Wednesday, November 9, 2016

Marketing in a Machine Learning World: Simplicity is the key!

I was really inspired to write this post after talking to a senior marketing executive of a Fortune 20 company. So if you work for a Marketing or E-commerce organization then you should read it.

Perhaps, it is one of the most exciting times to be marketing in this digital world because marketing is increasingly becoming more high profile job than ever in most of the organizations! But that also means that marketer’s life has become more complicated despite having more tools at her disposal. The reality is that the marketers have to worry more about problems like how to break through the noise in this hyper competitive era; how to drive loyalty; shift from product-centric to customer-centric model; deal with the saturation in social media – everyone is a content producer nowadays; changing demographics; understanding customer’s context; how to justify ROI internally; having consensus for creative assets; the confusion around attribution model and spend; the changing relationship between brands and consumers; keeping pace with technology; sentiment analysis; omni-channel customer journeys; rise of mobile; figuring out millennial; why so many shoppers are dropping out of their marketing funnel; lack of alignment with sales; lack of trust in the customer database and so on. The list is very long and it will continue to evolve. In the marketing world, what needs to be done is usually clear. But what isn't always clear is how to do it in an optimized way. 

Is there anything different Marketers can do to deal with these problems?

Yes, some of the answers are hidden in machine learning! It's time you start thinking about it seriously! 

As we move from hypothesis driven world to data driven world, we might realize that  we don’t need more theories – we need to rely on data to help us make practical decisions. Access to hundreds of data reports and interpreting it yourself is Not what I am talking about. I am talking about data recommending you a course of concrete actions that is possible only through Machine learning (ML). If companies are betting on building driver-less cars using power of machine learning then I am sure ML can help you in solving few of your problems also. Machine Learning in marketing is considered by many as one of the biggest game changing opportunity for marketers just because we have now more data than ever and it is no longer humanly possible to make sense of the data without the help of machine learning. Machine Learning can't solve all your problems but it does help you in giving a logical path forward to deal with many problems in marketing world.

So, Why is everyone talking about Machine Learning Now?

Machine Learning (ML), a branch of artificial intelligence, in simple terms focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning is almost like an intelligent assistant that draws from fields like Artificial intelligence, Statistics, data mining and optimization.
The reality is that Machine Learning technology has been there for decades but two trends have contributed significantly to phenomenal rise of Machine Learning:
  1. Big Data – You have more data than ever. Machine Learning becomes better and relevant with more data.  A lot has been written about big data so I won’t go into too much detail.
  2. Affordability - Till few years back, the machine learning technology wasn’t accessible easily to marketers – the cost to setup infrastructure and build specialized team was just very high. In the past, successful use of machine learning algorithms required made-to-order algorithms and huge R&D budgets, but all that is changing. IBM Watson, Microsoft Azure, Google and Amazon have launched turnkey cloud-based machine learning solutions. At the same time startups like Idibon, MetaMind, Dato and MonkeyLearn have built machine learning products that companies can take advantage of.  I have used some of these libraries myself and found it very simple to use but very powerful. Again, these models aren't perfect, but they're very useful.

    So, What is happening in Industry with respect to Machine Learning?

     There have been notable acquisitions in the machine learning startups in advertising, sales and marketing: Oracle acquired Crosswire for $50 million; Twitter acquired TellApart for more than $530m, Google acquired marketing-management startup Granata Decsion Systems, and Israeli Unicorn Ironsource merged with in-app advertising startup Supersonic, to name a few. Machine learning startups in marketing space like Appier (cross device marketing), Databerries (in store traffic), Drawbridge (cross device advertising), Emarsys (content personalization), Lattice Engines (predictive scoring for leads), Oculus360 (marketing Intelligence Paltform) and Personali (Uses ML to form emotional connection) have been funded with tens of millions of dollars receently. 

