Background Info

Details

As you go through this demo, you can open this panel at any time to see what is happening in the background and highlight how simple it is to integrate the demostrated capabilities via web services

Key Points

As you go through this demo, you can open this panel at any time to see what the key messages are that each part of the demo is trying to articulate

Power Name & Address Search

How It Works

Details

Name WSAddress WS
  • Normalisation & correct capturing of customer names
  • Easy Address Entry
  • Accurate Address Entry
  • Avoid duplicates
  • Check details agains global watch lists
  • Maximise chances of cross- & up-sell by recognising existing customer even if they are not logged in and using a different device

Key Points

With power search you can just start typing any address in a text box and select the matched address. It is formatted correctly to ensure no duplicates.

If there are duplicates then there will be a popup that can be used to encourage the customer to login to get a better offer.

As you enter names and addresses they are checked against a criminal watch list.

Instructions

Type any name, followed by any address. If no matches are found, make sure your country is correctly set in Settings on 'Select Vertical' page. When you see the correct address select it and it will populate the address field. At any time click on the 'WS Details' button in the 'Details' section above to see the how simple the web service calls are that are going on in the background.

If the entered first name is any of the following: Daniel, Harry, Luke, Dave, Ian, John, Matthew, Phil, Robert or Adam, then the audible in the personalised video in the next section will greet you by your first name. Otherwise you will be greeted with 'Hi there', but the entered first name will still be displayed visually in the video.

If you enter the name 'Charles H. Duister' (case insensitive) and select the address '3002 Summer Dr, Lakeside, AZ 85929-7704, United States' then you will see If you enter the name 'Charles H. Duister' (case insensitive) and select the address '3002 Summer Dr, Lakeside, AZ 85929-7704, United States' then you will see a popup appear warning that this customer is on some terrorist watch lists.

If you enter the name 'Jonathan Black' (case insensitive) and select the address '3002 Rabbit Rd, Gilmer, TX 75644-5672, United States' then you will see a popup appear informing user that they appear to be an existing customer and that if they login they migh qualify for additional discounts.

Tip 1: Press '=' key in the Name field or hold your finger in the name field for 1 second & name 'Charles H. Duister' auto-populates. Press '?' key in the Name field name 'Jonathan Black' auto-populates.

Tip 2: Press '=' key in the Address field or hold your finger in the Address field for 1 second & address '3002 Summer Dr, Lakeside, AZ 85929-7704, United States' auto-populates. Press '?' key in the Address field & address '3002 Rabbit Rd, Gilmer, TX 75644-5672, United States' auto-populates.

Tip 3: Clicking the 'Address' label reveals / hides some additional key fields used by banks to identify customers.

Demographics & Personalised Offers

How It Works

Key Points

The name & address selected in the previous section was geocoded and then enhance with demographic information. Checks were also performed to see if this user is an existing customer. If they are then all known data is retrieved.

All the above data is passed to the recommendation engine, including location and device details so that the recommendation engine can return the most relevant messages for this user and in this context. Previous interactions are remembered and considered when preparing new messages, irrespective of whether the user is logged in or not.

Personalised Interactive Videos can be composed on the fly by calling a simple web service and including customer data in the request. The first name entered in the previous section will be displayed in the video.

Personalised Interactive Videos can be composed on the fly by calling a simple web service and including customer data in the request. The first name entered in the previous section will be displayed in the video.

Personalised Interactive Videos can be composed on the fly by calling a simple web service and including customer data in the request. If you entered any of the following first names in the previous section: Daniel, Harry, Luke, Dave, Ian, John, Matthew, Phil, Robert or Adam, then the audible in the personalised video in this section will greet you by your first name. Otherwise you will be greeted with 'Hi there', but the entered first name will still be displayed in the video.

Personalised Interactive Videos can be composed on the fly by calling a simple web service and including customer data in the request. The first name entered in the previous section will be displayed in the video.

Location Intelligence

How It Works

Key Points

The Address which was entered in the ‘Name & Address’ section is taken as the input for this service. The objective of this service is to show drive time boundaries from a current location on the map.

The entered address is geocoded to get the correct Latitude and Longitude and this can be done by using various Geocoding databases configured within the system. The geocoded address is then passed onto a routing engine to calculate the drive time boundary.

