Touchdown Tuesdays - Snapdeal App Review

The retail industry is on a boom, Indian retail business is now standing tall at about 3 billion dollars; Snapdeal is no doubt one of the biggest players in the space, and a very promising one.

While the market is hot, the focus is shifting now towards making the purchase happen on mobile apps, the e-tailers are even doing campaigns to make sure that people buy using mobile apps, Amazon for example offers special discounts if you buy using their application, even the sales are targeted towards a certain platform only.

Snapdeal -

  The mobile application is at par with other competitors, with features like “shake to get exclusive offers”

I like to do my teardowns of any app on certain criterias like

1. Engagement - Knowledge – Competitors and User

2. Disengagement & Why ?

Engagement -

One of the most important part of any e-tailer application is to keep a pointer in the mind of its users that whenever they want to buy something “snapdeal” is the app they should be going to, Its a tough thing to do given that there are too many distractions in a user's life and too much competition, if your engagement message is wrongly targeted or even badly timed , it may be of no use to the customers, in fact the problem boils down to the fact that does the e-tailer know what his customer is intending to buy next.

For the online sales a bigger chunk of this problem was solved by facebook and google, they look at your internet traffic, your likes and shares and deduce what category of products you might be interested in, the retailers do the campaign of their ads and the relevant ones are shown to the relevant category of people.

But when it comes to mobile, a user is living in a silo, he may be doesn't even have an account with your mobile app and never bought a thing from you before so you don't even know his buying profile, and hence you (e-tailer) will suggest everything you can to him on your app, on the first day your app is blind, your app will remain blind until the day your user makes a move and buys something or browses something atleast.

What come out from the above discussion is

1. “Knowledge” about your user
2. Knowledge about his interests, what he likes and what he/she is really into these days.

The next big thing to know of is Competitors, which is vital to know but how to ? remains a question, what if the user is a total Flipkart buff, buys everything from Flipkart, does/should Snapdeal go an extra mile to convert him? Well I leave this question to them but do they know about the presence of their competitor in the user's phone, I doubt that.

Lets assume that we know the user, atleast what you present to user as the first experience will differ user to user; A hardcore gamer should see the listing of game titles rather than some ads selling mixer grinders,although mixer grinder ads may make more sense to a homemaker.

Bringing the user back in the loop is a hard thing to do, you got to keep making a pointer in the mind of user at “subtle” timings that “Our app is around for you”, one of the most used engagement model is sending interesting push notifications, make your users aware of sales and interesting events in your business, but the model is over-used now, infact the pushes are boring, people just swipe them off in morning, they just pop up at wrong times and with wrong deals; What do I have to do with a sale of sarees while I am a gamer, newsbuff, moviebuff, and a shopaholic. I never bought a saree in my life being a male, so you see “Engagement” is no more “send a Push Notification everyday” game , its much bigger than that, there are 100s of apps in each phone, whoever bugs too much is going to get un-installed, If I am a 25 yr old boy and you show me push notification of a sale of Ladies Kurties – I am not interested, I may be interested if you push a notification for a sale on Men's shoes; knowing your user is half the game won.

Usually I get Snapdeal notifications on very odd times, however one of these days the most popular sale was on its way, it was everywhere in newspapers and TV but no Push in the morning at 8 AM when it was about to start; I do get a push at evenings sometimes which I hardly entertain and I just swipe through, a normal hit ratio for e-tailers in push notifications is about 1% may be even less than that, the game is not to drive sales by push but to put that pointer in the mind of users.

The snapshot given below is of a notification promoting a music app, while the phone user is strictly a no-music person, and sitting at his office, the promotion should rather be focused towards music lovers.

While this is what facebook is suggesting me to buy

“Muscletech” - which is a protein supplement, purely because i read about keeping myself active, but does snapdeal knows about this? Perhaps not, and they won't.

Here is an example screenshot which I imagined for a female homemaker using the snapdeal app, powered with knowledge of contexts.

