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How MobileWalla Is Using Granular Data To Make Enterprises Reach 300 Mn Indian Smartphone Users

How MobileWalla Is Using Granular Data To Make Enterprises Reach 300 Mn Indian Smartphone Users

Data is the new gold. As PayPal co-founder Max Levchin says, “The global is now awash in information and we will see clients in a lot clearer approaches.” But to make such analysis, we first want the raw information, which can be stored for a required time period and can be dependent in an informative way, prepared to deliver any varieties of insights.

Globally, we have consumer facts agencies like Neilson, Gartner, IMRB, Grail, and so forth. Amongst others who excel in offering aggregated bundles of demographic facts throughout exceptional geographies in addition to over web. For instance, to answer questions which includes how many ladies elderly between 18-35 watch a selected TV serial or an internet collection inside the US, a organisation can continually find a inn right here.

But what approximately the rising elegance of millennials who are continually on the move and the number one mode to goal them is the ‘Smartphone’. Talk about India and you've three hundred Mn+ telephone users, predicted to attain 530 Mn by means of 2018, dwelling in 29 states, eating 22+ professional languages, and the sizable diversity in religions, training as well as work practices.

To take in this hard venture, US-based MobileWalla has come forward. It recently released its business operations in India, putting in place its headquarters in Kolkata.

As the serial entrepreneur and founder Anindya Datta averred in a recent interaction with Inc42, “We don’t do what Neilson or Comscore might be doing. If you want to reach ladies aged among 18-35 in India, we are able to provide you with an target market section, say of 6.5 Mn gadgets which belong to this precise age institution. You can take it and then with the assist of companies like Inmobi, can push data as in step with the want of your campaign.”

So Who Is MobileWalla? Take A Look!

Founded In: 2010

Branded As: A next-era client facts enterprise

Technologies Employed: Big data, synthetic intelligence and machine learning

Aim: To be the most granular customer intelligence platform in the world for B2C companies

To Reach For: Getting demographic, behavioral and systems-utilization facts

Sectors: Telecommunications, retail, FMCG, financial offerings and marketplace research verticals.

Products:  Segments and Data as a Service (DaaS),

Highlights: Claims  to have encompassed statistics approximately 1.Three Bn consumers in 31 specific nations

Key Investor(s): Indian Angel Network

So let’s dive in!

The Conceptualisation Of MobileWalla

Anindya, although working out of the USA for quite a few years, continues to be an Indian by using heart, and this is meditated inside the way he has named his startups so far. His previous startup was named Chutney, which was backed by means of Vinod Khosla and later sold to Cisco in 2005. The identical goes for MobileWalla, which he had in advance almost named as Appwalla.

“But the area name became already taken, so we needed to accept MobileWalla.Com,” he reminisced.

Anindya similarly found out that post selling Chutney, he spent nearly 4 years with his six-12 months-antique daughter and apparently, she is the only who landed him with the idea at the back of MobileWalla. “My daughter turned into quite obsessive about one specific recreation on her iPod. This became the time while apps have been starting to take off, and there were round 800 apps on the iTunes save. This made me dig deeper and I determined that local look for the app save become pretty terrible.”

With this realisation, Anindya first constructed an app discovery and search engine, which helped customers look for apps on the premise of Semantic Search. The foundation of Mobilewalla changed into Anindya’s quest to build a third birthday celebration seek engine for apps, but Mobilewalla turned into now not a right commercial enterprise at the time. Although they had been becoming famous, the financial sources had been depleting speedy and there has been no monetisation model in sight.

“So I realised that as we have been doing quite a few semantic seek, and it’s 2010, humans are now accepting apps. So, I concept why can’t we make something which could assist a corporation reach its target market on cellular better primarily based on how he or she is the use of the apps on his smartphone.”

MobileWalla, as it's far these days, took form in 2010. In its early days the corporation changed into backed by way of IAN and is now growing at a fast pace. It’s now monetising via its subscription-based DaaS offerings as well as its target market concentrated on and segmentation carrier.

It now claims:

Team Size: 50 (global), 20 (India)

Also Read:- How to Change the Default Start Menu Power Options (Quick Tip)

Revenue: $10 Mn Annual run rate, Grown 80% Q-o-Q for the beyond 8 quarters

No. Of Customers: eighty five

The Tech Behind MobileWalla

As Anindya explains, “One major source of our information is ‘advert requests’. When you open an app or a website, you're served with ads for the entire duration you're ingesting the content material. The instance an app is opened, it begins sending out ad requests on a constant foundation. These advert requests are structured and transmit facts approximately the device id and the place. This is the key part of the information that we acquire.”

“As the facts does now not consist of any privateness-associated statistics and is majorly dealt with as third-celebration data, data privateness compliances have no longer been a hurdle,” he introduced.

