Customer Support in the Big Data Era


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  • Hi I’m Tanya Shastri.
    A little big of background on myself before we get into the presentation. I’ve been plugged into the big data world for a while. A relatively short 5 years ago, there were no conferences, let alone conferences of this scale. Unconferences back in the day. Though not surprised at all to see the size of the conferences today. There is a lot of promise in data and tools like Hadoop are helping deliver on that promise.

    And while I’m not at Natero anymore, they are…

    I’ll be talking about how having access to big data can provide some actionable insights to reduce the cost of customer support and improve customer experience and customer satisfaction.

  • Here’s how I’ve structured the presentation.

    First some context
    Then I’ll go into the methodology – and some sample analytics

    For the previous part we’ll assume that all the data has is automagically prepared.. So a spotlight on data prep because any of you who’ve worked with data will know that data prep is often if not always the harder part.

    We’ll end with some considerations and learnings…
  • To provide a little bit of context for what kind of company this would apply to. A company that provides consumer products whether hardware or software.

    The can be extended to the internet of things in general.
  • Disparate sources, geographically distributed, last thing you want to do is add another source.. A source that is “big”.

    Combining a couple sources
    In some cases even using a source of data that isn’t typically used
  • Thousands of products – each having its own troubleshooting page. The intention of the troubleshooting page was to enable customers to self-help. There was no insight into how these troubleshooting pages were performing. If they were able to know which pages were not performing well, they could improve them and reduce incidents filed.
    For this two sources were used – the web-clickstream data from the support website and the incident database.

  • Thousands of products – each having its own troubleshooting page. The intention of the troubleshooting page was to enable customers to self-help.

    There was no insight into how these troubleshooting pages were performing.
  • Enterprises are looking for ways to improve customer satisfaction and reduce support costs, but often do not have actionable insights. Traditional approaches and tools fall short, often based on small biased datasets and requiring long turnaround times.
    This talk will cover the steps involved todevelop a big data solution for support through the example of a leading vendor of electronic consumer peripherals. Topics will include:
    The methodology and metrics developed:Metrics to track and improve self-help through support sites
    Metrics to track the end-to-end support process to find delays in processing of incidents, escalations, etc.
    Methods to identify problem areas based on data from discussion forums
    Data-driven discovery of paths that customers prefer for support
    The data wrangling required to implement the solution using a big data analytics platform:Big data analytics techniques to track customer behavior across channels
    Preparing the data for analytics: joining, merging and enriching the diverse datasets
    Validating the parameters and techniques used for analysis
    Considerations for an iterative analytical approach to get results with the highest confidence interval
    Metrics at various granularities to meet the needs of various business decision makers
    Automation for maintaining and tracking up-to-date results

  • Customer Support in the Big Data Era

    1. 1. Customer Support in the Big Data Era TANYA SHASTRI @tanyashas3
    2. 2. What will be discussed  Customer support context  From data to business benefit  Methodology  Metrics  Sample analysis  Preparing the data  Considerations and learnings
    3. 3. Customer Support Context
    4. 4. Customer Support Data Sources  Incident databases, call center data  Customer self-help website  In-product or in-app data, call home data  Discussion Forums Disparate ● Disjoint ● Silo-ed Structured ● Semi-structured ● Unstructured Transactions ● Events ● Logs Volume ● Velocity ● Variety
    5. 5. Methodology and Metrics  Tracking and improving self-help  Troubleshooting page score  In-product proactive support  Customer behavior based learning  Improving support efficiency
    6. 6. Tracking and improving self-help  Crude score ScoreC = #incidentsP1 #“P1 help page hits”  Detailed score ScoreD = (#incidentsP1 - #rmaP1) #“sessions P1 help page hits with t>10s” P1  Product1 RMA  Return Merchandise Authorization t  duration of time spent on the page
    7. 7. Self-help score (crude) 0 500 1,000 1,500 2,000 2,500 Product 1 Product 2 ScoreC HITS/1000 INCIDENTS SCOREx1000
    8. 8. Self-help score (detailed) 0 500 1,000 1,500 Product 1 Product 2 ScoreD S-HITS/1000 INCIDENTS-RMA SCOREx1000
    9. 9. In-product proactive support  Support integral part of product development  Customer behavior driven analysis  Analysis based on a sequence of actions ACTION 1 #A1 reach here T2 spent here ACTION 2 #A2 reach here T2 spent here ACTION 3 #A3 reach here T3 spent here
    10. 10. Analysis of sequence of actions 0 20 40 60 80 100 120 140 0 10000 20000 30000 40000 50000 60000 Action 1 Action 2 Action 3 Action 4 #taking this action Avg Time (sec)
    11. 11. Discussion Forums and Reviews  Discussion Forums and Reviews  Term frequency trend  Sentiment analysis  Time at each step in actual support process
    12. 12. Preparing the Data  Standardizing Product nomenclature  Enriching events/logs with traditional data  Session based analysis, session segmentation  Product hierarchy for drill-down to various levels
    13. 13. Be aware that…  The process is likely to be iterative  Data prep is a big deal  With “big data” simple analysis can be valuable  Analysis can sometimes feel like “hindsight is 20/20”
    14. 14. Questions?