Data-driven Marketing : 7 Common Misconceptions

It’s all around you. You hear about it in your weekly, you read about it on blogs. You bump into it almost everywhere now. Many of us know quite a bit about it, some of us have tried using it. And yet, there are these questions and assumptions that surface frequently.

1. It’s a new concept

2. Just a fad

3. Relevant to digital marketing

4. It’s complicated

5. Offline businesses don’t need it

6. Useful for big enterprises

7. I don’t have enough useful data

Data-driven marketing is simply gathering all your data, making sense of it, utilizing the insights to delight your existing customers & to acquire new ones. Read  “The 7 Misconceptions About Data Driven Marketing”to know how you can start using data and boost your marketing efforts.



How BIG is your data?

The dynamics of marketing are changing so swiftly, marketers are overwhelmed with the tools and channels that allow them to connect with thier customers. The biggest challenge for any marketer lies in understanding thier customers and delighting them every time they connect and across any channel that they choose to transact with the brand. This is where data has grown BIG and influences startegies and business decisions today.

Chetan Kulkarni our CEO talks about the changing marketing landscape and the new challenges that marketers must face in this realy cool blog that was published on CIO Review this month – Big Data, Small Data, Good Data, Bad Data, never mind! Read on to get soaked in data-driven marketing.

A CMO’s predicament: Can technology be the only answer?

The internet has redefined everything, but none more than the unprecedented change it has brought about in the state of advertising. Advertising now has lesser people, more algorithms, more machines, faster, precise, personal and a transformative experience. Ten years back, advertising was like throwing darts in the dark with a dozen salespeople assuring you that the target is somewhere there.

The digital advertising transformation was late in India, but has rapidly evolved into a colossal media empire. According to a study by Confederation of Indian Industry (CII) and PricewaterhouseCoopers (PwC) Indians consumed 13 percent more ads online than on print medium in 2013. If there ever was a watershed moment in the Indian advertising industry, this change is certainly newsworthy.

In other words, 2013 saw digital advertising spends eclipse advertising as we know it. There are many factors that make digital ads the giant it is today, but none more prominent than the chatter around the ‘programmatic’ manner in which ads are being bought and sold.

Using Supply Side Platforms (SSPs) and Demand Side Platforms (DSPs), companies are able to programmatically purchase ad real estate on thousands of publishers’ websites, in a matter of milliseconds.

With more & more inventory sources being lopped up by SSPs’ and Ad Exchanges, (AdX) programmatic is truly a force to reckon with. But with all the talk around programmatic, is this the only way the world is shaping-up to buy Media?

Old is not always gold- How non-programmatic paved the way for programmatic

The industry is abuzz with the new way of automating ads and ‘programmatic’ is increasingly becoming the norm. However, one simply can’t discount the non-programmatic manner of buying ads. A specific target market can be tapped into based on years of knowledge and experience. Tie-ups can be made with websites to allow for premium ad displays.

Non-programmatic buying does have its shortcomings. Chief among them is its inability to scale. For example, Nike can crack deals with ESPN, Stars Sports, Ten Sports, Formula1 etc… but can Nike find a 100 ESPNs? 1000 ESPNs? This is where programmatic buying is important. In the digital world, scale matters.

Coding your way to brand awareness

Linking several ad inventories and customer data, programmatic has the ability to display the ad to the right person, irrespective of the website he or she browses. Let’s say a customer visits Nike’s online store and browses around for Nike shoes. After browsing, he checks NDTV website which has nothing to do with Nike or sports. At this point non-programmatic buying would have been stumped. But programmatic buying is able to contact the ad exchange with the visitor info and pull out a Nike ad to display, in a matter of milliseconds. But that’s not the holy grail of advertising by any means. It’s important that a consumer ‘discovers’ a product as much as the consumer is targeted with a focused ad. An advertiser can only ‘inform’ and ‘educate’ a customer of new products if they acquire inventory from a publisher in the non-programmatic way.

But we’ve also witnessed a few snafus of programmatic buying when the machine gets it distastefully wrong. For example, an advertisement of an automobile was placed in an article talking about rising deaths due to substandard manufacturing. Another example could be an ad of a fairness product in an article talking about a society obsessed with false beliefs of having better prospects with fairer skin.

