For the first time in history mobile gaming has taken a larger share in the global gaming industry than the PC. Mobile gaming is expected to account for more than 34% ($52.5 bn) of the global gaming industry by 2019 (Newzoo, 2016). The market has become highly competitive and extremely saturated with large global players like Zynga, Tencent, EA Mobile etc. In addition to that mobile games are often given for free thus developers need to rely on creative monetization strategies. This thus requires active gamer engagement to recoup the cost of development. With plenty of choice in mobile games and low switching costs, developers are struggling to promote their apps. To eventually find out that users give up during the initial tutorial of the game.

AppOnBoard

AppOnBoard wants to change that, like the CEO Jonathan Zweig said “we want to take the tutorial out of the app, to the other side of the fence…. Before the app is ever downloaded” (Anthony, 2017). They have patent-pending technology that can create gamified in-game adds with chronological heat maps to track consumer behavior. This allows developers to see how consumers interact with their demo before the game is even completely developed. This aids developers, in turn helping gaming developing companies to weed out potentially bad app ideas.

The chronological heat maps is an integrated monetization boost for advertisers, as they can now sell of their ad space at higher prices because developers gain more analytics. Both sides of the platform benefits from running ad’s on AppOnBoards technology. Their tag lines surrounding their business model is “built by developers for developers”, and as such they state that they will be the next wave of app revenue.

The tool has been built by a team of engineers who have prior experience in building apps for over 200 titles in the App Store. The company has already signed up some of the largest game publishers including Zynga ($765 million Revenue) and Glu Mobile ($223 million Revenue).

Through a unique form of value co-creation made possible through this proprietary technology, developers can now generate better ideas and design through instantaneous feedback, whilst making ad’s more engaging and fun for consumers. Creating a customer centric approach to app game development.

For the complete story watch the following video!

Efficiency Criteria

Their are multiple stakeholders who gain from the new value system created by AppOnBoard. Under the joint profitability formula advertisers gain more money from their advertisement banners, developers gain more analytics and insights from their designs and consumers get more engaging and fun ad’s.

Under institutional arrangements, there are some judiciary implications, AppOnBoard will have to disclose and provide a “up-front explanation of what data is being collected, what they are doing with it, why they are doing and how long they will store it.” They would have to be communicate clearly to all stakeholders involved. This is required under the Consumer Privacy Bill of Rights. This might raise privacy concerns on behalf of consumers who used to have static ads and now are being tracked on their movement and user behavior.

Under social norms in terms of institutional arrangements, it is still yet to be seen how users will interact with these ads’. Users might find them intrusive over time, or they could simply stop interacting with gamified ads at all.

What do you think? Will app developers benefit from using AppOnBoards technology? And how will consumers respond?

Bibliography:

New Zoo, 2016. “The Global Games Market 2016 | Per Region & Segment.” Newzoo. N.p., 21 Apr. 2016. Web. 10 Mar. 2017.

Ha, Anthony, 2017. “AppOnboard Offers Fast, Playable Mobile ads.” TechCrunch. TechCrunch, 06 Mar. 2017. Web. 10 Mar. 2017.

 

 

 

 

Building robust web and mobile applications quickly is complex and resource intensive. In many cases this is simply not viable for smaller companies as they do not have the human capital nor the cash flow to afford the right developers. Creating significant problems in business innovation.

Gigster wants to change that, they want to revolutionize the software industry through democratizing access to great software at affordable prices. It follows a service-for-hire business model, whereby customers plainly indicate what they want to get developed and at what price. Gigster then connects them to the most appropriate developer who has the right skill set. Simultaneously, Gigsters softwares creates pre-written code based on keywords of the customer’s proposal, through its AI based technology. This makes the job easier and quicker for the freelancers, saving both time and money for the customer and effort from the developer.

Gigster has many professional developers in its network including MIT, Stanford and Caltech graduates. So far they have executed over 80 projects for large conglomerates like google and square to smaller incubator backed startups. Andreesen Horowitz one of the largest VC funds in Silicon Valley invested $10 million in Gigster after just having come out of the Y-combinator accelerator program two years earlier in 2013.

Gigster is here to stay, through an innovative business model that combines smart recommendation systems with a pay as you want scheme, they provide access to affordable personalized and creative web and mobile applications.