      Deep learning - a cutting edge branch of machine learning inspired by the architecture of human brain -is the hottest thing happening in machine learning if you consider the recent acquisitions by Amazon (Orbeus), Facebook (Wit.AI), Google (Dark Blue Labs, Deep Mind, DNNresearch), IBM (alchemyAPI) and Microsoft (SwiftKey). Deep Learning was the underlying technology which was used by the Google’s DeepMind AI who beat Lee Sedol, a legendary Go player. There is a big race among major software players for technical superiority

      Even has come out with their machine learning product called Einstein and Adobe has made announcement to embed machine learning in their  offerings.

 Is anybody in your organization looking at all the innovations happening in the  machine learning world; doing gap analysis and making recommendations about  your machine learning roadmap?

What can Machine Learning really do for a Marketer?

     Anticipating customer needs is not a new phenomenon but what is truly new is the ability to respond to customer needs automatically, in real time and at scale with the help of machine learning. The most common use cases of ML in marketing are primarily:
    • Finding and predicting best and least valuable customers from lifetime value standpoint
    • Building personas based on customer clusters and building appropriate creative, content and services for them
    • Recommending new products and content based on who you are which prospects are most likely to buy;
    • Tagging content with right keywords
    • Testing countless paths consumers may take through content
    • Programmatic ad buying
    • Optimizing moments of interests by personalization of content
    • Predictive lead scoring
There will be many more use cases in your organization if you explore and go deeper.

What is Missing? Why Everyone in my Organization is not using it? 

Despite having so much data and access to machine learning technologies, the rate of adaption should be better across the board in any marketing organization. There are two major reasons: Lack of training and Lack of Agility of Machine Learning Implementations. Let me explain in more detail:

  • Democratization, Simplification and Training of Machine Learning concepts - I believe what is lacking is bare minimum machine learning training at conceptual level for marketers – from executives to marketers who  are in trenches - so that they can start having the right conversations.  The whole concept of using machine learning needs to be simplified and they should know how to make it actionable. Customized machine learning training should be developed for marketing and e-commerce organizations. No, marketers don’t need to become data scientists for that. They just should be able to exploit the machine learning technologies already existing either in their organizations or outside in the cloud. The idea is to develop a primer for them so that they can start having meaningful conversations with data scientists.There are some very good books out there for machine learning but they are either too technical or very high-level to be actionable. Marketers just need to articulate the problem in a better way with some understanding of ML! Defining the machine learning problem precisely is the hardest part and it should be initiated by marketers – not data scientists.
Lets take a look at one of the most popular machine learning  algorithms and see how a marketer needs to articulate a question to a data scientist:

Example of Marketer’s Question
Machine Learning Algorithm by Data Scientist
How much should I spend on advertising to achieve certain sales volume?
Linear Regression
What are the chances that the customer will a) stay a customer b) re-purchase a product or c) respond to a direct mail?
Logistic Regression
Can the machine help me discover segments or clusters in my customer base by using already known factors in my customer base?
How can I match my customers’ interests with the description and attributes of my products?
Support Vector Means
How can I predict customer retention and profitability?
Random Forests
How can I identify the right audience to target on social platforms and what should I say based on what data tells us?
Deep Learning/Neural Networks
How can I develop Amazon-like recommendations on my website?
Collaborative Filtering

