Data Science and Digital Analytics

There was a time, not too long ago, when enterprises in order to understand their customers, were looking to get their hands on as much customer data as they can get. Nowadays, the amount of data, thanks to innovation in the space of analytics and data collection technologies, available to an organization is simply tremendous. Best of the brands are looking for help to manage the data and to fish meaningful insights from it. No wonder Data scientist is being hailed as the best job of the decade by the likes of HBR or Glassdoor, or more closer to home, by TOI. 

A data scientist  is responsible for mining through the massive amounts of data that an organization has an access to, and is required to come up with insights that could drive action. If your business has an online presence, chances are a major chunk of business critical customer data gets generated online.  In order to analyze your digital properties and their performance, you would already have deployed a digital analytics platform like Adobe analytics or Google analytics. These systems would be capturing metrics like visits, paths taken, time spent, products browsed, number of logins and pages visited etc.

As an enterprise business, your business intelligence unit would also be running their own series of analysis in order to assist the business in making strategic decisions. Some examples of these analysis could be assigning a propensity score for all the customers of the brand which details their propensity to buy products or services from that brand. Another analysis could be coming up with answers to questions like which products or services are in demand by the consumers and should be launched on priority by the organization.  These analysis help the top management to take decisions and shape the overall strategy and hence are of utmost importance. The data points used for running these analysis could come from customer databases(CRMs), transactional or order management systems, product information management(PIM) systems and digital analytics systems. 

Personalization is the basis for becoming an "experience business" and there is no bigger frontier to personalization than a mobile phone. If you look at any ten random phones from ten users, you would find that each would have their own personalized unlocking pattern. This proves that mobile phones are deeply personal devices and deriving personalization here is not as simple as adding someone's name to your message while sending an email. In order to personalize the content on your mobile app, you really need to understand the consumer in detail. You need to combine the online profile of the user with any other type of information that you have available for her and use this intelligence in providing the personalized content and offer to her. 

In the past,  I have been a business analyst as well as data scientist for few Indian and global brands and nowadays as a management consultant for digital initiatives, I get to speak to a whole lot of young as well as experienced data scientists and digital marketers from all sorts of organizations ranging from banks to media companies to ecommerce portals.  In this time, one thing which has remained constant across every type of industry is that the digital analytics is not given its due importance when it comes to data science.

This is resulting from the fact that the teams handling these two branches of analytics are completely different in almost all type of organizations. Digital analytics is primarily the domain of digital marketers and they usually have few analysts within their teams to come up with dashboards and distribute reports. Data science technologies are usually housed within the business intelligence units and their primary source of data input comes from all the offline systems. Now this is not to say that the data science teams do not use online analytics data at all but to suggest that the relative importance of online analytics data is quite low when it comes to various data modeling activities. Some of the new age companies, primarily in the ecommerce domain are trying to bridge this gap by assigning a single person or team catering to both of these different data sets. It makes sense for ecommerce guys to quickly realize the importance of this as their business is primarily done online and they cannot afford to ignore the critical online analytics piece.

In my opinion, there are two things which need to be done in order to speed up this change. First is the change in mindset - A data scientist is not the answer to all strategic problems for an organization but a decision scientist is. A decision scientist combines the knowledge of data science with the power of marketing communications and acts a bridge between the marketers and data scientists. Another need is to use those pieces of technology that could seamlessly connect the data from digital systems to the data from offline world. The idea is lower the overhead from putting in manual integrations in place which are usually not fool proof and do not always work as expected. But this is easier said than done.  Even if a data scientist wants to build his model using attributes from the digital analytics data, the data scientist is either not familiar with the best in class technology which can make the job of connecting different datasets easier or if known, is too technically cumbersome for the data scientist to effectively use it. 

One of the ways in which I have tackled this problem successfully in the past is to use open source technologies like R or Python. R is one of the most powerful languages for data analysis and provides the most up to date innovations, in form of packages, in its field. One of these packages that I have used extensively is "RSitecatalyst" which combines the digital analytics data from Adobe analytics with R and makes web and mobile app analytics variables available for modeling in R. There is a significant advantage of using these technologies because it lets the data scientist focus on the job at hand, which is to mine insights, and not worry about the data accuracy and leakages resulting from inefficient technology integrations.

