7 Things Retailers are Doing to Crank Their Mobile Shopping


People are definitely shopping a lot with smartphones. But buying? Not as much, at least not today. According to comScore’s 2016 Future in Focus report, people spend 60% of their digital shopping time on smartphones, yet spend 16% of their digital commerce dollars via mobile.
That leaves a gigantic gap – one that merchants are anxious to close. That’s why retailers are taking lots of steps to boost their mobile commerce revenue. Here are seven of the most common approaches they’re taking.

1. Speeding Up Sites for Easier Mobile Shopping

Whether they leverage responsive design, adaptive design, or separate mobile and PC sites, most retailers know that site speed and frictionless buying experiences are critical to driving their m-commerce growth. In the early days of mobile shopping, many retailers created an m.brand.com site to keep the “core” PC experience optimized for the dominant screen type. But when six shopping minutes out of ten take place on a handset, it’s clear that the core experience is now on the mobile screen.
And major retail sites are getting better at delivering to that core every day. Part of this improvement has come from a refocus on optimization metrics other than page weight. A couple of years ago, “time to Interaction” (TTI) enjoyed some popularity as a better alternative. It focused on the amount of time it takes for the user to be able to interact with a still-loading page.
These days, many retailers focus on a somewhat higher bar: the time it takes to render a decent looking and behaving web experience. Google’s algorithms emphasize the time it takes for above-the-fold content to load. Content farther down the page, or “heavy” plug-ins, can still be loading, but the consumer already has a decent experience with which to interact.

2. Launching and Growing mCommerce Apps

US merchants are adopting a strategy that’s very common internationally – focusing on building their app-based mcommerce. Apps enable shopping experiences tailored specifically to a phone and provide more experiential control. They also enable richer personalization. And people just plain like them better than mobile websites.
Latest figures show that almost 90% of connected mobile time takes place in apps versus the mobile web. Apps also make a variety of improved merchandising strategies and tactics possible. Push notifications, for example, enjoy open and interaction rates FAR more than email metrics. Then there are the thousands of mobile-only and app-mostly retailers that have sprung up worldwide. In developing markets, the success of these businesses reflects the massive role that mobile connectivity plays in the lives of their consumers.
Singular enables data-oriented marketers to connect, measure, and optimize siloed marketing data, giving them the most vital insights they need to drive ROI. The unified analytics platform tracks over $7 billion in digital marketing spend to revenue and lifetime value across industries including commerce, travel, gaming,entertainment and on-demand services.For more information, click here.

3. Beefing Up Companion Apps

Many brick and mortar sellers are also expanding distribution of apps that enhance the in-store shopping experience. Companion apps in retail provide special offers, access to reviews and content, pair messaging to a user’s location in a store, and the like.
Macy’s, for example, is working hard to improve its already strong companion app cred. They offer rewards points account management, store-location-triggered content and offers, scan and learn content, and exclusive discounts for app users when they shop the store.
Another fitting example is Chico’s the women’s apparel retailer. Chico’s views personal relationships between customers and associates as integral to their success and growth. Smartphones enable associates to recall and leverage customer preferences for a more tailored and personal in-store experience.
Apps can also help retailers better leverage unique mobile capabilities, like geolocation. Finally, apps are great for making loyalty programs easier. All the shopper data from such programs can help personalize app content to a specific user. And as we all know, more personal almost always means more profitable.

4. Streamlining Content

Mobile shopping often begins with a different mindset than desktop shopping. Users want to get to the goods faster and are often willing to transact more if the content available to them is succinct and focused.
Shorter headlines. Little or no body copy. An emphasis on optimized imagery. These are the watchwords of mobile commerce and shopping apps in 2019. And they are all implemented to appeal to mobile shoppers and how their preferences are different when surfing the small screen.

5. Improving Business Processes

Many retailers are working diligently to reduce the friction that can impede purchases. Here are some of the most common ways they are doing this:
  • Requiring Registration: This one can seem counterintuitive because the need to register is consistently rated a top reason for cart abandonment. But if you can get a user to register in a mobile app (or on a website) early, then you can auto-populate forms and steps later. Lost first-time sales can be more than made up for with repeat sales to registered users. Requiring registration is also proving invaluable as a strategy to reduce fraud – so much so that many retailers are reconsidering whether they should have a guest checkout path at all.
  • Fairly Distributing Coupon Codes: Have you ever gotten to a checkout page that asked for a coupon code that you didn’t have? It can be frustrating to realize that you are not getting the best deal. My strategy is to fill a cart and then Google for coupon codes – tens of millions of other shoppers do the same. But research also shows that many shoppers abandon carts they feel cheated out of getting the best possible deal. Sneaky coupon distribution can also be a profound way of turning off your best customers if you focus your discounts on new users only. Many retailers are moving to distributing codes on their own sites instead of (just) shopping comparison sites. Others are doing away with codes altogether, or making coupon code blanks less prominent – findable, but not front and center.
  • Showcasing Shopping Carted Items: Lots of people want to look over the items in their shopping carts before they transact. Retailers that make that task easy – and provide strong visuals of the goods on offer – tend to convert mobile shoppers better.
  • Requiring Fewer Pages and Steps: Amazon 1-Click is the best example of this. But many other retailers are also working hard to reduce the number of steps and amount of data entry required to make a purchase.
  • Providing Security Assurance: Many people still view mobile as less secure for transactions than PC. By providing verbal and visual assurance of strong security, retailers can mitigate some of the risk of losing wary would-be mobile transactors.
  • Ensuring Cost Transparency: Lots of people drop off when they get surprised by high shipping and handling fees. Brands that make shipping information and costs clearer seem to convert more mobile shopper.

6. Encouraging Mobile Payments

We’re all familiar with the optimized mobile experience for retail sales like Apple Pay – “proximity mobile payments” in the vernacular. Lots of retailers are experimenting in this area, with a broad range of success rates to increase conversion rates from the hassle of adding credit card information. In the US, Starbucks is widely believed to be the best at transitioning shopper behavior to mobile payment. 21% of their transactions now take place via mobile phone – something that also simplifies user participation in their loyalty program. The explosion of payment services is driving increases in penetration – but it also appears to be contributing to a bit of consumer confusion as shoppers try to identify services with the broadest reach and best features. In addition, in-aisle checkout options that leverage smartphones are also growing in popularity – both using kiosks and leveraging UPC readers on the devices themselves.