Of course, you will have to dig deeper and iterate over each of these questions. But asking the right question is the first step to start meaningful conversation with data scientists and technologists in your organization. This will also relieve too much burden from data scientists as they are generally overwhelmed with work and the complexity of their work is never understood properly.
  • Agility and workflow of Machine Learning Implementations – You can have a great team of machine learning engineers and data scientists but the process of deploying applications based on machine learning is usually so slow. How do you deploy models in 2 weeks versus 3-6 months? So there is a disconnect between what marketers are being asked to do versus what is produced by data scientists from timing standpoint. It becomes harder if you are dealing with millions of customers and large data. You will probably get two different viewpoints if you ask a marketer versus a data scientist in your organization! Agility in machine learning deployment models will not be exactly like agile software methodologies but there are many common elements. The big difference is that ML agile methodologies will be data driven. To solve that problem you need to customize good agile project management practices and apply them in ML context for your organization. But again you can’t do much about it unless you are trained, as mentioned in point above, in basic concepts. Machine learning in production is becoming less about algorithms and becoming more focused on the data workflows surrounding them. Data Flow is about all the steps required to train machine-learning models in the lab, deploy those models into production, monitor and evaluate their performance, and iteratively improve those models. If your data flows are long, expensive or manual than you have a huge problem. You need to rethink about strategy and consider alternate solutions to simplify it. The last mile problem of machine learning is well known. Most data scientists will not know deeply about marketing or/and marketing systems to embed predictions into the daily routine of marketers. 
In the end, it doesn't matter whether you own marketing strategy in your organization or have an operational role, you need to start thinking about taking concrete steps to integrate machine learning in the DNA of your marketing organization.  It really helps you in developing a solid long-term marketing strategy and an optimized operational model. The best time is now! Simplicity is the  key to success in this context.

    Thursday, April 9, 2015

    Insights from Adobe Digital Marketing Summit

    I read a profound quote recently in context of marketing from Marc Mathieu, SVP of marketing of Unilever. According to him, “The marketing industry is still putting too much emphasis on digital as a separate category, rather than marketing to people in a digital world.” Not sure, if everyone agrees to it or not but advertising and marketing are in an unprecedented time of growth and invention. According to IDC, from 2014 to 2018, marketing technology spending will reach $130B for the 5 year period. There’s been over $21.8 billion of venture capital and private equity invested in marketing technology companies recently – it doesn’t include any money raised from public offerings. It is a common knowledge now that the real innovation in marketing is a shift from producing communications to delivering customer experiences - basically, it is just not just art and copy, but also code and data. Also, the line is really blurring between sales, marketing, CRM and media in the digital world.

    The digital marketing summit organized by Adobe is viewed as one of the premier events in the digital marketing landscape. It was held in March 2015 at Salt Lake City convention center and had 7000+ attendees from more than 44 countries. In a nutshell, it revolved around Adobe’s marketing cloud, its value proposition and also a peek into what will be coming in the next twelve to eighteen months. Before we dive into the details of what Adobe is saying, let’s explore the concept of marketing cloud and its positioning and significance now.

    Why Marketing Cloud?

    Basically, in theory, marketing cloud is a concept of one stop solution, offered as a “Software-as-a-Service” model, for all your marketing solutions need.  The space is very new and evolving and it has some basic components like multi-channel marketing automation, content management tools, social media tools, testing tools, analytics platforms, media optimizer solutions etc.

    Today the big players in the marketing cloud are Adobe, IBM,, Oracle and HP. These vendors are still defining what marketing cloud can/should be based on market dynamics. Since these players can't offer all the solutions at once, each company has a head start in few areas mainly because of historical acquisitions.  Adobe is playing to its historical strength of content creation and data; is all about social and CRM integration; Oracle has great platforms for multi-channel marketing and ecommerce; IBM has overall dominance in commerce; and HP is placing its bets on Big Data.

    Today, these vendors just solve only few aspects of the marketing technology landscape.  Either they miss many important pieces or they lack integration because they have acquired many disparate products in a very short span of time! Adobe lacks integration with the sales side of the business as it lacks the CRM piece. lacks the content creation piece. Oracle lacks a web analytics platform and an ad tech solution. IBM is trying to catch up and still runs all new acquisitions as separate businesses. Both IBM and Oracle have a strong e-commerce solution which both Adobe and lack.

    What’s New in Adobe Marketing Cloud?
    Lets talk about few significant offerings and how it can potentially matter.