Making sense of an ideal media mix

Many marketers choose to allocate a sizeable chunk of their budgets on programmatic buying because of the transparency involved in its pricing. There is also the matter of accuracy in displaying the ad to a relevant viewer than just on a relevant website. But a great marketer knows to balance his budget allocation between both programmatic and non-programmatic buying. The changing requirements that come from a wide diaspora of clients spread across the globe should reflect this mix with programmatic and non-programmatic.

Programmatic is making waves and everyone wants to jump on the industry bandwagon. But a smart marketer is not a punter, but one who hedges his bets based on years of understanding and learning the dynamic constraints of advertising and the bounties technology has to offer.

Originally posted on Exchange4Media

Maximising Reach with Unified Messaging [PM Video Series 01]

“If your plans don’t include mobile, your plans aren’t complete”, says Wendy Clarke, VP Marketing, Coca-Cola. As much as it adds to your marketing plans, mobile complicates things with its web and app interfaces, iOS and Android platforms. But your customers are fragmented across these platforms.

Here’s our Shiju Mathew, Head Products- Mobile at Vizury talking about the fragmented mobile landscape and the need for effective user-mapping to make mobile marketing work.

Mobile is the centrum of marketing today and brands face the challenge of reaching out to customers across the web-app interface. Reason?

Each of them works independently and using different technologies. Mobile websites use ‘cookies’ for identifying customers, while apps know the customer via the device ID. And of course your customer will switch between web-app in the blink of an eye. The danger then, is that if your advertising partner identifies them as two different individuals and sends out different ads to the same person. That’s personalisation gone all wrong!

Know more about your technology partner’s ‘mapping algorithm’ to unify messaging across the mobile ecosystem. This solves the complexity of mobile customer identification and maximises reach. Be where your customers are- only with a highly personalized message!

Going the Extra Mile with Facebook Ads [Innovation Blog series 02]


If technology poses a problem – we have a solution. So was the case with Facebook retargeting. We were one of the first Asian companies to become a Facebook Marketing Partner – this, of course was a proud moment for us. Our innovations on the programmatic buying platform and integration are proof to our commitment at delivering greater ROI for our clients. So what did we do differently with Facebook ads? Read on to find out.

Facebook recommends a uniform ad template to all marketing partners thus limiting any experiments with colors, animations that typically come handy to grab user attention. This would make all ads look almost one and the same. Now, how do we ensure that our ads get noticed by Facebook users?

Our product specialists have an interesting answer to this.

Facebook ads


Contextual images and relevant text on image manage to strike a chord and get our ads noticed. For example, a user looks up tickets from Auckland to London on a travel app and then drops off, later she logs into Facebook. We show her a real-time customized Facebook ad for tickets from Auckland to London with a nice picture of the London Bridge. Since a trip to London is on her mind she would definitely notice the image ☺

Results? Relevant messages/images manage to grab user attention and resulted in higher ad clicks. This also led to driving back high-value drop-offs to our clients’ websites.

And, we continue with our explorations in the digital world – sometimes to beat technology, sometimes for better results.

Machine Learning Algorithms & Intelligent Recommendations [Innovation Blog Series Part 01]


The letter “I” in “Vizury” stands for Innovation! Well, we haven’t coined any such fancy initialism, but if we did the “I” would surely expand into nothing else but Innovation –it runs in our DNA here at Vizury. Innovation is one of our core values and symbolizes the way of work for us.

While we offer performance marketing solutions to brands and help them realize their ROI, we are constantly innovating in the background . These innovations could be centered around bidding optimization, sharpened recommendations, delivering meaningful analytics, optimizing spends and so on. But the end goal always is delighting our clients.

Beginning with this one, we present a series of blogs that talk about our super-successful product innovations and how they have impacted our clients’ businesses.

The Challenge:

Do shoppers always buy the last seen product from your website? Not all the time! Any visitor on your website does not necessarily follow a sequential purchase path. He might look at tee shirts, drop off, come back for shoes and then look at tee shirts again. The challenge that remarketing partners like Vizury face, is choosing the right product recommendations while showing ads to such users.

Do we show the shoe or the tee shirt or both?

Intelligent Recommendation Intelligent Recommendations_1








Intelligence that helps us choose:

As complicated as the shopper is, the machine learning algorithms that fuel the right recommendations are more so. The key here is to get a larger perspective of the user which is possible by understanding the user’s actions and behavioral trends across devices, channels, offline as well. Our product specialists have been constantly optimizing and experimenting with Vizury’s recommendation algorithm. We now have over 50 parameters including “last –seen” helping us decide the best suited product recommendations for every user on both desktop and mobile devices.