This functionality can show you how far you can reach in ‘n’ minutes from your current location. This can be overlaid with other information e.g. Point of Interest data. It is possible to know the different point of interest locations near you by using this service. For example you will be able to find all the restaurants within 5 minutes (drive time / walk time) from your current location.

We can deliver contextual location based messaging in real-time. Not just based on location or distance from a location but based on parameters such as drive time or walk time from a location too.

The solution makes it easy to visualise data by overlaying it on a map.

Instructions

  • Demonstrate location based messaging by touching or clicking the map once. Then drag the circle and move it into the drive time boundary and a location based message will popup.
  • On the map click the top right '+' button to control different data set visualisations by overlaying them on the map.

Customer & Product Data

How It Works

Key Points

  • The address fields were prepopulated when you selected an address in the free entry Names & Address section above. You can demo here how we can validate and enrich existing customer records.
  • Other fields are autopopulated as much as possible based on data that is already known on the customer.
  • Try to never request a customer to enter data that is already known to the organisation
  • The Stewardship module executes rules on the data fields as they are completed to predetermine in real-time if the application can be accepted. For example someone with an income of less than average income and aged over 75 might not qualify for the purchase of life insurance for million dollars.
  • Location data such as drive time from nearest fire station can allow you to calculate more accurate insurance premiums. In particular if it can be queried in real-time during the online application process.

Instructions

  • Click 'Validate Address' in the 'Details' section above to validate and correct the address and see the percent confidence we have int he address being accurate. Delete a field in the address and click 'Validate Address' again for a more in depth demo.
  • Click 'WS Details' in the 'Details' section above to see how simple the web service requests and responses are. Note beside the REST web service used in this demo we also have a SOAP version.
  • Demonstrate the Stewardship module by requesting a credit limit greater than 10% of annual income.
  • Demonstrate the Stewardship module by requesting home insurance for a Home Value above 500k and the 'Security' setting set to something other than 'Burglar & Fire Alarm'.
  • At the bottom of the form click on the Drive Time from nearest fire station to reveal details about how this was queried in real-time during the application process.
  • Demonstrate the Stewardship module by entering a blank Tax ID number.
  • Demonstrate the Stewardship module by entering a high weight that would give you a BMI (Body Mass Index) of 30 or higher.

Communication History

Details

This is where an agent can see a complete communication history

Key Points

Story

Communication History

Details

WS Details

This complete communication history is made available through REST calls to an e-Messaging service. Viewing of actual communications is by calling a simple web service

Key Points

Story

Communication Rendering

Details

WS Details

Key Points

The simple Web Service make it easy to render customer communications from any system - both internal and external, all in line with your organisation's security policy.

Campaign Treatments

Details

WS Details

Details on web service req / res to access campaign history across channels

Key Points

Easy access to all campaign treatments across all interaction channels.

Campaign Responses

Details

WS Details

Details on web service req / res to access campaign history across channels

Key Points

Easy access to all campaign results across all interaction channels.

Consumer Mapping by Twitter Sentiment

Details

Differentiators: Massive storage of 80M Tweets; rapid point data retrieval; Integration of several SAP/PB software solutions; large data warehouse of Twitter posts; integration with Google Maps API (solution could be Bing and therefore a replacement for Google as it retires its mapping products); Routing.

Technology: The demo uses SAP HANA located on an Amazon Web Services (AWS) node to store and the HANA query engine is used to create the spatial queries. The travel boundary-based trade areas are created with Spectrum Enterprise Routing Module (ERM). The demo is built as a J2EE web application and use Google Map API for the map visualization.

Data sets in use:

  1. 80 Million (M) out of 200+ Million geotagged tweets in US and Canada for 90 days of period (Oct-Dec 2013)
  2. Daily volume between 2M to 4M
  3. 9.5M POI data sourced from TomTom
  4. Store brands (others can be search but these are recommended because Twitter data is more relevant)
    • Starbucks
    • Taco Bell
    • Trader Joes
    • Walmart

Key Points

A marketing firm is using consumer sentiment data expressed through Twitter to understand brand identity and satisfaction with certain retailers. To do this, the geocoded point locations of tweets have been scored, mapped and color-coded by “positive (green),” “negative (red),” or “neutral (blue)” sentiment. The analysis will look at the clustering of sentiment nearest to store locations. A coverage area showing the predominant sentiment nearest to individual store locations will be mapped to ascertain whether the store or brand is resonating with consumers within a given time period such as by day of week. The objective will be to strengthen the brand where sentiment is low and introduce direct mail campaigns in those areas to induce more sales and improve sentiment.

