Things to Consider Before Replacing Google DSM
July 2019 Isn’t That Far Away Deprecation of DoubleClick Sales Manager (DSM) has caused some consternation in the media and publishing industry. After July 31, 2019, DSM users will no longer have access to the tools and data in the system, which enable publishers to manage digital channel sales. The reactions to this vary from frustration to denial. And like other deprecations, it’s something publishers must get past. For millions of affected organizations, the search for a replacement order management system has already begun, whether they are happy about it or not. Amidst the short timeline, you need to ask a few right questions while choosing a replacement for Google DSM. Below is a list of factors, a company should consider 1. Accomplishing the purpose served by Google DSM: Streamline business by providing media workflows with minimal to zero changes to the existing process a. Create proposals b. Create products and categories c. Manage rate cards 2. Automation and accuracy: Eliminate manual processes and errors by automating the business rules a. Data validations b. Calculate the metrics based on budget and product c. Dashboard for monitoring and reporting d. Define targeting rules 3. Integration with DFP: Implement a bi-directional integration with DFP for ease of managing campaigns a. Tracking the campaign status and basic reporting within the tool b. Integration should be updated with new releases of DFP APIs 4. Integration with Salesforce: Provide the ability to exchange information with CRM a. Access lead information b. Update opportunities automatically 5. Number of integrations: Create a seamless experience by connecting to in-house or third-party services a. Ad-exchanges/products: Provide a choice of ad platforms for bridging campaigns b. Data providers: Access audience segments c. Inventory Forecasting: Plan budget judiciously d. CDN: Upload assets e. Payments: Allow invoicing and acceptance of payments 6. Designed for digital ecosystem: The UX should be built keeping in mind the digital ad ops team a. Frictionless navigation b. Accessible from desktops, laptops, tablets 7. Cloud or on-premise: Can support your deployment model a. One click builds and deployment b. Easy to roll-out upgrades 8. Customers: Extent of experience with other advertising companies a. Domain expertise b. Depth and breadth in terms of technology choices available 9. Cost: The migration away from Google DSM needs to be cost-effective a. Application maintenance and enhancement b. No hidden costs 10. Customization and extensibility: Make changes based on the roadmap and additional requirements a. Add new modules b. Integrations with new services or platforms c. Modify existing workflow or UX based on the custom needs of the team 11. Value-added services: This can act as a differentiator amongst multiple available options a. Proposal templates b. Advance reporting and analytics c. Insights into the progress of media proposals d. Advance UX controls to improve operational efficiency Step Forward Don’t wait until 2019 to plan your transition. So as DSM gets ready to kick the bucket, what a perfect opportunity to upgrade to a smarter and more powerful solution. Backed by more than a decade of experience in building digital solutions for top media companies, Tavant deeply understands the specific needs of the advertising industry and have successfully delivered solutions to complex business problems ranging from media sales to advertising and reporting. Companies can now utilize Tavant’s media planning and sales manager for complete control and flexibility over your order management process.
Reverse Logistics Function – A Strategic Review
It’s June, the end of the planting season of the corn crop (i.e., one of the crops contributing to most of the farm incomes in the United States and our client), and a farm equipment manufacturer is loaded with a lot of warranty cases for repairs of its farm equipment. The timeline to deal with these warranty repairs is a few weeks before the harvesting season in October — when the manufacturer’s customers are expecting the defective farm equipment (for which he raised a service request for repair) to be up and running. If you closely look into the problem, there are a lot of things that should have been taken care of by the manufacturer before the planting season, even before planning the sales of its farm equipment for the year. The diagnostic areas for our client, the manufacturer, could be the development of a robust dealer network to deal with warranty repairs in locations near to the concentration of large farms, availability of technical expertise in dealerships to repair the high-tech farm equipment unserviceable by technicians without special training; logistics and technology capability for part returns to cater to the high seasonal demand; and above all, the customer service centers to ensure the process of a repair request to delivery of the farm equipment back to the customer location is smooth and hassle free, to prevent the farm owner from having second thoughts when he considers buying farm equipment from you next time. These are just broader areas of concern in reverse logistics. If you delve deeper, there are other problems — unpredictable demands that may eat into profits of any big organizations if not handled well, like the geographical separation of the supplier network; transportation and labor costs; recalls; disposition strategies of the returned goods; and government regulations affecting the reverse logistic functions, to name a few. The reverse logistics look more complex, and are more an area of concern as compared to the forward logistics, which are more organized and also a part of planned strategies of any organization in the business of manufacturing, selling, storing, distributing and servicing its goods. Historically, reverse logistics is one area that is often an overlooked and disorganized function of any manufacturing organization. But not anymore. For the organization that does not have a