Product Feed or User Level Feed – Part 2

In my previous blog, we tried to understand the differences between using product feed and user-level feed. This blog covers some industry specific use cases for user-level feed. I am also sharing some results that we have achieved after implementing user-level feed for India’s leading Insurance Aggregator.

Vertical specific use cases:

Insurance Aggregator: For insurance aggregator clients, we recommend showing the lowest quote and the relevant insurer offering that price to all users with identical inputs. But if each user within the input combination selects different quotes, they are then converted into user-level feed, along with user-specific landing pages. This ensures that the user-experience is intact, both at the display campaign marketing level and the website level when users click on a banner and come back.

Insurance Brands: For insurance brands depending on the input, users could be shown multiple quotes. At the quote search page, multiple users fall into same bucket. But if a user selects different offerings or does an action like “add-ons”, then we recommend maintaining a unique selection for each user, including user specific landing pages and generate feed. Subsequently, users are shown their last behavior based pricing and on click are brought back to a pre-filled landing page with all the user details.

E-commerce: E-commerce has product level feed, and we recommend showing the message related to products and take users back to product specific landing pages. Unlike insurance aggregators, a product on ecommerce platform has one price (if you ignore multiple sellers with different price points). One can innovate on user-level feed by taking shopping cart drop-offs back to shopping cart. But there hasn’t been much of an interest to support these.

Banks: Banks can have mix of product-level feed for new-to-bank user (for products like credit card etc.). In case of existing users, banks can provide user-level feed to recommend a combination of personal loan at specific interest rate and pre-approved amount, along with other products like credit cards at a user level. So yes, banks do have a classic opportunity to use both product level feed for new users and user-level feed for existing users.

Results: When we launched product-level feed for India’s largest insurance aggregator, we realized that the user experience was not the best. How did we measure it? By benchmarking CTR as a metric.

During initial client discussions, we realized that showing random quotes based on a set of inputs was confusing for the user. This is where we innovated and suggested the concept of user-level feed, and the need to maintain the user search state. We subsequently moved from product-level feed to user-level feed which also helped us in:

1.     Storing user-specific landing pages

2.     Taking users to quote landing pages or proposal landing pages (pre-filled with user input details)

3.     Displaying lowest quote or quote selected by the user

The above changes resulted in these impacts:

1.     Improvement in CTR by 2X (benchmarked by comparing default static banners served Vs. customized banners)

2.     Improvement in user-experience and conversions as confirmed the client

Scope for innovation: 

We at Vizury have been able to extent this concept to another level.

Offline data – User-level feed gives us the scope to interact with offline CRM user-level data. We can tap into historical user data to power the brand’s future marketing communication. This concept has been enabled both for insurance and banks. Thus creating a richer user-level targeting, both on internal inventory as well as external inventory like Google, FB etc.

We do not stop here. Another scope is to closely work with brands and their IT teams and help them power user-recommendation intelligence. Since we store user-level landing pages, we can also integrate third-party behavior at a user level, and pass them to the brand through the landing page. We then create internal mapping for each user to understand what they might be potentially interested in.

For example, if we start storing user level feed for an OTA, on banner click we cans pass hotel as an interest to user who searched for flight (hotel recommendation comes on the basis of 3rd party data). OTA’s system can then read this recommendation “hotel”, and after loading flight search result page can display a site notification for a special discount on hotels alongwith the flight bookings. This might just tilt the user to not only purchase flight ticket, but follow-up with a hotel booking as well.

Of-course, this kind of experimentation requires development effort. Both on the brand’s IT team, as well as the platform team.

Way Forward: Now that we have unlocked the world of potential opportunities by understanding the subtle difference between product level and user-level feed, it’s important to design your marketing campaigns keeping this in mind. And reason with your marketing solution provider, if the same makes sense and is possible.

I would love to answer any specific questions that you might have around using the right kind of feed and improving your marketing effectiveness. You can reach me at 


Growth recipes for Banks – Personalizing User Experience of Existing customers

Consumer behaviour has gone through a massive change in the past few years. The growing access to internet and the surge of smartphones have led to the inevitable transition to digital, especially in the banking industry. Banks are aggressively looking to grow the digital share of their business.

Every bank’s growth is driven by 2 key aspects,

  • Acquiring New To Bank (NTB) customers and
  • Engaging existing customers

It would be a crime not to personalize the experience of existing customers with the Next Best Action (NBA) across channels. Make the best use of your owned channels (website, email) before venturing into paid avenues (display, social).