Instructions

  1. Workflow 1: Sentiment
    Type “Starbucks” into BUSINESS NAME; click Search button (wait for map to refresh); CLICK Display Layer-Tweet; wait for display; then add Store.
    Narrative: The marketing firm reviewing consumer sentiment has mapped all tweets by sentiment value from the previous week. However, they are more interested in a particular area in Manhattan where sentiment is neutral or low; and there may be a possibility where more promotion needs to be addressed or perhaps a new store sited.
    Display sequence: ZOOM TO Lower Manhattan near City Hall. 4. In this area, there is area there are mostly neutral tweets with some positive and some negative sentiment as well. Notice also that there are few store locations just south of City Hall. In an effort to stimulate sales at existing stores, the marketing firm recommends that an advertising campaign using billboards and subway stations begin immediately.
  2. Workflow 2: Trade Area by Drive Time
    PAN South to Brooklyn and click on Store at 2201 Nostrand Ave.
    Narrative: In this area of Brooklyn, Starbucks locations are more disperse but management needs to determine to what extent the trade areas are overlapping.
    Make sure Driving Time is set to 5 Minutes; CLICK on store location at 2201 Nostrand; CLICK on Display Trade Area.
    We can see that the trade area encompasses two other stores at the outer extents of a 5 minute drive time radius.
    CLICK on each of the other two stores in the trade area, Display Trade Area for both stores.
    We can now see that while there is some overlap of the store trade areas, the Avenue U store does not seem to negatively impact either of the other two store’s trade areas but the Kings Hwy Store impinges on the nearly half of the Nostrand Store. Mangement needs to make a decision by looking at the store sales data whether there is significant cannibalization and whether further action needs to be taken to increase promotion, move each store or close one store.
  3. Workflow 3 [Tentative] – Walking Distance Trade Area Modify workflow #1 to show walking distance to each store and illustrate whether a new store needs to be located in the area south of City Hall to be able to capture more sales in this area.

Usage-based insurance demo to illustrate new data sources that identify a driver’s risk profile

Details

Differentiators: Easy integration with various PB software solutions; integration of myriad data sources. This demo is using HANA to store a massive amount of probe, traffic and speed profile data from various geospatial data sources. Pitney Bowes’ Spectrum Location Intelligence Module (LIM) is used to build the queries against HANA.

Technology: A SAP CAL (Cloud Appliance Library) is used to quickly provision HANA data store. The demo is a single node SAP HANA instance located on an Amazon Web Service (AWS) cloud to store the speed profiles and process spatial queries. It runs the free HANA developer edition on a high memory double large EC2 instance (r3.2Xlarge) with 8 CPUs and 32 ECUs and 61 Gb memory. This is the largest configuration available from SAP CAL for the free license of HANA developer edition. Pitney Bowes’ Location Intelligence Module (LIM) is the query engine and the map display is rendered using the Leaflet API with map tiles displayed using MapBox.

Data sets in use:

  1. Mobility Data Layer: About one month's mobile phone GPS trace data of a user in Boulder area. Each data point contains information of GPS coordinate and time. (Jon S. - 10505 points; Zhenan: 1236; Janna: 8300)
  2. Analysis Layer: Heat map analysis of the mobile phone GPS data.
  3. Traffic Data Layer: TomTom speed profile data (500,000 points in HANA; 5000 for Boulder). Data comprised of road segments data (e.g. Length, address, road type, toll info) with speed information in 4 buckets (am peak, inter peak, pm peak, night time)
  4. Risk Data Layer: INRIX traffic accident data (5982 points for Boulder). They are geocoded traffic-related events data including description of the event, time and location
  5. Zip Boundary: Zip 5 boundary data

