Three sleeps till Shop.org 2016 : Meet us at Booth 4095

Shop.org Retail Digital Summit 2016 is almost upon us, and this is probably the last thing we’ll be writing before we meet you face to face. We’ll be at the expo on the 26th and 27th of September, so drop in for a chat anytime between 9am to 4pm.
Hang on though. First time you’re hearing of the retail summit? No worries. Click here to check out the event website or here to read our introductory blog about it.

Anyways, we’re in Dallas already and quite pumped up to meet you all. We’ve also given a lot of thought to what we’re going to be talking about, and it’s all going to be built around increasing your sales this holiday season.

But before we get into all that, take a look at this. We’ve been studying the summit’s floor plan, and the booth marked with a red box is where we’re going to be located.

imgHandler.pngGreat, you found us. Now, here’s what we are going to be talking about,

Browser Push Notifications

The big idea that has kept us firmly rooted to our workplace desks these last few months, and we’re quite excited to share it with you. Come right this way to get a snapshot of what it’s all about, or click here to check out the comprehensive E-book we’ve compiled to get you up to speed. You can also check out Adexchanger’s fantastic piece on the options this provides businesses with here.

Of course, this is likely to be the main topic of conversation at our booth, so do make a note of all your questions and we’ll go over that one by one when we see you.

 

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Come say Hi to us at Booth 4095

 

But wait, it gets better.

Win a GoPro HERO5

That’s right, we’re having a raffle. We’ve been feeling pretty generous lately, so we’ve ordered a bunch of the latest GoPro HERO5 cameras to give away. We’ve only got a limited number of these, so claim your free raffle tickets by clicking on this link.

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So, that’s pretty much it folks. Roll on the 26th, and we hope you can make it down to meet us.

Got any questions for us before that? Get in touch at marketing@vizury.com or leave a quick comment below.

 

How Machine Learning can solve the Mobile App Uninstall Puzzle

The explosion of E-Commerce Mobile apps and their adoption by online businesses is a well-established phenomenon by now. Well on their way to becoming the toast of the digital world, 2016 is expected to see mobile ad spends dwarf their desktop counterparts by more than 200%. There are now well over 3 million apps across mobile app stores and they are expected to account for 4 billion users by 2020. That’s roughly 60% of the World’s population. Staggering!

 

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Since 2014, driving app installs and  enhancing Play Store ratings became key metrics or focus activities for E-commerce marketers. The app install spend stands testimony to the fact that the primary focus and attention of the marketer was towards driving installs, with more than 40% of marketing budgets and mind space on mobiles accounted for by the apps installed on them. In addition, tracking the installs gave rise to new businesses in Mobile Measurement Partners and we can see the pervasive presence across geographies indicating the importance of tracking Install campaigns.

 

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User Retention 

Until recently, the general assumption was that gaining app installs was the ultimate criteria that would ensure business growth. However, that line of thinking was shown up to be misleading when app marketers eventually started witnessing 70-90% of installs being lost in a period of 1-3 months.

 

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However, the fact that the top E-commerce apps successfully retain 30-40% of users on average suggests that this is not necessarily a bad starting point, albeit considering the next few steps are planned with an eye on preventing uninstalls from claiming a few victims . In simple terms, a higher retention curve is also possible provided sufficient steps are taken to focus on retaining app users.

 

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Preventing Uninstalls

Having seen that a more focused approach on user retention can do wonders to business growth, let us approach this challenge in 2 steps

1. Overhauling primitive strategies

Despite app user churn being a serious problem, the method initially adopted to tackle this was rather basic. Typically, marketers used to obtain the email/device IDs of the users who uninstall their app and reach out to them on social media and email channels. The flip side of this was, of course, that both are paid advertising channels.

This practice can also be quite inefficient as it requires manual intervention in setting up campaigns.

In other words, even though it helps in gaining lost users, this method incurs additional cost wherein a bit of intelligence from the systems could have, in hindsight, prevented channel isolation and saved thousands of dollars.

To put it succinctly, this campaign could have been way more efficient were marketers able to identify the users who are actually valuable to the business.

 

  1. Be a Smart Marketer – Reduce Uninstalls with Machine Learning

Going back a few years, RTB (Real Time Bidding) became a rage as it ushered in greater efficiency in media buying when compared to the existing bulk media purchase. The bulk media purchase had long demonstrated obvious shortfalls as it did not qualify the user to whom an impression needed to be shown.