Ensure a consistent experience across all digital touchpoints. Engage your customer with relevant upsell/cross-sell offers to boost retention and generate incremental revenue.

The first part of this guide spoke about personalizing user experience for New-To-Bank users based on website clickstream data and 3rd party taxonomy data. The second part will talk about user personalization for existing customers with the tons of offline CRM data and website behaviour data available at a bank’s disposal.

Before even getting into discussing growth recipes, it is critical for a bank to be able to identify existing customers among the pool of website visitors and then treat them differently.

Download the guide to get your hands on 10 unique ‘growth recipes’ as we call them to help you maximize the digital share of your business.

Write to us at for any thoughts or questions.

Three Omnichannel Trends At eTail East 2016

Retail has always been a game of high-stakes, and only those with the right combination of nerve, strategy and the all-important chip-stack find themselves at the table for more than a couple of rounds.

In 2016 the stakes are higher than ever, and the game is constantly changing and evolving. On the cards now is omnichannel retail – fast-paced, highly competitive and, for those that know what they’re doing, highly rewarding.

Keeping ahead of the curve is the only way to stay in the game in 2016. And that’s why Vizury is sponsoring eTail East 2016 – the number one event for ecommerce multi- and omnichannel innovators.


We’re very excited to be participating this year. As we see it, there are three major trends that are shaping the omnichannel game in 2016, and all – plus many others – are being addressed by the all-star keynotes at the conference this year.   

Here’s just a snippet of what you can expect.

Trend #1 – Much More Mobile To Come

We’re almost beyond the point where we can call the emergence of mobile retail and marketing a ‘trend’ any longer – it’s quite simply a transformation. And with such saturation, it is more important than ever to keep up with how consumers are using their devices to research and shop both in and out of store – especially as these devices become increasingly location-aware.  

Speakers drilling down into the ongoing mobile phenomenon include:

Trend #2 – Turning Social Into Sales

Whilst the vast majority of purchases still occur in-store, the power of shareable social media content – and particularly video – is still proving to be the driving force behind brand awareness online. But what can retailers do to turn their content from a bit of internet buzz into actual sales, both in-store and out?

Trend #3 – Customer Centricity

Putting the customer experience at the centre of your strategy is not a new concept. However, in the modern world of omnichannel retail, the opportunity to hone these strategies to ever-finer points and begin targeting customers on a micro -level is here. The question, however, is how? How do retailers leverage consumer data and available technology to continue to improve the customer experience across all channels?

Thoryn Stephens speaks on The Future Of Retail And The Convergence Of Customer Centricity, IoT And Omnichannel.

And Gary Mceldowney is on the case of Efficiently Moving Customers Through The Conversion Tunnel To Drive Repeat Purchases, and will also be Taking A Close Look At New Product Ad Channels: What Works And What Doesn’t.

Looking Forward To Seeing You There! 

These discussions form just the tip of the iceberg of what you can expect at eTail East 2016.

The conference is taking place in Boston between August 15th and 18th and is sure to be a buzz. Jam-packed with as much information and networking as fun and entertainment, we hope you can join us in the future of omnichannel at this spectacular event.

Find out more and register for the event at eTail.

We are setting up camp at TT4 by the foyer. Drop in to experience Engage Commerce, the world’s first Growth Marketing Platform for Commerce brands anytime during Aug 15-17.


Digital Marketing 3.0  – Why Marketers Need a ‘Growth Marketing’ Platform

Digital marketing has seen two big waves in the last two decades. The first wave, from mid nineties to the first half of 2000s, was dominated by martech systems like email, websites and SMS and used mainly for CRM marketing.

The next wave, which is probably nearing its end now, was dominated by ad tech. Programmatic and Real Time Bidding technologies exploded. The latter half of this wave also saw the rapid dominance of mobile as a channel, especially in developing markets that chose to skip desktops altogether to access the internet.

What are marketers faced with as a result of these two waves? A marketing stack that’s very fragmented, where systems often don’t talk to each other or where the integrations are superficial at best.

It’s no surprise the most marketers we talk to just want a massive simplification that allows them to regain control over user experience and focus on revenue generation.


