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.

What’s your API’s Cachiness Factor?

Published Originally on Apigee

“Cachiness factor” is the degree to which your API design supports the caching of responses. Low cachiness means that a relatively higher than optimal number of requests is forwarded to the back end for retrieving data; a high cachiness factor means that the number of requests serviced through the cache layer is reduced and optimized.

Every time a request is sent to the API provider endpoint, the provider incurs the cost of servicing the request. Investing in a good caching mechanism reduces the number of requests that hit the endpoint, leading to a faster response time, lower servicing costs and saved bandwidth. Resources can then be spent on servicing requests that otherwise would have had to compete with cacheable requests.

Cachiness in an API design refers to understanding how a piece of retrieved data can be reused to serve other API requests. Such an understanding can be transformed into a set of actions that store the retrieved copy of the data in an optimal form for reuse.This coupled with insights from API usage analytics can provide direct benefits in terms of app performance and operational costs.

An API proxy can be designed to do a number of things when a request arrives:

Determine the quality or fidelity of the data requested by the app or end user

This information can then be used to
– Transform the API request to retrieve the data from the endpoint data store at the highest possible fidelity and breadth
– Save the retrieved data in the proxy cache
– Extract the appropriate fidelity and breadth (determined by the original request) and send as the response to the app/end user

For example, if the request is for weather patterns for a city, the system can potentially map the response to a response for all zipcodes in that city and store it accordingly in the cache.

Pre-fetch based on temporal and spatial locality
Predict based on usage patterns what the next request is likely to be and pre-fetch this data from the endpoint for storing in the cache.

For example, given a request for browsing a list of plasma TVs on sale at a retailer, it might make sense to cache the entire response set and serve subsequent requests for more data (e.g. the next set of TVs) from the cache.

Pre-fetch based on similarity
Use the idea of similarity in data sets to predict the next request and retrieve the data for storing at the proxy cache.

For example, for the scenario in which our user requests a list of TVs from one manufacturer, it might make sense to pre-fetch a list of TVs from another manufacturer with a similar product line and store this information in the cache.

Parameters-based selection
If your API supports “select” on your data through parameters, another option to optimize the cachiness of your API is to retrieve the entire data set (within certain bounds) from the backend, store it in your cache, and return only the appropriate data set for the request. Similarly, filtering of data can be performed at the proxy as opposed to the end point, increasing the cachiness factor for the API.

Using Data Analysis to improve cachiness

You can also use data analysis techniques to understand request patterns for your data and use this information to pre-fetch or over-fetch data from the endpoint to increase the cachiness factor of your API.

Caching Diffs
Another possible technique is building a mechanism where updated data is automatically sent from the end point data store to the cache as new updates are generated in the backend. At the cache level, instead of expiring the entire data set, the part of the data set that is least likely to be relevant is automatically expired and the new updated “diff” is appended to the cache data set.

The technique that will work for an API will vary from API to API. You might need to experiment with various techniques to identify the one that makes sense for your scenario and API.

Measure What Matters: Six Metrics Every CDO Should Know

Published Originally on Apigee

The chief digital officer has to juggle multiple priorities, foci, and investments, ranging from within the enterprise to its edge. Having been charged with growing an enterprise’s business, CDOs need to enable their companies to successfully undergo a digital transformation. This digital transformation includes enabling the enterprise to plan, build, and maintain products as well as market to, sell to, acquire, retain, and support users through digital or digitally enhanced products and processes.

Defining a successful digital business strategy requires a deep understanding of users’ preferences and behaviors and also requires the ability to track changes in user behavior and their consumption and demand patterns. Understanding users’ preferences and behaviors requires the ability to track customer behavior over time and across channels. Tracking changes requires a set of analytics that enables the CDO to measure, at an aggregate level, the behavior of segments and micro-segments of users and also to understand, at the individual level, the current state, engagement, and problems faced by a user or a partner.

Building a digital enterprise requires the ability to track and accelerate innovation, agility, and experimentation in the enterprise. Democratizing access to data and building-block services for developers requires a systematic audit, curation, and exposure of enterprise capabilities as reusable APIs with the ability to track, monitor, and aid the usage of such services by developers and partners efficiently, quickly, and successfully.

Here are the six dimensions of an analytics plan that a CDO should build and track to enable better decision making.

Business KPIs

A CDO’s main goal is to grow the enterprises’ business. To achieve this, the CDO must track two key types of business KPIs: traditional business KPIs and digital KPIs.

Traditional Business KPIs are those that the enterprise uses to run the business, such as customers, average revenue per user (ARPU), churn, and revenue/profit.

Digital KPIs include traffic and revenue from digital touchpoints and the total and rate of acquisition of new users, customers, developers, partners, and devices. Tracking business KPIs involves tracking both the absolute numbers and the trends, which can signify changing consumption and demand patterns and serve to alert the CDO about potential problems with customer satisfaction or the services supply chain.

