value data and data enabled use cases.

Conclusion

The App economy fueled by the shifting enterprise edge is and is expected to produce increasingly diverse and disparate streams of data that provide a wealth of opportunity for enterprises to apply big data and contextual analytical principles to solving the key problems in the app economy.

An enterprise’s competitive edge depends on their ability to uncover deep insights through platforms like Apigee Insights that offer the ability to gather, model, analyze the app economy data, generate insights, act on these insights, observe the change and adjust if necessary andmaking strong progress towards the enterprise’s strategic objectives.

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.

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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