    • A New Data Management Platform - Adobe audience manager (formerly Demdex) is a new data management platform that helps build unique audience profiles that can identify your most valuable segments and use them across any digital channel. In the past, we could define an “audience” in Adobe Analytics for analytical purposes, but to recreate that same segment to, say, send an email from Adobe Campaign or to personalize content on the website or mobile app, required manual step-by-step recreation. Adobe has made strides to automate this process which is a significant step.

    • Streaming and Monetizing Videos across devices - Adobe Primetime delivers TV to every IP-connected screen. It gives programmers and operators modular capabilities to stream and monetize video across desktops and devices. Media companies can now deliver personalized ads across platforms, and ensure that a single user with more than one device doesn’t have to watch the same ad with more than the desired number of exposures. It is used by NBC Sports, Comcast, Turner Broadcasting, Time Warner Cable and others.

    • Programmatic ad buying continues to be a challenge for today’s advertisers, with too much focus placed on display ad bidding and multiple data vendors providing different buying methods and billing practices. Adobe announced a solution, combining a new algorithmic engine and key advancements to Audience Core Services to unify audience targeting, buying, data and billing in one platform. The solution integrates audience and behavior data from a broad range of sources (including Web, mobile app and CRM systems), automates the execution of paid media campaigns through Adobe Media Optimizer (formerly Efficient Frontier – a demand side platform) and lets marketers use the same audience segments across earned and owned campaigns to deliver consistent experiences. Today only 5% of advertising is bought programmatically but that number is growing 50% year over year

    • New Campaign Tool - Adobe Campaign (Formerly Neolane) allows marketers to create and manage sophisticated email campaigns across devices. Oracle, IBM etc. already have products and it allows Adobe to have a similar offering in its marketing stack.

    • Better integration of Adobe Creative with Adobe Marketing – Adobe announced the 25th anniversary of Photoshop which reminds you that creative is a bigger business than marketing for Adobe. Historically, the creative business (based out of SFO) and marketing business (based out of Salt Lake city) have operated separately but you can see steps in trying to integrate them seamlessly in some of the products like AEM.

    • Significant Improvements in Adobe Experience Manager – Adobe 6.x (formerly CQ) has some significant architectural, HTML5 compliant templates and DAM (Digital Asset Management) improvements. It also offers Assets on Demand option which is SAAS version with many features of DAM and Scene 7 combined. The new DAM will help us to manage our digital assets in a much better and collaborative way.

    • Contribution analysis – Digital Analysts spend countless hours searching for explanations to change in metrics. They have had to carry out this time- consuming analysis on their own by importing large amounts of data in data warehouse. Until now. Contribution Analysis scans all variables (conversion traffic etc.) to explain changes in metrics and identify what contributes most to an anomaly.

    • Mobile app development - Adobe announced a new mobile app framework that gives companies an end-to-end workflow to manage the complete mobile app lifecycle — from app development and user acquisition to app analytics and user engagement. Also, half a dozen mobile app technology providers are integrating their tools into Adobe Marketing Cloud.

    • Marketing extends to IoT (Internet-of-Things) - Adobe Marketing cloud enables brands to extend the impact of marketing across more touch points including wearables and IoT devices. Adobe Experience Manager Screens and Adobe Target now bring personalized experiences to physical spaces like retail stores and hotel rooms and enable marketers to optimize content across any IoT device. The new IoT SDK lets brands measure and analyze consumer engagement across any of those devices. And new Intelligent Location capabilities allow companies to use GPS and iBeacon data to optimize their physical brand presence.

    Key Observations

    It gets reinforced in a summit like this that while the growth rate of overall marketing spend will probably not change in a big way, its composition will change dramatically and technology will command a much larger share in the coming years.

    Is it a good idea to standardize on One Marketing Cloud?