RTB, on the other hand, basically assigned a monetary value to the perceived value of the user to a business,a flexible quantity which could be further modified in real time and define a user’s perceived value to the business.

Similarly, uninstalls could be handled more effectively if marketers can assign a value highlighting the risk of a particular user who is uninstalling the app. This would help them identify what they are willing to spend to retain that user.

For instance, a high risk user who has purchased earlier with the business could be offered steep discount for the next purchase, while this sort of indulgence might not be afforded to a user who has installed the app that very day and is yet to make any kind of transaction on it.

We here at Vizury use proprietary algorithm for uninstall predictions which gives precisely this information to the marketer. An uninstall score of sorts is assigned to each user based on their engagement behaviour with the app, helping the marketer define a hierarchy of users categorized based on how likely they are to uninstall the app. This empowers marketers to prioritize their users by segmenting them thus,

  • Purchased User with Uninstall risk of >=70%
  • User with Products Added to Cart & Uninstall risk of >=80%
  • New User with Uninstall risk of >=90%

This also gives one more key advantage – Ability to engage with high risk users through the app push notifications channel without incurring any additional cost. Once the user uninstalls the app, that’s when email or display/social remarketing take over as the major channels to him/her. Since this consumes a pretty significant budget, it makes sense to keep this as a backup option.

So there you go. A smart way of approaching a problem is all you need to see your retention rates spike drastically. This may well prove to be the panacea for your business growth!

Got any questions for us? Get back to us at marketing@vizury.com with your thoughts and queries.

 

3 tips to optimize your App Marketing spends and grow conversions

Ecommerce companies have seen a slower quarter. According to Business Today, investors in India have turned cautious about placing big bets on ecommerce start-ups and the party is clearly over. The total VC money invested and the numbers of deals have dropped from a peak of 43 deals worth $831 million in March 2015 to just 24 transactions worth $112 million (Rs 730 crore) in less than a year according to Economic Times. In a similar state, brands from leading ecommerce markets like SEA and the Middle-East are looking at tighter marketing budgets and greater value for their mobile ad spends (the focus is on metrics like In-App user engagement and conversions).

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To cope up with shrinking budgets and bigger marketing goals, you need to try and use your marketing channels effectively.

Here are 3 powerful ways to optimize your marketing channels and grow In-App conversions.

  1. Use owned channels first – The cost of engaging users on channels like Push, sms, email, In-app message is much lower when compared to Display and Social. You can start by prioritizing channels that come with lower engagement costs. Personalized messaging across these channels could help you engage upto 25% of your users. Use paid display/social ads only to reach users who have not been engaging with you on free channels and optimize your display ad spends.
  2. Block inactive channels – It is important to determine active channels for every user. A careful look at their online behavior and past engagements will help you understand your users’ preferred channels for engagement. Activate the most active channels and block inactive channels at a user level. For example, if I have always opened your sms but never responded to display ads, send me an sms every time you want to reach me and drop me out of your display ad campaigns.
  3. Treat uninstalled users differently – It’s meaningless to retarget me if I have uninstalled your app. You might want to put me into the re-engagement/download segment instead. Track uninstalls with a silent Push. Use display ads effectively by tailoring it to the user’s present state – active, inactive, uninstalled user. 
    • Run retargeting display campaigns for active users to prompt conversions.
    • Target inactive users with relevant offers and re-engage them.
    • With aggressive re-download campaigns, bring back uninstalled users.
    • Show relevant ads for every user and make good use of your display spends.

Wondering how you can action these smart strategies? Remember these 3 things:

  1. Get a cross-channel Mobile Marketing Automation Platform that will choose the right channel at the right time for every user.
  2. Use a single platform for multiple channels, it is easier to optimize your spends that way – like this infographic below.
  3. And finally, the platform must have a robust feedback loop, so that user activity and online behavior is gathered and assessed to determine the next best action on a continual basis. 

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We have implemented similar App marketing strategies for leading ecommerce Apps. One online retailer got 3x higher conversions by using just his Free channel before paid! Download the case study below.  

I would love to hear about how you optimize your marketing mix, do share your stories below.

 

Latin America: E-commerce takes off, Retargeting follows suit

According to the Latin American E-commerce Insititute e-commerce sales in Latin America surpassed the milestone of $ 70 billion, up from just $ 1.6 billion in 2003. This means that the Latin American ecommerce market grew by almost 40 times in just 10 years!