planned strategy for reverse logistics, the trends of its financial performance and market share may be a gloomy picture. Statistics show how “Reverse logistics—the management of returned and recyclable goods” is, in fact, an important business activity. It is more expensive than expected, costing companies approximately US $100 billion per year in the United States alone. Costs associated with returned goods can be anywhere from 8 percent to 15 percent of a company’s top line. In fact, the cost of processing a return can be two to three times that of handling the original outbound shipment. Product returns exact a toll not only on a company’s financial performance but also on its image and sales. A major recall done by any automotive company can spread the negative sentiment about the company brand image like wildfire. So, the way of the future is looking at reverse logistics as more of a strategic and diagnostic tool to differentiate from competitors. The strategic approach demands strong infrastructure backed with the technological capability to have data visibility throughout the reverse logistics cycle. Big data and predictive analytics can be used to make important strategic decisions in network planning and cost optimizations. Many organizations have chosen to outsource their reverse logistics function completely to optimize cost. But choosing a third-party service provider is a big decision, before which a company needs to understand its current returns flows, identify the total cost of returns, profile the end-to-end returns, and quantify and categorize its return flows. The diagnostic tool approach demands looking at the root cause analysis of failures in logistics and manufacturing, recalls, and repairs to come up with metrics of predictive analytics and performance management that can identify areas of risk, improvement, and performance in both the forward and reverse logistics. The reverse logistics function should be viewed more as a profit center than a cost center. Companies should develop a financial framework to look at all financial transactions in the reverse supply chain and map them to the P & L and cash flow statements. Last but not the least, performance management of the reverse logistics functions using key performance indicators (KPIs) and metrics to ensure that the function is performing consistently and is in line with the strategic planning of the organization is important. Financial KPIs can include return costs as a percentage of sales, return processing costs by category/channel/supplier, shipping costs, inventory levels and carrying costs, and write-offs. Sources: http://www.supplychainquarterly.com/topics/Strategy/201201reverse/ http://www.supplychain247.com/article/managing_reverse_logistics_to_improve_supply_chain_efficiency_reduce_costs/fedex_supply_chain Meet our Warranty Experts at Booth #11, WCM Conference 2018 to learn more! CLICK HERE to schedule a personalized DEMO.
AFTERMARKET 4.0 – A Consumer’s Perspective
Who hasn’t heard of IoT? Who doesn’t know about Cloud Computing? Everybody, right! It is everywhere — in our homes, cars, home assistants, and phones. But did you know that it’s what organizations are using to improve their processes, their productivity and their profits? Probably not. With the advent of the steam engine came the first industrial revolution — the 1.0, so to speak. When we found the assembly line production technique, mass production became the foremost manufacturing process — the 2.0 of the industry. The invention of computing and computer-based machinery was the third revolution of industry, which was, until very recently, the dominant feature in manufacturing and many other industries. The current and ongoing revolution in the entire industry is the Industry 4.0, which is the culmination of Cloud Computing, IoT, Analytics and Cyber-Physical processes to automate, improve and manage entire businesses and manufacturing processes. A selective case of applying all these technologies and perspectives to the aftermarket and warranty is “Aftermarket 4.0”. Let’s say you own a brand-new BMW i8 with all its bells and whistles. It’s only natural that you would have a mobile app to manage your car. You have sensors in all the nooks and corners of the car to measure the speed, check if your seat belt is fastened, check the tire pressure and balance, know the number of people in the car, check the parking sensors, check the fuel mix sensors, and even check if you’re sleeping at the wheel. All these sensors would collect data and pass it on to an intelligent machine that would make a decentralized decision on what should happen next. And you would see all the collected data in a report in your mobile app. Then, when you take the car for a service at your dealership, which, by the way, was notified to give you a discount, for routine maintenance. Because the dealership has data about your good use of the car, you get an additional 5% off. Feels good, doesn’t it? Through this process, you’ve used cloud computing and mobile technologies to access the car details and take actions. You’ve experienced IoT with the full set of sensors in your car, and the analytics on the data, which provided actions you needed to take. There were cyber-physical processes that were managing the whole thing for you with little intervention. Welcome to Aftermarket 4.0! Imagine every need that you can regarding your vehicle. Fuel? Check! Engine Health? Check! Part Change? Check! With intelligent systems and sensors, the manufacturers and dealers can proactively help with any and every need of the vehicle. This doesn’t just start and end with servicing your car. Warranties, replacements, returns, trading, bartering and much more can be accomplished with standardized and identifiable sensors and nodes of a IoT. Not only does it create a much easier way to identify assets, estimate the value and trade them, it also creates a better environment for transparency for the customer and an experience that is much easier for purchase, repair, and return of vehicle necessities.