If you would want to discuss more ways to make analytics power meaningful insights to digital marketing strategies and for your business, please feel free to reach out to me.  

   

So You’re Digitally Transforming Your Business – What Now?

A blueprint and a must read for Indian business looking for digital marketing driven transformations -

http://blogs.adobe.com/digitaldialogue/news_and_reports/digital-transformation-of-businesses/

Google walling off its inventory garden

A lot has been said and talked about the impending move by Google going to ban third party cookies, web beacons or other tracking mechanisms on its Google Display Network. The move will come in effect from the new year and it will prohibit collecting of impression level data by third party pixels for purposes of subsequent re-targeting, interest category categorizations and/or syndication to other parties.

In the wake of this scenario becoming a reality soon, lets see what are the effects of this move on various entities in the display ecosystem -

Impact - Marketers are affected the most because they will not be able to figure out the number of times their ad is seen by users on Google inventory versus non-Google inventory. Global frequency capping will not work now for the campaigns running on GDN. 

Publishers,on the other hand, will not be able to lay claim to the success of marketer's campaign(running on GDN, except where they are last touch points in the campaign funnel) to themselves  and hence will have some effect on their ability to command premium price for their audience data and inventory. 

Reality and Way forward -The impact on publishers side of equation is minimal though, because the publishers deploying the services of a DMP would not be selling their inventories using adsense in most cases. Marketers should try other inventory sources. With the advent of programmatic, multiple supply side exchanges have gained prominence. Chief among them being FBX, Right Media Exchange, Microsoft Exchange, Pubmatic, Rubicon Project, Appnexus, OpenX etc.

Also this rule of not allowing third party pixels will not have any effect wherein the DMP also owns the system that is biding for the impression and hence there is no data flow from one system to other. This will give more weight to the much talked about consolidation in the display ad-tech with more of the DMP and DSP players coming together. Rocket Fuel bought [x+1] few months back. Adobe has Audience Manager(DMP) and have built out a DSP through their Efficient Frontier acquisition. Turn and Ignition One also offer a combined solution to their customers. This is an okay approach for now but not a great one given that different DSP's give differing results and hence marketers prefer not to stick with just one and use many depending on campaign objectives, inventory qualities and  win rates. Also DSP's are now vertical specific and hence more and more DMPs vendors wanting to be platform agnostic. 

Why is Google doing this? There is some merit to Google's point of view. With the ad technology expanding every day, the number of pixels placed on the publisher's inventory are huge and it is difficult to figure out the pixels resulting in data leak. Google in its bid to curb the data leak across its inventory has decided to ban all the third party pixels. While the intentions are good, this has some undesirable effects too. And the worst ones of these will be for the Google itself as publishers will start shying away from putting their inventories through adsense. 

In the end, Google will have to come up with a solution which can identify and certify certain pixels. DMPs give brands a chance to trust the digital channels and result in a bigger and healthier ecosystem and that is something that Google is a pioneer of.

Confused about web analytics tools to use? Here's the solution.

Google Analytics and Adobe Analytics are two of the industry leading tools that are used most commonly by businesses in India.

Many marketers and business owners are generally confused on which web analytics platform they should use. Most also don't know whether they should be investing in a paid analytics platform or should make do with a free one. 

In reality, both these tools(GA and AA) are great and your requirements define the tool that suits your needs the most. Answers to some of the questions below will help you figure out which tool fulfills your need.

The first thing to understand is how big is your website? Are you selling hundreds of products and have a vast traffic and transactions volume? Do you publish a lot of content which generates huge traffic volume and powers advertising revenues?

Adobe Analytics suits you here

Or do you have a simple operation wherein the transaction volume is low and the website is simple? Google analytics works just fine for you.

How important is the real time data across marketing campaigns and attribution to you? Do you want to see the performance of your campaigns, products sold, articles and sections read in nearly real time? Do you want to optimize things on the website daily based on real time data? If the answer is yes, you should go for Adobe analytics.