Specifically, a successful CDO will:

  • set up organizational structure and processes to understand and attribute KPI changes to market, competitive, or product forces
  • define marketing and product strategies to drive usage, revenue, customer, developer, and partner acquisition and retention

CDO’s Business KPI Dashboard

Digital Transformation

Digital transformation can be defined as, and measured by, the acceleration of innovation and agility in an enterprise that ultimately leads to new, compelling user experiences and is marked by higher usage and revenue.

CDOs should measure the following aspects of digital transformation to detect organizational and personnel challenges around innovation, process roadblocks that hurt agility, and a lack of crisp product and/or platform positioning that impacts both reach and the level of partner and developer engagement.

Innovation: The ability of the enterprise to bring new, compelling products and services to market, measured in the APIs and apps delivered to users. New products can be defined as new products for existing users, products designed to attract new users, or new markets and products designed to attract users of competitive products.

Agility: The ability of the enterprise to improve its products and services, measured by the rate of improvement of apps and APIs. In other words, how quickly can an enterprise expose its services as APIs and how quickly and easily can these APIs be adopted by developers and consumed by apps?

Reach: The ability of the enterprise to attract new users, developers, and partners to their platform, products, and services, measured by the rate of new user, developer, and partner acquisition and the churn rate of these users, partners, and developers.

Time to Maturity: The time taken by APIs and apps to “go live” and be used by real users, as measured by the time from the first definition of the app or API to when it is available for consumption.

Partner and developer engagement: Developer and partner engagement with the enterprises’ platform as measured by the rate and breadth of platform features usage over time, including the rate-of and time-to success of developers and partners.

Ecosystem density: The measure of the “consumption” and “supplier” relationships that an enterprise has with other businesses (through APIs). Let’s take as an example an API that allows you to send photos to print from your mobile device. When used by and offered from services like Shutterfly, Flickr, and Instagram, for example, this API is the core of a much denser and robust ecosystem than if it were being used solely by any single app or website.

Similarly, say an app were to consume not only the print API but also APIs that offer users related services, such as viewing photos online and creating albums and slideshows. Then that app offers a richer experience to its users than if it only offered the print API functionality. The progress and success of an enterprise’s digital transformation can be measured by the density of the ecosystem—by how connected and how integral a part the enterprise plays in its digital supply chains and how robust the complex partnership models and supply chains are.

Specifically, a successful CDO will:

  • audit and optimize organizational setup and process efficiency regularly to understand rates of innovation and agility and identify internal roadblocks
  • commision strategies for improvement of developer and partner engagement through new products and services and better support and training
  • understand and remove bottlenecks in partner and developer onboarding, including the most common reasons for failed or prolonged onboarding process

CDO’s Digital Transformation Dashboard

Channels

The most pronounced impact of a digital transformation is evident in the changing behavior and transaction patterns of an enterprises’ users. Digital channels sometimes replace or cannibalize traditional channels, but more often they help and enhance multi-channel transactions. As customers navigate and interact with the enterprise across multiple channels, a CDO needs to be constantly aware of the shift in those customers’ product access and acquisition patterns. This awareness is shuttled into strategic investment decisions across channels and often into building bridges between channels to enable easy context switching for users.

Channel awareness is turning out to be one of the key tenets of data-driven decision making for the CDO.

A successful CDO will:

  • define product strategies to enable better cross-channel usage of your products and services
  • define and design channel-specific workflows and cross-channel workflows to adapt to end user usage patterns
  • track
    • traffic and revenue by channel
    • the most common channels where high value transactions begin and end
    • transactions that transcend multiple channels
    • users that use multiple channels to start and end transactions
    • users that shift and change the channels through which they interact with the enterprise

CDO’s Channel Tracking Dashboard

Apps and APIs

The CDO brings the app and API revolution to the enterprise by exposing old and arcane services as reusable, lightweight, and accessible APIs, designed to be consumed by lightweight and purpose-built apps.

CDOs track app and API metrics to understand and track the adoption, engagement, and usage of their products and services and to determine, optimize, and fine-tune investment decisions. Metrics include revenue, traffic, QoS, unique users, ratings of apps (consumer) and APIs (partner/developer), active apps, devices, and geo-distribution of traffic and revenue.

A successful CDO will:

  • track and understand trends and changes in KPIs, inlcuding unique users, usage, and app ratings to implement product strategies to build better products
  • use KPIs as an impetus to explore new market and customer segment opportunities and make timely investment decisions

CDO’s Apps and APIs Dashboard

Developers and Partners

Developers and partners are the channels to grow the enterprise. A healthy developer community and a diverse partner ecosystem is a sign of a thriving enterprise and a leading indicator of digital success. CDOs should measure the cost and likelihood of developers and partners successfully onboarding to their platform and launching new and innovative apps that are desired and used by users. Metrics such as cost of developer acquisition (CODA), partner onboarding success rate, and partner onboarding time are key to tracking the health of the developer and partner community. At any point, the CDO should have information about the revenue and traffic from a partner/developer, the QoS experienced by the partner/developer, apps built by these partners and developers, and the unique users delivered via these apps. This information is used by the CDO to fine-tune developer/partner onboarding processes and to craft marketing strategies that attract, retain, and engage developers and partners.