7. Making Mobile-Exclusive Offers

To grow m-commerce, retailers need users to buy more and buy more often, and therefore need a user experience optimized for mobile. Many retailers are implementing mobile-exclusive offers to both drive more app launches mobile site visits and motivate incremental purchases. Lots of such merchants emphasize discounts in their mobile offers and we’ll continue to see this spike during holiday shopping, especially on Black Friday. But an increasing number are also testing and implementing approaches in which unique, upmarket goods and experiences are made available only to mobile shoppers. Fashion house Zegna was an early mover here, offering live streaming and exclusive merchandise way back in 2012. But a variety of other online retailers has followed suit.

The Stores, They are a Changin’

Retail is a category in tremendous transition, and mobile is a key pillar as they chase new mobile and online sales opportunities and adapt to be more relevant to today’s shoppers. From the big box discounters to couture emporia, most retailer leaders know that success today requires flexibility. And a robust mobile marketing plan.

To build or not to build: making build vs buy decisions for mobile attribution and aggregated campaign analytics (part 1)

Some of the larger marketing organizations we talk to in EMEA think about building aggregated campaign analytics and ROI insights themselves. They generally don’t see the full difficulty and continuous maintenance this project involves. In this article, I explore the challenges of building and why a solution like Singular meets and exceeds these need. This is part one; part two will arrive in a month.

EMEA is a hub of marketers big and small representing every type of app developer and web-centric marketer you can think of. The data explosion has affected each one. It has made actionable insights, which make all the difference in this competitive landscape, the holy grail of every growth marketing team.

Build vs buy

One question that is a serious challenge for them all: should we build an in-house mobile attribution solution or buy it from a third party?

Our customers are smart and between them own over 50% of the top 100 grossing apps. So it’s no surprise that they employ intelligent engineers and data-savvy growth teams who already have the knowledge of how to achieve aggregated campaign analytics and could have a good shot at the greater challenge of getting ROI in an accurate, timely manner … although getting ROI at the most granular levels would be a massive challenge.

Therefore, it’s not a question of whether they can do it, but rather should they do it. We found that when addressing this question, the same considerations led even the largest enterprises out there to outsource this crucial work to a marketing intelligence platform like Singular.

The first thing to take into account is the cost of undertaking such a huge project and the time to completion.

Engineering time is not cheap and a company can rack up several hundred thousand dollars to build the required infrastructure even before considering the ongoing cost of maintenance. Not to mention that a project of this size and complexity will take months to complete and in such a fast-paced industry, this is long enough to start falling behind the competition.

Cost is not just measured in currency

However, the cost of this is not just monetary.

Valuable technical resources likely need to be diverted from core product projects, which impedes innovation and custom developments that address the specific needs of the business, allowing even more breathing room for competitors.

Getting the foundations right is no easy feat: you have to get a framework for your BI system, make sure that your MMP matches that framework, and then map your cost APIs into it correctly to get full aggregated campaign analytics. Furthermore, if your marketing efforts extend beyond Google and Facebook, you will have to set up multiple APIs with all the different networks you run with and for any new networks you want to test in the future.

If engineering time is limited, as it often is, and new networks are not integrated – what is the impact of the inability to test on the business? The cost of passing on new inventory and networks with new targeting and ad format capabilities cannot be underestimated.

Once you have your APIs connected, additional work is required to configure the internal dashboards to display the new data. It’s a manual process that is prone to human error which can easily render datasets inaccurate and therefore unsuitable for optimization purposes. If you’re going down the build route, you’ll need to put in place time and resources for checking accuracy before you even start thinking about which data visualization platform you’ll use to make sense of it all.

From aggregated campaign analytics to marketing intelligence platform

That’s another reason why our customers choose Singular, a marketing intelligence platform built with the modern growth marketer in mind, addressing their requirements of instant access to reliable data for granular optimization.

Even if all the above is accomplished so that data is flowing in and is accurate which we’ve seen can be done, the issue of combining it with internal data sets poses a true challenge.

Filling in the gaps and delivering the insights requires a complex infrastructure with strong identifiers for combining purposes to enrich campaign and publisher granularity, which almost certainly still leaves creative level combining — and therefore creative ROI — beyond reach.

All this means a lot of data and heavy queries that slow down the internal systems.

Our research and customer feedback reveals that the above challenges, opportunity cost, and continuous and expensive maintenance of self-built infrastructure are what drives small and big enterprises alike to a conclusion that a third party is a better solution for this essential need.

What you actually buy from Singular

Here at Singular, we understand these challenges well — after all, we went through the pain of building it ourselves.

Our product is our bread and butter and we’ve gone far beyond the basics to build a true marketing intelligence platform that frees up engineering time of our clients to build marvelous things that uniquely aid their goals while giving growth marketers the tools that they need.

What you buy from Singular is beyond the aggregation and standardization you’d expect to build yourself: you buy a world-class solution that is focused on continued innovation and automation, to give you unrivaled insights and optimization capabilities.

You buy teams that build and support integrations, improve infrastructure and system performance, and constantly work to add new features. You buy a data science team that make it their business to spot discrepancies, a support team that handles data flow errors and API issues, and a stellar (if I may so so myself) customer success team that makes sure the platform is serving your business.

If you had engineering and BI time to spare — what would you build?

See how DGN Games grew 85% and saved 15 hours each week with Singular.

Next month we’ll hear from an EMEA customer about how Singular has enabled their business and aided their growth strategy. If you have ambitious goals and are thinking of buying or building, reach out to us about a demo to see what Singular could do for you.

DGN Games hits the jackpot with 85% growth YoY

#1 fastest growing social casino game

85% growth YoY

15 hrs saved per week

Singular has been a gamechanger for our growth efforts and has become an integral part of our marketing stack. They provided us with a comprehensive solution to analyze our marketing funnel, and was key to becoming the fastest growing social casino company in the industry.

– Uriel Shklanovsky, VP of Marketing at DGN Games

DGN Games is the fastest growing company in the Social Casino vertical with hit-titles Old Vegas and Lucky Time Slots. DGN Games bases its success on a combination of state-of-the-art technologies, in-depth industry knowledge, and top-notch gaming content.

DGN Games Lucky Time Slots
Lucky Time Slots, a casino gaming app by DGN Games

Their story

The team at DGN needed to aggressively scale their User Acquisition (UA) efforts while ensuring a profitable ROI. However, achieving massive scale efficiently isn’t something you can leave to luck; it takes the right strategy, people, and tools.

In order to build out a winning strategy and optimize their campaigns efficiently, the UA team required both a high-level view of their overall program’s performance as well as granular insights for optimizations. This critical need was proving to be extremely time-consuming and costly for DGN’s BI team to undertake.