Aftermarket 4.0 – A Manufacturer’s Perspective
How many times have you faced the scorn of your customers because they could not return or claim warranty on your products? Have you ever wondered why, or if there was something you could do about it? As a customer myself, I have had more bad experiences than good when it comes to product returns and warranties. I always used to think, “Why doesn’t the manufacturer want his product back? After all, it is an opportunity to resell it, understand the product failure, retain a customer and create higher brand value. But many companies trade all this for the MRP of the product — seems out of balance, doesn’t it? But I don’t exactly blame the manufacturers because it is not that easy to quantify the benefit in a streamlined aftermarket process, a product return process, or a warranty process. On the other hand, the costs are easy to identify, but the benefits outweigh them three to one in this case. What if there was a way to reduce the cost of returns and warranty management, and be the brand your customers love most? Wouldn’t you be the first one to grab the opportunity? Let me introduce you to “Smart Factories”, an ongoing 4th revolution in the industry earmarked as “Industry 4.0”. Manufacturing always seems to be the first industry where optimization and profitability methodologies are created. Every business and processes in it are going to a level of automation, decentralized and intelligent decision making by machines and robots. This is not happening somewhere in the future; this is happening as we speak.Factories that can understand the objective of their operation, organize the raw materials and machinery, analyze the requirements, make in-production decisions and create the most optimized output are at this moment in use across the world. IoT is not new, but it is a new perspective on how the information captured by objects that are interconnected is used for improving the entire system. We need a core structure that can analyze and decide on what to do with the information. This is achieved by a Cyber-Physical system that can monitor the network of IoT nodes, make a digital copy or map of the entire physical process, and make decisions that optimize the use of all the resources available in the factory. loT has not been discussed yet, so will readers know what it is? The scope of such a system is not bound by just the confines of the factory or the manufacturing facility. The interconnected system of things (IoT) can consider the market demand in real time — the spikes and dips and the geographical distribution and many other factors — and feed this back to the system to be analyzed. The system can then decide which product, at which time, in which location must be produced at the optimum use of all the raw materials and resources. The system can extend up to any level of penetration across the value chain from the raw materials to the end consumer. Consider the flipside, which becomes the – the current industry state that is creating great opportunities in cost reduction and revenue generation for manufacturers and other players in the value chain. Consider a product recall or even a repair due to an isolated incident or part breakdown. Because you know the specifics of each product and where has it been dispatched, a major recall and assigning a nearby technician to resolve the broken/worn out part become easy to manage. Many lives can be saved by just knowing the condition of your product at a given time; the lack of information is what causes most accidents. Many lawsuits can be averted by just analyzing what happened to the product at the time of failure. Lives saved? What types of products are we talking about? Assessing product quality has multiple advantages. Not only does it tell you how well your product’s performance is in the long run, it will indicate when a failure may occur, what conditions might create the failure, how much impact your product has on the environment, and the product’s total footprint. Analysis alone is not the objective of such a system; decision making based on all the information and inferences is. Designing, prevention, and correction become that much easier with a smart factory. Industry 4.0 and Aftermarket 4.0 are only the start of what is going to be the norm for managing your business processes in all business functions and levels. It is up to you where and when to start adopting it. Meet our AfterMarket experts at Warranty Chain Management conference, WCM 2018 in San Diego from March 6-8, Booth 11.