Key Points

Narrative: Most of Insurance companies today determine a policy holder’s rate based on the driver’s home ZIP code, driving records, claims data and estimated mileage per year. As mobile-sourced data, vehicle sensor technology and analytics provide improved new insights of driving behavior, such as speed, frequency of breaking, and acceleration/deceleration insurance companies are moving to usage based insurance (UBI) model to improve the rate assessment criteria for policy underwriting. However, underwriters would benefit by having additional information about the driver’s travel patterns and data collected from the local transportation authority about accident incident locations and other factors such as road impedances. With location Intelligence, insurance companies can reduce their risk exposure by better understanding the myriad factors related to driving behavior. In addition, if a driver’s accident claim is due to bad driving behavior or accidents occurring in heavy traffic areas then the insurance company can use this information and adjust the policy rate appropriately and with fundamental proof. This demonstration can illustrate how visual and location analytics supports the underwriter, claims department and actuarial process.

Instructions

  1. [SELECT USER Jon S] Jon S. travels regularly between certain destinations such as between his home and his workplace during the work week. Jon and his insurance company have agreed to use a sensor to monitor his driving behavior in hopes that Jon will be able to reduce his insurance rate.
    [TURN ON MOBILITY DATA LAYER].
  2. The points on this map represent places where the sensor has captured data over the past 30 days. The point data may contain information such as driving speed, frequency of breaking, acceleration/deceleration, tire pressure and time of day.
    [TURN OFF MOBILITY DATA LAYER].
  3. We also have information about the type of traffic conditions that Jon may encounter during his drive-time.
    [TURN ON TRAFFIC DATA].
  4. We see that some of Jon's route is very congested where RED indicates slower speeds along the route and green indicates less congested routes where the speed limit is maintained routinely.
    [TURN ON MOBILITY DATA LAYER].
  5. So, now on the map we can compare the high traffic areas with Jon's driving pattern.
    [TURN OFF MOBILITY & TRAFFIC DATA LAYER].
  6. Also, we know where the frequency of most accidents, road closures and other route impedances occur in this area of Boulder, Colorado
    [TURN ON RISK DATA LAYER; ZOOM IN AND OUT TO SHOW DENSITY OF TRAFFIC ACCIDENTS; CLICK ON SINGLE INCIDENT - SHOW BOTH ACCIDENT AND ROAD CLOSURE INCIDENTS].
  7. Now let's see where Jon is most at risk.
    [TURN ON ANALYSIS LAYER].
  8. This "heatmap" - an analysis of Jon's driving patterns whereby the more intense or "RED" areas indicate the frequency of locations visited as collected by the vehicle sensor - can now be compared to the higher frequency of accidents or impedance zones.
  9. We see that Jon travels a route with not too many risk factors indicating that Jon may be eligible for a rate reduction. Let's see if Jon is at risk.
    [TURN ON MOBILITY ANALYSIS].
  10. Our map know shows Jon's home and place of business
    [ZOOM OUT TO SHOW SHOPPING AREA WHERE JON SHOPS].
    [TURN ON RISK ANALYSIS]
  11. Indeed Jon's seems to be a low risk. Seeing the map overlays provides a unique perspective that would not necessarily be seen by looking at reams of data on a spreadsheet or other key performance indicators (KPIs)
  12. However Jon's insurance company applies rate adjustments and aggregates risk by ZIP code level geography.
    [TURN ON ZIP CODE LAYER].
  13. And within Jon's driving zones there appear to be quite a number of risky areas. Should Jon still receive a rate decrease?

In summary, using a vehicle sensor data to estimate driving behavior and applying a usage based insurance (UBI) model might save drivers money. However, it will depend on many location-based based factors to ultimately determine rate structure and the ability for underwriters to estimate risk. Here, location intelligence becomes an absolute necessity for insurance companies to evaluate the best way to score drivers and help them to save money and to reduce their own book of business exposure.

In this demo, we utilized SAP HANA to store our location-based information and extract these data to Pitney Bowes's Spectrum Spatial. Using visualization software rendered using the Leaflet API with map tiles displayed using MapBox we are able to display a variety data types to provide visual intelligence to underwriters responsible for risk analytics.

Cellular Coverage of Cell Towers (Germany)

Details

Technology: HANA is used to store point locations of GSM, LTE and UMTS cell tower locations. Spectrum Spatial Analyst is used to create heat maps, and cell tower coverage maps using Pitney Bowes’ Enterprise Routing Module to simulate signal attenuation.