Their solution

DGN placed their bets with Singular! The team leveraged Singular to aggregate, standardize, and analyze their campaign analytics across all of their apps and +10 media sources in a single platform. Singular empowered the team to expose insights, such as publisher level ROI, and ultimately enabled faster and smarter optimizations. Additionally, the team exported their insights, such as cohorted KPIs by App and Source, from Singular to leverage in their internal BI systems, freeing up precious BI resources.

One of the most valuable capabilities for the team was being able to create tailored reports with Custom Dimensions. Custom Dimensions combines specific attributions to create a new value. In DGN’s case, they combined select platforms to report on Desktop (Canvas, Web, Mixed) vs. Mobile (iPhone, iPad, iPod, iOS, Android, Amazon, Blackberry).

Thumb-stopping creative is key to engaging and acquiring users, especially for social casino companies. Singular’s Creative Reporting enabled DGN to compare image and video performance across all their media sources, and use the learnings to influence the development
of future creative assets, whether it be to iterate on current assets or test new concepts. The savvy team at DGN also leveraged Creative Clustering to automatically group creative performance by asset attributes like theme and color via image recognition technology.

Their results

“Jackpot! With the help of Singular, the innovative team at DGN Games became the fastest growing social casino company, growing a massive 85% YoY*. #winning

From an efficiency standpoint, the team was able to save ~15 hours a week that they previously spent aggregating campaign analytics from multiple media sources into a single reporting view. They’re now allocating that time savings to constantly test and optimize their creative, audience, and overall UA strategy. We’re very proud of DGN’s impressive growth and honored to be a partner in their success.

* Source: Social Casino Gaming Tracker – 2Q18 Report by Eilers & Krejcik Gaming

Are you ready to kick your growth efforts into overdrive? Let’s connect!

Using attribution data to calculate mobile ads LTV

Eric Benjamin Seufert is the owner of Mobile Dev Memo, a popular mobile advertising trade blog. He also runs Platform and Publishing efforts at N3TWORK, a mobile gaming company based in San Francisco, and published Freemium Economics, a book about the freemium business model. You can follow Eric on Twitter.

Note: if you’re looking for ad monetization with perhaps less effort than Eric’s method below, talk to your Singular customer service representative (and stay tuned for additional announcements).

Various macro market forces have aligned over the past two years to create the commercial opportunity for app developers to generate significant revenue from in-app advertising. New genres like hypercasual games and even legacy gaming genres and non-gaming genres have created large businesses out of serving rich media video and playable ads to their users by building deep, sophisticated monetization loops that enrich the user experience and produce far less usability friction than some in-app purchases.

But unfortunately, while talented, analytical product designers are able to increase ad revenues with in-game data by deconstructing player behavior and optimizing the placement of ads, user acquisition managers have less data at their disposal in optimizing the acquisition funnel for this type of monetization. Building an acquisition pipeline around in-app ads monetization is challenging because many of the inputs needed to create an LTV model for in-app ads are unavailable or obfuscated. This is evidenced in the fact that a Google search for “mobile app LTV model” yields hundreds of results across a broad range of statistical rigor, but a search for “mobile app ads LTV model” yields almost nothing helpful.

Why is mobile ads LTV so difficult to calculate?

For one, the immediate revenue impact of an ad click within an app isn’t knowable on the part of the developer and is largely outside of their control. Developers get eCPM data from their ad network partners on a monthly basis when they are paid by them, but they can’t really know what any given click is worth because of the way eCPMs are derived (ad networks usually get paid for app installs, not for impressions, so eCPM is a synthetic metric).

Secondly, app developers can’t track ad clicks within their apps, only impressions. So while a developer might understand which users see the most ads in their app and can aggregate that data into average ad views per day (potentially split by source), since most ad revenue is driven by the subsequent installs that happen after a user clicks on an ad, ad view counts alone don’t help to contribute to an understanding of ads LTV.

Thirdly, for most developers, to borrow conceptually from IAP monetization, there are multiple “stores” from which ad viewing (and hopefully, clicking) users can “purchase” from: each of the networks that an app developer is running ads from, versus the single App Store or Google Play Store from which the developer gathers information. So not only is it more onerous to consolidate revenue data for ads, it also further muddies the monetization waters because even if CPMs for various networks can be cast forward to impute revenue, there’s no certainty around what the impression makeup will look like in an app in a given country on a go-forward basis (in other words: just because Network X served 50% of my ads in the US this month, I have no idea if it will serve 50% of my ads in the US next month).

For digging into problems that contain multiple unknown, variable inputs, I often start from the standpoint of: If I knew everything, how would I solve this? For building an ads LTV model, a very broad, conceptual calculation might look like:

What this means is: for a given user who was acquired via Channel A, is using Platform B, and lives in Geography C, the lifetime ad revenue they are expected to generate is the sum of the Monthly Ad Views we estimate for users of that profile (eg. Channel A, Platform B, Geography C) times the monthly blended CPM of ad impressions served to users of that profile.

In this equation, using user attribution data of the form that Singular provides alongside internal behavioral data, we can come up with Lifetime Ad Views broken down by acquisition channel, platform, and geography pretty easily: this is more or less a simple dimensionalized cumulative ad views curve over time that’d be derived in the same way as a cumulative IAP revenue curve.

But the Blended CPM component of this equation is very messy. This is because:

  • Ad networks don’t communicate CPMs by user, only at the geo level; [Editorial note: there is some significant change happening here; we will keep you posted on new developments.]
  • Most developers run many networks in their mediation mix, and that mix changes month-over-month;
  • Impression, click, and video completion counts can be calculated at the user level via mediation services like Tapdaq and ironSource, but as of now those counts don’t come with revenue data.

Note that in the medium-term future, many of the above issues with data availability and transparency will be ameliorated by in-app header bidding (for a good read on that topic, see this article by Dom Bracher of Tapdaq). In the meantime, there are some steps we can take to back into reasonable estimates of blended CPMs for the level of granularity that our attribution data gives us and which is valuable for the purposes of user acquisition (read: provides an LTV that can be bid against on user acquisition channels).

But until that manifests, user acquisition managers are left with some gaps in the data they can use to construct ads LTV estimates. The first glaring gap is the network composition of the impression pool: assuming a diverse mediation pool, there’s no way to know which networks will be filling what percentage of overall impressions in the next month. And the second is the CPMs that will be achieved across those networks on a forward-looking basis, since that’s almost entirely dependent on whether users install apps from the ads they view.

The only way to get around these two gaps is to lean on historical data as a hint at what the future will look like (which violates a key rule of value investing but is nonetheless helpful in forming a view of what’s to come). In this case, we want to look at past CPM performance and past network impression composition for guidance on what to expect on any given future month.