Their business model can best be described as an economic multi-sided platform; the developer side of the network grows through an invite only strategy, this ensures the quality of the applicants and motivates the network to be organically grown. Currently they have over 700 developers, 300 product managers and 100 designers, people inside the network refer to them as “Gigsters”. Gisgsters have studied at respected intuitions and have worked at large conglomerates. This in turn also helps feed the demand side of the platform, as current developers already have existing experience with client certain clients providing a seal of approval for the platform itself.

The platform performs many functions and ensures joint profitability across the value chain. The platform provides high quality web applications at competitive prices, and provides interesting, well earning work for developers across the world. The platform is capable of reducing the overall costs for all stakeholders through its AI machine learning technology increasing the systems welfare, by reducing time spent designing and building the application. Gigster has created a strong institutional environment to mediate the network, by an invite only method of developers the platform ensures a form of social control through reputational and relational elements. There are also forms of contractual ownership that is managed by a team of customer experience Gigsters, they develop the system requirements and mediate contract relations between the customer and the developer. Intellectual property rights are strongly protected, Gigster even requires customers to sign a non disclosure agreement before signing up.

Will companies ever need developers again? How viable is this business model in the long run?

Bibliography

Feldman, A. (2016, October 19). Next-Billion Dollar Startups. Retrieved from Forbes: https://www.forbes.com/sites/amyfeldman/2016/10/19/next-billion-dollar-startups-2016/#49067b35549d

Gigster. (2017, February). Gigster is the world’s engineering department. Retrieved from About: https://gigster.com/about

 

 

Online advertising efforts now account for more than a third of total ad spending in the US (Media Buying, 2016). As such competition to gain banner space and consumers attention has intensified. Therefor in order to gain higher effectiveness retailers have begun using an instrument called retargeting in order to create more relevant ads. Retargeting is a type of algorithm that can design ad banners featuring images of products that match consumers’ recent browsing behavior. This extreme form of personalized advertising has received a mixed response from consumers. On the one hand these ads more relevant and therefore more useful for consumers. However, on the other hand individuals at times find these advertisements inappropriately close to their personal preferences, raising concerns regarding privacy. As such retargeting is fueling the debate in between privacy and personalization.

This paper examines the impact of retargeting on click through rates and adds to the aforementioned debate by examining how retailers trust may moderate this relationship. The researchers partnered up with two ad agencies in order to gain enough ad impressions for their banners. They state that ad personalization with retargeting can be described along two dimensions, a banners depth and breadth. A banners depth of personalization refers to how closely the banner reflects that person’s interest. For example, a featured ad of a product that was recently in a persons virtual shopping cart reflects a person’s interest closer than an ad that features a product that a consumer merely inspected. A banners breadth refers to how complete the banner reflects that person’s interest (all the products in the shopping cart vs. only a few).

Their findings indicate that for banners of more trusted retailers, click-through rates are particularly high when a banner has high depth and narrow breadth as these adds appear more relevant and useful. However, for less trusted retailers a higher depth of personalization decreases consumers’ click-through rates as there is an increased reactance to privacy concerns. In general, though regardless of trust, retargeting is still more effective than normal advertising (Figure 1).

Figure 1:

These findings appear to be intuitive but it has important implications for retailers. Retailers should carefully asses their corporate standing and adjust their retargeting strategies dependent on the trust consumers bestow on them.

This article however does not comment on one critical aspect, which is consent. A consumer report found that 52% of people feel violated when personal information is used for advertising without their consent (SAS, 2015). In some cases, personalized advertising has even lead to lawsuits, a case based example is Target an American retailer that sent coupons of baby items to a young teenager. These ads were seen by her farther who found out her daughter was pregnant before she even knew. (Hill, 2016)

This raises an important question should retargeting be allowed from an ethical perspective? and to what extent should companies be held liable for algorithmic accountability?

Academic Article: Bleier, Alexander, and Maik Eisenbeiss. “The importance of trust for personalized online advertising.” Journal of Retailing 91.3 (2015): 390-409.

Bibliography

Hill, K. (2016, February 2). How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. Retrieved from Forbes: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#fab066e34c62

Media Buying. (2016, September 13). US Digital Ad Spending to Surpass TV this Year. Retrieved from eMarketer: https://www.emarketer.com/Article/US-Digital-Ad-Spending-Surpass-TV-this-Year/1014469

SAS. (2015). Finding the Right Balance Between Personalization and Privacy. SAS. SAS Institute Inc.