Data sets in use:

  1. Cell Tower Database
    • GSM Cell Towers
    • LTE Cell Towers
    • UMTS Cell Towers
  2. Options for map data display
    • Bing Roads
    • Bing Hybrid
    • Bing Aerial
    • MapBox
    • MapInfo StreetPro
    • Open Street Map
    • Open Cycle Map

Key Points

Narrative: A wireless carrier needs to better understand cellular coverage of the existing infrastructure in the Frankfurt, Germany region. The carrier is planning an expansion of the LTE network to determine areas where coverage is necessary to compete. The GSM and UMTS cellular coverage is extensive but the next generation Long Term Evolution (LTE) network is sparse. There are gaps in coverage within estimated coverage areas that, along with other factors both within and beyond the carrier’s control (network problems, software, signal strength, individual wireless device quality, urban structures, buildings, weather, geography, topography, etc.), will result in dropped and blocked connections, slower data speeds, or otherwise impact the quality of services. This demo will serve to illustrate the coverage extents of each type of cellular service, GSM, UMTS and LTE in order to locate gaps and plan for new cell tower construction.

Instructions

  1. [USING THE GEOCODE TOOL, SELECT COUNTRY AND TYPE “FRANFURT; ZOOM to Frankfurt but make sure the coverage shows at least a 50Km wide area; individually display GSM and UMTS cell tower locations]
  2. Narrative: A wireless carrier needs to better understand cellular coverage of the existing infrastructure in the Frankfurt, Germany region. The carrier is planning an expansion of the LTE network to determine areas where coverage is necessary to compete. The GSM and UMTS cellular coverage is extensive but the next generation Long Term Evolution (LTE) network is sparse. There are gaps in coverage within estimated coverage areas that, along with other factors both within and beyond the carrier’s control (network problems, software, signal strength, individual wireless device quality, urban structures, buildings, weather, geography, topography, etc.), will result in dropped and blocked connections, slower data speeds, or otherwise impact the quality of services. This demo will serve to illustrate the coverage extents of each type of cellular service, GSM, UMTS and LTE in order to locate gaps and plan for new cell tower construction.
  3. [USE THE INFO TOOL TO CREATE A REPORT OF THE INDIVIDUAL CELL TOWER LOCATION AND RANGE; Report will be displayed at the bottom of the page.]
  4. [TURN ON UMTS CELL COVERAGE; TURN OFF; TURN ON GSM COVERAGE] Explain that coverage is extensive for older generation cellular service. [TURN OFF GSM COVERAGE]
  5. [TURN ON LTE TOWER LOCATIONS] Note sparse coverage of LTE towers.
  6. [TURN ON LTE COVERAGE MAP; ZOOM TO DOWNTOWN FRANKFURT; TURN OFF GSM AND UMTS CELL TOWER LOCATIONS]As we can see on the map, coverage is sparse [ZOOM OUT]; looking at a larger area we can sell that many areas are underserved.
  7. [CHANGE DISPLAY TO OPENSTREETMAP] Using the OpenStreetMap backdrop map, we can see higher population areas that are underserved such as Hofheim and the areas surrounding Frankfurt International Airport.
  8. [CREATE HEATMAP OF GSM CELL TOWERS] The heatmap can now show the relative intensity of GSM coverage versus the LTE coverage maps.

Location Based Marketing Analytics

Details

In this demo, location based messages are triggered when customers enter the location parcels of interesting businesses. The number of times each message is displayed and clicked is tracked.

Seeing this data illustrated graphically on a map gives business users new insights into which messages are working best in what types of business. They can also gain insights into the effect that income and household formation have on the number of views, clicks, proposals and purchases.

Key Points

This data is updated in real-time and its display is mobile optimised allowing marketers to adjust their marketing campaigns and messaging based on real-time insigths mid-campaign. This empowers businesses to grow real customer life time value.

Instructions

This demo works best if you first show the parcels, in the location (last) section of the onboarding demo and click in some of the parcels to generate location based messages and then click on some of these messages.

Next go into the Marketing Results on this page and show the number of views and clicks. Then go back to the location (last) section again and display / click on the same messages. Finally return to the Marketing Results on this page and show how the number of views and clicks have increased - highlighting the real-time and visually compelling marketing insights that we can provide.

