How to Harness Your Data for Growth

How to Harness Your Data for Growth

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 top line 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.

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 annually upfront 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 the 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 a team?
  • Marketing
    • Who is the right set of customers to go after (ICP)?
    • What demand gen channels have the highest ROI?
    • What is CAC? How does that vary by the team?
    • What is the LTV to CAC ratio by a team (ramped versus ramping reps)?
    • What marketing campaigns drive the highest deal sizes or the shortest sales cycles?
  • Sales
    • Pre-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 of 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?

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