If you are okay with looking at the audience, behavior and acquisition data at a 24 hour delay and you optimize things monthly instead of daily, go with Google analytics.

Does your business have a mobile app as well and you want to understand the mobile app analytics as well? Or do you have a video website with a lot of videos playing and you are interested in detailed video reporting? Adobe analytics in a single platform covers web, mobile, social and video which helps in understanding the holistic picture of the visitors of your online properties.

Most of the businesses in India in Ecommerce, Travel, Media and entertainment domain think their website data is very valuable and confidential to their business. These businesses do not want to share that data in lieu of a free analytics tool. With Adobe analytics, you own your data. In case of Google analytics, Google owns the data and your website data can be used to power Google's interests in advertising and other domains. If you do not have concerns with respect to Google using your data,  Google analytics is an option for you.

Cost and Support: Google analytics, base version, is free and will process upto 10 million hits per month. Google analytics premium is priced at $150,000 annually and will process upto 1 Billion hits a month. Adobe analytics does not have a free version and is priced as per the traffic volume of the site. Adobe analytics comes with 24x7 customer support and set up services. With Google analytics free version, you are on your own and support is provided only with the premium version.

So in order to find the right analytics tool for your business, you need to look at the business and answer some of these questions above.

6 things to note before investing in advertising platforms

I discussed in my last post the need to have a technology platform in place for managing your digital media effectively. Once you decide to go ahead with that decision, the next step is to choose a technology platform from among the many that are currently being offered. The problem in doing this is that often these platforms makes similar claims about functionality and features and end up confusing the marketer more than educating him.

In order to avoid that, a marketer needs to be smart enough to see through the bull shit and to not get swayed by falsely claims.As an enterprise digital marketer, you must strictly adhere to the below points during evaluating an adtech platform so as to get the bang for the buck.

Multiple channels - Digital marketers work across multiple channels from Search, Social, Programmatic and direct Display, Video and Mobile Apps. A single platform should be present across multiple channels to suffice many of these needs. This helps in efficient management and removes the overall burden of correlating the massive amounts of data generated in individual platform silos.

Not a Point Product - Even if your need of the hour is to cater to just one channel, you should always be looking at platform which does more than just fulfilling your current needs. A new channel may gain prominence tomorrow or your digital strategy might change. Changing a platform, although now much easier because of the cloud based platforms, still requires considerable investments in terms of time and effort.

Analytics Integration - An adtech platform works best when it is tied to a good analytics platform and by good I mean a two way native integration. Data should flow seamlessly between your adtech and analytics platforms so that you can do a deep dive on your campaigns performance in analytics and build specific metrics within analytics, in line with your business goals, for optimizing campaigns in your adtech platform. Look out for platform providers who claim to have integration but in reality involves a lot of manual effort in data transfer. Safest bet is to look at adtech providers which also offer analytics platforms. A good example here would be Adobe which offers both Media Optimizer and Analytics.

Simulations - Gone are the days when marketers used to spend money on advertising campaigns and hope for the campaigns to perform well. An adtech platform should have predictive analytics built-in, in order to run the campaign simulations across channels before a marketer actually goes ahead with launching the campaign. This feature helps in saving a lot of marketing dollars which otherwise
would have gone into ineffective campaigns.

Attribution - It is not a secret anymore that correct attribution of your advertising money is the key to successful media planning and buying. An adtech platform should provide you with detailed attribution reports that can clearly tell the channels that work for you the best and the channels that assist your business objectives.

Local Support - Technology works towards success when it is provided as a service and not just as a product. Make sure that the product you are going to license comes with support from the provider. I cannot emphasize enough the need to have local support here and not from someone sitting thousands of kilometers away from you because you are going to need it a lot. You should clearly ask the platform provider who their support guys are and if they sit out of your country of operation. Any platform provider with support teams based in a different geography should be strict no.

Should you be investing in tools to manage your digital marketing monies?

This is a question which multiple marketers are trying to figure out for their organisations.
Marketers are confused since they are unsure of whether to invest in a tool, which invariably
includes investing in the hands-on training of the in-house people to use these tools, or to outsource the entire complexity of advertising management to a media agency.