A successful CDO will:

  • define strategies to reduce cost of developer and partner acquisition and onboarding
  • remove bottlenecks and provide a better developer experience to strengthen platform adoption

CDO’s Developers and Partners Dashboard

News: Internal, Ecosystem, and External

Last but not least, CDOs need to stay abreast of relevant news and information that impacts their industry, ecosystem, enterprise, organization, or specific app or API team. CDOs should track how their apps and APIs are being talked about on social media, and listen to learn about product or service issues that are likely to cause dissatisfaction for developers, partners, and users. In addition, they should closely track how new releases and versions of their services, APIs, and apps impact usage and revenue.

A successful CDO will:

  • manage the perception of a business’ products and services on social media and arrest and address negative trends and user and developer dissatisfaction
  • design and implement market and competitive research pipelines to uncover new trends and changing end-user behavior patterns

CDO’s News & Releases Dashboard

Conclusion

A CDO is tasked with a challenging job: to be the chief digital strategist for the enterprise, and to shake up an enterprise and make it digitally relevant and able to successfully adapt to changing user preferences, behavior, expectations, and access patterns. A data-driven approach and culture is the best asset that a CDO can nurture in the enterprise to make objective decisions and track the impact of those decisions and actions.

If you are a CDO, we would love to hear from you about analytics and other techniques that you are using to bring about the digital revolution in your 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.

3 Signs That Your Partner Program Is Going Belly Up

Published Originally on Entrepreneur.com

While launching partner programs can offer many benefits to businesses, if not set up correctly, they can also spell disaster.

For those a little confused about what exactly a partner program entails, it is basically a formal program and process operated and managed by a business with the goal to attract, engage and retain other partners. For example, Google offers an advertising service to publishers and advertisers. The ultimate goal of these programs is to increase revenue generated from these partners. In addition to the services offered to partners (i.e. advertising platform), a partner program includes trainings, tools, support, documentation, help and strategic services to empower partners to succeed.

For a program to be considered a success, there should be a strong partner membership, robust usage of the services offered and large stream of revenue generated per partner.

If your partner program is struggling to stand on its legs, you may be making one these three following mistakes:

1. Your return on investment is low or not being measured.

Without clear metrics and goals, most partner projects end up getting defunded as the return on investment (ROI) is simply not there (or not measured) to justify continued investment.

Another symptom of a flawed partner strategy is a misalignment of expectations between senior management/executive sponsors of the platform and the actual implementers. If ROI does not match the expectations of the sponsor, a partner program can be classified as a failure.

To ensure everyone is on the same page when it comes to ROI, determine metrics right off the bat. To do so, companies need to know exactly what metric is important to them and to their partners. For example, the rate at which partners sign up or the time it takes for a partner to go from onboarding to generating revenue might be relevant metrics. Companies need to understand and establish the baseline for these metrics and then measure the efficacy of their program by ensuring the metrics move in the right direction.

Once you have figured out metrics, make sure your platform can deliver. A well-designed service platform makes it easier and cheaper for other teams in the enterprise to go to market with new services, products. If the cost to enhance and expand the platform is not getting smaller for each additional new service, your program might be in trouble.

2. You have built an ineffective service platform.

If your partner program has struggled to attract, retain and enable partners to develop and grow their businesses, it is an ineffective service platform.

To fix this, you have to make your platform sticky, meaning partners cannot succeed without your services. If this isn’t the case, you have a serious problem.

For example, if your partners are signing up to work with both you and your competitors, it might be because they consider you a risky investment. If your partners build only a single one-off application on your platform, your program is probably not on their investment roadmap and growth strategy.

One company that does offer a sticky program is Microsoft’s BizSpark. Initially, startups can build on the platform for free but after a certain time frame – and after a lot of resources have been built on it – Microsoft starts charging. And this creates a huge revenue stream for the company, as what startup is going to want to jump off the BizSpark platform and start over?

3. Your monetization strategy is all wrong.

If you are not making money off of your partner program or generating leverage from its usage, your monetization strategy needs to change.

The key to successful monetization is identifying and expose only products and services that have a high demand from partners. For example, a great tactic may be to survey your potential partners or analyze the needs of your targeted partner persona. This will inform you about the matching subset of services in your arsenal that offer enough value to the partners that they will pay for it.

If you not thinking about your program monetization by investing in marketing, pricing optimization, adoption and growth or performance management of the platform, you are probably stuck with a stagnant partner program with sluggish usage and high churn rate. A successfully monetized partner program requires that it be constantly analyzed and optimized.

If your partner program is an IT initiative and does not have a business team behind it thinking about its ROI, effectiveness and monetization, it will not only struggle to get adopted but also starve for investment dollars from your executive sponsors and revenue dollars from your partners. As these investment dollars dry up, the partner program that runs on top of the platform will be considered a failure and will eventually shrink, get branded as a failure and cease to exist.

The key to success with a partner program strategy begins with carefully selecting what your program offers, convincing and onboarding partners and doing everything and anything to make them successful. Once that is the case, you can monetize the partner program and as you open up new revenue streams for your enterprise, see the program grow, expand and become a line of business.