Having a context knowledge of each user locally with api's could eleviate this to a lot of extent, you show the relevant categories of products and you push the right notifications at the righr time to your users to make better chances of them buying the products or atleast to make that cue in their minds about your products.

Disengagement – An app developer may never know when a particular user decided to stop using the app. Disengagement is inevitable, its bound to happen with any application, the game is to get installs, make sure people use your application (engagement), and after a certain period of time the disengagement starts happening and it could be for various reasons – May be your new versions are not stable enough, your push notifications are on wrong time in wrong context, worse your advertisements are not well targeted, your customer support sucks big time it could be any of the reasons, and while finding out who came on network is easy finding out finding the un-installs is not, so whoever stopped using your app is probabaly the dis-engaged one and that's your only cue.

I uninstalled the snapdeal app, and there seems to be no special push or emails to get me back in the game, Why ? Because they don't know it, had they knew how much I spend usually and when exactly did I got dis-engaged, they might have had an idea, simply an A/B test of finding out un-installations since last version update and what version your users were running when they un-installed is vital to know for finding the answer to “why did they un-installed ?”.

So you see you can't just stay CRUD for e-commerce, the game is evolving, so evolve, now is the best time.

A case for push(y) notifications

Push notifications have long been maligned as "intrusive" and overwhelming. I am going to go out on a tangent and make a case of them. 

Push notifications should be viewed as a modern day avatar for "text messages". We are used to receiving a constant stream of text messages throughout the day. Push notifications just add to that stream and hence they are no more "intrusive" than text messages. 

The problem however stems from the fact that a lot of these messages are not informational messages like text messages, but are a a "call-to-action" and ask you to play a game, open a message or complete a transaction. 

So if you really want your users to take an "action" you have be aware whether your users are in the right context and "in the moment". For e.g. if I get a push notification to play a game when I am in a business meeting I am surely going to feel annoyed, and dismiss it. However, if that same message came when I was waiting at the subway station for the train to arrive, I will feel that this is the right time and right place for the message to have shown up. What a difference, the context of a user makes to the same push notification! 

Ideally, a new model of push notification should be there that should take the context into account like below: 

This could be the basis of a new push notification system that can have many other properties

For example the sender could define three types of notifications

(1) High Priority: These notifications have to be delivered immediately. For e.g. change of password or PIN. They may use non-data channels like SMS to deliver them as well just in case the user doesn't have a data connection or its switched off.

(2) Normal Priority: These are equivalent to current notifications and will use data channel only and will deliver notifications and can link to an app or website for further call to action.


(3) Contextual Notifications: These are notifications that will deliver the "best engagement" results and are delivered via a context layer that builds a predictive  model to deliver the notification at the right time and right place 
              to the right user! 

           Till then, keep swiping 'em away! 

SemusiSDK Accuracy - A Comparative Analysis

Semusi has been developing cutting-edge Machine Learning solutions with its SemusiSDK for Human Activity Recognition, Demographics recognition, Place labeling and Interest detection. As our systems have matured, we are now benchmarking our solutions against popular Android applications in the market.

This benchmark is against “Moves”, a popular pedometer applications that measures the steps taken by a user and the duration the user has been walking. We asked 6 people - 3 females and 3 male in their mid-20's, to walk 500 steps. The people were between 5 feet 3 inches to 6 feet in height and between 60-80kg in weight. We used 4 high- and low-end devices held in hand, which took 4.47 minutes of walking time. The GPS in all devices was turned off. The devices used were -

1. Samsung S4
2. Samsung S2
3. Moto G
4. Micromax A21

Summary:

Counting Steps -

As we can see from the graphs, SemusiSDK performs with 63.39% better accuracy on average in counting steps than Moves. SemusiSDK logged 323.5 steps on average, as against 182.58 by Moves.

SemusiSDK is 63.39% more accurate in the best case when using Micromax A21. SemusiSDK logged 285 steps on average, as against 104.33 by Moves.

SemusiSDK performs 59.80% better for females and 25.48% better for males than Moves for counting steps. SemusiSDK logged 340.83 steps on average for females and 306.17 for males, as against 137 and 228.17 respectively by Moves.