Many enterprises that are serious about digital now have a dedicated function for e-commerce or digital acquisition. In some cases, interestingly, this function is called “Growth”. Growth Marketing implies that the function is responsible for driving revenue growth – through acquisitions and through retentions leading to upsell/cross sell. And a growth marketer often has different priorities and expectations from their marketing stack. These are:

  • Measurable and rapid RoI on marketing investments
  • Consistent and highly personalized cross channel user experience
  • Managing digital data velocity and volume with machine learning

At Vizury, we strongly believe that the only way to achieve this is through an atomic tightly integrated marketing stack that is geared towards growth. So what exactly are the key architectural elements of a growth marketing platform?

  1. A universal user identifier technology at its heart – which is able to connect a user across channels.
  2. Algorithms that drive decisions or aid decision making and reduce the need to generate and maintain “rule based segments”.
  3. Simple intuitive features that let the marketer focus on the message experience.
  4. Industry specific features that demonstrate a deep understanding of growth marketing requirements for a specific vertical.
  5. A connected and comprehensive set of channels that intelligently orchestrate to drive results.

This does not mean marketers have to give up platforms that they have been using for years. We believe these systems have traditionally been used for “transactional CRM”. E.g. “You just spent $XXX on your card” or “Here’s your monthly loyalty statement”. And they will continue to find usage while growth marketing would develop as a separate and parallel stack.

So that’s what the next wave looks like in digital marketing. A growth focused stack where ad tech and mar tech converge, where data and channels are woven together seamlessly and one that uses prediction and personalization at its core.

[Video Blog] Growth Hacks: Daily Use Tips for your Mobile Apps

The role of an Ecommerce marketer has progressed beyond the bounds of traditional campaigns and they’re now looking beyond website traffic and app installs. As marketers begin to look at growing customer engagement and retention, even conversions in most cases there is a constant need to look at innovative marketing tools and techniques in order to achieve marketing goals. Here’s a presentation by Deepak Abbot around growth hack and daily tips that mobile app marketers could use.  Deepak presented this at the UnPluggd event in Bangalore.

Also, here’s the video of the above presentation at Unpluggd in Bangalore.


Should mobile apps cut down on choice overload?

Conventional wisdom has always maintained that the more choices or features you offer your customer, the happier he/she is and therefore more likely to love and use your app. Right? Surely this is pretty much straightforward?

Turns out it’s actually not. Sometimes, there’s quite a good chance you’re simply confusing, or worse, overwhelming your user by cramming loads of options into a tiny app, leaving him less likely to go ahead and perform the actions within the app that you want him to. Display advertising done wrong.

Innumerable choices are great when you want to make an impression, but may not necessarily result in solid conversions.

A classic demonstration

This interesting experiment lends some real credibility to the argument put forward above. A few years back, a couple of researchers put up two jam stalls, with a slight difference. The first offered 24 different flavors you could sample and eventually buy, while the second exhibited only 6. The result? While more people stopped to browse at the table with more choices, only a mere 3% went on to make a purchase. In sharp contrast, 30% of the ones who stopped at stall 2 went on to buy a jar of jam.

While this study may not have necessarily taken other psychological aspects into account, it is fair to imply that plain confusion was one of the major turnoffs for the shoppers in scenario 1. We might even reasonably infer that with a more optimum quantity of choices, people tend to pursue a particular choice with more vigor, since it makes it easier to eliminate the ones they definitely know they don’t want.

                                                                   Image source :

App notifications need a focal point

Let’s extend this argument to the re-targeting ads that an e commerce app typically displays. I recently used an advertising app to buy a second hand laptop, and narrowed it down to a couple of choices. But the cluttered homepage kept pinging me with more choices than I could handle. Five new ads would pop up each time I clicked on one, almost as if the app was flaunting its options, making me hold out a bit longer each time in the hope that I could find a better deal. I still haven’t bought the laptop.

This is a classic example of in-app features and ad pop ups done wrong, ending up having the opposite effect of what it intends to do.

Take push notifications for example. An app that wants to send out notifications about a big sale has two choices. The obvious one is to send out a generic sale banner Push, talking about all the products the sale covers, but this is a haphazard cluttered approach. Worse, it means the push has nothing particularly relevant to talk to them about, and will most likely go unseen.
Hiding in plain sight

A push that goes unnoticed is even worse than a badly timed one that infuriates the user, or even one with irrelevant or non personalized content. But, that is exactly the kind of risk you’re running if your pushes contain too much information.