Estimating mobile ads LTV in Python

To showcase how to do that, we can build a simple script in python, starting with the generation of some random sample data. This data considers an app that is only serving ads to users from Facebook, Unity, and Vungle in the US, Canada, and UK:

import pandas as pd
import matplotlib
import numpy as np
from itertools import product
import random

geos = [ 'US', 'CA', 'UK' ]
platforms = [ 'iOS', 'Android' ]
networks = [ 'Facebook', 'Unity', 'Applovin' ]

def create_historical_ad_network_data( geos, networks ):
 history = pd.DataFrame(list(product(geos, platforms, networks)),
 columns=[ 'geo', 'platform', 'network' ])

 for i in range( 1, 4 ):
 history[ 'cpm-' + str( i ) ] = np.random.randint ( 1, 10, size=len( history ) )
 history[ 'imp-' + str( i ) ] = np.random.randint( 100, 1000, size=len( history ) )
 history[ 'imp-share-' + str( i ) ] = history[ 'imp-' + str( i ) ] / history[ 'imp-' + str( i ) ].sum()

 return history

history = create_historical_data(geos, networks)

Running this code generates a Pandas DataFrame that looks something like this (your numbers will vary as they’re randomly generated):

[code / table]
geo platform network cpm-1 imp-1 imp-share-1 cpm-2 imp-2 \
0 US iOS Facebook 2 729 0.070374 9 549 
1 US iOS Unity 7 914 0.088232 3 203 
2 US iOS Applovin 7 826 0.079737 4 100 
3 US Android Facebook 2 271 0.026161 2 128 
4 US Android Unity 5 121 0.011681 9 240 
5 US Android Applovin 6 922 0.089005 9 784 
6 CA iOS Facebook 2 831 0.080220 9 889 
7 CA iOS Unity 8 483 0.046626 5 876 
8 CA iOS Applovin 7 236 0.022782 9 642 
9 CA Android Facebook 8 486 0.046916 4 523 
10 CA Android Unity 1 371 0.035814 5 639 
11 CA Android Applovin 8 588 0.056762 7 339 
12 UK iOS Facebook 2 850 0.082054 8 680 
13 UK iOS Unity 7 409 0.039483 3 310 
14 UK iOS Applovin 1 291 0.028092 5 471 
15 UK Android Facebook 7 370 0.035718 6 381 
16 UK Android Unity 3 707 0.068250 6 117 
17 UK Android Applovin 3 954 0.092094 3 581

imp-share-2 cpm-3 imp-3 imp-share-3 
0 0.064955 8 980 0.104433 
1 0.024018 4 417 0.044437 
2 0.011832 3 157 0.016731 
3 0.015144 7 686 0.073103 
4 0.028396 3 550 0.058610 
5 0.092759 8 103 0.010976 
6 0.105182 1 539 0.057438 
7 0.103644 6 679 0.072357 
8 0.075958 5 883 0.094096 
9 0.061879 1 212 0.022592 
10 0.075603 8 775 0.082587 
11 0.040109 6 378 0.040281 
12 0.080454 6 622 0.066283 
13 0.036678 8 402 0.042839 
14 0.055726 7 182 0.019395 
15 0.045078 2 623 0.066390 
16 0.013843 2 842 0.089727 
17 0.068741 1 354 0.037724

One thing to consider at this point is that we have to assume, on a month-to-month basis, that any user in any given country will be exposed to the same network composition as any other user on the same platform (that is, the ratio of Applovin ads being served to users in the US on iOS is the same for all users of an app in a given month). This almost certainly isn’t strictly true, as, for any given impression, the type of device a user is on (eg. iPhone XS Max vs. iPhone 6) and other user-specific information will influence which network fills an impression. But in general, this assumption is probably safe enough to employ in the model.

Another thing to point out is that retention is captured in the Monthly Ad Views estimate that is tied to source channel. One common confusion in building an Ads LTV model is that there are ad networks involved in both sides of the funnel: the network a user is acquired from and the network a user monetizes with via ads served in the app. In the construction of our model, we capture “user quality” in the Monthly Ad Views component from Part A, which encompasses retention in the same way that a traditional IAP-based LTV curve does. So there’s no reason to include “user quality” in the Part B of the equation, since it’s already used to inform Part A.

Given this, the next step in approximating Part B is to get a historical share of each network, aggregated at the level of the Geo and Platform. Once we have this, we can generate a blended CPM value at the level of Geo and Platform to multiply against the formulation in Part A (again, since we assume all users see the same network blend of ads, we don’t have to further aggregate the network share by the user’s source network).

In the below code, the trailing three-month impressions are calculated as a share of the total at the level of Geo and Platform. Then, each network’s CPM is averaged over the trailing three months and the sumproduct is returned:

history[ 'trailing-3-month-imp' ] = history[ 'imp-1' ] + history[ 'imp-2' ] + history[ 'imp-3' ]

history[ 'trailing-3-month-imp-share' ] = history[ 'trailing-3-month-imp' ] / history.groupby( [ 'geo', 'platform' ] )[ 'trailing-3-month-imp' ].transform( sum )

history[ 'trailing-3-month-cpm' ] = history[ [ 'cpm-1', 'cpm-2', 'cpm-3' ] ].mean( axis=1 )

blended_cpms = ( history[ [ 'trailing-3-month-imp-share', 'trailing-3-month-cpm' ] ].prod( axis=1 )
 .groupby( [ history[ 'geo' ], history[ 'platform' ] ] ).sum( ).reset_index( )

blended_cpms.rename( columns = { blended_cpms.columns[ len( blended_cpms.columns ) - 1 ]: 'CPM' }, inplace = True )

print( blended_cpms )

Running this snippet of code should output a DataFrame that looks something like this (again, the numbers will be different):

geo platform CPM
0 CA Android 5.406508
1 CA iOS 4.883667
2 UK Android 4.590680
3 UK iOS 5.265561
4 US Android 4.289083
5 US iOS 4.103224

So now what do we have? We have a matrix of blended CPMs broken out at the level of Geo and Platform (eg. the CPM that Unity Ads provides for US, iOS users) — this is Part B from the equation above. The Part A from that equation — which is the average number of ad views in a given month that we expect from users that match various profile characteristics pertaining to their source channel, geography, and platform — would have been taken from internal attribution data mixed with internal app data, but we can generate some random data to match what it might look like with this function:

def create_historical_one_month_ad_views( geos, networks ):
 ad_views = pd.DataFrame( list( product( geos, platforms, networks ) ), 
 columns=[ 'geo', 'platform', 'source_channel' ] )
 ad_views[ 'ad_views' ] = np.random.randint( 50, 500, size=len( ad_views ) )
 return ad_views

month_1_ad_views = create_historical_one_month_ad_views( geos, networks )
print( month_1_ad_views )

