Let us first try and define this beast.
[Sidenote: As we work with several of our clients, it’s amazing how analytics “jargon” confuses business users across the globe.]
In our world, alternative data is all such data that is currently not available to businesses through their “usual” structured application systems (such as CRM, ERP, Transaction Systems, etc.). Moreover, it has come to signify data available in real-time. In more practical terms, it is data that is being generated by consumer-facing devices, systems and applications, such as mobile devices, social network interactions, sensors, smart homes, etc.
What we are talking about, though, is readily available data generated on mobile devices.
With mobile phones gaining a huge prominence, attention, and share of wallet in everyone’s life, all apps on the device are (can be) privy to a lot of information. This would include, for instance, social media data, device usage information, emails, apps, notifications, messages, and so on. Android, through carefully crafted structures allows you to take user permission and, hence, the data for business purposes. All this data would come under alternative data.
Is it relevant for me??
Over the last couple of years, we have seen a big change in the way alternate data is being perceived. From “you’re kidding me!” to “this-sounds-good-but-is-of-no-use” to “I-want-that-shiny-thing-too” to “hmmm-this-could-be-valuable”, alternate data has come a long way.
Alternate data use cases are all about contextual awareness, faster decision making, and bringing a little bit of a-ha to your customers everyday and at every possible opportunity.
What businesses further need is the ability to convert all this alternate data into meaningful information that they can use. Well before we let artificial intelligence take over. In most cases, we are looking at automation being the fifth step, while businesses are still struggling with the first step of meaningfully capturing and structuring this alternate data. [Preliminary reporting/ MIS, descriptive analysis, and predictive/ forensic modelling would be the other stages of analytics adoption]
Say, if you are a digital lender, would the awareness of the multiple financial relationships, investment profile, income information or good Samaritan behaviour, their mobile recharge/bill payment behaviour, utility bills, the company they keep(!) (in real time) allow you to feel better about this prospective loan applicant? And what about their social behaviour idiosyncrasies? Would being able to process all of this fast allow you to focus your verification and approval processes on the real “tricky” things, rather than spending time on multiple rounds of documentation? And isn’t this a lot more information than just a bureau score that you’ve come to rely too heavily on, with little or no differentiation between lenders?
Or, if you are an e-commerce company, say Flipkart or Snapdeal, how much would knowing the “true worth” of a customer who’s shifted all their transactions to Amazon affect your customer retention and reactivation strategy? Again, real time? Or, maybe find that serial abuser, who loves returning items all the time? As a habit? Would it matter if you do that “nice thing” around key events? Like that “candlelight dinner voucher” that accompanies what you clearly should know is a gift for the missus? Or, the Julia Donaldson book you throw in as a surprise gift for a father buying something for his little one? On the birthday? Sometimes, they work more than the Rs. 100 discount coupon, don’t you think?
Or, if you are an insurer, would the awareness of key life events, such as children’s birthday, pregnancy care, a physical injury, or a negative claims experience, an increase in salary, a significant incoming bonus, allow you to position your product offering a lot better and at a better time? Would someone with a family history (undisclosed) of diabetes be considered a higher risk? How about pre-processed car insurance discounting/ waiver well before the car is purchased? Or, preventive health check-up for someone who’s worried about their weight gain? Or, a discounted gym membership for someone who’s started clocking more steps on their wearables?
We believe that while the use cases and applications are many, the bottomline is just one – the more you know about your customer, the better you can manage the “customer lifetime value”.
So, where do we begin?
We have this 5 question checklist that we start most of our discussion with –
What are your top 3 business challenges today? Do any of them get impacted by availability of alternate real time data?
Do you have a customer facing mobile presence?
How much data are you really capturing from this presence?
How much of that data are you really integrating with your customer data/ app data/ design team/ marketing team/ risk team/ strategy team?
On a scale of 1 to 10, where do you rate your mobile analytics capabilities? (we hear numbers like 8 or 9 at times, but more often than not, businesses today are in the 1-5 range.)
Our accelerators and products vision is to completely transform mobile analytics, right from the ability to bring alternate data to business (Algo360), to bringing robust analytics to businesses (AppSights), to bringing analytics solutions (MuSeD – micro-segmentation for individualization of app based marketing/ Predix1 – Recommendation engines for contextual recommendations) to businesses whose focus, rightly, is business. If you need putting your alternate data strategy in place, Algo360, a 360-degree view of the customer, might be the way to go. Just give us a shout, and we will happily bring you your dose of alternate data to ease your credit risk assessment, credit underwriting process, loan underwriting, underwriting mortgage process and more over digital lending platforms. Algo360 SDKs are easy to integrate, and our analytic data feeds generous to the point of being overwhelming.