Too much information is as bad as no information, and you effectively have nothing specific to hold the user’s attention in the two seconds in which he/she ponders whether to swipe away this spammy notification.

Customers are so used to hyper-personalization that anything less will probably be ignored. And isn’t narrowing down the choices the whole point of personalization? Using previous user history to personalize the Push can help narrow down on the product most likely to tempt that particular user to notice it. Highlight that on the message and you’ve got your man. Hook line and sinker.


Know your user

While this may admittedly be easier for users who have carted certain products, the ones who have dropped off your home or product view is a different story. Here, it is recommended that you build user personas and map them with look alikes in order to categorize these users better and craft product-specific pushes to send their way.

‘Limit your choices’ is the first thumb rule to keep in mind here. It aims to draw the users’ attention to the aspects that are most relevant, and prevents overwhelming them with too many complex choices. The most intelligent apps strike a perfect chord in targeting their users, displaying only the most applicable choices in plain sight, subconsciously almost guiding the user to the end action by manufacturing interest in the ad.

What do you think? Can too many choices be overwhelming, or just plain beneficial? Write to us at with your thoughts.

[Video Blog] Interview: Chetan talks Growth Marketing with Destination CRM

Chetan Kulkarni, CEO of Vizury was at the New York City office of CRM Magazine to talk about the new wave of personalizaiton in marketing and Vizury Engage, the Growth Marketing Platform custom built for eCommerce, Finance and Travel verticals.

Chetan spoke about the inception of Vizury and how, in the past 8 years, we have evolved into the Growth Marketing Technology company that it is today. Our growth platform, Vizury Engage helps marketers grow digital conversions through user personalization across channels.

The interview was focused on Engage and how it is different from other marketing automation platforms in the industry.

You can watch the interview here:

You can read the full article with excerpts from the interview here.


Creating mobile apps that meet customer expectations

This article was orginally published at Chief Marketer –

Though many leading mobile apps have garnered millions of installs worldwide, retaining users remains the key challenge in determining success. In order to survive, mobile apps must combat churn by meeting the consumer’s ever-changing expectations.


The average mobile user has begun to expect more personalized interactions with brands. This is at the crux of enhanced user engagement and retention rates, and marketers have been exploring various methods to engage and re-engage their app users. Among these, push notifications have proven to be the most powerful, owing to their ability to reach users and their (now) non-intrusive nature as compared with other channels.

Push notifications have gone through several phases of evolution in terms of content format, trigger points, and personalization scale. Each of the four major phases of evolution of the famed push notification has its pros and cons in features, some of which have been completely phased out.

Static push

The static push was fairly primitive, rather like the primate phase of human evolution. It couldn’t stand on its own two feet, but provided a great launching point for future evolution. An app marketer would send a push message—such as “20% off on shades this summer”—to all app users at a particular time of a day. This message sent to the entire user-base was generic, time-based, and completely un-personalized. CTR of such notifications used to be a meager 1% or less, meaning users mostly ignored static push messages.

Segment level personalized push – text format

The next step in the evolution of push was the neanderthal stage. In this phase, user data was integrated to add the “fire” that push notifications needed. Information about app users began to play a critical role in the marketers’ efforts to understand user behavior and customize messaging. Users were segmented based on distinct personas that reflected their in-app behavior. Messaging was then customized for each segment, and an in-app event was defined as the trigger point, such as a product page view, drop off from cart, etc. CTR levels climbed a bit to about 3%, owing to a relatively greater level of customization. The fire created by user data and persona targeting reinvigorated customers looking for engagement.

User level personalized push – with product image

The next stage of evolution brings us to the upright-human equivalent of the push notification, capable of using various tools such as segmentation, which was a level deeper in personalization. Push messaging wasn’t customized to entire segments of user personas any more; instead, it was customized to each individual user. Another significant development on the content front was that, along with text, images could also be included in the push — such as of the product last viewed by each particular user. This product banner would be deep-linked to the product view in the app, and helped marketers connect with more users. CTR thus went up to an average of 8%.

Multiproduct push carousel

The latest iteration of push is likened to modern man, fully capable of accomplishing everything its predecessors could, but adding the ability to anticipate. This is the multi-product carousel. It allows push messages to feature not only the user’s last visited product, but five other recommended products as well. The push is more dynamic, as the user can scroll through the products within the push itself. Machine learning algorithms help marketers determine which five products to recommend based upon a number of parameters.