There are various reasons that have contributed to this quantum leap. Firstly, the digital environment in the region has witnessed a widespread acceptance with increase in the number of internet connected mobile devices in Latin America. According to e- Marketer, the number of internet users in LATAM will increase to about 300 million this year. Besides, ecommerce has other practical benefits such as the ease to buy. You need not step outside and visit multiple stores – you can do so while you watch television back home. Further, most e-commerce transactions are done using credit cards. In mature e-commerc markets, it is common for customers to use credit cards for online purchases. In recent years, Latin American markets have witnessed an increase in usage of such cards as well.

With the user willing to explore e-commerce, it is important that advertisers engage these users and provide products & services that delight the users and transform website visits into sales transactions and further into relationships. Marketing channels like desktop retargeting, mobile retargeting and social retargeting with dynamic banner creative optimization are extremely beneficial to achieve marketing goals and in building a customer relationship. To know more about retargeting click here, here and here.

dynamic creative retargeting

Since each of these channels are relatively new there was an initial reluctance among advertisers to take the first step. But once advertisers realized the huge benefits that retargeting can provide, we witnessed a swift adoption of these channels by all the leading advertisers in LATAM. However, it is important to reinforce that retargeting must be used intelligently and combined with a good e-commerce marketing platform for dynamic creative banners optmization and Real Time Bidding with great intelligence, flexibility and creativity to achieve the desired results.A combined strategy of desktop, mobile and social retargeting has proven to be most beneficial for our clients.

why retargeting works

Some of the big players in retail and e-commerce operating in LATAM are already working with us and combining all these channels very well. This experience and knowledge of their customer helps e-commerce or retail companies to work better and achieve thier ROI. Further, these companies are able to engage more effectively with their users and take the next big step and work with omni-channel and omni-consumer strategies. With all the insights generated and the commercial benefits possbile through effective utilization of these marketing channels – a combined retargeting strategy will no doubt be the next big thing in Latin America’s E-commerce market.

[New Feature] Dynamic Emails with our Mobile Marketing Platform

There’s some exciting news I would like to share. We have just launched “Dynamic Emails”, a new feature available on our Mobile Marketing Platform. Using this feature, online commerce marketers like you will be able to reach both website and app users with highly personalized emails, enabling you to further improve your overall marketing effectiveness.

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What are Dynamic Emails?

These are highly personalized emails that can be sent out to your target audience based on their intent and activity. And these mails can be personalized for every single user that you want to reach.

Inner workings: Dynamic Emails with Vizury’s Mobile Marketing Platform

  • Real-time data: Our platform gathers behavioral data from your website/app and analyses them at a user level. This helps in determining the user’s intent and generating relevant product recommendations/offers.
  • 1:1 personalization at scale: The platform allows you to create niche target segments and engage them with personalized content.
  • Rich media: The platform draws relevant product images, description, recent price via a live product feed. These parameters are included within the email.
  • Reach across devices: Reach users even when they are not available on desktop and social websites/apps.

Some use cases to try with Dynamic Emails

There are a bunch of things that you can do with Dynamic Emails. Here are some typical use cases that you must try.

  1. Send “First Buy” offers to new users and prompt them to complete a purchase.
  2. Retarget cart drop offs by sending a quick reminder email about the pending purchase. This could be an event based trigger.
  3. Everytime a product price drops, the platform can trigger emails to users who have carted that product to prompt a purchase.
  4. Retain inactive users with personalized re-engagement offers. These could be tailored based on last activity, inactive period to generate product/segment level recommendations.
  5. Our platform’s machine learning capabilities allow you to build user personas and map them for look-alike recommendations via emails. You can use this feature to cross-sell/upsell relevant products.

Results

We are thrilled with the beta results of dynamic emails. Typically, static emails sent out by ecommerce brands get upto 2.5% CTR. Dynamic Emails sent out from Vizury’s Mobile Marketing Platform garnered upto 3X higher CTR. Also, these emails were able to drive significant improvement in conversions as well.

To know more / try Dynamic Emails, sign up for a demo.

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Drive new users down the sales funnel, quicker!

The great BFSI digital migration is upon us. In the last one year alone, this is what has happened,

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BFSI customers are slowly but surely moving to digital and the imperatives to marketers is beyond obvious,

  • Make your website conversion centric rather than just an information dispensary
  • Create personalized experiences on data marketing platform across all channels

While most BFSI marketers have already jumped on the wagon, only baby steps are being taken yet. The potential for revenue generation through digital channels in the BFSI domain is unprecedented.