    When it comes to standardizing on one marketing cloud, the reality is that very few companies have a green field when it comes to their marketing stack. Almost everyone has different legacy components, across different business units. For instance, CRM uses its own set of technologies and the ecommerce team might be using say its Oracle/Endeca stack.  Basically, it is a heterogeneous marketing technology world. One marketing cloud vendor will not be able to satisfy all requests because marketing in Fortune 100 companies is just too big for that. Also, we are in an era of unprecedented innovation, especially as it applies to digital marketing, and companies who lock themselves completely into one marketing cloud are at risk of missing out on hugely disruptive technologies both now and in the future.

    Having a Holistic view from Implementation Standpoint

    Marketing cloud concept is pioneered by software vendors – probably not by organizations like yours. So let’s ignore what the vendors are saying for a moment and try to understand what it really means for us? Many organizations have implemented many different pieces of Adobe stack already. So you need to have better clarity about what new technologies and incremental changes in the existing technologies will mean for you. You need to understand it holistically and have an integrated view of all the pieces of marketing cloud. Technology management is all about deciding which changes are adapted – also when and how those changes are adapted.

    Connecting the Dots  

    Technology and new initiatives are always changing and impacting the whole digital ecosystem. You have to tirelessly connect the dots and use data as the glue which binds different pieces together. Related bits of data are worth more when they’re combined. In order to be successful in a digital world, you still have to go to the basics and spend more time to understand your changing requirements. No vendor or a marketing cloud can really help us in that. In the end, data, insights, our vision, processes and governance are the true differentiator. If all of your competitors also use the same pieces of technology in the marketing cloud then how can we truly differentiate?

    Sunday, March 15, 2015

    Do you want to know about a Blind Girl’s Online Retail Experience?

    I am talking about Amy here – not any fictional character. She likes to call herself a blind chick because she is young and blind. She is actually getting married in a week to Dave who is also visually impaired.  Amy is funny, full of life, always smiling and talks with lot of passion about her online retail experience which is definitely not great.
    Online Shopping gives her privacy, dignity and makes her feel more independent.  The Online retail experience is a very broken experience for her today but she remains hopeful.  
    There is just no bitterness in her about it. Amy was recently stuck in Boston during a snowstorm for many days and wanted to do most of her wedding shopping online but had a hard time doing it. According to her, “Most of the time it is almost like going to a car dealership when you don’t know how to drive.”  Unfortunately, many times she is forced to talk to customer service representatives! Sometimes she is asked if she could find someone else to do her online shopping! How fair is that?
    Amy also gives examples of websites like and who provide a great experience to people with disabilities.

