Applying data science to sales pipelines - for fun and profit
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Applying data science to sales pipelines - for fun and profit An overview of data science and machine learning at C9
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Applying data science to sales pipelines - for fun and profit
- 1. Applying data science to sales pipelines – for fun and profit! ! Andy Twigg! CTO!
- 2. Data! science! Domain! expertise! Machine! learning! Data! • 62B sales pipeline records! • Structured, unstructured! • 3rd party public data! • Fine-grained temporal data! Deep expertise:! • sales! • forecasting! • revenue models! • Automated ML infrastructure! • ML models tuned for specific problems!
- 3. CUSTOMERS!
- 4. DATA SCIENCE @ C9! • Opportunity Scoring! • What is Pr(win) for this deal?! • What is Pr(win in quarter) for this deal?! • How does this compare to sales team commits?! • Which deals can we influence most?! • Forecasting! • How much will we close this quarter?!
- 5. SALES PIPELINES & OPPORTUNITIES! • Opportunities are temporal creatures; while ‘open’ they proceed through a number of observations and terminate in one of a discrete set of ‘closed’ states – typically won or lost! • Usually they proceed through ‘stages’, except:! • An opportunity can be entered into the CRM system as closed (no open observations)! • Stages are only a partial order - can skip / revisit stages! • Can be re-opened after closed! • As the opportunity evolves, we get more and more data about the opportunity! • A pipeline is a set of open opportunities! Lead created! Stage: Qualifying! Email sent! Email sent: response! Amount= $1000! Call! Stage: demo! Meeting! Demo! Push close date! Stage: negotiation! Closed/ won! Reopened Amount= $2000! Closed/won!
- 6. ANATOMY OF AN OPPTY!
- 7. ANATOMY OF AN OPPTY! Pushed out Pulled back in Final outcome: won Committed here (by the sales rep)
- 8. ANATOMY OF AN OPPTY! Pushed out Pulled back in Final outcome: won Committed here (by the sales rep) Predicted won from the start Predicted won in the correct quarter
- 9. PREDICTIVE ENGINE! Build a fine-grained history of closed opportunities • Cleaning! • Preprocessing! • Featurizing! • ~10 GB/customer! • ~ 1M training rows! ! 1,000s of raw signals per opportunity • Structured (CRM, ERP)! • Unstructured (NLP)! • Firmographic! • Gov sources! • SEC filings! • Crunchbase! • …! ! Identify historic deals with similar behavior Continuously re-score opportunities as they evolve Update model as opportunities close • Fully-automated model rebuilding and scoring platform! • Model input features:! • Historic observations of opportunity! • Sales-specific features e.g. momentum! • Temporal features e.g. std(amount over last 30 days)! • Industry-wide features e.g. avg_sales_cycle(target)! • Continuously cross-validated model tuning! • Extensible, scalable platform using Hadoop (HDFS), Python!
- 10. Win/Loss Model (Random Forest) Estimate Pr(win) Duration Model (Poisson Regression) Estimate Pr(win in quarter) Influencer Model (Linear) Positive/Negative Drivers • Standard Features • Temporal Features • Derived Features BEHIND THE SCORES!
- 11. ©2014 All Rights Reserved
- 12. ©2014 All Rights Reserved
- 14. sales team: good precision (~70-80%) but poor recall (~10-40%)! C9 won precision ~ sales team won precision! C9 won recall ~ 3 x sales team won recall! First observation Last observation precision recall f1 precision recall f1 C9 scoring 0.65 0.86 0.74 0.75 0.93 0.83 Commit 0.70 0.07 0.13 0.87 0.45 0.59
- 16. FORECASTING: TOP-DOWN VS BOTTOM-UP! Top-down: Predict current quarter based on previous quarters! ! Accounts for seasonality and trending! ! Ignores state of current pipeline! 0.0e+002.5e+08 observed 5.0e+072.5e+08 trend −5e+065e+06 seasonal −1e+075e+06 2013.0 2013.2 2013.4 2013.6 2013.8 2014.0 2014.2 2014.4 random Time Decomposition of additive time series Bottom-up: Predict current quarter based on currently open pipeline! ! Considers quality of deals in pipe! ! Ignores trends, deals not in pipe! $265,410! $157,000 77% $200,000 37% $82,000 86% +! -! +! -!