The website is the primary digital touchpoint of any BFSI brand. You can have 3 types of visitors to your website,

  1. Existing customer
  2. Not a customer, but a repeat visitor
  3. Not a customer, first time visitor

You don’t need me to tell you that you’re committing a crime if you aren’t personalizing the experience of existing customers on your website. So let’s keep that for later.

What about a repeat visitor?

A simple example: a user has visited your website before and has looked for auto loan products

And this user has visited your website again. Are you able to personalize the experience of this user? Or are you just serving him static banners of your usual promotional offers which he is going to ignore with almost cent percent certainty?

So what do we know about this user? Based on your clickstream data, you know he’s looking for an auto loan.

The Home page banner of your website can be personalized with an auto loan offer. Here is an example screenshot:

The exact same offering can be mirrored in a paid channel through programmatic display retargeting and Facebook. Reach out to this user with the same personalized auto loan offer. Here is an example screenshot of a car insurance offer for a user who has looked for car insurance products in his previous visits:
Ensure a consistent and personalized experience for your users across all digital touchpoints to boost engagement and push them towards the eventual transaction. Make the best use of your owned channels (website, email) before venturing into paid avenues (display, social) to optimize your marketing spends.

You can download this guide to get your hands on 5 growth recipes to maximize the digital share of your business through new-to-bank users.

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In the next article, lets look at personalization for existing customers and the unique use cases through which you can surprise your customers to generate incremental revenue through relevant upsell and cross-sell.

Write to us at marketing@vizury.com to let us know your thoughts on the great digital migration in the BFSI domain.

 

Demystifying Marketing (Series 01)- What is Omnichannel Personalisation?

Activate omnichannel personalization using your online and offline data across channels including second screen engagement for personalized advertising that is powered by predictive analytics and is optimized with programmatic buying for a marketing strategy that works.

Are you still with me?

Well I wouldn’t blame you if you lost me somewhere in the second line. The above statement is pretty much the state of marketing for most brands today – filled with jargons which more than half the people in any given board room might not even begin to comprehend.

Especially with the surge of digital marketing channels and platforms, it is no wonder that your company’s marketing handbook is getting fatter with fancy jargons added to it each day.

Let’s take a breath… hold the reign here. Where do these jargons come from? Are they actually a thing? Do they mean anything to your marketing?

Matter of fact, they do. They do mean something and they are not as complicated as they are made to sound. Now take a look at the opening statement again. Actually just look at the highlighted terms, ‘coz the sentence as such doesn’t mean much.

We will take each of the 5 terms and dissect them. We will figure out what they mean with the help of certain use cases, one by one. To start off, let’s take the term – omnichannel personalization. We will come to the other terms in the following blogs.

Enough jargons. Let’s talk some real marketing.

So, what is omnichannel personalization?

 

marketoonist_marketing_Declutter By Tom Fishburne, marketoonist.com

This is an easy one – Personalize your conversations with every customer based on their interests as you connect with them on different touch points. Essentially, what this means is, if you are a fashion retailer and you find out that Emma is looking for a Louis Vuitton handbag, then you must recommend the handbag to her with the same discount on your website, app, social, email, sms, push notifications and offline as well.

It is easier said than done though. To be able to personalize messaging to your customer, the first thing you need to do is to identify a customer across these channels and then find out what she really wants.

This is precisely why ‘data’ is gradually becoming the crux of marketing at all brands. A Data and Marketing Platform becomes a powerful tool to fuel personalization.

Customer data from any source or format, both online and offline, can be onboarded to a single platform and the data and marketing platform will give you a unified view of the customer in terms of demography, product preferences, buying behavior, preferred channels and devices etc.

Let’s say you are an ecommerce brand and you have a special discount on certain products for registered users alone. You want to reach out to these users using different marketing channels and tell them about the offer. The key here is to keep the messaging consistent across channels and platforms (website, mobile web, app). A single message on all channels keeps it simple for the customer and pushes them forward on the purchase cycle.

This is omnichannel personalization. It’s that simple! More importantly, it’s found to improve conversion rates by 30%.

Sounds good?

Here are 4 reasons to consider data driven marketing (a real good read, trust me!)

Next week, we’ll de-clutter more.