Counting Walking Duration -

As can be seen, SemusiSDK is 3.67x better on average for calculating minutes walked than Moves. SemusiSDK logged 3.21 minutes on average, as against 0.69 by Moves.

In the best case, SemusiSDK is 9.18x better when calculating minutes walked than Moves on Samsung Galaxy S4. SemusiSDK logged 3.39 minutes on average, as against 0.33 by Moves.

As in the plots, we can see that SemusiSDK 5.38x better for calculating minutes walked for females and 2.56x better for males than Moves. SemusiSDK logged 3.45 minutes for females and 2.97 minutes for males on average, as against 0.54 and 0.83 respectively by Moves.

 Figure 1: Average steps calculated by SemusiSDK and Moves, and the actual steps walked

 Figure 2: Average steps using Samsung Galaxy S4 over 3 Females, 3 Males

 Figure 3: Average steps using Samsung Galaxy S2 over 3 Females, 3 Males

 Figure 4: Average steps using Moto G over 3 Females, 3 Males

 Figure 5: Average steps using Micromax A21 over 3 Females, 3 Males

 Figure 6: Average steps calculated by SemusiSDK and Moves for 3 Females

 Figure 7: Average steps calculated by SemusiSDK and Moves for 3 Males

 Figure 8: Average minutes walked as calculated by SemusiSDK and Moves, and the actual steps walked

 Figure 9: Average minutes walked using Samsung Galaxy S4 over 3 Females, 3 Males

 Figure 10: Average minutes walked using Samsung Galaxy S2 over 3 Females, 3 Males

 Figure 11: Average minutes walked using Moto G over 3 Females, 3 Males

 Figure 12: Average minutes walked using Micromax A21 over 3 Females, 3 Males

 Figure 13: Average minutes walked by SemusiSDK and Moves for 3 Females

Figure 14: Average minutes walked by SemusiSDK and Moves for 3 Males

Table 1

The actual logged data is as below.

Actual Steps – 500

Actual Minutes – 4.47

User	Device	SemusiSDK Recorded Steps	SemusiSDK Recorded Minutes	Moves Recorded Steps	Moves Recorded Minutes
Female 1	Samsung Galaxy S4	507	3.37	383	0
	Samsung Galaxy S2	259	2.92	0	0
	Moto G	53	1.17	60	0
	Micromax A21	310	3.9	5	0
Female 2	Samsung Galaxy S4	794	4.9	463	0
	Samsung Galaxy S2	380	4.5	0	0
	Moto G	281	5.22	18	0
	Micromax A21	310	3.9	5	0
Female 3	Samsung Galaxy S4	434	2.78	440	0
	Samsung Galaxy S2	295	2.81	270	2
	Moto G	265	3.32	0	0
	Micromax A21	202	2.55	0	4.47
Male 1	Samsung Galaxy S4	335	3.05	319	0
	Samsung Galaxy S2	273	2.6	101	2
	Moto G	278	3.29	0	0
	Micromax A21	225	2.55	0	0
Male 2	Samsung Galaxy S4	554	3.38	469	2
	Samsung Galaxy S2	232	2.78	163	0
	Moto G	293	3.94	176	0
	Micromax A21	329	2.69	435	0
Male 3	Samsung Galaxy S4	274	2.88	390	0
	Samsung Galaxy S2	290	2.58	270	2
	Moto G	257	3.31	234	4
	Micromax A21	334	2.5995	181	0

#BeContextAware

Drumming the #BeContextAware beat !!
E27 is a leading startup ecosystem publisher in Asia.  Semusi made the short list to showcase our technology at Echelon 2014 in Singapore.  Had a great day - talking to many mobile developers who were intrigued and excited about the new ways to develop exciting engaging context aware apps.  

We demoed Semusi API's, samples apps, customer apps and our Semusi Smart Targeting beta.