    There are millions of people in USA and all over the world who have blindness or very low vision; are hearing impaired; have mobility-dexterity challenges; have speech difficulties or have various cognitive disorders.  
    Due to modern miracles of medicines impacting longevity, we have an aging population who are likely to develop many of these symptoms. The Web is almost 25 years old and there is a whole generation of us, including millions of people with disabilities, who grew up with World Wide Web. Sir Tim Berner Lee, the inventor of the web, always envisioned a web that is truly for EVERYONE and is accessible to all and one that empowers all of us to achieve our dignity, rights and potential as humans. He also felt that it was very important to keep the balance between commercial and social needs of the web.
    So where did we go wrong? How could we have such a big miss?
    Web accessibility is an area that needs serious work by all of us. The laws like Section 508, American Disability Act, Section 255 and others are not very clear and are interpreted differently by companies. Government/Federal, Non-profit and University websites are more compliant than the commercial organizations because they have to be section 508 compliant in order to exist – basically they have no choice. Many people consider WCAG 2.0 standard by W3C very hard to implement and also  very blind centric. Some of the commercial companies have begun becoming accessible but most have a long way to go. Lawsuits in the web accessibility space have increasingly become more prevalent. But in addition to legal concerns, the focus on user experience is equally important from accessibility perspective.
    The ecosystem for web accessibility has developed in last decade but we are just not there yet. Today, you have screen readers like JAWS, NVDA and Apple voiceover to help out visually impaired people. Wordspace from Deque Systems can help you in auditing your website. Accessibility management tools like SSB Bart, Audioeye, Amaze Deque and IBM Browse Out Loud help in management aspects of web accessibility.  According to web accessibility practitioners, eighty percent of the responsibility still lies with website operators even if you buy any of the assistive technologies or related tools. Website owners need to create environment that is more conducive to their content authors, developers, designers, testers, project managers as well as agencies.
    Most of the online retailers are unaware about the number of disabled people visiting their website or the type of disability they have. Besides the impact to their conscience, they may be missing financial opportunities. People with disabilities have increasingly become very web savvy, and they love their smart devices like all of us. According to, iphones are more popular than Androids devices among people who describe themselves as disabled.  If you are interested in more survey results then please go to
    Companies like Apple, Google, Amazon, IBM and have taken web accessibility very seriously and are ahead of the curve than others. BBC is considered a gold standard in the web accessibility area. IBM has even appointed a Chief Accessibility Officer recently.  Over the last few years, AT&T  increased focus in the web accessibility and are leading in the telecom space. Target, after settling an accessibility lawsuit, has the most interesting turnaround in this area. They have ramped up their team  and  are very proud of what they have achieved so far. In the end, it is not that hard and like any successful initiative in any organization, it needs executive sponsorship and commitment at all levels.
    If you are an online retailer then the first step is to recognize your shortcomings in this area and make a very serious and focused effort to fix web accessibility. Enabling accessibility on your website is not about building a feature but is more about right processes, culture, training, tools and discipline.
    To be successful, you will need to build a team and culture to embrace accessibility. You will have to increase awareness and do training  to make it part of your processes. Automation is key to accessibility so you will have to start thinking in terms of accessible components and issue automatic test failures where possible.
    Winston Churchill, rightly said, "We make a living by what we get, but we make a life by what we give.”

    Sunday, October 5, 2014

    Digital Data Quality – Can you Improve it by Auditing and Governance?

    I have heard someone say that life was much simpler when Apple was only a fruit and we just had to worry about one web analytics tool for our web site. Apple, the company, and proliferation of digital technologies have turned everything upside down - in a good and exciting way.

    Talking about digital data quality is not considered as hot as talking about analytics, big data or machine learning. Probably, that will never change! But I am sure companies will do more about the digital data quality in the future than they do with it today. In the end, data quality is one of the underlying foundations for better reporting, analytics and decision making.

    Everybody agrees about the importance of data quality but collectively we probably need to figure out an overall strategy from a data quality perspective. Who can argue about the need for your digital data to be accurate, relevant, complete, timely etc.? Haven’t we worked on data quality, data lineage and data stewardship for more than a decade in an offline world of datawarehouse, MDM and CRM? We still have, however,a long way to go in a digital world as far as data quality is concerned.

    We definitely have become smarter, more practical and more sensitive about data quality.  We have also learned to live with imperfections and realize that 5-10% discrepancy is probably acceptable given the huge effort required to fix it in a real world. We are also learning to understand how to improve data quality iteratively by understanding the gaps, shortcomings in the process, developing feedback loops etc. But more methodical and structured approach is required to improve data quality instead of reacting to it after a glaring discrepancy in any critical report.

    Before we talk about data quality, we need to talk about digital data collection process. You collect data with the help of tags. For those who are not aware, a tag is a simply a chunk of code —usually JavaScript — that performs the task of data collection for various purposes. It has always been painful historically as it was very hard to make it agile both from business and IT perspective given the constraints of release cycles in an enterprise environment. Unfortunately, the level of complexity in managing it manually without a tool and the effort behind it is sometimes not recognized. All of this has been changing in last few years with the new category of tools called Tag Management Systems.

