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

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

How to Collect Data (Big Data) for Real-Time Next Best Action (NBA)

Data is more vital for sending Promotional Messages, Recommendations or Any Action for any Customer / Segment

The main focus for any retailer/bank/airline/hospital is to build a comprehensive customer signature. The customer signature provides vital information of the customer in the form of :

• Demographics
• Geography
• Behavioral
• Location
• Purchase Behavior
• Social behavior

Collecting this data and rolling up accounts to 70% of Customer Analytics. However, there is always a limitation to it. The customer might shop only specific items with the Retailer / avail certain services from the Bank. If the Retailer / Bank is a specialized one (like Apparel/Jewelery) the information for individual customer will be limited to few tens / hundreds of records /year.

Payment GatewayLocation Based Offer / Message using NBA

 

To overcome this a Bank / Retailer can have alliance with Loyalty firms / use syndicate data etc. The Credit Card / Payment gateway company holds the key.

Take a case of the customer shopping in a high-end Apparel retailer, a Jewelery shop and have paid using Credit card in both places.

The Credit Card company doesn't have the Transaction level information but knows that Customer X has shopped for the following in the same locality

a) Apparel
b) Jewelery

If this information can be used prudently by the speciality footwear store in the same location. Probably the customer might be interested to drop -in provided he / she knows about the store. If the Footwear retailer forms an alliance with Credit card company and push appropriate messages, the sales would definitely go north!

+MasterCard for example collects and stores the transactional data from the customer for data analysis. Customers have the option to opt out.

Credit Card Industry

 Companies like BeyondAnalysis use the Payment Card  (Visa, +MasterCard )'s Transaction data and provide the Retailers like  +Waitrose information about the customers who are purchasing from other supermarkets as well. This information would be limited to aggregate level and not at an individual level there by protecting the privacy of the individual and at the same time generating necessary customer insights

 

 

 

 

 

 

 

How to Deliver Next Best Action (NBAs) in Real-Time

How to create Location-based Real-Time Next Best Offers (NBOs)


NBAs are supposed to understand the customer's need and fulfil them even before he/she wants to act on it.

The main purpose of the NBA/NBO is to happen real-time. With the advent of smartphones delivering the Action in appropriate location and time is now easy.

Any Long distance passenger have to wait for few hours for the connecting flight; The Airline knows the customer’s demographic info, location, time (both are very much accurate) - a good amount of info for Analytics

The data of all passengers are fed to Analytical system and their past travel history, lounge purchases, social habits and syndicate data is crunched to figure out the need for each passenger. The needs are then matched with the Offerings of the Airline's partners (Hotels, Boutiques, Spas) in that particular airport and appropriate recommendations are created

Real-Time Next Best Offer (NBO) Flow

Timely message / offer is sent to passenger’s mobile phone (e.g., offer on antique jewelry in Airport boutique ) based on the time/location and her Inflight purchase/interaction behaviour. The last part - knowing his/her immediate purchase/interaction is more important. This is fed into the Analytical system to validate against the recommendaion that are waiting to be delivered - the recommendations are refreshed and delivered. This is what makes the 'Offer' - Real-time (Right Product/Service)

The above example uses Mobile Phone as the Delivery Channel. Please refer What are the key channels to be considered for Promotions for other Channels.


The omni-channel world requires product information to be managed consistently. The main focus areas are
• Provide consistent data across all channels and update the information real-time
• Offer product information at the right time and place;
• Compile a complete data set and deliver rich, unique data;
• Optimum utilization of social media information