 

Marketing in the Age of Context

"In marketing, content is king but context rules. Context rules because it makes the experience relevant”

Last Sept (2013) I attended the Mobile Marketing Forum event in India where I gave a give a talk on 'conquering mobile advertising holy grail: Context-Awareness matters'. The premise of my talk was that mobile marketing needed to become more engaging, relevant and smarter! In a world of 'one size fits all' marketing, I urged mobile marketers to start to think about realizing the full potential of mobile marketing and to deliver on the promise of engaging 'right message, right place, right time' sometimes referred to as 'marketing in the moment'. A prerequisite to deliver on this promise, was for marketers to start thinking about a user's context, succinctly referred to as 'context awareness'.

To illustrate the potential power of context in marketing I previewed Semusi work on activity context and demoed how mobile banner ads could change in real time based on the user activity - sitting, standing etc

In Nov (2013) Robert Scoble & Shel Israel‘s launched their high anticipated book 'Age of context'. The authors did a great job in making the case that the combination of 5 forces; Mobile devices, data, sensors, social and location will give rise to a new era where context will play a central role.

“As discrete entities, each force is already a part of life. Together they have created the conditions for an unstoppable perfect storm of epic proportion"

The great thing about a high profile book launch is that its gets everyone talking. Over the past few months I have enjoyed talking to many marketers about the relevance of context in marketing.  In these discussions, marketers often ask for comparisons with native advertising and web based contextual marketing where content is used to understand context. We believe content consumption for a user is at best one signal of user context. To truly understand user context requires deciphering multiple signals - content, location, sensors to name a few.

Semusi is 100% focused on fusing multiple signals to provide an easy to use context awareness platform for marketers and developers. In my interactions with marketers,  many of them remarked that whilst understanding the activity context of users in exciting, it would be great to understand the Demographic Context for users; Gender, Weight, Heights...using sensors.  Well the Semusi team got to work ....today  at the Mobile Marketing Forum in Singapore (May 2014) I will be demoing Demographic Context in action! I look forward to meeting marketers and brainstorming how marketing will change in the Age of Context.

The Places in our lives

This post is about how “Places” as a semantic notion is more powerful in some ways than “Location” as a geophysical notion. 

Take a look at how you interact in offline or physical world. Do you say “I am at A-41 Corenthum, Tower A….  (or equivalently I am at 28.6272, 77.3735)“ or do you say “I am at office”

The former is a way of expressing Location and the latter is a way of expressing a Place.

A lot of services over time have been built using LBS (or Location Based Services) but unsurprisingly none of them have been successful. Whilst people have given several reasons, one of the reasons could be that just because you are in a particular location doesn’t mean that you are interested in services for that location, even though the likelihood is there.  For e.g. just because you are in the vicinity of a Pizza Hut doesn’t mean you are interested in buying a Pizza. You could be passing that location regularly everyday on your commute.

So what really matters is the significant locations in your life, like Office, Home or Transportation (if you consider a car or a bus as a Place)

What about business establishments that want to target users who are in vicinity – the poster child of LBS? Well, they should look at other signals – for example is the user found in other “Places” like the current business? Is the user in the vicinity of a Place that serves food and its lunchtime?

I think that the discussion has to move beyond Location to Place, Time, Demographics, and other aspects of a User to enable these services to be meaningful and succeed.

Use Cases for Mobile Context Aware Systems

In the previous post I had discussed why are context aware systems important now (link)

Its natural to ask that given this importance, how can these systems be useful.

Well, before we answer that question we have to define what "context" is

We like to define context by breaking it down into 4 parts (Who, What, Where, When)

Who: This is your identity and relates to your gender, age, weight, height, background, profession etc
What: This looks at what your current activity, ambience or interests are.
Where: This is your location (not just a lat, long) but also what type of taxonomy can we attach to that location for e.g. home, office, commercial, residential, restaurant, etc and also add more attributes around that location.
When: This tells you what time of the day, week, month, year etc is relevant to the current context.

So, given this definition of context we can define that a context can be used (i.e. personalised) as follows

WHEN a WHO is doing WHAT at WHERE then do ACTION

The use cases can then be derived as following