It is no secret that if used properly, a tool increases the productivity and efficiency of the organisation thereby deriving higher rate of returns on marketing spends.

So the real issue is to decide whether to invest in media management tool and equip the internal teams with it or to outsource this entire business to a specialized digital media agency. Both the options comes with their own pros and cons.

The answer to this problem differs from business to business and depends on multiple factors like organisations strategy, capability of the internal teams, type and amount of media to be managed and also the relationship and trust of the business with their agency.

Lately many of the big enterprise marketers are investing in enabling their in-house teams with the advertising platforms to drive efficiency to media spending. Chief among many reasons to go down this road are to bring the branding/product/marketing teams to work in conjunction with the media
buying and spending towards common goals. Investing in technology in-house also allows businesses to circumvent the hefty commissions that agencies charge. Also investing in technology to solve problems brings in a culture which is self sustaining, forward looking and more future safe.The rapid growth of cloud powered technology solutions have made taking this option more appealing than it has ever been in the past. Marketers are free to experiment with these solutions without the risk of being stuck with one type of technology forever. This in-turn results in pushing the
marketing platform companies to out perform each other by building and offering more capabilities and services.

Agency business on the other hand is not dying anytime soon. Most of the big agencies have successfully transformed and have made themselves useful in the increasingly digital world. These agencies have the experience and know how of the real world media business and are steadily building the skill sets required in the digital age.Agencies themselves use multiple technology platforms and most of these agencies have people who are trained on using these tools. In many cases, businesses license these platforms and agencies use them thereby providing the best of both worlds.

Some marketers also attempt to hire a lot of workforce in order to outdo the benefits of a tool and to avoid the related cost. This is definitely not a wise decision given higher attrition rates, increasing demand of higher ROI from marketers, complex algorithms working across digital channels ,challenge to manage large teams
working on different digital pieces and to make sense of it all to drive unique marketing objectives.

I would like to conclude this piece by saying that investing in a technology platform is today's demand and a critical necessity for tomorrow. The way you choose to go about it - via agency or through internal teams, depends on your organization's strategy. Once you decide to invest in a platform, equally important is the decision to choose the right platform from the many currently being offered in the market. In my next post, I will list down the factors that a marketer must look at while evaluating any media management tech platform.

Rocket Fuel acquires [x+1]; signs of consolidation in display ad-tech

Rocket Fuel, a programmatic ad network focused on providing performance advertising to digital marketers, today announced that it has entered a definitive agreement to acquire [x+1], an ad-tech firm with dual Demand side platform (DSP) and a data management platform (DMP) offering. The value of the total deal is $230 million.





Six months ago, Rocket Fuel had tried to bid for BlueKai but Oracle got there first.  Rocket Fuel was searching for a decent DMP technology for a while now. Since the other major DMP player, Demdex was acquired by Adobe in 2011, this had left only [x+1] and Aggregate Knowledge, other 2 possible leaders in the DMP space, to be considered.

Rocket Fuel has a complete managed services offerings(except in Japan, where a self service model exists) for its clients wherein it runs their performance advertising campaigns. This takes away the headache of campaign management for the clients and need to invest in people, tech and processes. [x+1], which has a fantastic DSP+DMP product, on the other hand is a tech-only vendor with self serving offerings to its clients. This allows for a much greater control and customized execution to the campaigns.

Customers of both these companies is set to benefit from the amalgamation since Rocket Fuel gets access to world class technology which can power its offerings and [x+1] customers can look forward to the experience of Rocket Fuel in deriving success from the programmatic display advertising .Still a lot of future success will depend on the integration of technologies and the go-to market strategy of the combined entity.

This acquisition ,and a few others in this space in the last one year, also signals  the growing need to provide a  complete set of solutions for marketer's needs - starting from tag management and ending at marketing attribution. End to end solution also allows for a higher share of the marketer's wallet and his/her confidence in you. Quite frankly, no marketer would want to work with multiple technology and service vendors and spend their days in only correlating the data and information.

The display programmatic ad-tech space is still somewhat murky with a lot of point products solving small parts of the marketing puzzle. Consolidation is imminent and necessary for the growth of the category and we will see some more players getting acquired in the days to come.