For example, if a purchaser is at Big Bazaar and occurs to test the cricket rating on ESPNcricinfo, that app will continue sending advert requests embedding his or her place. Tracking it'll let MobileWalla recognize in which the purchaser is. A further demographic analysis is carried out on the basis of the person’s engagement on her smartphone.

To give an explanation for further, he referred to a US-based totally patron’s case look at. Consider that a organization desires to promote diapers and attain a ability purchaser base to goal. So, here’s the analysis that runs at the back end:

 We pick out the audience section which has the maximum potential to grow to be a centered patron base for the company. In this example, they're ‘moms’.

 Where are we able to attain out to this audience segment? In the USA, maximum mother and father drop off their kids at their colleges. So, there's a high chance that we are able to attain out to moms at simple faculties. So, let’s say, there are 780K elementary schools in our records, whose place can be effortlessly fetched.

 We will then start tracking the ad requests we acquire from these primary colleges and over a time period, we are able to examine and section this records.

This will enable us to discover the gadgets owned by the moms of these young youngsters.

With this records, the agency can then send centered notifications thru its marketing marketing campaign, leading to better effects.

Challenges On The Tech Execution And Data Analysis Front

The first and predominant task is the information garage. As claimed via Anindya, every day they acquire approximately 25 Bn advert requests. Here is how the garage-value evaluation is achieved.

25 Bn ad requests per day –> Each ad request is 4 KB in length –> Totals to 100 Terabyte worth of uncooked information ordinary –> Need to keep records over a period of time, say  years, totals to at least one.Five Hexabyte

“Amazon Web Services today has the garage capacity of 15 Hexabytes. If we shop this records there, it will consume 10% of their garage and our information storage bill will by myself shoot up to $five Bn. This isn't always economically possible,” shared Anindya.

The crew has as a consequence constructed its own information compression technology based on information semantics wherein the facts can be compressed right down to 1,000th of its length. “This reduces the storage by using ninety%, and consequently our records storage prices,” he brought.

Further, the information analysis is primarily based on statistical and probabilistic measures. So, how does MobileWalla maintain the percentage of blunders down?

Usually, patron information companies do two styles of evaluation – deterministic and probabilistic. So for example, if a retail employer asks for its competitor’s purchaser statistics, this is deterministic analysis and may be finished inside a time body of per week to fourteen days. However, if  the group need to discover facts for campaigns like ‘Mother’s of standard school kids’, it is going for probabilistic analysis, which takes a much larger time frame to complete.”

Further in records, even as taking the inference, one makes  forms of errors, Type 1 and Type 2 i.E. False negative and False wonderful, respectively. False positives are “after I am telling you some thing is proper however isn't always”, and false poor is “after I am now not providing you with some thing which turned into proper”. Simply put, a kind I mistakes is to falsely infer the existence of something that isn't always there, whilst a type II blunders is to falsely infer the absence of something that is. “So we are very cautious that we do Type 2 errors and now not Type 1,” he added.

Anindya’s Bet On India

India, with its range, offers a very massive purchaser market and therefore a big wide variety of B2C organisations. As Anindya explains, “Imagine rivals like Flipkart-Amazon, Ola-Uber, Zomato-Swiggy, or maybe offline inn chains like ITC-Hyatt. Amidst this reduce-throat competition, the business enterprise with higher records will really be in a role to advantage an facet over its competition. Take the case of someone like Paytm seeking the adoption of its digital bills platform amongst each small e-store. That could require granular data and that is where MobileWalla steps in.”

He further delivered that the variety inside the facts intake patterns further made it technically difficult for the corporations to gather and segment the facts and get the answers at a granular level. “But being a pure play generation enterprise, MobileWalla is focussed on fixing such hard tech problems,” introduced Aditya.


What approximately the challenges? Anindya believes that one main venture MobileWalla may face in Indian adoption is the price-touchy nature of the market. Also, the lack of talent specialised within the Data as a Service area.

The Increasing Importance Of ‘Data’ In The Indian Economy

According to a few enterprise experts, Big records analytics area in India is predicted to witness an 8-fold bounce and attain $sixteen Bn by means of 2025 from the cutting-edge $2 Bn. The Indian advertising and marketing analytics industry’s present annual sales is pegged at around $2.03 Bn and is predicted to grow at a CAGR of 23.8% till 2020.

With the Indian authorities displaying a bent in the direction of the promoting of new age technology, there has emerged a brigade of startups using information as a key to solving issues for the organisations and marketers. Fractal Analytics, TEG Analytics, WNS, Datalicious and Bridgei2i are a number of the names leading the charts among others.

MobileWalla, with its target audience segmentation and targeting as well as DaaS services, is aiming to provide one holistic set of offerings to its customers beneath one roof. The employer is now looking to scale and is seeking to make investments greater in its research and technical talents.

Author Biography.

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