Running the above snippet should output something like the following:

geo platform source_channel ad_views
0 US iOS Facebook 73
1 US iOS Unity 463
2 US iOS Applovin 52
3 US Android Facebook 60
4 US Android Unity 442
5 US Android Applovin 349
6 CA iOS Facebook 279
7 CA iOS Unity 478
8 CA iOS Applovin 77
9 CA Android Facebook 479
10 CA Android Unity 120
11 CA Android Applovin 417
12 UK iOS Facebook 243
13 UK iOS Unity 306
14 UK iOS Applovin 52
15 UK Android Facebook 243
16 UK Android Unity 106
17 UK Android Applovin 195

We can now match the performance data from our user base (gleaned using attribution data) with our projected CPM data to get an estimate of ad revenue for the given month with this code:

combined = pd.merge( month_1_ad_views, blended_cpms, on=[ 'geo', 'platform' ] )
combined[ 'month_1_ARPU' ] = combined[ 'CPM' ] * ( combined[ 'ad_views' ] / 1000 )

print( combined )

Running the above snippet should output something like the following:

geo platform source_channel ad_views CPM month_1_ARPU
0 US iOS Facebook 73 5.832458 0.425769
1 US iOS Unity 463 5.832458 2.700428
2 US iOS Applovin 52 5.832458 0.303288
3 US Android Facebook 60 5.327445 0.319647
4 US Android Unity 442 5.327445 2.354731
5 US Android Applovin 349 5.327445 1.859278
6 CA iOS Facebook 279 6.547197 1.826668
7 CA iOS Unity 478 6.547197 3.129560
8 CA iOS Applovin 77 6.547197 0.504134
9 CA Android Facebook 479 4.108413 1.967930
10 CA Android Unity 120 4.108413 0.493010
11 CA Android Applovin 417 4.108413 1.713208
12 UK iOS Facebook 243 4.626163 1.124158
13 UK iOS Unity 306 4.626163 1.415606
14 UK iOS Applovin 52 4.626163 0.240560
15 UK Android Facebook 243 5.584462 1.357024
16 UK Android Unity 106 5.584462 0.591953
17 UK Android Applovin 195 5.584462 1.088970

That last column — month_1_ARPU — is the amount of ad revenue you might expect from users in their first month, matched to their source channel, their geography, and their platform. In other words, it is their 30-day LTV.

Putting it all together

Hopefully this article has showcased the fact that, while it’s messy and somewhat convoluted, there does exist a reasonable approach to estimating ads LTV using attribution and ads performance data. Taking this approach further, one might string together more months of ad view performance data to extend the limit of the Ads LTV estimate (to month two, three, four, etc.) and then use historical CPM fluctuations to get a more realistic estimate of where CPMs will be on any given point in the future (for example, using a historical blended average doesn’t make sense in the run-up to Christmas, when CPMs spike).

The opportunities and possibilities for making money via rich ads at this point of the mobile cycle are exciting, but they don’t come without new challenges. In general, with the way the mobile advertising ecosystem is progressing towards algorithm-driven and programmatic campaign management, user acquisition teams need to empower themselves with analytical creativity to find novel ways to scale their apps profitably.

. . .

. . .

Next: Get the full No-BS Guide to Mobile Attribution, for free, today.

Solving cross-channel and cross-platform marketing with a modern tech stack

How do you build a modern marketing tech stack for cross-channel and cross-platform marketing? A good start might be emulating some of the best practices of top marketers from Instacart, Match.com, HER, and Riot Games.

Dominic Kelly SVP Sales, Singular; Tim Hsu, Head of Growth, Riot Games; Noa Gutterman, Head of Growth Marketing, HER; Guillaume McIntyre, Head of Digital Marketing, Instacart; James Peng, Consultant (formerly: Match.com)

But don’t expect it to be easy.

Finding the right solutions is tough.

“The martech landscape has grown over 40% year over year,” Tim Hsu, head of growth for Riot Games, said recently at Singular’s UNIFY conference. “The 2018 version of the martech landscape came out in April … in 2011 there were 150 solutions.”

“In 2018 there are 6,800 solutions by 6,200 providers in over 48 categories,” he added. “That is really complex.”

Adding to the challenge are all the new telemetry points you can track as a brand.

IoT adds data from internet-connected fridges, smart door locks, and app-controlled lighting. OTT movies and shows add data from providers like Hulu, Netflix, Apple TV, and Chrome TV … and the emerging ad networks that advertise here. Add it all up and you’re dealing with billion of additional data points even compared to marketing five years ago, Hsu pointed out.

And that’s not even mentioning new avenues marketers are exploring: Alexa skills and Google Actions, augmented reality and mixed reality, plus the whole messaging explosion via Facebook Messenger, WhatsApp, SMS, and other platforms.

James Peng dealt with those challenges at Match, one of the biggest dating services on the planet.

Singular SVP Dominic Kelly

“Match is a two-decade old business … they kind of piled on the marketing stack,” he explained. “My job was to adapt to mobile … how to consolidate all the data from all the sources was challenging.”

As a company with its roots in the dot-com explosion, Match was initially web-based. Making that mobile was the right way to go, but trying to report on cross-platform marketing data — web and mobile data together in a simple, normalized, usable fashion — was challenging, to say the least.

But executives need a single source of truth to sum up overall performance.

For Match, Peng decided Singular was the right solution.

“Singular … was a way to attack that entire structure and allow reporting across all the platforms in a linear fashion,” Peng said. “Singular was a core solution for reporting and replaced the need for the same solution on the desktop side … the solution actually solved for web also as well as mobile at the same time. That was a nice plus.”

One big benefit?

Having your attribution provider and your overall marketing analytics reporting together reduces your need to standardize events, and pre-emptively avoids many of the complications and discrepancies that otherwise marketers have to solve with BI staff or data science experts.

It’s an even tougher challenge for Noa Gutterman, who is the head of growth marketing for HER. Gutterman’s data requirements include meetups and other live events.

Attendees at the UNIFY conference by Singular

“We use 10 to 15 solutions at any given time … we spend most of our money on Google and Facebook, but look hard for non-traditional sources,” Gutterman said. “Assessing metrics from live events is a big struggle … the data we were missing was data from the ticketing platform.”

For Guillaume McIntyre, the head of digital marketing for Instacart, the way to find the right marketing technology solution is in the wisdom of crowds … as long as those crowds are composed of smart marketers.