4 Ways to Harness the Power of Digital Transformation in the Aftermarket Industry
Reality Check The automotive aftermarket is undergoing dramatic changes in evolving customer expectations, acceleration of technological innovation, and shifts in competitive power. These changes are revamping the way business in the automotive aftermarket is conducted and value is created. Interestingly, Auto aftermarket sales becoming an omnichannel experience.Today’s consumer visits and researches a variety of channels — including websites, catalogs, social media, advertisements, and stores — before making a purchase. Furthermore, online channels are also giving customers quick access to the information on the prices of parts while online forums are giving customers a peer perspective on the quality/value of workshops. Before moving further, let’s quickly define “Aftermarket” The aftermarket, which is a broad term, can be at times, an afterthought in the automotive world and many consumers may not even be aware what the term means. However, it represents an enormous industry that offers significant value in improving the driving experience. Aftermarket parts are replacement parts that are made by a company other than your vehicle’s original manufacturer. The need of the hour is modernization of legacy systems in the aftermarket industry With legacy and disjointed systems, aftermarket processes suffer from high latency and lagged response. This may be because of restrictive technologies and interfaces or high cost of wrap up solutions. For example, waiting time for a service ticket is so high that it may dissuade the customer from reaching out to OEM or their dealers. Moreover, legacy systems do not enable the customer to do self-service. Additionally, without digital technologies, the customer interaction with a dealer or an OEM is constrained by time and geographic reach. This is where the digital transformation plays a vital role!!! It leads to a better understanding of the customer, helps in personalizing responses, streamlines operations, enhances customer experience and improves revenue by serving manifested and latent demands. Digital technologies allow companies to derive total life cycle value of their incumbent customer base. So how digital transformation is changing the Aftermarket landscape 1. Social Media UPS Online shoppers survey shows online buyers are diligent about research, extensively use online reviews, ratings and social media. Interestingly, 70% of business buyers purchase from an online catalog rather than through another channel. By using social media and analytical tools customer touch points can be identified as social media has made customers more comfortable with connecting and engaging with one another and sharing their concerns and thoughts. Also, with the help of social media, aftermarket suppliers can eliminate the unwanted waiting and reneging. 2. Mobility and connected devices High smartphone adoption, millions of connected devices using IoT and other technologies and ubiquitous connectivity are creating new opportunities at multiple levels for OEM’s. These technologies are reorganizing and redefining internal and external structure and the process of companies. Mobile is critical in the shopping journey and mobile phones account for 34 percent of retail e-commerce sales transactions. That percentage is expected to increase to 48 percent by 2020. Needless to say, to stand out from competitors, a business needs to provide a smooth, frictionless experience and customer engagement by providing quick product searches, delivery and in-store pickup options, and mobile-friendly access to online sites. 3. Cloud Cloud and IoT enabled infrastructure enables a highly cost-effective, rapidly responsive and elastic IT, better aligned with the business needs. Cloud enables aftermarket business to innovate faster while leveraging existing systems and capabilities. Cloud-based tools provide visibility to aftermarket suppliers for every party in the supply chain to look at same data and analytics so that defects can be detected and corrected early in the chain. 4. Data & Analytics Digital technologies connect ecosystem-wide processes so that assets are efficiently managed using predictive analysis of potential errors and initiate. Aftermarket digital transformation pushes business strategies to evolve from selling a product or service to a customer experience-centric value proposition. By using data and advanced analytics, aftermarket suppliers can accurately forecast demand, deepen customer engagement and can also drive loyalty and sales. The Bottom Line: Digital Disruption is forcing companies to recognize the aftermarket’s enormous potential and understand the entire lifespan of a sold product, including supplies, repairs, selling and servicing spare parts, installing upgrades, handling inspections and add-ons, training, and customization. To stay competitive, it is imperative for aftermarket suppliers to change their mindset, create a vision, invest in digital content and analytics, lean on data to stay in touch with customers permanently, provide service through the traditional and digital channel and deliver exceptional aftermarket capabilities coupled with self-service.
Why Cloud Paradox in the Digital Age?