    For those who are new to this area, using a Tag Management system lets marketers easily insert snippets of code, called tags as mentioned above, which enable third-party tracking, analysis, reporting, remarketing, conversion tracking, optimization, and much more. A marketer can log in to the tool themselves and add, edit or delete tags as they see fit, without needing code-level access. To make it even simpler, these technologies have already integrated with other ad-tech companies, so the marketer can now just tick a box to activate the appropriate tags. Tag managers allow marketing to have control over their own little space on a web page. For example sake, if you have 5 to 20 tags on any given page then they are replaced by a single container in a Tag Management System. That container contains code that listens to rules dictated in the tag manager's backend as to when to fire what tags. The Tag Management systems can also do some cool things like reducing cost to POC a new tool in your ecosystem; correction of campaign issues in real time among many others. Innovative techniques like data layer is being implemented by companies which separates data collection, manipulation and delivery from web page structure. Data layer defines events and information uniformly across the site – basically a consistent place to store and retrieve data values so that different tags can easily and quickly find the same piece of data.

    The world of tag management continues to gain more traction. The recent 47.2 million dollar funding of Tealium – one of the leading tag management vendor – validates the upside of this new space. There are dozen vendors like Tealium, Tagman, Ensighten, DC storm, Site tagger, Google Tag manager etc. with their own strengths and weaknesses and often rated based on number of tags they can support among other criteria.

    But before you plan your tag management strategy, don’t you need to think about the existing tagging gaps and errors in your web pages? Even if you bought a brand new tag management tool today, you will still have existing tags throughout your website deployed probably in the last decade. The most common tagging problems are incompletely deployed; incorrectly configured; not configured; duplicated and non-removal of old tags. How do you know where to start to implement your new Tag Management tool? Basically, you need to audit your existing web pages landscape in order to develop any approach.

    I still think that importance of tag auditing is not well understood because first it is new as well as process around tag auditing needs a broader conversation across different business units. Using a tool like Observepoint, founded by John Pestana (cofounder of Omniture), to audit your website can be a good option. There are few other tools also in the marketplace if you want to explore. Tag auditing is about validation and identifying tag placement and configuration problems. It helps you find missing tags; tags which are not firing and incorrect variables and parameters. And it presents its finding in an easy-to-read reports.
    There are two components of a tag audit – a site scan and monitoring. During the scan, the system tests the web site and catalogs tag data for every page. The monitoring component – also called as “simulations” – is put into place to detect sudden disappearance of tags, or unexpected tag variable changes.

    A tag auditing tool like Observepoint enhances your investment in the tag management solution - It doesn’t matter which tag management tool you own. An improperly deployed data collector can result in broken web pages, loss of site traffic and subsequently lost sales – you definitely want to prevent all of it from happening. If you worry about compliance then there is also a risk of data leakage also if tags without permission are deployed or not removed by mistake.

    Even if you own Observepoint and generate reports with it, you still need to define your digital data quality governance process to make it work. There is no standard way of doing it as all of it is new and every organization is different. There are many questions to answer! You still need to define who owns to fix various issues identified in the audit report? How do you prioritize? How do you know it is working well? Do you know who owns digital data quality in your organization? Should the team who is collecting and normalizing data be responsible for data quality? Should it be your Analytics team, IT team, QA team or different business units should be accountable for every type of tag/data? What should be the frequency of your audits? Who makes a decision whether it is critical issue or not after an alert from your tag monitoring system?

    We do need to recognize the learning curve in the world of tag management and auditing as it is still not mature. Enhancing digital data quality is a hard job and often tedious for the people who work hard for it. But it can be very rewarding in the long run! Also, improving digital data quality is a collective responsibility – not something which can be just owned by your analytics or your IT team though they can certainly lead the effort.

    Monday, September 22, 2014

    Online Video Personalization - Why is it becoming So important?

    All of us have heard the phrase, “A picture is worth a thousand words.” But according to Dr. James Mcquivey of Forrester research, a minute of video is worth 1.8 million words. I am not aware of the exact math Dr. Mcquivey used for his conclusion but through experience we know that video does leave a lasting impression.