While it’s tough the say what will come next, it is clear that the mobile app is here to stay. And because of its adaptability, push is one its most compelling engagement tools. Mobile marketing automation platforms are helping app marketers make sense of user data and personalize push for better engagement and eventual transactions.


How not to become ‘impersonal’ while being ‘personal’

Am I alone in thinking that, when I receive a “personalized” email from a brand – i.e. it greets me by name, it actually feels pretty impersonal?

To me, “Hi Prasenjit” just means I handed over my personal details to someone and now my name is sitting on their database waiting to be used in marketing communications. But do they really know me? And will they be using my data for something that will be of benefit to me? I’m pretty sure the answer to both of those questions is a ‘no’.

Personalization now means more than calling your customer by their first name. I don’t know whether it’s laziness, or a lack of the right tools, brands today know so much about their customers – so why aren’t they using the data they have to really tailor their messages and make them relevant to the individuals they’re talking to?

Personalization has to be a key priority for marketers in 2016.

Why is it important?

When executed well, personalization, has clear benefits for brands. For example, for a bank, the CTR on a personalized “Next Best Action” recommendation banner on the Home page is thrice the CTR of an equivalent, non-personalized recommendation.

But it’s not always easy to do it right. Personalization is a tricky beast…

For customers, it can be a sensitive – and sometimes contradictory issue. People are very aware that organizations collect data about them and, as a result, have come to expect brands to know their preferences and market to them accordingly. They want brands to be open and honest about what they know and to be helpful in return. They also want confidence that their data is secure.

Get the balance wrong between what you know about your customers and how you use what you know, and customers can be left feeling very suspicious. You can lose their trust, jeopardize business and even appear intrusive if you start using information inappropriately or that customers didn’t know you had.


True personalization is about really knowing your customers and using what you know to mutual advantage. As marketers we know that a customer’s purchase and transaction history, behavior and preferences can steer us towards offering similar products and services – and that’s absolutely what we should do.


Imagine the power of being able to offer a host of tailored benefits as well – from discounts and offers, to useful information right when they need it and through channels they prefer the most. We’re able to collect more data about our customers now than ever before – but we’ve got to make them want to give it to us and once we’ve got it, we need to use it for their benefit, not just our own. When you know people’s motivations, tendencies and pain points you can really start to personalize what you offer them. And when you start to offer them something relevant that makes their lives easier or better, that’s when the rewards come back to your business. That’s what all marketers are looking for, the power of 1:1 personalization.


First, Second & Third party: The Devil is in the Data

Before we delve deep into this headline, let us briefly understand as to what the above data sources mean:

  1. First Party – Data generated by the self is classified as first-party data. In the context of ad-tech, the data generated by users online belongs to the respective advertisers and are first party data (to be used for themselves). For example, data generated by users while they browse Coverfox belongs to Coverfox and are to be used by Coverfox
  2. Second Party – Data generated by a source but to be exclusively used by another partner is second-party data for the partner buying the data. For example, data generated by if used by Shopclues as part of a specific partnership, then the data is second-party data for Shopclues (which is buying the data as part of strategic tie-up)
  3. Third Party – Data generated by a source, but distributed by an intermediate platform, and is consumed by clients of this platform, is third-party data for the client consuming it. For example, data provided by a partner X and is made available on Vizury platform and is consumed by Vizury clients, is third-party data for Vizury’s platform clients.

The way ad-tech has evolved over the years, value of first party data has been (and continues to be) the most prominent. When a user lands on the website, one gets the perfect chance to understand as to what the user is interested in and then through a series of steps take these users towards the purchase or whatever the end objective is. For example, if a user lands on the Coverfox website means that the user was in some way interested in purchasing some form of an insurance product (Car, Bike, Health, Travel etc.). But for some reason, these portals have only been able to convert a fraction of these visitors into customers.


What should they do next? This is where the ecosystem evolved and complementary marketing partners evolved to help these portals mine their user data and use it to perfection. Some of the tools available to achieve maximum efficiency are:

  • Call center led conversion (if user dropped a lead)
  • Remarketing (based on first-party data)
  • On-site personalization (for next-visit)
  • On-site recommendation

and many more. These tools or partners did help improve conversion and engaging users more effectively. They tap into all sorts of user footprints, apply all sorts of algorithms (machine learning, deep learning, AI, predictive modeling etc.) and have definitely created an added level of intelligence, thus creating a user experience.