Don’t let fear keep you from harnessing the power of the cloud When cloud computing was initially introduced, many organizations didn’t understand the capabilities of this technology and were extremely apprehensive about placing their data on an external server mainly due to security reasons. As technology has improved and as the business world has become increasingly dependent on remote teams and off-site workers, accessing critical company data from the cloud has become crucial. Organizations are still unsure about moving to the cloud. Are you concerned about having your data in the cloud? If yes, then discover the truth about cloud computing! According to the study by Cisco, more than 83% of all data would be based in the cloud within the next three years. While a study by Gartner reveals that by 2019, more than 30 percent of the 100 largest vendors’ new software investments will have shifted from cloud-first to cloud-only. Gartner also predicts more cloud growth in the infrastructure compute service space as adoption becomes increasingly mainstream. Furthermore, a recent IDC survey on cloud market predictions indicates that 50% of IT spending and 60% of IT spend will be on cloud-based infrastructure by 2020. Additionally, rising demand from the migration of infra to the cloud as well as from compute-intensive workloads such as Artificial Intelligence, Analytics, and the Internet of Things— both in the enterprise and startup arena — are further driving this growth. Sadly, in a world where security breaches at large organizations dominate the headlines, the ambiguity that encloses cloud computing can make securing the enterprise seem daunting and a few organizations are still apprehensive and not able to maximize the full value that the cloud offers. And some businesses still remain apprehensive. Common Concerns In no particular order, businesses hesitant to adopt cloud computing are often concerned with: Security. By far the biggest concern. Are you afraid that your data will not be as safe in the cloud, as it is in on-premise systems? Control. Do you feel that you will lose control of your data if you move it to the cloud and it’s more assuring to know that you have it nearby? Compatibility. Do you fear critical applications will not be compatible with cloud computing solutions? A Passing Fad. Apparently, Do you strongly feel that the cloud is just another passing phase? Put your doubts about the Cloud to rest Cloud is undoubtedly a way for your organizations to cut down your operational cost and streamline your business process. However, before jumping on a bandwagon, it is better if you look at some of the key benefits of transitioning to the cloud: Cloud is secure: Surprisingly, according to Gartner, through 2020, public cloud infrastructure as a service (IaaS) workloads will suffer at least 60% fewer security incidents than those in traditional data centers. While 60% of organizations that implement relevant cloud visibility and control tools will experience only one-third fewer security failures by 2018. Needless to say that the cloud is more secure than traditional approaches. Reduced cost– A study commissioned by Cisco shows that on average, the most “cloud advanced” organizations see an annual benefit per cloud-based application of $3 million in additional revenues and $1 million in cost savings. These revenues boosts have been largely the result of sales of new products and services, acquiring new customers faster or due to accelerated ability to sell into new markets. Decreased headcount: With significantly fewer servers to look after, and with standardized platforms, you will subsequently find you require fewer IT staff. In fact, many organizations figure out that they can reduce their staff maintenance by 50 percent. Quicker deployments: Cloud may or may not have a drastic impact on application performance, but in just about every case, you’ll be able to get them up and running much sooner. Creating—and eliminating— environments for new applications is a much faster process, allowing your development team to use their time most efficiently. Improved Agility. Cloud computing drastically increases application delivery as there’s no associated waiting time to access or allocate the infrastructure. Subsequently, by embracing continuous delivery and cloud DevOps, your business can significantly improve its agility. 20%+ faster time to market for new services 50% fewer application failures and faster recovery time (in 10 minutes or less) 30% more frequent new code deployments and a 38% improvement in overall code quality High Availability: The complete cloud computing facilities are routinely protected from system failures and outages using redundant network switches, servers, and storage facilities. In particular, by leveraging off-site backup and redundant servers and storage facilities make these well-equipped cloud computing facilities less vulnerable to disaster or malicious attack. Fewer servers: Moving infra, application, and platforms to a cloud model can undoubtedly help you with enormous savings, as you can stand down or redeploy servers that were previously hosted applications now moved to a shared model. Final Thoughts It’s time to try the cloud! Legacy systems often prevent responsiveness and derogate service levels and a lack of speed or agility often results in inconsistent and disconnected experiences for users, partners, and employees. Moreover, aging systems should not prevent you from harnessing digital technologies. However, the big question that often worries every business is what should and what shouldn’t be moved to the cloud. The answer has proven to be remarkably simple. Everything is potentially cloud-able – bizarrely, even mission-critical survival solutions like disaster recovery. The need of the hour is to focus on delivering solutions faster to meet customer demand in today’s hyper-competitive market and make a big difference. Stay tuned for Part 2 blog post on Application Modernization and Cloud Connection of our Cloud Computing blog series!!!
