Location Based Predictive Analysis using Crowd/Social Data - Traffic Congestion Analytics / Parking Space Analytics

How to use Social/Location data for Big Data Analytics / Real-Time analytics


The real use of Analytics is slowly emerging. This time let's review some Mobile Apps build using +Android, Apple and +Windows Phone

Parko - an App to find the Parking spot uses the user's mobile behaviour and predicts when the user will vacate the spot and alerts the other drivers who have registered. The behavioural analytics is the USP of Parko


+Waze - that was recently acquired by +Google, Inc. uses real-time information from nearby drivers to find the best path. It is basically a GPS-based navigational app which uses turn-by-turn navigation and the historical user-submitted travel times and route details

The +Traveling Salesman algorithm can be tested here.

Real-time analytics need Big Data infrastructure where companies like +Cloudera and +Hortonworks play a key part. With Internet of Things gaining popularity the Apps will be replaced by the Cars itself. Some models from +Ford Motors  have the chips that can be used to relay Vehicle information to a central repository, which can be instantaneously mined and their insights reported/shared.

This also needs a co-ordinate approach from the government / local body. The results from the analytics is not just for the drivers - it can help the Local police plan Signals appropriately, the Schools and Hospitals can plan their routes.

Location based analytics also involves predicting the behaviour of the user based on his/her current navigation.

Geo Fencing in Marketing

Geofencing is the new buzzword for location based marketing.  The success of FourSquare has led to location based analytics and marketing. Location based marketing needs to take care of

• Delimiting Geofencing Perimeter
• Analyze Perimeter Segments
• Data Integration (Location with Transactional and Customer data)
• Location sensitive content / message / offer creation
• Privacy Filtering
• Location based Delivery

Customer analytics from Anonymous In-Store Customers (In-Store Shopper Analytics)

How to Perform Data Analysis on Anonymous Customers who have Purchased In-Store with Cash

Customers who shop irrespective of whether they have loyalty card leave a trail during payment, Their Credit Card Data stores vital information and can be used for subsequent analysis (Payment Card Industry - Crucial for Real-Time Next Best Action (NBA) - Big Data)


How to Track Anonymous Customers

Tracking Anonymous customers (visitors) on the web is quite easy nowadays. The Cookies, IP
Addresses etc provide a wealth of information and this can be used when the visitors return. Tools like SAS, SPSS, +IBM Smarter Analytics and Manthan Systems's ARC Customer Analytics provide anonymous visitor behaviour analysis .

How about Anonymous Customer in Store and analysing their behaviour. Companies like Catalina uses the information of buying preferences of customers to drive promotions, merchandizing and sales. Catalina claims their data and insights are unique.

Aggregate data from Customers provide rich information about Brand Preferences etc.


Video Analytics is also common in Retail nowadays. RetailNext, uses video footage to study how shoppers navigate and pushes appropriate messages/promotions

RetailNext also uses data to map customers’ paths. What is interesting is the ability to differentiate men from women, and children from adults from anonymous data

How it Works:
Most often the Analytics provider rely on Wifi Pings, the Shopper's phone when it searches for WiFi provided by the retailer, pings the Router and the Router captures the signal strength to identify the location

Euclid Analytics also uses WiFi data to understand the footfalls, the time spent at each aisle etc. Also it measures the signals between a smartphone and a Wi-Fi antenna to count how many people walk by a store and how many enter.

+Cisco's Meraki wireless router has a feature called Presence, which has a good location analytics dashboard that measures key anonymous visitor tracking metrics - Capture Rate , PasserBy, Visitors, Median Visit Length, Repeat Visitors etc.

This information can be used for better in-store promotions, Point-of-Sale displays etc. Cisco Meraki has partnered with Facebook to provide Free WiFi in lieu for providing some information

Amazon is testing its own WiFi network. Currently Amazon devices access the internet through its Whispernet service, which runs on AT&T’s 3G cellular network

Real-Time Customer Feedback

Annik has launched ‘Rapid Insights’ specifically for companies who operate retail stores and want to understand & track customer feedback in real time. The real benefit of this solution lies in taking corrective action in real time to improve overall customer experience. This revolutionary technology is extremely user friendly and can be implemented in retail stores easily.