“You have to be very curious and open-minded to assess new solutions. But you can’t just talk to new vendors all the time, or that’s all you’ll be doing every day,” he said at UNIFY. “I’ll try to talk to smart people, and if they all mention one solution, I’ll investigate it.”

For Instacart, it’s also all about prioritization.

“As soon as you bring in all the sources, the complexity increases significantly,” McIntyre said. “We really prioritize what data what we need.”

Managing complexity is a massive component of digital marketing success today, especially for cross-platform marketing.

Without organization, marketers drown in data. With consolidation and normalization, marketers make smart real-time data-driven decisions that boost performance and turbo-charge ROI.

“I was an early customer of Singular when I was at Twitter,” Hsu said. “We were using two dozen supply sources … so the data explosion that Gadi talked about was very real for us. The reason we partnered with Singular is that I had my data science pod doing the work initially … and it’s the opportunity cost of what they could be doing otherwise. Partnering with a platform that has done the data integrations and has done the sanitization is a pretty big deal.”

That’s true for both “traditional” customer acquisition and, on mobile, user acquisition.

Dig deeper: See how the best marketers are making sense of cross-channel and cross-platform marketing data.

Singular & Grow.co Release State of the Industry: Digital Marketing Measurement

Digital ad spending continued to swell in 2017, increasing 16% to reach $224 billion worldwide, with mobile ad spending accounting for a record 63% of total spend, according to estimates by eMarketer.

As the cost and demand for digital ads grow, so too does the importance of digital marketing analytics. Yet the amount of data sources funneling information into the modern-day marketing stack is continuing to expand, placing a heavy burden on marketing teams to effectively ingest, analyze and optimize increasingly large and diversified sets of marketing data.

In Singular & Grow.co’s State of the Industry: Digital Marketing Measurement, we set out to understand how marketers are handling today’s marketing data deluge. Survey results highlight a host of fascinating trends in the digital marketing world and reveal where leading digital marketers are making their largest investments in terms of ad partners, infrastructure and analytics providers.

Produced in partnership with Grow.co, the world’s leading community for growth-focused mobile apps, the State of the Industry survey drew responses from Singular customers including StitchFix, Kabam, Lyft, NexonM, Yelp and Ibotta as well as non-Singular customers.

Respondents represent a broad array of verticals, geos, team sizes, budgets and revenue streams, with more than three-quarters of respondents identifying their businesses as “mobile-first” companies whose primary touchpoint is a mobile app.

Below is a snapshot of findings from the survey. For a complete list of findings and analysis, download the full report here.

  • Media Sources Surging: The average digital marketer spends across 12 different media sources at any given time, but in 2018, 51% of marketers say they plan to increase the number of media sources they use.
  • MarTech Complexity: On average, marketers utilize 17 marketing tools and services to run their digital marketing campaigns.
  • Analytics Woes: Notably, only 13% of marketers are confident in the accuracy and completeness of their data, while just 8% of marketers are confident they’re attributing campaigns across devices and platforms.

Average number of media sources, 2017


With this wealth of new data comes major analytics challenges. In 2018, as marketers increase the media sources they use, go deeper on specific platforms, and advance their analytics toolset to support both efforts, the size and diversity of the data they can collect is constantly growing and evolving.

To remain competitive in 2018, mobile marketers understand they must meet these challenges head-on with automated solutions that unlock granularity, quickly expose data inaccuracies and fraud and, above all, centralize the full gamut of marketing data in a single platform. Call it the Catch-22 of modern-day marketing: an ever-increasing need for crystal-clear analytics amid an ever-increasing flood of data.

It’s no surprise, then, that 98% of marketers prefer a single platform for measuring marketing performance.

A single source of truth for marketing data allows marketers to analyze, optimize and automate marketing data with far greater precision and far better results. These are the capabilities that will allow growth-focused apps in 2018 to distinguish themselves in an increasingly competitive digital economy.

So how do you stack up against the competition? Download the report now.

All About Snapchat and Measuring Snap Ads with Singular

Over the last several months, we’ve heard from many clients about their interest in implementing Snapchat app install ad campaigns. Singular is a Snapchat measurement partner. This post provides a bit of background information on the platform and its ad products and outlines how Singular clients can leverage our attribution and analytics toolsets to measure and optimize Snapchat campaigns.

A Massive Mobile Platform

Unless you’ve been living under a rock, you know about the meteoric rise of this powerful mobile platform. Some eye-popping stats:

  • More than 173M daily active users (DAUs)
  • An average of 20+ app opens per user per day
  • Over 60% of users create Snaps with the Snapchat camera every day

The Snapchat audience has celebrated strength among Millennials. But its reach and footprint have grown rapidly in other audience segments as well, and the social media giant offers significant reach into many major audience cohorts globally,

Snapchatters love the platform, spending more than 30 minutes per day on average posting and viewing content. With that kind of reach and engagement, it’s only natural that many advertisers are looking to add the platform to their acquisition programs.

Digital analysis shows that Snapchat has a variety of unique characteristics that heighten user engagement and keep people coming back again and again throughout the day. That’s making brands more interested in working with this major mobile player and leveraging its digital advertising products.

Some of What’s Unique About Snapchat

For those that aren’t super familiar with Snapchat, you should really take a look, as it is different in a variety of respects from other social platforms. At its core, this platform is built around content: vertical videos and images, not text. Images and videos are the center of virtually every Snapchat screen. It is also a mobile-centric experience, relying on the uber portability of smartphones AND their mobile cameras to gather content, and their vertical phone screens to view it.

From the beginning, Snapchat created a personality and set of tools that seem to have made millions of people more people comfortable with frequently creating content. Snapchat constantly makes it clear that authenticity and timeliness are what matters, not necessarily million-dollar production values.

That focus on organic experience is part of the reason why users have been so quick to adopt Snapchat and happily devote big blocks of their time to Snapchat. Years ago, the percentage of content creators on social or mobile platforms was relatively small. But on Snapchat, 6 in 10 users create content every day.

Snapchat also enables users to set specific privacy and sharing parameters for each piece of content they produce.

Users can create and share content with individual friends, as part of a “user story”, or they can submit content to “Our Stories” which lets Snapchatters build community narratives together (think first day of college or Coachella). These temporary experiences play off what is arguably Snapchat’s most powerful trait: authenticity. Snapchat attracts a lean-forward, mobile-centric, youth audience coveted by many types of apps.