Automation Solution for Network Calls Validation
Requirement Traditionally all the network calls related testing has been performed manually. A proxy was created for the device/app and then manual verification was done for every parameter for the various calls generated for both HTTP and HTTPs throughout the application. Because there were more than 150 calls, with every call having more than 30 parameters to verify, the manual effort required to validate these calls was quite high and sometimes prone to manual errors. Thus, there was a need to develop an open-source packet analyser that could automate the validations of any type of network call, supporting both HTTP and HTTPs protocol, and on any type of application (mobile web, app, and desktop based). The focus was on implementing a solution that could easily be integrated with any of the popular automation tools and techniques, and in turn, could be easily used, modified, and maintained per the need, and that additionally supports network throttling and analysis of base64 encoded network calls. Implementation Approach Our team at Tavant developed the framework/solution using Browser Mob Proxy (BMP), an open source tool, to achieve automation for network traffic validations. The developed framework consists of the main layers below: Test Case Layer: Test cases are developed using appium/selenium automation tools based on Java. Implementation Layer: This layer consists of the re-usable methods, utilities, appium and selenium/webdriver APIs and the BMP server. The BMP server is available in two flavors: embedded and stand-alone utilities. The embedded version is mainly used for selenium-based desktop web network call validation/automation, whereas the stand-alone version can be used for creating a proxy to be used by any 3rd party medium/applications like the mobile web and mobile apps. The BMP server creates a proxy to route and captures the traffic from the application to the internet and further export the performance data as a HAR/JSON file. The BMP server uses certificates to be deployed on mobile for capturing SSL-based network calls. Click here to understand how the BMP server works completely. The framework also implements a common object repository to maintain the objects and their types at a single location, which makes test case scripting easy and maintainable. Execution Layer: The framework initiates the test case execution two ways: test rail and command line. The tester needs to provide the platform and application information based on which execution is triggered. Every test case initiates/launches the BMP server for any functional verification, and quits once the generation and analysis are complete for that scenario/network call. This takes cares of the unique call validations generated with different scenarios. Test Data: This is the expected data that needs to be validated for network calls stored primarily in the form of Excel. Reporting: JSON parsers are developed to parse the HAR/JSON files generated using BMP and were compared and validated with the expected/test data. The detailed test results are stored in the Excel sheet and pushed back to the test rail. The framework can be easily integrated with different 3rd party tools like Jenkins, TestRail, Github, etc., depending on the project requirements. Further, test reports can be easily integrated with the test management tools. It also supports the analysis of encoded calls using base64 decoding techniques. Tools and Technologies BMP Server, Appium/Selenium-Webdriver, Jenkins, Maven, TestNG framework, Github, Test Management Tool, etc. Challenges Faced The curl command, used for generating the HAR data for network calls, was not providing the data consistently. So, we used some of the options/switches provided by curl and stabilized the network call generation process. We faced issues with generating network calls for mobile-based applications, which got resolved by deploying SSL certificates on mobile devices. Benefits Reduces the manual effort substantially to test the network calls/traffic routing to the app Provides a common end-to-end solution to analyze all types of network traffic (videos and non-videos) for various applications Integrates easily with any automation tools and technologies Enables reusability of test scripts/methods Saves significant effort in test case creation/update Keeps testers from having to resolve framework level complexities Provides an easily modifiable framework Delivers a solution that is stable, efficient and robust enough to drive the complete end-to-end approach with minimal maintenance Provides a solution that can be supported on any of the test environments and network settings Provides a solution that has active support for various online forums
To Risk It or to Identify and Fix It? What’s Your Take?
What is a risk? Risk is a potentiality of failure. When the risk is discussed in the software industry with respect to delivery, it is either attributed to project risk or product risk. How do we avert risk from our project delivery? This can be taken care of by the testing team, but how? With today’s dynamic market, where the technology is made obsolete by a better technology being developed, Time to Market has become most vital. To meet these shorter time spans, organizations are adopting agile concepts. Iterative models are being followed for incremental deliveries based on the priorities defined by the stakeholders. The answer to all this from a testing perspective is risk-based testing. Identifying and mitigating risk play vital roles. When was the last time you approached testing using a risk-based model? This is possible by designing a test plan that aligns with delivery and operations. Risk-based test management is the solution to achieve timely delivery, focusing on business-critical requirements. The methodology that provides an evaluation of requirement risk (business risk or technical risk) as an input to test planning is a full-lifecycle proposition. Sometimes it becomes difficult and taxing to identify the potential risk(s) as things might not be evident and straightforward. Locating the upfront risks is as important as contemplating the potential risk(s) based on the market in which our client