Enterprise Video Search ROI

Transcription

1 November 2012

2 1 ROI for Enterprise Video Search The Challenge Video is exploding as an enterprise communication medium. According to Cisco, video conferencing alone is growing at a rate of more than 40% annually. Whether used for external facing communications such as marketing, promotion, and/or customer service, or internal uses such as videoconferences, training, or compliance, IT managers need new approaches for dealing with video as a content format. Video posed particular challenges because it lacks the metadata and tags needed to enable proper discovery. Enterprises are often faced with the problem of looking across their file systems, seeing thousands of videos, and not knowing what they are about or what s in them. This creates major challenges in terms of getting value for the investment in this content, understanding compliance risk, and understanding how the enterprise knowledge worker is creating and consuming video. The fundamental problem to solve is how to make video a first class citizen in the enterprise information stack. Measuring Search Lift Using basic search concepts, the challenge of any kind of information discovery problem is one of recall and precision. Recall is defined as the ability for an information portal to discover and include all relevant information on a given topic to the user. Precision is defined as the information portal s ability to present the user with information most relevant to the user. Applied to video, the task is in creating robust metadata such that a search technology can be applied to deliver recall and precision across a collection of videos. RAMP s own studies of video search across its customers show an increase in recall of between 100% and 900% when compared with video search that relies only on basic video metadata like titles and keyword descriptions. Just taking the low end of this range (100%), this means that on average ½ of the results returned by a RAMP-powered video search would otherwise not be found at all. Precision is also dramatically enhanced as a time-coded transcript means that not only are more relevant videos discovered, but the actual search terms can be presented with their associated time-codes from within the video. This is vital when the videos are more than a few minutes in length. Video indexing improves content discoverability by % This is critically important if you re a media company in the business of monetizing online video. Videos that can t be found can t be monetized, so a return on investment (ROI) for adding transcript search is easy to obtain. If you know the value of each video served on the site (e.g. ad revenue potential), and you can measure the improvement from better search recall, you can calculate the business benefits. Easy, right? But what if your application is not for a public-facing Web site? How do you calculate the benefits of advanced video search when your business isn t about selling video advertisements?

3 2 As interest and adoption of RAMP s content optimization services has increased for inside-the-firewall scenarios, so to have the requests for a Return on Investment (ROI) story for video search in the enterprise. Anyone who has tried to sell an enterprise search solution will tell you that it s much harder to come up with an ROI story around something as intangible as employee productivity. The promise of making money (candy) is always an easier pitch than one of saving money (medicine). People have tried, though, and we ll steal shamelessly from this work to apply it to enterprise video search. Enterprise Search ROI IDC s oft-cited paper, The Hidden Costs of Information Work (last updated in 2009), provides a model for calculating the costs for common information tasks in the enterprise, including search. This is considered a soft ROI model because it is not tied to a particular business use case or desired outcome other than generic productivity. The concept is simple: if the average information worker spends some percent of his or her time searching for information, then any efficiency improvements in that task will result in cost savings proportional to the cost of employing that individual. Business videoconferencing traffic is growing at a CAGR of 41 percent from Cisco Visual Network Index, June 2011 Google converted this idea into a formula for calculating the productivity savings of enterprise search. Their formula is below: (# of workers) X (average annual salary/2,080 hours) X (hours saved/worker) = total productivity savings/year Soft ROI models like this are criticized for, among other reasons, using parameters that are difficult to pin down. In this case, determining hours saved/worker is fraught with challenges. Even defining and counting up the information workers in the organization can be difficult. It s really left to you to come up with values that best reflect your organization. Even so, both IDC and Google offer some guidance for the hours saved at least. The IDC report states that, according to their survey, the average information worker spends 9.5 hours a week searching for information and that an effective enterprise search implementation can decrease this time by 53%. Google points out that even a highly conservative 10% improvement for a 1000 person company translates to a $1 million in annual productivity gains. Not bad. Adding Video to the Equation There s an argument to be made that if you did nothing but add video content to an existing enterprise search implementation, you would see a direct improvement in search efficiency among your employees. We ll assert,

4 3 however, that in most enterprise search use cases, you will not see this improvement unless you include full-text transcripts on the video. Enabling video search without transcripts will provide an efficiency lift, but it will be at most half of what transcript-powered video search will provide. We know this from the media customers we serve and the impact we see transcripts having on search recall (see earlier comments). It s also the case that enterprise video is typically longer than most online video designed for consumption on the public Web. In the enterprise, a recorded webinar or training session may be an hour or more in length and packed with information captured in the otherwise inaccessible spoken word audio. What s more, finding the most relevant section in a one hour video compounds the problem further and reinforces the value of time-coded transcripts. With this assertion in place, we can then say that the productivity gain from enabling enterprise video search (with transcripts) is proportional to the amount of video in the overall search collection. For example, if half of the collection is video then it counts for half of the efficiency improvement the search provides (so, 5% if you use Google s number, 26.5% if you use IDC s). Without transcripts, you might get, at most half, of this improvement. How Much Video Do I Have? Determining the percentage of video you have (or will have) in your search collection is not just a matter of adding up the number of files or the number of bytes. It s just not the case that a 100 page research report has the same amount of content as a 3 minute introductory training video. Nor does a one hour training video have the equivalent information as a one page meeting summary. What we need is some way to normalize the amount of content from these different asset types. Since length in minutes is the most readily accessible piece of data we have for video, we ll use that to equate an amount of video with a number of documents (# of documents being the usual way of measuring text content in an enterprise search system). We ll also use number of words spoken or written to characterize the amount of information. The average rate of speaking in a presentation setting is about 150 words per minute. Assuming an averaged sized document is about 1500 words, we can say that, in a search context: 10 minutes of video content are equivalent to one average length document Average document length is obviously highly variable and dependent on what s in your search collection. There is not a lot of information on this topic, but there are references to the average length of a blog post (about 225 words, according to Wordpress) and the average number of pages in a Word document (about 9 pages, according to LexisNexis). Do some Web searches and you ll find some data as well, but it would be better to just take a look at your own collections.

