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

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 to Segment Social Media Users (Influencers / Detractors / Recommenders)

Segmentation of Social Media Influencers

We have seen in the past about the Retail Specific Segmentation Standards and also some Segmentation Tools that help to achieve that.

Here we would delve bit deep into segmenting the social media users.

Behavioral Segmentation of Social Media Users

Social media users are classified as shown by the Klout Influencer Matrix given below

Depending on the Need / Goal, one can select the specific segment of users. IBM's Social Analytics Tool (SPSS) helps to identify the following from Social Data

• Influencers
• Recommenders
• Detractors

The above combined with customer and transaction data would give necessary insights into customer behavior

Merging Social Data with CRM / Customer Data

The social users can also be segmented by demographics, geographics, which would provide information that might not be captured in CRM (for example, +Microsoft Dynamics ERP )

The 7 elements of social data

Sandile Mayambala a digital analyst as come up with the following combinations

• demographic
• product
• psychographic
• behavioral
• referrals
• location
• intention

It may not be possible to get all data points for every customer. In that case the data is normalized and analyzed

See also :How to Find Social Influencers using analytics,
Five Types of Social Media Influencers

How to Find Social Influencers using analytics

How to Identify the best influencers from a given data


Who are these Social Influencers ?

One can associate a Social Influencer with :

One who has the maximum followers
One whose opinion is considered by his social circle (who can influence others easily)
One who creates and shares content regularly
One who makes most calls; accepts most calls etc

Benefits of identifying Social Influencers

Identifying influential persons within a circle will help the marketers expand the business; for example, they might be selected for a Test Drive (as Ford did), evaluate a product/service (Beta Programs from Microsoft etc), conduct surveys and thereby spread the word.

How to Identify the Influencers

Identifying Influencers is a broader statement. There are scores like Klout score etc available to weigh the influence of one in social media

The Klout score is an useful one for Social Media

However, a more deep data mining exercise would be more helpful. This would be more helpful than a specific score as the business goal of the datamining would pave the way for the most appropriate social influencers for a goal.

For example, if a Telecom group wants to acquire (poach in fact) new customers, it can identify customers who are good influencers and whose circle/friends are from different telecom network etc.

Building a robust dataset is very important. One needs to collect all possible information (from the call records - CDRs ) and roll-up the same for each customer,

Once the Data is created, one can use Supervised learning techniques or Link analysis to find the best scoring customers.