Digital Analytics Enablement Overview

Any organization’s successful Digital Analytics execution is solely dependent on the quality of data available. With multiple platforms used for measuring and optimizing Marketing Attribution, Customer Experience improvements and Audience segments, having ready availability of quality data is the primary challenge which can enable or disable any well-articulated Digital Analytics Strategy.

Successful execution of a Digital Analytics Strategy begins with the right analytics enablement plan which involves

1. Business consulting – business analysis, interviews with key stakeholders, understand the business objectives, document business needs

2. Functional analysis of different departments which use Digital Analytics information for making business decisions and preparing a measurement strategy for each of them

3. Technical analysis of digital properties like websites, mobile apps, kiosks etc. digital channels and social media channels and developing a technical implementation plan for each of them

Tag management is the primary method used in Digital Analytics for collection of data used in Marketing Attribution for improving marketing efficiencies, driving improvements from Customer Experience Analytics and is the most important method for collecting first party data used in Audience Analytics.

A successful analytics enablement plan to drive best in class Digital Analytics strategy requires efficient systems and people for Tag Management.

Signature:

Roopkumar T.V.

Mobile App Analytics Tools

A lot of new vendors have started emerging in the mobile app analytics space

1. Google Analytics for Mobile Apps
2. MixPanel
3. Localytics
4. Appsflyer
5. Flurry

With the exception of Google Analytics, every other vendor listed above have been mobile first, focusing first on Mobile App analytics. Google Analytics has been able to quickly catch the changing trends in customer behavior and preferences, such as reduced Average Time Spent on PC websites/ web interfaces and increased Average Time Spent on mobile apps or mobile websites. 

In the largest Internet market by Revenue and RPU the US, Average Time Spent by the users browsing on mobile devices has already overtaken the Average Time they spent with PC interfaces making US a  'mobile first' market. 

Be it in US, EU, China, India, South East Asia or any other large internet market by number of Unique Active Users, Total Time Spent by users on mobile apps or mobile browsers has overtaken the Total Time Spent on PC websites / web interfaces.

Google Analytics is a 'must have' for every organization which deploys digital analytics, given it's powerful integration with Google Adwords, the leader in Digital Marketing execution and many other powerful features included in Google Analytics 360. Also, additionally MixPanel is a very useful tool which needs to be deployed in every important mobile app, given a lot of case studies of top mobile apps (like Uber, Airbnb etc.) using MixPanel.

Signature:

Roopkumar T.V.

Manage Targets feature in Adobe Reports and Analytics

In Digital Marketing and E-Commerce, we always compare the actual performance of various KPIs against the benchmark performance on a daily basis, and even hourly/real-time basis, during peak sales seasons like holiday shopping. Generally the actuals are measured using a Digital Analytics application, imported into Excel or Tableau where it is compared against the benchmarks. Benchmarks are typically the forecasted plan numbers based on the historicals, industry and economic performance trends.

Now Adobe Reports and Analytics, part of Adobe Marketing Cloud (@AdobeMktgCloud) provides a new feature Manage Targets using which, benchmarks for virtually any KPI (Traffic, Order Volume, Order Dollars, even custom metrics like Product Views, Checkouts etc.) can be uploaded into Adobe Marketing Cloud. Benchmarks can be uploaded for daily, weekly or any selected frequency including hourly.  Navigating through the reports menu in the Adobe Reports and Analytics application, the report users can view and compare the actual performance of various KPIs with the benchmark performance within a single view on near real time basis (maximum 1 hour lag). This allows the business users including Analysts, Marketing Managers, Product Managers, etc. to spend less time benchmarking the performance, and instead use their valuable time more productively in taking timely actionable decisions.

Navigation to the Manage Targets menu in Adobe Reports and Analytics, is through Reports and Analytics>Targets>Manage Targets

We can set targets for measuring the performance of actuals versus the planned benchmarks, for not only the basic digital KPIs like Visits/Traffic but also for more advanced KPIs like Order Volume, Revenue, Cart Additions etc. Also the actual performance versus planned benchmarks can thus be viewed and measured within a single view in Adobe Reports and Analytics, using Manage Targets feature. 

Segment Manager in Adobe Marketing Cloud

Adobe Marketing Cloud provides excellent segmentation capabilities with its Segments feature. Once a Segment is created it can be used/reused across each of following products, all part of Adobe’s Marketing Cloud

• Reports & Analytics (formerly Omniture SiteCatalyst )
• Ad-Hoc Analysis (formerly Omniture Discover )
• Datawarehouse (formerly Omniture Datawarehouse )

In this post, I will focus on the Segment Manager feature of Adobe Marketing Cloud. With Segment Manager we can create on-the-fly, advanced segments out of the data stored in Adobe Marketing Cloud. Types of data available in Adobe Marketing Cloud include

• Digital clickstream data collected from websites, mobile and tablet apps and marketing campaigns using JavaScript tags.
• Structured meta-data for Campaigns, Products, Customers, Offers, Pages or any dimension uploaded into Marketing Cloud using SAINT Classifications.
• Structured offline data uploaded into Marketing Cloud using Data Sources.   