 

 

Implementing Real Time Logging

Note: Knowledge of Flume, Kafka and AWS EC2, Auto-scaling, S3, Spot instances would be helpful for understanding this article.

Overview

Being a big data marketingcompany, we do a lot of analytics on the data that gets generated by this traffic to websites. This poses considerable scale challenges when we begin to think of implementing systems which are real-time. One such challenge was seen when we wanted to implement real-time logging for all our Real Time Bidding (RTB) bid servers. Our bid severs mainly has the jetty web service running in it which process HTTP bid requests sent by ad-exchanges and generates some log entries for the requests that it has processed. These log entries are later used for Analytics.

History

Here are some numbers to understand how the RTB infra is distributed.

  1. We are geographically spread in 6 regions (US-East, US-West, EU-Weast, AP-Southeast, AP-Northeast & China).
  2. Each of the bigger geographies (APSE & USE), has about 20 c3.2xlarge serving bid traffic during peak hours. And across all geographies, there could be about 75 c3.2xlarge machines handling bid traffic during these peak hours.
  3. Each region is treated very independently i.e. every region has its own set of cache servers, front-end bid servers, back-end cache population servers, etc.
  4. Each bid server handles approx. 4000 requests per second and the size of logs generated by each bid sever is around 300 MB / min. So, at peak times, the bigger regions would be generating around 20 * 300 ~ 6 GB of uncompressed data every minute or 100 MB / second.

All these logs are written to files and uploaded to S3 to be consumed later by analytics systems. Earlier, we used to use the below method to make these logs available for Analytics:

  1. Uncompressed logs got uploaded from each bid sever every half-an-hour to AWS S3.
  2. As soon as a log got uploaded to S3, the bid sever would send a SQS message giving details about the log that got uploaded.
  3. There used to be a set of offline logging instances, which were subscribed to the SQS queue. As soon as a message was recieved in the queue, one of the offline-logging server, would download and decipher the message.
  4. The offline-logging server would then proceed to download the log-file mentioned in the message, compress it and upload to S3. We preferred to do the compression on the offline-logging instances rather than on the bid severs as we would otherwise end up with spikes in CPU utilization on bid severs at the time of compression.

This worked well as S3 was acting as the intermediate layer and each offline logging server was able to independently download any newly uploaded log file, compress it and re-upload it to S3 for consumption by analytics systems. However, there was one major concern with this architecture. If a bid server got terminated suddenly, upto the last half-an-hour data on that bid server would get lost. Also, our bid servers were on auto-scaling and we were thinking of using spot instances rather than on-demand instances. With spot instances, there is a high chance of sudden termination. So, this implied that if we were to use spot instances, we had to make the bid-servers stateless and so we did not have the luxury of accumulating log data for half-hour and uploading it periodically. We had to push the logs out of the bid-server real-time!

Architecture And Design of New Implementation

So, here was the solution that we implemented:

  1. We decided to use kafka as a broker for the log events.
  2. Each bid sever would push its log events/messages to a kafka topic.
  3. The offline-logging servers would connect to the specific topic as a kafka consumer and keep reading the log entries and writing to files.
  4. Periodically (currently every half-an-hour), the consumers would stop reading data and upload the files that got generated to S3, which would later be consumed by analytics systems.

architecture
The java process running in the offline-logging severs is a multi-threaded multi-layered appliaction with the important thread pools being as follows:

  1. Consumer Thread Pool: This is responsible for running one consumer per thread to read data from Kafka Broker
  2. File Thread Pool: The consumer thread pool would submit data to the File thread pool which would be responsible for compressing it and writing it to files.
  3. S3 Upload thread pool: Periodically (currently every half-an-hour), the consumer in the first layer, would stop consuming data from the kafka broker, close all the files that were being written to by the File Thread pool, and then inform the S3 Upload thread pool to upload the files generated from this consumer thread to S3. Once this is done, the offsets in the Kafka partitions would be committed, indicated that the data in the topic partitions from which the consumer was reading has been successfully uploaded to S3 upto the committed offset.

Challenges Faced

We faced several challenges, while implementing this solution. I am documenting some of them here:

Ensuring that data uploaded to S3 is complete and not redundant:
If you observe the design of offline-logging java process, it can be observed that there are multiple points at which if something fails, we could end up with missing data or redundant data uploaded to S3. For ex. if a file is uploaded to S3 from offline-logging server, but before it can commit the offset to Kafka, let us say the java process crashes. In this case, another offline-logging consumer would re-process the data of the partition (from the last committed offset) and we could end up with redundant data in S3. To address this, we suffixed each file with the partition and the offset from where it was read. So, if a partition data was re-processed from the last committed offset, this uploaded S3 file would just get overwritten.