All campaigns running with the renewed recommendation algorithm have delivered 10% higher ad-clicks and conversions.

And The Winner of VizHack 2015 is…


Lines of code, take away pizzas, exhilarating gaming night, huge prize money and great ideas that turned to reality- would best summarize VizHack 2015.

Last weekend, we saw some ingenious minds at work trying to crack the code at our HQ in Bangalore. We had some brilliant ideas flowing in from hackers across the country. Our team had the tough task of shortlisting the final few teams that would join us for the hack. The shortlisted teams came in for a 24 hour hackathon and showcased their work the next day.








The finalists were evaluated rigorously and team “Anonymous” bagged the grand prize. We’re sure their idea is not going to let them be so anymore 😉

Team Anonymous built a product that would enable targeting ads based on the contents of videos and images a user is interested in rather than on psychographics, demographics, behavioral traits etc. It analyses viewing patterns, audio and object content of the video. When the whole of marketing world is starting to revolve around personalisation, this sure is a much needed product!

Check out the coolest ideas that made it to the finals of VizHack 2015.

Congratulations winners!

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Memoirs of ad:tech New Delhi 2015 – when advertising meets up


With some compelling conversations and interesting rounds of shooting, we truly had an awesome ad:tech. Our think:tech session saw great minds in advertising trying to decode the data-fuelled marketing mystery.

Our Performance Marketing Hub officially launched during the event has garnered tremendous interest and some great feedback.

We know you love our poems by now 😉 Here’s one dedicated to all of you who made this event a grand success!

Packed cookies night and day

So what did your fortune say?

Hashtag was set, Guns locked and loaded

Booth 43, was super crowded

The Performance Marketing Hub launched

Panel Debate and think:tech rocked

Not a minute to pause

What an ad:tech it was!

ad tech VIzury_resized


Cool Hack Ideas That Made To The Finals of our Hackathon VizHack 2015


Here are the three winning teams and their Hackathon ideas:

First prize: Anonymous (Ajay Narang, Anshulika Prasad, Prerna Srivastava, Vidushi Khatri)










Their idea was to build a technology that can help targeting advertisements at an entirely different level. Right now the ads are targeted to the users based on psychographics, demographics, behavioral traits etc. This product will enable targeting ads based on the contents of Videos and Images a user is interested in.

Second Prize: Image feature extraction (Abhinay Swaroop, Kushal Wadhwani, Praveen DS, Ravi Prakash)










They were trying to detect whether an image has objectionable content or not. The scope of this project is limited to skin exposure and not object detection. This was a Vizury team!

Third Prize: (Satyendra Bilthare, Krishna Kumar, Kunal Dexit, Partha Konwar)










Generally, when someone submits a post on Facebook, and if we find it attractive, we start searching in the Internet about the availability of the product/services nearby our place of stay. This process itself requires a lot of manual work and reviews of the recommendations, before actually purchasing the product or utilizing the service. This manual process will take a lot of time to give him a desired result. They search for the specific ‘#category’ specified with #HashTag nearby to his place of stay, and provide him all the #ads sorted by the maximum popular one. Further this idea can be generalized to more sectors such as Job Posting, Bookstores, Movies, etc. These ads will be a great source of revenue generation.

Here are some of the coolest hack ideas (and the brains behind them) that made it to the finals.