Within Adobe Marketing Cloud, the Segment Manager can be assessed  under

Analytics>Reports & Analytics>Favorites>Segment Manager (use the left nav to navigate to Segment Manager)

Once inside the Segment Manager, we can reach the required segment in multiple ways

1. By doing a search  , provided we know the full or partially identifiable name of the segment

2. Click on Show Filters and do either of below

a. Select a tag (for example: all the segments I had created were tagged as Roop_Shared – There were in total 104 segments added to this tag)

b. Select a Owner (for example: all the segments I had created earlier appear under my user name Roopkumar_Tundalam – There were in total 450 segments under this user name)

Once you have reached and selected the desired segment, through any of the means explained above, you can perform a variety of operations on the selected segment. I will discuss about 2 important operations below

1. Share – you can share your segments with other users in your organization who also have access to Adobe Marketing Cloud. Easiest option is to click on All, which will share the segment with every Marketing Cloud user in your organization.

2. Approve – If you are an admin user of Adobe Marketing Cloud, only then this operation will be available. Using Approve, you can review segments created by new or junior users within your organization and approve the segment for larger usage if it’s setup properly. 

In the next blog posts, I will discuss the more important topics on Segments with Adobe Marketing Cloud such as

1. How to create or configure a segment in Adobe Marketing Cloud
2. Discuss a few scenarios in Digital Marketing, where segments in Adobe Marketing Cloud will be highly useful for analytics and optimization.

Real Time Insights using Apache Storm

Real Time Insights using Apache Storm

In the previous post on real time monitoring and alerts, discussed about the importance of real time optimization needs of Digital Businesses. 

Opensource community now provides Apache Storm - a powerful solution for this purpose.

What is Apache Storm?

As per Apache Foundation: Apache Storm is a free and open source distributed real time computation system. Storm makes it easy to reliably process unbounded streams of data, doing for real time processing what Hadoop did for batch processing.  Storm is simple, can be used with any programming language, and is a lot of fun to use!

Real Time Alerts, monitoring and optimization needs of Digital Businesses

Digital businesses spend millions each day on online advertising and in product promotions. Real time alerts are important for monitoring and optimizing the performance of online ad campaigns, promotions and making adjustments in real time to optimize the return on spend.

In addition to alerts on performance of online advertising, there are other specific needs for alerts such as

• E-Commerce businesses want to know in real time how the traffic,conversions to different product categories are performing.

• Almost all digital businesses want to understand which traffic segments to target at a given point of time. Which customers to target when?

• Similarly what offers are to be provided at a given point of time.   

Hopefully new Analytics Architectures and Toolkits such as those based on Apache Spark, Apache Storm, NoSQL datastores etc. provide real time alerting capabilities on performance of specific KPIs and metrics. Large volumes of data in many varieties including streaming data in motion, is processed in Apache YARN/Hadoop and similar platforms, using clusters of distributed computing nodes. Any anomalies in data for specific metrics can be detected and reported in real time by sending alerts to business decision makers. Specific algorithms are written to run on these advanced Analytics platforms using programming languages like Java or Python or R. The need is to actually develop boxed applications around these algorithms, which can be installed and executed by end users on their own client machines, connected to these scalable, highly available, low latency and real-time data processing platforms over the cloud. 


Signature: Roopkumar T.V.

Application of Machine Learning algorithms in Digital Marketing

Some of the digital marketing challenges that can be addressed through machine learning algorithms include: 

• Helping digital media planners and buyers determine each media or ad position based on qualitative and quantitative research that is outside your own website or app. This expanded view may encourage media planners and buyers to focus on quality buying of of ad inventory reducing cost of untargeted or unqualified ad inventory.

• Enabling digital marketers to anticipate, identify and qualify audiences at the point of entry, and personalize content to maximize conversions or session outcomes. This will make content much more relevant and tailored to consumers. Also this personalization can be extended to multiple devices the audience uses. 

• Providing content feeds to digital users based on their interests, past interactions or other important factors.  Social media platforms have some limited capacity in prioritizing newsfeed content order based on viewing, timings, and likelihood to consume.  So many top tier online experiences train consumers to expect a custom experience. So not providing priority content will disappoint.

• Ranking visitors by value to the business. Not all traffic has equal value. Rapid website testing can be enabled and also personalization of page layouts, offers, messages, creative and content can be greatly improved by algorithms that have the means to assess the expected value of each visitor