Handling Kafka (Broker) Server downtime
It sometimes happens that we might have to move to a new Kafka server and so there could be downtime for Kafka broker. During this time, the jetty process running on bid sever would not be able to submit log events to the kafka topic. To overcome this problem, we run flume agent on each of our bid severs. The main jetty process which actually generates the log event, submits it to the flume agent running on the same machine, which in turn submits to kafka broker using its Kafka sink. In the event that the kafka server is unreachable, flume spools them locally (we are using FileChannel) and resubmits to kafka broker once it becomes available.

Flume throughput
Initially, we observed that flume was not able to submit data fast enough to Kafka. One of the major reasons was the flume batch size and transaction capacity was small. We realized that increasing the batch size and transaction capacity helped considerably. See http://blog.cloudera.com/blog/2013/01/how-to-do-apache-flume-performance-tuning-part-1/ for more information on this.

Disk and Network I/O bottleneck on the kafka server
We are using one c3.2xlarge for running the kafka server. We saw that the peak bandwidth on these instances is generally around 150 MB/s. We were further using EBS volumes for storing kafka data logs, which peaks out at similar bandwidth. Considering that we were generating about 100 MB of log data every second, and that this 100 MB data again had to be consumed by offline-logging servers as well, the single c3.2xlarge instance became a bottleneck as it couldn’t support 100 MB/s in as well as 100 MB/s out traffic. To work around this, we used snappy compression while pushing data from kafka. This helped us reduced bandwidth by 50%. Also, see http://nehanarkhede.com/2013/03/28/compression-in-kafka-gzip-or-snappy/ on an interesting comparison between using snappy and gzip compression for kafka.

Kafka broker throughput not increasing in spite of available CPU on the kafka server
We also observed that even when the number of bid severs were high, the CPU utilization of the kafka broker server was low and there was a backlog building up on the bid server side (in the flume agent). This was due to the lower number of io threads on the kafka broker. The solution to this was to increase num.io.threads in the Kafka broker.

Rebalancing of offline-logging consumers
Another edge case issue that we faced was related to consumer rebalancing in Kafka. Let us say that we have a offlien-logging server which is consuming data from a particular kafka topic partition. Now, after reading some data from this partition, let us say that partition rebalance has occurred and this partition is now consumed by another offline-logging sever. Now before this first server begins upload to S3, suppose rebalance again occurs and the partition is assigned back to this first offline-logging server again, then it would read data from the last committed offset and hence could end up with duplicate data. To address this, we wrote a rebalance notifier, which notifies whenever a partition rebalance occurs and in that case, the offline-logging java process would clear all data that it had written for that specific topic partition until then. So, if the partition is reassigned to the same offline-logging instance, it has no older data related to that partition. An alternative solution to this problem could have been using low-level consumers rather than high-level consumers, but then we would lose the flexibility of dynamically scaling up and scaling down offline logging instances in an elegant manner.

Key Takeaways

  • Increasing flume transaction capacity helps improve throughput
  • Use EBS Optimized instances in cases where you want reliable EBS bandwidth
  • Using compression helped us reduce disk and network bandwidth considerably on the kafka server
  • Increasing num.io.threads in the kafka broker helped increase kafka throughput and utilize CPU to the full
  • Higher read throughput on the kafka server is observed in times of backlog as data is no longer available from disk buffers and it has to be read from disk
  • Parallel stripe of LVM for EBS volumes does not help much as EBS bandwidth generally becomes bottleneck. On the other hand parallel stripe of ephemeral storage helps increase disk throughput (this was seen when we later moved our Kafka Broker to d2.2xlarge)

How to use push notifications for content apps

Push notifications are the bread and butter of the mobile commerce menage. But if your app is one that deals with dynamic content, and uses images, videos or even maps as a crucial tool to interact with the user, then it might need something more to boost engagement rates.

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iOS 10: The E-Commerce Marketers Guide to Push Notifications

This article was originally published at mobileFOMO.

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How Push notifications can enhance Pokemon Go

A bit late to the bandwagon, but we’re finally trotting out the obligatory Pokemon Go blog. With a marketing twist, of course.