How it works?
-- Placing of tablet at the retail outlet with platform agnostic proprietary customer feedback module
-- During the retail journey, customer shares experience through tablet
-- The management accesses real-time dashboard to view customer feedback and take corrective action

Create Right Mood at Right Time using Experience Players 

Customers doesn't come for Price/Offers, they come for good customer experience, which is why the retailer need to create Right Mood at Right time. Philips Retail Solutions (PRS) helps retailer create memorable in-store experiences with its Experience Player (EP). EP is a clever little black box that allows one to control individual experiential assets, whether it be animated on-screen content, atmospheric light levels or audio. The player can be scripted or programmed with a sequence of events within a set timeline. This solution is ideal for a Garment boutique or Clothes Retailer

iBeacon and LTE Direct

With more devices on Customer's hands, getting their attention becomes a top priority when they are inside / near the stores

iBeacon
Apple's new mobile notifications system iBeacon is being touted by big brands as the next great opportunity in advertising. Using tiny Bluetooth wireless transmitters affixed to buildings, iBeacon lets companies send iPhone owners specific pop-up notifications based on their location and proximity to stores and products. These iBeacon transmitters need not be a separate device but some of these transmitters are simply iPhones as iPads running iOS 7, which lets these device act as both iBeacon transmitters and receivers

LTE Direct
+Qualcomm's LTE Direct technology is touted as an alternative to iBeacon because of the Privacy it brings. LTE Direct is a device to device platform (synchronous) that helps proximity detection apps in battery efficient manner.
Its discovery is connectionless and is only based on proximity, allowing the devices to discover others without revealing their own identity or exact location. Devices in proximity read the 'expressions' for determining the relevance of one another. This is meant for the autonomous discovery

Fabric is the product of Powered Analytics, Inc., aiming to bring an Amazon-like shopping experience into the physical store. Fabric works using Beacon technology -  it learns the store floormap and tags products and categories with their location in-store. Integrating the application with Web and Mobile applications (Apps) would ensure that the shoppers are tracked across channels, and their location is used to predict the right product at the right time across channels

This way In-Store (Brick & Mortar) customers can be delighted by leveraging the data from their online experience and vice-versa

Read then Follow Now - How to Chose Right Channel - Using Social Media Analytics

Derive Customer Insights from Social Media Information by Big Data Analytics


Not so long before people (or customers) read newspapers, browsed magazines, visited libraries . Then came Web - people browsed, then came the +Amazon Kindle Fire a Book library in your hands. Now it's an era of Social Media where +LinkedIn , +Twitter and +Facebook have made them as Followers!

Yes people follow a lot - +The Boston Globe , and +Bloomberg News is available on Twitter and Facebook. You can subscribe, follow them . You can follow +CIO , +TechCrunch if you are a technology geek. If you are a marketer then +marketingprofs might be in your following list, +Nike and +adidas will be for a Sports geek

So what these tells us .. in the first place it gives some understanding about the customer's behaviour. Why is this so important for any marketer.

Social Media, Marketing and Segmentation

Marketers now use Social media extensively for communicating with their customers. They also use the massive volume of Tweets, Likes to get more insights about the customer.

A simple analysis carried out by a Watch manufacturer shows that those who have bought their premium watches are followers of  GeorgeMichael (+GEORGEMICHAELORG GMORG) Is it time to tap them?

How to Merge Social and Customer Data

Clustering or Segmentation of the data using a combination of Customer, Transactional and Social data is beneficial as it will find homogenous groups.

To arrive at the point - Social media data needs to be aggregated. This is where Big Data comes into picture (Refer How is Apache Hadoop used Big Data Analytics and Inteligence ). Companies like +Hortonworks , +Cloudera Inc provide Hadoop clusters where Social media data can be crunched to get the relevant information out.