How App Marketers are Leveraging Snapchat

With its broad, youthful demos and massive reach, Snapchat is fast becoming a popular way for app marketers to quickly grow install counts. For many of our marketer clients, Snapchat is interesting as a way of both growing scale and diversifying install streams – something many marketers are interested in doing as the industry consolidates to fewer, larger players. With Snapchat, companies get access to a massive, deeply engaged audience.

Advertising Products

Snapchat offers a full-screen ad format for app install campaigns. Its foundation is a Snap Ad; an up to 10-second full-screen vertical video unit. After seeing the video, those Snapchatters interested in learning more or downloading the app can swipe up for options. Here a brand can connect the user to a longer-form video, other local content, or directly to one of the app stores to download. The introduction of goal-based bidding for install ads further enhances the unit by allowing app advertisers to optimize towards installs.

Currently, advertisers can buy directly from the Snapchat company sales team, through a self-serve ad manager, or field campaigns through one of Snapchat’s growing number of Snapchat Partners. Here is an example of a ten-second video app ad.

Singular’s Relationship with Snapchat and What it Means

As an official Snap measurement partner, Singular has comprehensive access to Snapchat ad campaign performance and spend data metrics, for maximum insight into Snapchat campaigns.

Singular clients can easily measure their Snapchat campaigns using the tools and workflows that they already know and appreciate.

As with each of our more than 1,000 partner integrations, we can deduplicate installs and re-engagement events, as well as provide the full range of ad measurement at the campaign, creative and provider levels. All Tracking and Cohort reports are available.

Singular can also capture your Snapchat campaign spend data down to the ad level. That means you can perform precision ROI analysis, just as you can with any other Singular media partner. This will contribute to unprecedented campaign insights for your Snapchat efforts.

With Singular, you can monitor Snapchat campaign performance in real-time for maximum insight. Use our powerful, unified analytics platform to measure:

  • Impressions: Keep track of every Snap Ad exposure across Snapchat
  • Video Views: Measure the number of Snap Ad video plays that occur on the platform
  • Swipe Ups: Get a precise count of the swipe-ups that occur on your campaigns and executions
  • eCPM: Get a precise measure of the effective cost per thousand impressions for your app campaign
  • eCPV: Ensure maximum insight with comprehensive measurement of the effective cost per video play for your creative
  • eCPI: Get true visibility into your effective cost per install on Snapchat
  • App Install Conversions: Learn how many clicks result in installs
  • Deduplication: Prevent double payment and accurate data for ROI analytics with mobile app tracking deduplication from Singular

With our access to time stamps for Snapchat-driven clicks, we can precisely measure ad performance. Since Snapchat is a “self-attributing network” like Facebook, Google and Twitter, you do not need to create special tags for your Snapchat campaigns. Simply use Singular’s easy-to-use campaign set-up and you’ll be measuring in minutes. Visit this page for information on trackable app event metrics on Snapchat.

We’re pleased to be a Snapchat measurement partner and expect more and more of our clients to field programs in the coming months.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

The Big Trends at Grow.co MAU Las Vegas This Year

I love attending Grow.co’s signature annual event, MAU, held every year in Las Vegas.

This app-centric event has grown into one of the most important mobile events in the world. I thought I’d take a few moments to bullet out some of the most salient themes from the event. This community of more than 1,000 marketers represents a big chunk of the cream of marketing talent in our industry.

What this group thinks and does sets the trends and tone for the industry in the US and around the world. Here are just a few of the big topics from the show:

Massive Growth

I’m struck by how quickly MAU has grown — from a couple hundred attendees a few years ago to well over a thousand today. All that growth is a testament to the strength and commitment of the Grow.co team. It also reflects how the app space has grown and evolved into a powerful business channel in so many categories. Everywhere you turn, you see “who’s who” in app retail, travel, gaming, personal finance, and on-demand services, to name just a few of the sectors that were extremely well represented. More proof that apps have more than “arrived” – they are now at the center of digital commerce for many of the world’s leading businesses.

User Quality and Remarketing Ascendant

As we’ve been discussing on this blog for many months, marketers now care deeply about the quality of users they attract to their apps. There were lots of strategic and tactical discussions about remarketing and ways to encourage ongoing user engagement. I’ll wager that any attendee that wasn’t focusing time and resources on retention and retargeting before last week left Las Vegas with a keen sense that they need to do so in the months ahead.

Holistic View of Marketing and Users

App UA and retention have become more challenging, and marketers are clamoring for more holistic views of their businesses and users. Marketers are talking about good versus bad complexity; having more information has made marketing more complicated – but in a good way. This complexity ultimately leads to unprecedented levels of consumer insight. The challenge is that they are tired of getting all their information from different silos and interfaces. This piecemeal access to information is a bad form of complexity – one that has no benefits but many downsides for the marketer.

Thinking B2C for B2B

There’s a popular expression in B2B marketing – that B2B is “really just B2C with a tie on.” Meaning that we need to understand and appeal to the needs of individuals as we work to build interest and adoption of B2B apps. When a brand can make a case to both a prospective business client and its individual users, app adoption and retention can grow rapidly. This was a theme in multiple presentations at the event – definitely food for thought.

Seeking Early Indicators of Success

This came up again and again in presentations and informal discussions. More and more marketers are focusing time and energy identifying early indicators of user quality so that they can be nimbler in optimizing both UA and retention efforts. This is a particularly important trend in the commerce side of the app business because it may take days or weeks for a user to transact. Marketers can’t wait that long to understand whether their marketing investments are yielding the right sort of users. They need early indicators so they can improve their results faster.

AI and Machine Learning

These aren’t the industry’s buzzwords-of-the-moment for nothing. Lots of companies are developing products that leverage machine learning and other elements of AI to improve customer experience and user insights. Expect to see more innovations from leading solutions providers and new companies. This trend is about what you do with data to unlock its kinetic business value.

Events like Grow.co MAU Las Vegas 2017 are a terrific opportunity to reconnect with clients, partners and prospects. They are a chance to learn, share and promote the growth of the industry. I want to thank the talented team at Grow.co for a fantastic program and event, and all the participants for being active participants in another great app industry conference.