- 17. HYBRID FORECASTING! top down + bottom up! 20 40 60 11 10 9 8 7 6 5 4 3 2 1 Weeks to EOQ Amount($M) C9 Final Amount Actual Amount Amount Forecast • Augment time-series model with side information from bottom-up model, e.g.:! • Amount predicted to close in current quarter! • Average score of currently open opportunities! • Average predicted days to close! ! • Sometimes known as ARIMAX! log(yt) ∼ 12 i=1 log(yt−i) + log(x (1) t−12) + log(x (2) t−12) + log(x (3) t−12)
Editor's Notes
- why is data science work @ c9 different to other MLaaS/BI/SaaS companies? it’s ML put to work for a specific application, using domain knowledge
- Answering each of these questions uses different techniques
- Let’s dig deeper into what goes into the scores. We take more than 1,000 raw signals per opportunity – this is structured CRM data, unstructured text data (NLP), firmographic data including government sources (registration filings, credit unions, SEC filings), and other sources such as job postings, crunchbase, etc. This data is noisy so a lot of effort goes into cleaning and preparing this data, removing highly correlated subsets of fields, matching across tables, etc. Finally, this preprocessed data is transformed into a form that can be used for machine learning. For each customer, we have a large, fine-grained history of closed opportunities, stored in a temporal database. Each customer will typically have around several GB of raw historical data. The predictive model pipeline is fully automated and runs in production using HDFS, Java and Python. The This shows some of the detail surfaced by our predictive models for a specific opportunity. We surface three main results: Is the deal likely to win? Is it likely to be win this quarter? What are the main positive/negative influencers? Can I win: Random forest model, estimating Pr(win) regardless of time horizon # signals ~ 200-500 raw fields, covering > 1000 signals after cleaning Structured CRM data, NLP on unstructured data, ~30 government sources (registration filings, credit unions, SEC filings - # employees, revenue), crunchbase, NLP Can I win this quarter: We construct a duration model that attempts to estimate the number of days until the opportunity will close, assuming the eventual outcome is a win. We then transform this into an estimate of Pr(win in quarter). Key indicators: We use a model to that takes in specific influencing features, such as ‘budget secured, deal momentum’ etc. It includes both standard features in CRM data (eg industry), but also domain-specific and temporal features (eg ‘momentum’, ‘amount is fluctuating’) It then surfaces the 3 features that most strongly positively or negatively influence the overall score of the deal Constructing these fields requires domain expertise – in knowing what measures are importance, and which are also actionable by the managers and sales reps
- This shows clearly the temporal nature of an opportunity Opportunities are (usually) either open/closed Usually proceed through stages, but: Can go straight to closed Stages are only a partial order - can skip / revisit stages Can be re-opened after closed As the opportunity evolves, we get more and more data A pipeline is a set of open opportunities
- Let’s dig deeper into what goes into the scores. We take more than 1,000 raw signals per opportunity – this is structured CRM data, unstructured text data (NLP), firmographic data including government sources (registration filings, credit unions, SEC filings), and other sources such as job postings, crunchbase, etc. This data is noisy so a lot of effort goes into cleaning and preparing this data, removing highly correlated subsets of fields, matching across tables, etc. Finally, this preprocessed data is transformed into a form that can be used for machine learning. For each customer, we have a large, fine-grained history of closed opportunities, stored in a temporal database. Each customer will typically have around several GB of raw historical data. The predictive model pipeline is fully automated and runs in production using HDFS, Java and Python. The This shows some of the detail surfaced by our predictive models for a specific opportunity. We surface three main results: Is the deal likely to win? Is it likely to be win this quarter? What are the main positive/negative influencers? Can I win: Random forest model, estimating Pr(win) regardless of time horizon # signals ~ 200-500 raw fields, covering > 1000 signals after cleaning Structured CRM data, NLP on unstructured data, ~30 government sources (registration filings, credit unions, SEC filings - # employees, revenue), crunchbase, NLP Can I win this quarter: We construct a duration model that attempts to estimate the number of days until the opportunity will close, assuming the eventual outcome is a win. We then transform this into an estimate of Pr(win in quarter). Key indicators: We use a model to that takes in specific influencing features, such as ‘budget secured, deal momentum’ etc. It includes both standard features in CRM data (eg industry), but also domain-specific and temporal features (eg ‘momentum’, ‘amount is fluctuating’) It then surfaces the 3 features that most strongly positively or negatively influence the overall score of the deal Constructing these fields requires domain expertise – in knowing what measures are importance, and which are also actionable by the managers and sales reps