    In the last few years we have seen an unprecedented growth in online video. According to Cisco, 74% of all internet traffic in 2017 will be video. And there are other cool things we keep hearing about video:
    ·         Web pages with video are 53 times more likely to appear on first page of Google.
    ·         52% of marketers say that online video has “among the best ROI.”
    ·         Visitors who view videos are 2 times more likely to purchase.
    ·         Every survey will tell you that there is a significant increase in digital video production budgets
    ·         10% of all video viewing in 2013 was online. And more and more people are using mobile and tablets to watch videos and for longer duration.

    But there are enough challenges for most of the companies in the video space:
    ·         They don’t really understand how video contributes to sales in quantifiable way. It makes it more difficult to understand impact of video on sales if you are also a brick-and-mortar retailer i.e. you have some kind of Omni-Channel strategy
    ·         Most of them have very little idea about how many online videos should be produced every year. There is no quantifiable rationale behind those decisions.
    ·         Online video still has challenges of volume as it relates to professional grade content
    ·         It is not well understood how to target videos – basically personalization of videos. Basically, using data to identify the consumer and serving the relevant video based on context. Though, we understand at a high level at the viewing habits of large set of people but most of us remain clueless about viewing habit of individual users.

    Personalization of online video is a big opportunity for online retailers from a sales and marketing perspective. It should be a big part of their overall personalization strategy. They need to work to tie their audience profiles and context to their video inventory through the help of analytics. A targeted video has all the benefits of personalized content but in addition to that it increases the engagement time. It has more probability of influencing a potential customer to buy from you than any other type of content on your website. It is also more economical to target videos through online channel. On top of that, video always has the potential to go viral.

    Personalization of online videos should also take the customer/prospect journey into account. If the prospect is just considering your product/services then you should target a product/service video or a case study. For conversion, a presenter led video makes more sense; a training or a customer service video might be appropriate for loyalty purpose for a returning customer.
    It is also rare to see web properties recommending videos in a similar way as you see on “Youtube” or watching videos on “Netflix.” Why?
    Utilization of video is probably better understood on self-service side when it comes down to “How to” videos. It does help in significant savings in reducing calls to call centers.
    Maybe, one of the reason for lack of video personalization strategy is that most of the companies are still trying to figure out the foundational pieces of online video - Basically, things which relate to video management, infrastructure and performance, SEO, video players, e-commerce support, mobile support, responsive design etc..  They are relying on one of the video platform vendors like Brightcove, Ooyala, Adobe/Scene7, Kaltura, KIT digital, Invodo etc. to take care of the foundation pieces. Personalization of video is not generally in the near term roadmap. Most of these vendors do a good job in building foundational framework for you and have their strengths in certain areas. You will have to figure out an evaluation process to choose one of them.
    HTML 5 players, for videos, mobile/tablet support and video interactivity are one of the important things happening in online video world. Companies like Adways and Wirewax can offer you great options to integrate with your video platform if you are interested in producing interactive videos.
     But despite these new breed of video vendors available to you, in the end you will have to own your video personalization strategy because it is based on the data you have about your visitors. It will also depend on analytical and machine learning capabilities of your organization. Also, how efficiently, you can make different teams like content production, IT, CRM, creative and analytics work together.
     There are also other types of personalization happening on video front. AT&T tried using a personalized video to explain mobile bills to their customers. It uses the technology developed in its AT&T Foundry innovation center in Ra’anana, Israel.  It will be interesting to see how this trend evolves. We will probably see this kind of personalization being applied in re-targeting of video ads.
     In the end, personalization and analytics driven video strategy is still a distant thought for most of the companies. But companies who are serious about offering the best online experience don’t have all the time in the world to figure out their video personalization strategy. Most of the large companies have all the tools, content, data infrastructure and resources in place. They just need champions, right thought leadership and executive sponsorship to make it happen. Video personalization shouldn’t be an afterthought in your personalization strategy – it should be one of the key drivers.