  1. ML_Hackers (Srikanth Vidapanakal, Pratik, Nikhilesh): Their idea was to classify Malware using Large Scale Machine Learning. Malware classification requires analyzing about ~0.5 TB of data which is a tedious process.
  2. techvine (Aditya Srivatsav, Adit Chauhan, Rohit Gupta): Most of the time we get to know about the important dates and notes from some other person via Whatsapp, Email or Facebook. Now after recieving them we just save them in reminder apps like google keep or evernote. In all these reminder apps, we have to save the reminder or stick notes so that we can reminded by them. But these apps aren’t smart enough to create reminders from your Whatsapp, Emails or Facebook messages. Now with the app remify, it automatically saves the important messages by its own. Now with this people may be able to set reminders and alarms in their friends, collegues, classmates or in their co-workers phones. In this way they can remind people about important dates and meetings.
  3. @lpha_d0gs (Aravind Sundaresan, G Arun Kumar, Irfan Basha): Their idea was to perform analysis of data obtained from tweets sent out on an event recently hosted. This data can be used by companies to see where their products (launched at the event) stand among the users in terms of popularity. This also allows company managers to see the amount of positive and negative feedback given by those interested in the product thereby depicting market trends and the needs of customers.
  4. 5minus2 (Sanjana Arun, Tarun S, Rahul Nagaraj): Skin related diseases are one of the most irritating health issues nowadays. Be it pimples, patches, infections etc or serious skin cancer patches. Medical imagery containing Gigabytes and Terabytes of data sets or image sets,this large big data to be processed,trained and classified based on the samples of the input of the skin patches to get accurate results. They include image processing to process the image samples and machine learning + big data to train and classify the image sets into different stages of severity. Our hack will thus focus on this field of Big data Medical imagery. Focusing on skin cancerous images, they scan the mole or patch from the image sample and process it thus monitoring the differences time to time, calculating total dermatoscopic value TDV hence showing melanoma content, giving severity conditions. The person will be given, either a reference to a dermatologist through the app, mail or otherwise.
  5. Chronological Behavior (Jaydev Acharya, Amit Kumar): Their idea was to target specific user based on their on-line shopping trends. Based on the users shopping trends like date and time they collect data and use its intelligence to build a profile which will target those users on a periodic basis and will notify them with our banner ads for their shopping and offers. For example:
    1. A user is travelling for certain festivals/ vacation trips. We collect data of the trips and based on our data manipulation we will notify them on the same time regarding flight offers and making their booking easier.
    2. A user shops for grocery every month on a specific date range we will collect their grocery list through their shopping cart and each month prior to their grocery shopping day we will show them ads as a reminder for their shopping with list which they have bought every month. This will make it easier and will not have to add each items into shopping cart.
  6. 17– (Rahul Dominic, Vivek Vaidya, Ankit Siva): Their idea is to build an app which is a multi-platform application that works on the principles of machine learning, NLP, and textual analysis to bring crisp and precise content to consumers. It initially asks the users to input free form interests into the application. This serves as a basis for their entire experience in the application. A web script curates articles from various sources on the internet of various fields and condenses it to its essential points. This is stocked back-end for a single day. Based on the information by the user, the application pulls relevant data and presents it to the user. It is for people who want a personalized news app that gives them content suited to their tastes, in a crisp and concise format. For deeper reads, they will be referred to the parent site for the article. This is made for those who are busy and want information right then and there but is not limited to that segment of people.
  7. R Power (Mahesha Hiremath): Their idea for the hack was around predictive text analytics. That is, if there is are lots of unstructured text documents, and suppose each text will have some impact on user behavior (for example it can be ad, or email message, or even posts in social, media), the use of predicitive text analytics helps predict which text idea will bring good result in terms of likes or sales of the product or something similar. This can have good application in ads, social media marketing, etc.
  8. HyperLoop (Amitabh Das, Ashrit Shankar, Jeevan Raghu): Their idea was to implement a recommendation system that makes use of both item and collaborative filtering techniques. In theory it overcomes a lot of the standard problems that a normal recommendation engine faces.
  9. Verloop (Guarav Singh): His hack idea was to push contextual ads to mobile, based on user location.
  10. 2PC (Mayur Mohite, Santhosh Kumar, Somanath Reddy): Apache sqoop is a tool for loading bulk data from HDFS to relational databases in a horizontally scalable manner. Current implementation of sqoop ensures atomicity of data load by loading the data from HDFS into a staging table in RDBMS by using a map only job. The data export to staging table succeeds if all the mappers succeeds, otherwise it fails. If the data load is successful, the data from staging table is moved to the “main” table via a single transaction. This method of copying data from HDFS to staging table and then to the main table has several disadvantages:
    • Duplicate copying of data from staging to main table
    • Space consumed by the staging table will be large if the data set size is large.
    • Time taken by this process is also huge. Two phase commit protocol is a distributed algorithm which ensures that the distributed processes participating in a transaction do it in an atomic manner. In our problem, multiple mappers loading the data in RDBMS are the distributed processes, we will use two phase commit protocol to copy the data from HDFS directly into the main table in the RDBMS without using staging table. Two phase commit will ensure that this distributed transaction is atomic.
  11. Bid by Price Difference (Premnath Thirumalaisamy, Supreeth Chandrashekhar): Given the frequency of daily discounts and offers, the product price changes very frequently. The idea is to update the user profile with the product price at the time of visit, and whenever bid request is made, bid higher for those products where there is a significant drop in price. This can lead to increase in CTR in markets like India, where drop in price is one of the decisive factors for purchase.
  12. LumberJacks (Anurag Bhowmick, Madhavi Gokana, Sukrut Hukkerikar, Varun Pahwa): Their idea was to store images which are in distributed NFS to Document Store like Mongodb. And for cases like refresh images or delete stale images, use some graph database which can ease the join efforts and give more insights by analyzing the nodes in the database.
    Main Motivation of trying out some database instead of NFS:
    1. Linux File System: Directory have a limit on maximum number of files.
    2. Replication and sharing is hard to manage in case of NFS.
    3. Scanning for the stale images on NFS can choke the system.
  13. Troublemakers (Chandrakanth Reddy Angeri, MathanV, Sanketh Dhopeshwarkar): The idea here is to build a framework for end-to-end verification of new servers getting build prior to deploying them. We replicate live traffic from one-of-the production servers and pass it to new machine. We will then capture all application/system/performance metrics of this new machine. This will help in deciding whether the new machine can be taken live or not. This feature is useful in all pre-production testing scenarios when testing new features/code changes or a new hardware.
  14. Onliner (Jatinderpal Singh, Sumit Khanna): They planned to do: 1. Hash Join through bloom filter 2. Better control over join through cogroup and detecting left skewness, right skewness, data duplication in sets 3. Decoupling parallel code from sequential code – 10x improvement over intellibid.

