To truly harness data, organizations need a scalable approach to develop.
How to Harness your Data
May 08 2017
To truly harness data, organizations need a scalable approach to develop:
  • cutting edge insight into the drivers of the business
  • predicative indicators of future performance;
  • clues on stemming churn or;
  • capitalizing on low-hanging fruit for driving topline growth.
Today these types of insights are generated by teams of business analysts, consultants and data scientists. To start working on such a set of complexes analyses all organizations need to have the ability to store this data in real-time where all of this information is married together. This is where most organizations stumble: the ability to get the right set of data scientists to develop a scalable data warehouse that can store and retrieve queries in real-time is extremely difficult. It’s too expensive and time consuming to attract data scientists and execute on this.
It’s equally expensive and time consuming to attract the top 1% of consultants and analysts (“BizOps professionals”) who can digest, make sense of the data and provide repeatable actionable insights.
Not all companies can afford this as the war for talent – especially data scientists—is at an all-time high with no signs of slowing down as organizations engage in a data-driven, data-centric world.
Datatron is an AI-based company that can provide Data Science as a Service. Using advanced deep learning technology, its algorithm is trained to answer the key questions that move the needle for different stakeholders of the business:
  • Customer Success:
    • Who are the profiles of customers that tend to pay annual up front based on billing?
    • Who are the profiles of customers that don’t pay (and need to be removed from ARR)? Use that and feed into sales process.
    • Who are the profiles of customers that are late in payments?
  • HR:
    • Who are the profiles of sales reps that ramp the fastest?
    • What are the profiles of reps that have the longest tenure (feedback into the recruiting funnel)?
    • What types of profiles are most successful in the organization:
    • Sales: rep attainment as a metric;
    • Non-Sales: career growth, performance review feedback.
    • What recruiting channels drive the best talent profiles?
    • What is the employee churn in the organization (desired and undesired)? How does that vary by team?
  • Marketing:
    • Who are the right set of customers to go after (ICP)?
    • What demand gen channels have the highest ROI?
    • What is the CAC? How does that vary by team?
    • What is the LTV to CAC ratio by team (ramped versus ramping reps)?
    • What marketing campaigns driven the highest deal sizes, or the shortest sales cycles?
  • Sales:
    • Pre-sales:
      • See SDR Management Questions.
    • Sales:
      • Are there sufficient opportunities per rep or do reps have too many opportunities (for ramping and ramped reps)?
      • Who are the best performing reps and what are they doing differently (or are their profiles different)?
      • Are reps on track to hit their targets? Where are the gaps (insufficient late stage opportunities, low deal sizes)? Where are the opportunities versus the stage and age against the average?
      • Which opportunities have the highest likelihood for an upsell or cross-sell?
      • At what point should more sales rep be hired?
      • Should quotas be increased? What should the next optimal level be?
    • Post-sales:
      • What are the different engagement profiles of customers?
      • What is the likelihood for churn for each customer base?
      • What are the profiles of customers that churn?
      • What are the profiles of customers that have high net dollar-based retention?
      • When is the right time to pitch upsells/add-ons to customers?
      • Is there sufficient rep coverage for customers?
      • What % of customers are referenceable? How many customers have CxO vs VP vs Directors as primary points of contact?
  • Product:
    • Which customers have the highest usage on the platform?
    • What is the user adoption curve by feature?
    • What features have the highest levels of adoption?
    • Which features have low adoption?
    • What is the adoption of features by customer revenue size?
    • Which features need to be highlighted in marketing/pricing?
    • Which features should be killed?
    • What is the engagement levels by feature?
    • What is the feature engagement level by customer revenue size?
We developed a sophisticated (AI) assistant “Emma" that provides actionable insights NOT just visualization of data.
Filed under Datatron Platform | Tagged: Data Cleansing Predictive Analytics ML Platform