End-to-End Document Integrity

Details

Our End to End software and hardware Integrity solutions are all about Accuracy and Precision. We offer print job manipulation, transformation and management through our Production Intelligence suite. We also help you know where your mail piece is in your shop at all times, being guaranteed that the right information is in the right envelope with our Direct Connect – closed loop system. In addition we give you productivity reports for your hardware equipment, including down time and operator productivity. From our full line of inserters, sorters – we provide you with postage accounting and manifesting ensuring complete control over your internal mailing operation. We also prepare eDocs for USPS mail submission and acceptance.

Instructions

These images loop through continuously. On a touch device you can swipe the images to progress to next slide.

VideoGraph Expo 2015

Customer Journey Optimisation

Details

By ingesting all customer interaction data across all channels and associating it with a single customer, the solution can present a timeline of all the interactions that have happened with any individual customer - both inbound and out. Furthermore each interaction can be rated by sentiment.

View Timeline

Most importantly, for any customer action (e.g. purchase of a particular product, opting into paperless communications, etc.) the platform enables you to determine the sequence of interactions that most commonly led up to the successfully completed customer action. Furthermore, the platform can also provide insight into the stage of the customer journey where customers most commonly dropoff from taking the desired action.

Objectives

Instructions

Select a business objective and then a customer action you would like to increase the frequency of. The first table displays the sequence of customer interactions that most commonly lead to the selected customer action. The second table displays the stage in the customer journey at which customers most commonly drop off and do not continue to complete the desired customer action.

Use the sliders in the filter section to see how the sequence of customer interactions most commonly leading to a customer action varies for different demographics.

Income group:

  1. Wealthy Households
  2. Prosperous Households
  3. Comfortable Households
  4. Less Affluent Households
  5. Poorer Households

Household formation:

  1. Pre-Family Couples & Singles
  2. Young Couples With Children
  3. Families With School Age Children
  4. Older Families & Mature Couples
  5. Elders In Retirement

Click the + / - button in the communications sequence table to show / hide additional communication details.

Interaction Timeline

Details

By ingesting all customer interaction data across all channels and associating it with a single customer, the solution can present a timeline of all the interactions that have happened with any individual customer - both inbound and out. Furthermore each interaction can be rated by sentiment.

Most importantly, for any customer action (e.g. purchase of a particular product, opting into paperless communications, etc.) the platform enables you to determine the sequence of interactions that most commonly led up to the successfully completed customer action. Furthermore, the platform can also provide insight into the stage of the customer journey where customers most commonly dropoff from taking the desired action.

Journey PlannerObjectives

Instructions

Click the arrows, swipe or move the rangeslider to scroll through all the interactions that have been had with this customer across the different channels. The sentiment of each interaction is displayed as well as the last NPS given by the customer prior to the communication that is currently display.

  • Click on the eye icon to view the actual communication in the archive (not currently implemented).
  • Click on the info icon to view additional details on the commuication.
  • Click on the bars icon to see all the response actions from the customer on a given communication. When there are multiple actions on a communication then the currently displayed actions is highlighted in the displayed list.
  • Click the NPS / Sentiment button to display a legend explainging the colors and NPS value.

You can switch the number of communications displayed in the timeline by changing the time period.

Risk Data

Risk Levels

Details

Risk data from PB includes wildfire, flood, earthquake, fault lines, sink holes, abandoned mines, hurricane history, hail history, tornado history, wind history and more.

The solution exposes web service and batch interfaces to perform geospatial queries on the data to discover if a location is inside a risk zone or the distance from as risk zone.

PB provides the fastest and most accurate global geocoding solution on the market today. Together with the geospatial querying capabilities this allows insurance and finance institutions to determine the risks associated with their client's properties with unmatched accuracy.

Instructions

When you first visit this Visualise Risks page, all available risk layers are displayed. You can use the filter to only display the risk you are interested in. Using the filter will also update the legend for the currently selected risk data. This demo only implements visualisation of risk data for the US. Hence you may need to zoom the map out if no risk data is available near the address that you entered in the Name & Address section.

Data Quality

Relationships
Videos
Demo type:

Server Statuses:

    Language:
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    Geocode:
    Set Geocode
    Settings

    Industry

    Instructions
    Menu

    Credit Card Application

    Details

    Name & Address

      Identify Yourself

      Which card best matches your needs?