5 4 Example Let s try this model out with an example. A corporate IT department at a major high-tech firm is enhancing the portal that employees use to access the company s library of research and online training content. As part of the enhancement, they are adding a number of training videos and recorded webinars to the existing content. Here are the data points: 20,000 employees 50% are information workers (or candidates for the training material) $80K average fully loaded salary 60,000 existing documents (Web pages, Word, PDF, and PowerPoint) (Adding) 30,000 minutes of training video and recorded webinars 9.5 hours searching per week (IDC s factor) 10% search efficiency improvement (Google s factor) Using the video length to document ratio we derived earlier, the effective ratio of new video to existing content is: 30,000 mins 10 mins/doc = ,000 = 5% Using the IDC and Google factors, the hours saved per worker per year using is: 9.5 X 0.10 = 0.95 hours per week = 47.5 hours per year (assuming 50 weeks per year) Going back to the Google formula, but applying it only to the lift of adding video we have: or (# of information workers) X (avg fully loaded salary/2080) X (hours saved/worker) X (% video) = productivity savings/year (20,000 x 0.5) X (80,000/2080) X (47.5) X (0.05) = $913,462 productivity savings/year Again, this applies only if transcripts are part of the video search equation. If we are relying solely on video metadata attributes (title, description), we would see at most ½ of this savings and likely much less if most of the video search collection is dominated long-form content (e.g. training videos, recorded seminars, etc..). This example assumes that video amounts to about 5% of the total content in the search collection. As video increases as a percentage of all available enterprise content, we would expect its contribution to total productivity savings to increase proportionally.

6 5 Lastly, if this seems like a big number to you, consider that if we apply Google s formula to get the overall productivity boost from enterprise search we get an annual savings of over of about $1800 per information worker or, in this example, a total savings of $18M! Other Hard ROI Models As mentioned, soft ROI models are often and appropriately criticized because of the subjectivity of the parameters they use. Another criticism of the particular model above is that it focuses solely on productivity costs as opposed to opportunity costs. As a result, it may understate the true cost savings that enterprise video can bring. An opportunity cost for enterprise search results when an information worker fails to find what they seek. Both IDC and Google point out that one impact of this scenario is that the information worker may end up reproducing work that has already been done. A valid point, but it is still a productivity cost. What happens when the worker, because of a failed search, ultimately fails to accomplish his or her task? This is where ROI models built around specific business scenarios come in. It would be impossible to describe all these possible scenarios for a video search ROI, but here are a few examples, described in the abstract: Sales Training Ready and efficient access to sales training videos results in a more effective sales force and a better revenue generation engine. Even a 1% improvement in sales effectiveness for a business with $100M in annual revenue is not insignificant. Corporate Communication Thanks to the growth and demand for video on the public Web, video is now an in-demand format for executives to communicate new business directions, new products, new benefit programs etc Effective corporate communication improves employee satisfaction, productivity and retention and decreases recruiting and HR costs. R&D and Product Development Product development summits, workshops, and seminars are increasingly being recorded. These recordings are an important source of knowledge for R&D and Product Development professionals. While presentations may be captured in written or PowerPoint formats, the full breadth of knowledge can only be extracted from the actual recordings. The implications of a not having ready, searchable access to these assets can mean product delays and even missed product opportunities. Corporate Training Effective employee training is the heartbeat of any successful business and the pedagogical benefits of video training material are an establish fact. What s more, new employees those just out of college who have been weaned on Youtube and Vimeo expect rich, online media as part of their training experiences. Training manuals are a thing of the past.

7 6 Compliance With many industries facing stringent new compliance regulations regarding the content they produce and its accuracy, compliance officers are faced with the daunting task of monitoring audio and video content across the organization without a scalable means of searching and reviewing this content. An enterprise video search solution addresses this new requirement. Social Media Monitoring/Voice of the Customer More than ever, customers are taking to the web to express their opinions about corporate brands and products. One of the most popular venues for this is YouTube. Unlike monitoring Facebook and Twitter, YouTube videos are not easy to search, and even more difficult to mine for negative customer feedback. Video search enables a scalable approach to monitoring YouTube and other rich media voice of the customer sources. Conclusion IDC s model and Google s formula provide a framework for calculating the costs savings that come with effective enterprise search. We can tie video into this model, but the savings of adding video to search without full-text transcripts is less than half the savings when transcripts are included. Independent of the savings in employee productivity are savings in opportunity costs associated with video search when applied to business scenarios like sales training or corporate communications. Organizations considering adding video to their enterprise search systems will benefit from considering both the generic productivity cost savings as well as the business impact and benefits for specific applications. About RAMP RAMP is a leader in Content Optimization and Universal Search. RAMP s software-as-service solution delivers the world s most advanced technology for creating rich metadata on audio and video content and is covered by over 20 US patents. RAMP s online video player, MetaPlayer, provides an online video viewing experience complete with time-stamped metadata integrated into the playback. Millions of video assets are currently published using RAMP. Already deployed by major media publishers, RAMP delivers on all the key criteria described in this paper for a true universal search solution. RAMP Contact Information RAMP 27 Wormwood St. Boston, MA info@ramp.com tel fax