But at times, there is scope to do something more. What if the questions are,

  • Can I know something more about my visitor/user, when they land for the first time
  • Can I influence user basis their external behavior (outside my site)
  • Can I achieve cross-sell, up-sell basis information which is derived from multiple sources

Some of the above questions have resulted into the concept of second & third-party data. If a portal can get into strategic tie-ups wherein the 2 partners are complementing each other, then such data-exchange (in closed private-view) is what is known as second-party data. For example, insurance portals (Coverfox) can tie-up with travel partners (like MakeMyTrip) and reach out to all the users who have purchased flight tickets and sell them travel insurance. Now this can be done either within the context of MakeMyTrip site or outside on display networks as well by leveraging partner platforms like that of Vizury Engage.

Another way is where a platform also can get into generic tie-ups with large data-sellers and humanize the information foot-print into actionable insights. Subsequently overlap these insights with client data and help them deliver relevant messaging. For example, basis 3rd party data if Vizury is able to predict that a user is a heavy traveler, next time this user comes to the Coverfox portal for the first time, s/he can directly be taken to the travel insurance section (and not default homepage). Or if this user is found on a banking site, one can personalize an onsite notification, selling them travel related credit-card. Not just this, intelligent platforms can link data-views to first understand if this user already has a credit card or not, or specifically a travel credit card. Only if the user does not have a travel card, will he be shown a travel credit card, else some other travel up-sell product.

What next? Keep thinking and come up with use-cases which can challenge the contours of digital and traditional marketing. And then seek platforms which can help you execute such use-cases, while helping make data available.

Write to us at with your thoughts on the devil data.

Cohorts to LTV, CAC to MAU: Tips to make marketing meaningful, avoid Vanity

This article was originally published at


We keep hearing about a lot of growth metrics all around us, but ever wondered, if all that ever made sense? Learn what real, meaningful metrics are & use them to drive real growth.

Here are some of the popular terms which can be termed as Vanity if not reported rightly:

Install Base — Barring highlighting the Install buckets milestones (check the note), there is no point in dwelling on the install base as we all know how most Apps are grappling with over 70% uninstall rates. Net Install base any one?


Active Users — Happy to see that active user is now the widely used metric than awfully flawed Install Base. However, how you define an active is important too. For example a chat app highlighting MAUs is a clear eyewash when companies like Snapchat are taking about HAU (hourly active users). MAU is losing relevance for most apps, so stick to short durations if you really want all of us to believe your great numbers. (Here is a great hack to find MAUs for most apps)

Retained Users — If you spend consistently on acquiring new users which leads to an overall increase in MAUs, it will an incomplete analysis if the overall MAU % is the only metric you track. It can be a bit misleading as you may fail to notice the actual leak in users. A good analytics person will measure the growth of increase in active users minus the new users acquired that month.

Here is a sample illustration on arriving at Retained user’s growth:


Category Rank — Yet another “How cool we are” metric. It’s useless unless you are visible in Top 25 overall Apps. For example, in India Play Store (as of 30/9/2015):

  • No.1 Sports App = No.206 overall Free
  • No.1 Business App = No.168 overall
  • No.1 Medical App = No.100 overall
  • No.1 in Books & Reference = No.109 overall
  • No.1 Finance App = No.71 overall

So prepared to be judged if you boast about high category rank. Is your app among top 5 sports Apps of India? 😉

Trending Apps — It’s another myth and a favorite flaunting badge for some marketers. It means nothing to appear in trending if you are not in Top 100 overall. For example, in India Play Store, out of top 25 trending apps, only 7 are visible in Top 100 overall free and almost 90% apps never make it to Top 50.

Monthly Retention — Cohorts are often discussed among growth hackers as the definitive way to assess the App’s health. However, it is extremely important to understand what not to quote. For example, Week 8 retention is different from Month 2 retention. Month 2 in all probability will show a much higher retention as it would even count someone who must have visited the app 29 days ago.


CPI — It’s another term which is quite popular among marketers. I have hardly interviewed any product/business/marketing person who didn’t boast about how they went about optimizing CPIs. Think again — is low CPI a good benchmark? For a news app, it is easy to get installs by showing attractive looking Bollywood pics in its Facebook/Google Ad — will the user acquired by such misleading Ad likely to stay active? So please move to CPaU (Cost Per Active user model).


