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?
- 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?
- 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?
- 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?
- 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?
- 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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