operates. Potential risks can be the technologies involved, the current competition and potential competition, and the possible security issues around the flexibility and limitations of the software being designed. The substantial uncertainty that may occur in the future can endanger the project objectives. All the potential risks are not for the vendor to solve unless if the client provides enough data and looks forward to such consultancy from the vendor. Projects will never be subject to the same kind of risks, so the risk management exercise should be conducted each time. Nothing remains constant and risks change over time; hence, the need for organizations to forecast and assess the potential risks before the critical decisions are made. There are two dimensions to potential risk. We can qualify the risk as well quantify the risk. If we have to analyze something we need to know the volume of occurrence as well as the level of impact. If we prioritize the identified risk on a scale, considering the probability of occurrence against the level of impact it can have on the project, particularly on the key attributes of budget, schedule, or quality — this is a qualitative approach of analyzing the risk. Now, this prioritization is in turn consumed, and additionally, highly processed data is used to quantify the probability of the high-priority risks numerically. This acts as input to make decisions amidst the uncertainty; to verify the alignment towards specific project objectives; and to compute the achievable margins, release date, and the scope. This is a quantitative approach to analyzing the risk. Identifying and Analyzing Risk – There are certain methods that can be used to identify the risk and impact, and for analyzing the probability of recurrence based on past data. Cause and Effect Matrix. This is a useful method for the root cause analysis conducted at the end of the project delivery. To identify the possible causes, the participation of all stakeholders is essential for brainstorming and forming a Fishbone diagram. Assigning scores to each of them helps to understand which activities created the risk and the critical steps present in the process. Why and how? Control Manage Cause Controlling the risk cause Pre-impact recovery planning and preparation Cause-Effect Linking Delinking the cause and effect Identifying post-impact recovery measures Failure Mode Effect Analysis (FMEA). It is a systematic and qualitative tool, widely used in early development cycles for analyzing potential reliability or quality problems. FMEA is measured by 3 factors: Frequency: Tracked on a scale of 1 to 10, indicate how frequent a discrepancy is likely to occur. Severity: Factor that determines the possible impact on the client. Detection: The probability of the discrepancy event getting detected. Prioritization based on the 80-20 principle proposed by Pareto is done for each of the criterion identified. Risk Control. This method is used to define an acceptable level of risk for an organization. Senior management sets this by having thorough discussions with the stakeholders. Once the risk tolerance level is earmarked for the organization, called Risk Appetite, the assessment is carried out to identify if the risk foreseen is exceeding the defined value. If so, mitigating actions are taken accordingly. Respond to Risk. This is more of a corrective-action-taking method to mitigate risk and eliminate what has gone wrong in an effective way. These are the most effective actions to take towards a potential risk: Accept: To perform risk assessment, not do anything, and continue the same way, which is accepting the risk. Avoid: To identify the risk and prevent it by not taking part in any risk-causing act. Transfer: To avert and transfer the risk to a different entity altogether, if possible. Mitigate: To mitigate risks by adding suitable controlling measures or by manipulating the risky behavior by modifying its probability or at least the level of impact. The main aim of risk mitigation is to reduce the probability of occurrence to a manageable level of impact. The process is structured in the below steps: Discussing the probable controls. Measuring benefits. Estimating the associated cost. Evaluating the resultant probability. Effect and residual risk. So, in short – Identify the critical blockers as quickly as possible (at the lowest price). Target the business-critical area first and provide confidence to the business. Justify testing effort + cost of business and technology risks. The solution as a Tool to Manage Risk – Taking the current market into account, there is an acute requirement for a tool that could at least do the following activities, while managing the risks and helping us with the risk-based test management: Synergetic Review and Feedback – A platform to have collective review and feedback by all the
Doing the Analytics Right for Video Platforms
Today, almost half of the population in the US streams Over-the-Top (OTT) content directly to their television for an average of 1 hour 40 minutes daily. Users are getting inclined toward having an increasing number of live experiences for sports and news content. OTT has inevitably unfolded into a multi-million-dollar industry and it is set to grow at 17.2% CAGR to 2020. Most of the OTT platforms provide video metrics out of the box. However, the broadcasters, content providers, and advertisers face inimitable challenges when they try to holistically comprehend the performance of OTT content, target the right segments and formulate an effective business strategy. Also, compiling data from numerous data sources to make meaningful insights can get quite exhaustive and time-consuming. The existing platforms fall woefully short to help uncover intelligence and insights to drive effective business outcomes. The key stakeholders for this OTT analytics comprise of 1) Advertisers – who want to get associated with the right content and target the right audience segment 2) Content Providers – who want to understand and invest in ideas comprehending audience behavior and interests 3) Marketeers– who want to be enabled with the right audience to succeed in their marketing initiatives. There are various challenges faced by these