Real CAC — Next step after CPaU is to find CAC which sets the basis of any marketing campaign. CAC traditionally is calculated by dividing the total marketing budget by converting users. However, with lucrative discounts being offered to acquire new users, the amount of discount needs to form a part of your acquisition cost to arrive at rCAC

LTV or LNP — Lifetime Value of a User is an important metric to allow a marketer to continue spends as long as CAC < LTV. Real Growth Marketer would go one step extra by measuring Lifetime Net Profit of a user to ascertain if the startup would ever make profits from the users they acquire thru marketing. Below is a dummy illustration to show how Real CAC & LNP Ratios should be calculated:


CTR or CVR — Marketer loves to boast about CTRs (Click-thru) as it highlights the quality of creative used but did it result in your app download? Banner might be enticing enough to click but not useful enough for someone to download the App. This brings the concept of CVR (Conversion Ratio from Click to Download) — click leading to an app download is the real measure of campaign’s success. Facebook optimizes campaigns based on conversions under oCPM and CVR plays a direct role in lowering CPCs (despite fixed CTRs) thus reducing the Cost Per Install.
Hence a true Growth hacker would focus on increasing CVR for their App campaigns.


Play Store recently made available a new report which makes calculating CVR much easier.

Qualified Visitor — Lots of Apps rely on push/email/social marketing which can bring in hoards of visitors. Counting all visitors as equal is not advisable. For example, in the image below, how many visitors would you treat as “Qualified” — 23mn or 30mn?


The real growth hackers are the ones who are true to themselves and don’t cave in to pressure of tweaking the data to make the stakeholders happy. So if you are the one and follow all of the above metrics truthfully, then accept a high five from me. Cheers!

I will be happy to hear your feedback on this. Feel free to comment here or tag me on twitter @deepakabbot or mail me at


Stuck with an old school Mobile Push Notifications Platform?

Personalised Push Notifications with Machine Learning on Vizury Engage App

Marketing Automation Tools have always been rule-based. And this trend started with email marketing automation tools. The marketer was expected to know whom to send an email to, what message needs to be sent & the automation tool would send emails to thousands of subscribers. This worked well in the early computing days when value-add meant automating mundane tasks using computers.

The Mobile Marketing Automation landscape today

So much has changed since then. With the advent of Mobile Apps, a major chunk of email marketing got ported over to Mobile Push Notifications. Since Push was presumed to reach users anytime via the mobile, Push automation (sending a Push notification out to thousands of Mobile App users) took prominence in many mobile marketing strategies. These notifications were sent to broad user segments and were not really relevant to every user. And so a whole new category of products called “Mobile Marketing Automation” was born. Companies like MixPanel, MoEngage, Localytics, LeanPlum, Appboy & Kahuna lead the pack as “Poster Boys” today.

So, what went wrong?

But there’s something amiss with all of these tools. This was first felt when marketers saw that 90% of users either ignored or unsubscribed from Push Notifications.

While porting the mass segmentation & mass emailing feature-set to Mobile Push Notifications, the Machine Learning improvements (that have happened over the last decade) were missed out.

Today, with Machine Learning algorithms, you can predict what product/service each of your app users will want next. With such razor sharp insights, you can target every user with extremely customized Push Notifications multiplying the probability of a sale. Such focused conversations with every user help you retain and grow loyal app users.

Executing millions of personalized conversations in real-time – now, that’s Mobile Marketing Automation in it’s true sense.

Now, how to choose a Mobile Marketing Platform?

Before choosing a mobile marketing platform, ask yourself the following questions:

  • Does this platform figure out whom to target, on its own or does it expect me to tell?
  • Does it suggest messages that meets your business goals- depending on what worked and what didn’t?
  • Does this platform care for my marketing dollars? Does it pick and show an optimum channel (Push, email, third party app /social display) for user engagement to make sure my marketing dollars are spent well?

It’s time to get machine- learning do the job for you!

Does this sound too complex? Take a look how Engage Commerce uses machine- learning to make your app marketing a walk in the park.

Experienced the power of machine learning already?

To know more about chrome notifications, google notifications, android notification Browser Push Notifications and  push messages visit our website


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