Here’s us thanking you all for your participation in VizHack 2015!

Programmatic or Non Programmatic Buying: The Ideal Media Mix Strategy

There is significant buzz around the ‘programmatic’ way in which media is traded. With more inventory sources being lopped up, by SSPs’ & ADX, programmatic is truly a force to reckon with. But is it the only way forward and is it how the world is shaping-up to buy media? I think not.


I am not undermining the value of programmatic buying; it is a great way to optimize media as it provides great control at the very heart of optimization i.e at cookie level. Add to it, the fact that there is a great deal of intelligence and data that goes into buying every impression here; you have an awesome and supremely powerful tool/platform to buy inventory. While this is a definite positive over the ‘blind CPM buy’, there is something that is missing here, something that cannot be overlooked – the human element.

Human element is what differentiates us from being robotic. While, data and technology continue to break new ground every day, there is still something very pungently tantalizing and charmingly different about the way we behave. And while data might get you right 80 per cent of the times, there is still great value that can be unlocked, with that 20 per cent, where the human relations or conventional ways of buying and selling matter.

I personally feel that for a perfect media-mix strategy, an ideal blend of programmatic and non-programmatic inventory is a must. The sheer value that one can unlock owing to an exclusive hold over a source of inventory, that is not available on any of the aggregated platforms, is truly amazing. I, for one, am a big fan!

Such exclusivity or preferred buying is possible, only if one shares a great rapport with the original inventory source; this is where the human element comes to play. It is no surprise, that some of the biggest performance drivers have succeeded, owing to having access to such exclusive sources of inventory and that big programmatic platforms, such as – ADX, Rubicon and Pubmatic, have come-up with their own versions of PMP (Private Market Place), to support such conversations between buyers and sellers. These companies know that there still is a strong underlying value that can be unlocked, when you put a buyer and a seller in front of each other.

Coming-up with a prefect media-mix strategy, involving programmatic and non- programmatic sources, is every marketer’s/performance marketing partner’s dream. If you get the combination right, you will not just be able to deliver great RoI and volumes to the advertiser, but enhance your media margins as well. Also, with such a strategy, one is not overtly dependent on a particular source of inventory and this brings in great leverage while one deals with a wide diversity forces that impact businesses and the changing requirements that come in from a wide diaspora of clients spread across the globe.

But there is no set recipe for this mix; everyone needs to figure out their own perfect mix of things. It’s perhaps is more like baking a “cookie” and every mother/grandmother has her own recipe that has been perfected over time. Noteworthy is the fact that, the core ingredients don’t change.

Since we are talking of ‘cookies’, let me sign off taking a cue from one of my favorite movies – Bruce Almighty – A perfect media strategy is like baking that perfect cookie; just the dough and butter won’t do, neither will an increased proportion of yeast or raisins or eggs or choc-chips. You throw in the right proportion of everything and then bind it with love and you have your perfect cookie; it will take time in getting it right, but, there is great satisfaction when you do get it right.

“And, that’s the way the cookie crumbles.”

Originally posted on Digital Market Asia.