Note: This blog post was published first on the Apsalar blog, prior to Apsalar’s merger with Singular. Learn more about our united company at Singular.net.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

Top Takeaways From AppBoy’s LTR Summit

At Singular, our goal is to help grow the app industry by participating in key mobile strategy discussions wherever they occur. Last week Singular attended AppBoy’s Long-Term Relationship Conference, where mobile marketing strategy and product leaders gathered to discuss the future of mobile marketing and user communication. The event focused on today’s rapidly evolving mobile app marketing stack and featured talks with innovators at companies like Lyft, Ibotta, OkCupid and Wallapop (all Singular customers!). Here are top mobile marketing strategy takeaways from the event:

1. An inflection point for “buy vs. build”

OkCupid historically built most of its mobile app analytics services in-house. Yet things have changed in recent years as the team has started to hook more third-party services into their mobile app marketing stack. “For most our history we felt we were ahead of the curve, but many of these components have rapidly advanced over the years,” said Mike Cirello, Software Architect at OkCupid, who mentioned mParticle, Looker, Amplitude, and AppBoy as some third-party tools used by his team. Cirello’s sentiment echoed that of several speakers who reported that their mobile app marketing teams are using more third-party vendors to achieve efficiency gains in areas like data management, data processing, product experimentation and customer support.

For Lyft, the decision to build or buy components of its mobile app marketing stack largely comes down to speed. As Milan Thakor, Lyft’s Passenger Engagement Lead, said:

“We need to know [the third party] moves really quickly and will build faster than us, and that their vision is aligned with ours.”

2. The danger of disconnected data feeds

In recent years there’s been an exponential increase in the number of mobile marketing data feeds inside organizations, said Michael Katz, Founder of mParticle. Marketers, meanwhile, have quickly learned that siloed data can lead to poor user experiences. For instance, if mobile app customer data is not connected to marketing automation, a user who submits a complaint because of an incorrect order might still get emailed with new deals prior to the complaint being resolved. Or an iOS or Android app user visiting a city might receive location-specific offers in that city even after they’ve departed. It’s clear how those two experiences could frustrate app users. To avoid such messaging mishaps, companies must ensure that their marketing tools effectively communicate with their customer data.

3. Optimizing for downstream events

Bait and switch ads might drive high iOS and Android app ad click rates, but they don’t pay off in the long run, said Rich Donahue, Ibotta’s SVP of Marketing. Instead, Ibotta optimizes its ads and messaging for downstream events, including testing 97 different onboarding flows in its Android or iOS app. In addition, Ibotta runs cross-channel messaging tests that leverage interconnected analytics systems — for instance, tests to determine how paid ads affect the open rates of emails and push notifications.

The Wallapop business, a mobile marketplace for secondhand goods, is similarly focused on optimizing its user acquisition and re-engagement for key actions in its app. Users of Wallapop who haven’t opened a conversation with a seller in the first 7 days after registration are much more likely to drop off, said Nicolás Herrero, Wallapop’s Lead Data Scientist. Such mobile app strategy findings have led to Wallapop building campaigns around custom events and triggering messages if a user hasn’t performed a certain action within a given period of time.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

Observations on App Marketing in India from ad:tech New Delhi (2017)

I absolutely love visiting and doing business the the Indian app industry, and the incredible dynamics inherent in app marketing in India. While we established a sizeable local team based in Bangalore, India in 2016, I continue to stay deeply involved with our Indian mobile app clients and prospects in this market. This year, I again had the good fortune to attend ad:tech New Delhi, India, the biggest digital event in the subcontinent. Given the importance of the Indian market for the iOS and Android app industry, I thought I’d jot down a few notes and observations from our exhibition and our more than 1000 conversations with clients, prospects and partners.

The incredible dynamism in this market is both inspiring and addicting. Every time I visit, I get to learn more and have new experiences that keep me so passionate about this marketing and innovation hotspot.

Continued Mobile Application Industry Dynamism in India

Apps continue to propel the Indian digital economy. Not surprising given the amazing growth in smartphone penetration we’ve seen over the past several years, and which we expect to continue.

Some estimates are that smartphones in use in India have actually crossed the 300 million mark. That’s about 50% more than there are in the US these days, and the market is really just getting started!

Given how apps drive a higher quality user experience than mobile websites, it’s natural that apps continue to be an area of business focus and investment.

Why the Fundamentals of the Indian App Industry are So Strong

Apps are seemingly tailor-made for a digital market dominated by mobile connectivity, and with a rapidly expanding mobile device install base. While average Indian incomes somewhat constrain mobile and other commerce, the $0 download price for most apps makes them an appealing way to shop. But it is not just retail that is doing well in this market. Mobile banking apps, message apps and ewallets have grown rapidly after the 2016 currency adjustments temporarily affected people’s ability to access cash. Android devices dominate in this market, and in addition to Google Play, consumers have ready access to Android device apps via sideloading and niche app stores created especially for India.

Evolving Indian VC Investment Environment

Two years ago, global and Indian VC dollars were absolutely pouring into the Indian app developer market. Now, while investors continue to demonstrate faith in the Indian economy and in particular the mobile device category, they want to see revenue and engagement results – quickly.

Brands that monetize well can continue to score big in investment. Brands that don’t, however, are facing a significantly tougher investment environment.

Retail App Consolidation in India

We all know that the app industry is consolidating in India, as the largest players in many industries snap up medium-sized competitors and create mega-brands. Being at the show, however, made that trend even more vivid. As mobile app development clients and colleagues shift companies, we see that even in a consolidating market there are truly outstanding professional opportunities and challenges for “the best and brightest.”

New Focus on User Quality in India

As we are seeing in most leading app markets around the world, Indian marketers are paying even greater attention to the quality of users they attract. Marketers care deeply about how many of their users eventually become payers or buyers, and are actively seeking out ways to improve user monetization.

We’re seeing an explosion of interest and growth in post-install marketing among leading Indian app marketers. We heard about the increasing focus on user quality in six different ways:

  • Continued interest in finding ways to combat install fraud
  • Partner research and messaging related to the quality of users they attract for their customers.
  • Growth in the number of marketers who say that they optimize to revenue and engagement rather than install counts.
  • Number of prospects interested in mobile analytics and how it can help them deliver data-driven post install marketing that drives maximum ROI.
  • Gradual media consolidation toward larger and/or more sophisticated media providers who can deliver higher quality users.
  • Explosion of interest in mobile marketing automation, push platforms, and more precise user level measurement.
  • Importance of service and insights so that clients can avail themselves of all platform value

At Singular, we’ve long believed that having a strong account management and support team is absolutely critical to driving the most value for our clients. My time at this year’s ad:tech convinces me even more that our investments here are key to our continued success in India.

Shows like ad:tech New Delhi give us a great opportunity to reconnect with our clients, partners, friends, and prospects. To talk about our services and the value they can bring to the industry. I want to thank every member of our team who helped provide a strong presence at ad:tech New Delhi. And thank our many Indian app industry and multi-channel clients for their business.

Note: This blog post was published first on the Apsalar blog, prior to Apsalar’s merger with Singular. Learn more about our united company at Singular.net.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.