      Default Heading 1

      Default Text 1

      Default Heading 2

      Default Text 2

      Default Heading 3

      Default Text 3


      Click here to view a personalized interactive video explaining the difference between each of the above 3 products and decide which is best for you.

      Additional Customer Information

      Proposal to be generated in:

      Name

      Generate Proposal

      Tell us about your next travel destination

      Local Businesses Offering Discounts With Your Card

      Person is at POI

      Point of Interest & Parcel boundary data sets

      Person is in the building

      Point of Interest & Building Footprint data sets

      Person is in a specific store of a Mall

      Point of Interest & Indoor map data sets

      Radius based geo-fencing does not know a customer’s precise location

      Building Footprint, & Property Boundary as geo-fences to pin point customer’s exact location

      Proactively interact with customers based on location context

      Mobile context awareness engages customers based on location context through their mobile app and PB provides the location context with high precision and high accuracy


      Back

      Proposal for: ... ...

      Back

      Communication History for:

      Details

      Communications Exchanged

      In / OutDateDescriptionStatus1st OpenedLast OpenedOut of OfficeReply

      Campaign Treatments

      DateCampaignMessageTypeAgent

      Campaign Response

      DateCampaignMessageTypeResponse TypeAgent

      Content, Actions & Sentiment Timeline

      logo
      Twitter Photo
      Letter

      Physical Letter

      Date: 5 Jan 2015, 13:54

      View

      Outbound

      Description: GRN proposal for credit card

      Details

      Read

      Action: Read

      Date: 7 Jan 2015, 18:56

      Other actions

      17 Sep 2014, 14:45

      16 Sep 2015, 14:45

      History

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      Details
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      Videos of Demos

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      Visiting Nurse Application

      Location analytics to minimize waste and misuse of healthcare funds and improve the efficiency and safety of visiting nurses

      Admin Console shows schedule of nurses and patients to be visited. Information is shared with nurses via mobile app

      Mobile App allows nurse to view scheduling, patient and routing information. When a job is accepted, device sends trace data until the nurse completes the job

      The solution matches trace data with parcel data and analyzes if the nurse was on-site with patient and for how long. Exceptions (e.g. visit too short or long) can be flagged for follow up

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      Customer Insights:

      Details

      Address Validation


      Suspicious Behaviour Check


      Net Promoter Score (NPS)


      Customer Lifetime Value


      Most Likely Cross-sell

      The most likely cross-sell opportunity is for: Mobile Phone Insurance


      Insurance Risks

      Existing Customer


      Likelihood of Churn


      Household Purchasing Power


      Most Critical (CLV)

      The most critical item to improve to increase customer lifetime value is: Increase Product Holding


      Products Owned

      ProductTypeSinceValue (CLV)
      InsuranceHome25 May 2012$6,123
      InsuranceCar1 June 2014$1,437

      Paperless Propensity

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      Marketing Results

      Details
      1000 - 20000000000
      1000 - 2000
      1000 - 2000
      1000 - 2000
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      Document Production & Integrity

      Details
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      Customer Journey Planning

      Details

      Interaction details


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      Risk Visualisation

      Details
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      Feedback

      Thank you for taking the time to provide feedback on this DCS demo. Your feedback will help us improve this demo environment. We will respond to all feedback via email in less than a week of receiving it.


      Select you primary area of interest:

      What industry is your primary focus:


      How easy is the demo to use (1 = very easy | 5 = very difficult) :

      How likely are you to recommend using this DCS demo to a colleague or business partner? (0 = very unlikely | 10 = very likely) :


      Submit
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      Entity Resolution - 8 Common Obstacles Addressed

      Details

      This demo uses these 4 incorrect customer data records. Find out what is wrong with them by clicking the the buttons below.

      Field NameRecord 1Record 2Record 3Record 4
      Address15 Jones Avenue15 Jones Av.71 Jones Ct524 Water Town
      CityNewtonNewtonNwetonNewton
      Emailbob.williams@yahoo.combob.williams@yahoo.commburkes@gmail.com(617) 554-1329
      NameBob WilliamsRobert WilliamsBurkes, Mike & IdaMarshall Fender & Marsha Fender
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