- Let’s dig deeper into what goes into the scores. We take more than 1,000 raw signals per opportunity – this is structured CRM data, unstructured text data (NLP), firmographic data including government sources (registration filings, credit unions, SEC filings), and other sources such as job postings, crunchbase, etc. This data is noisy so a lot of effort goes into cleaning and preparing this data, removing highly correlated subsets of fields, matching across tables, etc. Finally, this preprocessed data is transformed into a form that can be used for machine learning. For each customer, we have a large, fine-grained history of closed opportunities, stored in a temporal database. Each customer will typically have around several GB of raw historical data. The predictive model pipeline is fully automated and runs in production using HDFS, Java and Python. The This shows some of the detail surfaced by our predictive models for a specific opportunity. We surface three main results: Is the deal likely to win? Is it likely to be win this quarter? What are the main positive/negative influencers? Can I win: Random forest model, estimating Pr(win) regardless of time horizon # signals ~ 200-500 raw fields, covering > 1000 signals after cleaning Structured CRM data, NLP on unstructured data, ~30 government sources (registration filings, credit unions, SEC filings - # employees, revenue), crunchbase, NLP Can I win this quarter: We construct a duration model that attempts to estimate the number of days until the opportunity will close, assuming the eventual outcome is a win. We then transform this into an estimate of Pr(win in quarter). Key indicators: We use a model to that takes in specific influencing features, such as ‘budget secured, deal momentum’ etc. It includes both standard features in CRM data (eg industry), but also domain-specific and temporal features (eg ‘momentum’, ‘amount is fluctuating’) It then surfaces the 3 features that most strongly positively or negatively influence the overall score of the deal Constructing these fields requires domain expertise – in knowing what measures are importance, and which are also actionable by the managers and sales reps
- what’s the competition? reps’ commits we have found that typically, sales people have good won precision (~70%) but poor recall (30-45%) but they aren’t good at identifying _which_ deals will close we can identify about 2x as many deals that will eventually win, at the start of the quarter def f1(p,r): return 2.0*(p*r)/(p+r) Many customers follow this pattern.
- world’s most interesting salesman
- Slide Objective: Present the unique capabilities of C9 OppScore, show those capabilities in the demo and then connect those capabilities to business benefit. Questions: Let’s dive into the product. We’ll start by talking about how C9 OppScore helps reps determine which deals they should focus on and the specific actions they can take to improve the probability of winning. How much quality coaching time do managers have with reps each week? How do your reps prioritize which deals to pursue and how do they determine when to walk away? How long does it take a new rep to ramp and what are you doing to enable new reps? How do you ensure they reps are identify and execute on the sales strategies that have had the highest rate of success? Talking Points: C9 can help you address all of these points. In the next few minutes you’re going to see how C9 allows you understand how data science can be used to get reps focused on the right deals and applying the best actions to close those deals. After the demo recap the business benefits
- Slide Objective: Present the unique capabilities of C9 OppScore, show those capabilities in the demo and then connect those capabilities to business benefit. Questions: Let’s dive into the product. We’ll start by talking about how C9 OppScore helps reps determine which deals they should focus on and the specific actions they can take to improve the probability of winning. How much quality coaching time do managers have with reps each week? How do your reps prioritize which deals to pursue and how do they determine when to walk away? How long does it take a new rep to ramp and what are you doing to enable new reps? How do you ensure they reps are identify and execute on the sales strategies that have had the highest rate of success? Talking Points: C9 can help you address all of these points. In the next few minutes you’re going to see how C9 allows you understand how data science can be used to get reps focused on the right deals and applying the best actions to close those deals. After the demo recap the business benefits