The digital age sees things go viral every couple of hours for no reason, but this trend seems to be here to stay. For the uninformed, Pokemon Go is a game by Niantic studios that comes with a social prerequisite; you actually need to go out and walk about to progress in the game. Already better than crushing rows of jellybean, you say. We hear you. Picking up similar ideas and methods from their previous augmented reality game Ingress, Pokemon Go allows you to walk around catching your favorite canonical critters in places you encounter in real life (yes we do mean that there is a ponyta running about in your neighborhood park).
In fact, the game is yet to be released in several major Asian and European countries but has still generated more downloads in a week than Tinder did in nearly 4 years. That is just phenomenal.  Reducing stoic adults worldwide to a giggling mass fan boys, the game has also made sure people are actually getting a bit of exercise and spreading goodwill in the process. Win-win, surely.
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                                                image source: game rant
But, for all its popularity, what can the app do better from a customer contentment angle to interact and make the user experience better?
After a few nights of catching growlithes and pledging allegiances, we noticed a few places where the app is missing openings to re-engage with users via Push messages and keep them hooked. I mean, we’re self confessed fans, but sometimes we all need a little PUSH,
Push alerts on the GO – Ok the game is fun, but who wants to walk around literally peering into the phone all the time? I know I’d rather multitask, catching pokemon on the go while walking my dog or something.  But right now, you need to keep your app open at all times to get alerted. This basically means once I turn off the app, or the phone automatically changes screens in my pocket like it annoyingly tends to do, I’m completely disconnected from the game, even if I’m literally walking through a horde of pokemon on my route.
Right now I use a touchlock app to counter this issue, but there really needs to be an option where the game can be allowed to run in the background. This is where dynamic push messages can come in handy, since the user won’t actually be looking at the screen in such instances. Sending out short buzzes as and when they walk into a Pokemon will do quite nicely indeed. If they chose to have the tracking mode on during a previous futile hunt, a carousel push alerting them that the particular pokemon has respawned nearby would definitely bring them back to the app.
Pokemon Go- Vizury Blog2.jpg
                        image source: independent.co.uk
Update Pokedex after crashAs you would expect from a game with overwhelmingly positive virality, the app keeps crashing quite a bit. This is especially frustrating, especially since it tends to happen right after you’ve caught a Pokemon, and you have no idea whether it broke free
In such cases, a push message if the app crashes, along the lines of ‘The Pokemon you caught was successfully added to your Pokedex‘ with a relevant image/silhouette will go a long way in re-engaging the exasperated user and persuading him to come back to the app. 
Team recruit Pushes – Now, this is an interesting one. We’re not going to bore you with the details of the teams that are available, or recommend the one we think you should pick (let’s just say it’s a real shame if you pick Valor or Instinct). But, this is certainly an area where the app would benefit from dynamic Push messages in quite a few ways. The obvious one is to gather your location and send you data on other users of the same faction near you and where they’re located (which is useful since you take down and defend what is called a Pokemon ‘gym’ with members of your own faction against the other two). I know that people right now are collaborating on social media networks for this purpose, so an in-built sidebar that lets them do this, and corresponding Push notifications for this, will be incredibly useful.
pokemon-go-team-vizuryblog
                                                 image source: magiquiz
Gym notifications – Remember the gyms we talked about earlier? Right now, there’s no way for you to know if one of yours is being taken over by someone from a rival faction if you’re away from the app. You won’t have the slightest idea you’ve lost control of the territory and that your Pokemon has returned to you, until you actually go back to the app and check your Pokedex. A relevant Push notification would be extremely handy to react quickly when your defeated Pokemon is waiting to be healed.
pokemon-go-vizury4
                                       image source: pastamagazine

 

So what do you think? You can write to marketing@vizury.com with your thoughts and opinions. For now, we’ll leave you with a heartwarming story on how people are using the game to make sick kids happy. And, remember, you gotta catch ’em all!
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To know more about chrome notifications, google notifications, push  notifications in android Browser Push Notifications and  push messages visit our website https://www1.vizury.com/

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When I was young, I enjoyed riding my bicycle for hours. When I grew-up, I observed a mechanic repair my bicycle one day. Thereafter, everyday I would spend some time fixing things on the bike myself. Well many times, the repairs were not needed 🙂 As I grew up a little more, I found that I enjoyed fixing the bike more than I did riding it. Let me blame the additional knowledge and curiosity I had developed subconsciously in the technical aspects of the bike for that.

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