stakeholders while analyzing the OTT content. How to retain and increase subscriber growth rate? Who is watching and how are they getting disengaged? How do we measure the performance of content across audiences? Which region drives the most engagement for the content? How can the audience be segmented to offer personalized programs or ads? Which platforms provide the best ROI? How do we effectively market the content? To overcome the above challenges, we need to start with access to quality data. Lack of quality data is the biggest challenge in data analytics. Initially, broadcasters’ view was limited to Nielsen ratings from sample audience data. However, data collection has expanded progressively with the launch of streaming services like YouTube, Netflix etc. and now spans to thousands of parameters of metadata collected. The clickstream data has transformed into big data. Data that gets generated directly from the video platform is the key data for content analytics. This is also called first party data. In addition to this, the second and third-party data collected from data management platforms help to provide correlations and enrich the analysis. Once we have access to the right data, we can perform predictive analysis engaging techniques like data mining, statistical modeling, and machine learning to take the data to the next logical level. We can create a dashboard that can help in formulating the content strategy, promotions, personalization of content, etc. The success of any OTT solution lies in being able to decipher customer behavior and optimize omnichannel marketing efforts to explore better business direction, garner personalized insights through segmentation and predictive modeling to boost operational efficiency and extract value from copious data for smart decisions. It should also be able to automate data aggregation and empower us with better decisions. The objectives of the OTT solution should be clear. The approach, cost, and complexity will vary based on the objective. We can get profound insights by applying techniques like Machine Learning (ML) and Artificial Intelligence (AI). We need to remember that, data may not contain the answer, but if you torture it long enough- it can tell you anything. Garnering data is not the end objective; neither the reporting or building of dashboards. Rather, it all starts with asking the right questions. What are you looking for? What is it that you want the data to answer? Only if you have the right question, you can derive answers from the available data. We’re listening. Have something to say about this blog post? Share it with us on LinkedIn, Facebook, Instagram and Twitter. OR Please add your thoughts, ingenious analysis and novel strategies in the comments section below. We look forward to hearing from you.
Can Dynamic Pricing Work for the eCommerce Segment?
Dynamic pricing, a strategy which enables businesses to provide flexible prices for products and services is now catching on across hospitality, retail, travel and entertainment industry segments. Whether the aim is to stay profitable, fill up an airplane or sell as many sports tickets or products online as possible, companies today are using dynamic pricing to achieve their business goals. While this model has been in existence for several decades, it is only now that is gaining momentum, and is likely to grow more pervasive in the years to come. How effective is dynamic pricing? In 1978, the airline industry in the U.S. was deregulated and this gave companies the freedom to follow different pricing models. Some companies adopted a dynamic pricing model, and were quite successful. Other companies held on to the standard pricing model and tried to find loopholes in their competitors’ marketing strategies. Many of them went bankrupt! Does this essentially mean that the company which offers the lowest price for a product will win over their competition? Fast forward to the 2000s when Buy.com used a dynamic pricing strategy which relied on a software agent to search its competitor’s websites for competing prices, and in response, reduced its own prices. This approach helped Buy.com gain significant customer traction, but its profit margins suffered. To summarize, it is crucial to arrive at a balance between having competitive prices and maintaining healthy margins. Some pertinent questions to be asked when considering this model are: What should be the cost of the product? What should be the duration for an offer? And, How to arrive at that point? The answers to the questions above depend entirely upon the individual businesses and their respective products. This is because inventory, demand and competition are individual attributes which differ from product to product and company to company. Nevertheless, in general terms, the factors which might drive a company to opt for dynamic pricing are: Sectors with relatively high start-up costs compared to operating costs Sectors with finite markets, i.e. markets with finite time horizons, finite seller inventories and finite buyer population This model has actually helped industries with high perishables like the airline industry, sports ticketing companies etc. to improve profit margins. If it works for others it should work for eCommerce too, right? The eCommerce industry is not a finite market and it does not have a finite time horizon, finite seller inventory and finite buyer population. Also, start-up and operational costs are considerably lower for eCommerce companies because of technological advancements. So, the question is, does the eCommerce industry really need dynamic pricing? The answer is `yes’ and, in this case, the business’ goals might not be tied entirely to improving profit margins, but also to build a unique brand identity and gain a competitive edge. The good news is that customers have reacted well to dynamic pricing models over the years, as seen in the deregulated airline industry where the technology is perceived as offering lower prices in many situations. In summary, by implementing inventory-based, data-driven, game theory or, simulation models, eCommerce companies can capture the volatile internet market and get to a consumer-centric, product-specific dynamic pricing strategy.