Here care must be taken to include/excluded Social data as segmentation variable as it has huge potential to skew the result

Social media information can be used either as :

• Segmentation Variable
• Interpretation variable

The cluster profiling would reveal the impact of the social data. For example, a Retailer found that customers who retweet have constantly responded to a type of promotion. Share your own experiences



See also
How to Segment Social Media Users (Influencers / Detractors / Recommenders)

How is Apache Hadoop used Big Data Analytics and Inteligence

Big Data Analytics - Usage of Apache Hadoop / What is the use of Apache Hadoop in an Analytical Project

There are various ways Apache Hadoop is used in a Big Data Project.  +Hortonworks latest report

Apache Hadoop Patterns of Use: Refine, Enrich and ... - Hortonworks categorizes it into

• Data Refinery Pattern
• Data Exploratory Pattern
• Application Enrichment Pattern

In Data Refinery - Hadoop is used for Cleansing up the data and sending the output (probably as aggregation / refinement) to the Enterprise Data Warehouse which might be +Oracle +SQLServer etc

In Data Exploration pattern - data is analysed in Hadoop environment, using Hive etc. and the results are shared with the Applications. There are many Big Data Visualization Tools that provide good reports. There are some Open Source tools like Pentaho Business Intelligence Studio that offers Big Data Visualization and Reports

In Application Enrichment Pattern the entire data is stored in Apache Hadoop. For example, the Web session is stored in Hadoop and appropriate actions are taken based on the user's web navigation .


These are some broad classification of the use of +Apache Hadoop

Free Tools for Data Mining

I got to know about RapidMiner and thought of testing it out (under progress). Tried K-Means clustering. The selection of raw data / input file was pretty easy, then I tried to link the process to K-means.  +RapidMiner threw an error that there were some missing values (also suggested to select a method/process that is available for missing value imputation.

I added the process (refer figure below) and then linked the process back to the +KMeans. So far so good. Wanted to know more from statisticians and analysts who have used  Open Source Software - like R,  and also SAS,   +Microsoft Dynamics ERP and SPSS

Segmentation of American Teens Internet Usage

American Teens - Segmentation

Smartphone adoption among American teens has increased substantially and mobile access to the interTeens and Technology 2013 findings. +Teens of America are +Hyperconnected
net is pervasive  by Pew Research Center's

This also shows the predictions for 2020 by +Pew Research Center and +Internet Provider.org

How to Measure Effectiveness of a Campaign using Marketing analytics (Banking)

Marketing Analytics - Measure Effectiveness of Email Campaign for Bank


In our Case Study: Customer Analytics for Telecom Operator to Cross-Sell and Up-Sell we had a look at the Telecom Analytics .. Now let's have a look at how Analytics has changed the communication between the bank and its customers

Banks have a good understanding of customer as the customers part with various data points (SSN #, Tax #, Address, Employee, Salary etc.) at different points of time (Enrolment, Address Change, Mobile Banking etc..)

This information is used for personalized marketing through email (This is the Channel for analysis - multiple outbound channels can also be fed) .

Processes in Marketing Analytics for Personalized Promotions for Banking

The processes involved are as follows

1. Gather Data
2. Analyze Data , Document Discovery and Create Model
3  Execute Campaign
4. Collect Responses
5. Measure the Results (Efficiency)
6. Fine tune / Optimize the model
7. Refresh Model and Segment

Since the necessary data for understanding the customer is already available / collected, the data is segmented to understand different customer segments that are available and their profiles

Each segmented is treated in unique way based on their behavioural patterns

The segments are divided into control and test groups. The customers in the test groups are subjected to the personalized marketing campaign while the control segment customers are part of regular campaigns

The most important step after sending those mailers is to collect responses. This is a key differentiator between a good and bad campaign management

The responses are subjected to Response Segmentation analysis and the results are measured. What are the parameters that are clearly visible in the responders? What are the feature of the non-responders .. this is an area where business needs to play some role rather than IT / Analysts

The Bank's Personalized Campaign has created a 500% increase in response compared to the generic mailer (Refer Figure)

The model is recreated with the understanding from the new findings - this is called Model Recalibration. The model is refreshed in campaign management system and the process continues



The Next Best Action (here the Mailer) is a continuous learning process

+Microsoft Dynamics ERP   was used as the CRM for the Bank. Third party software was used for Mail blast and custom code was used for model development \


There are also custom tools/solutions for campaign / personalized promotion. For example, Manthan Systems' TargetOne tool uses data from various touch points like point-of-sale (POS) and store vicinity, during ecommerce transactions, email click-throughs and social media engagements to ensure right conversation is initiated with the right customer at the right time through the right medium

See also:
Payment Card Industry - Crucial for Real-Time Next Best Action (NBA) - Big Data