Collaboration and Productivity. Collaboration can lead to new products…
By: Lisa London
Tags: Regression Analysis, Collaborative Consumption, Meetings, Salesforce Productivity, Productivity

Business Case: Collaboration has its proven benefits. It can lead to new products, new processes and innovative ideas. But can too much become a hindrance? We looked into quantifiable measurements of collaboration, such as total number of emails and meetings, to better understand their effect on the productivity of a hardware manufacturer’s sales team.

Details: An automated hardware manufacturer noticed the sales team of over 200 salespersons had uncharacteristically uneven results. They sought out their people analytics team to boost/increase sales and productivity.

Purpose: Detect patterns, recommend actionable solutions based on data

Methods: Correlation, regression modeling, scatterplots and histograms

Step1 — Business Question:

Why are attainment results uncharacteristically uneven across the sales department? Any solutions to increase sales quota attainment?

Description of Data Set:

Figure 1: Data dictionary Part 1

Figure 2: Data dictionary Part 2

The data we have on whether or not quota is attained is from the Sales Department, so this is where the following analysis will be focused.

Let’s look at the workforce architecture of the sales team…

Frequencies of Employment Level in Sales Department:

ggplot(df_sales, aes(factor(level),

fill = factor(level))) +

geom_bar()

Figure 3: Levels in Sales Department, and relative # of employees

Most of the sales department is made up of “Junior IC” level employees, followed by “Support” and “Senior IC” employees.

Next, we look at the median quota results (% of attainment), by level of employee. The box plots display the minimum, maximum, median, and first and third quartiles.

Figure 4: Level of employees and attained quotas

Highest performers include the Senior Executive, Directors and Executives. Managers and Junior IC level employees could learn from Directors or Support, on relevant sales tactics that work, in order to increase quota attainment. If executives focused some of their efforts on improving their Junior IC employees and Managers, there is a likelihood of added quota attainment and employee growth.

Step 2 — Correlations Between Variables in Data Set:

Figure 5: Measuring the strength of various correlations between variables

Highest Correlating Variables in data set:

Centrality and emailcount— 0.70

Centrality and emailcount_int — 0.83

Emailcount and emailcount_int — 0.89

Emailcount and emailcount_ext — 0.87

Meetingcount and centrality — 0.59

Meetingcount and meetingcount_ext — 0.93

Meetingcount_int and meetingcount_ext — 0.89

Insights: These three factors — meetings, emails and centrality are all highly correlated with one another.

Mild Correlation:

Attainquota and emailcount — 0.28

Attainquota and meetingcount — 0.36

Attainquota and centrality — 0.35

Attainquota and meetingcount_int — 0.37

Insights: In order to understand the effect of collaboration on attainment % of quota, we look into email count, meeting count and centrality.

Part 3 — Visual Analysis: Histograms and scatterplots to explore the hypothesis and data categories/points

Histogram of Employees and their Sales Quota % attainment:

Figure 6: Histogram of sales quota attainment

Email Count and Attain Quota % (color coded by level of employee):

Figure 7: Attain quota % and email count, color coded by level

Here, we notice that the relationship between email count and quota attained is not linear. After a certain point, the quota attained % decreased with additional email count. This shows that emails/collaboration may help achieve goals up to a certain point, but excessive email amounts could hinder productivity and progress.

Part 4 — Data Subsets — Non-Linear Relationships

We create data subsets to more accurately depict non-linear relationships.

Attain Quota % and Email Count, with Data Subsets:

ggplot(data=model_1, aes(x = emailcount, y = attainquota)) +

geom_point(col = "blue") +

geom_smooth(method = "lm", formula = y ~ x, data=subset(df_sales, emailcount<7500),

se = FALSE, col = "coral") +

geom_smooth(method = "lm", formula = y ~ x, data=subset(df_sales, emailcount>=7500),

se = FALSE, col = "darkgreen") +

theme_classic()

Figure 8: Attain quota % and email count, subsets of collaboration effects

Centrality and Attained Quota:

Centrality (the measure of how central the employee is to the flow of information within the organization) produces a similar result. This data too, was separated into subsets, to more accurately depict the non-linear relationship between quota attained and network centrality.

ggplot(data=model_1, aes(x = centrality, y = attainquota)) +

geom_point(col = "blue") +

geom_smooth(method = "lm", formula = y ~ x, data=subset(df_sales, centrality<0.30),

se = FALSE, col = "darkgreen") +

geom_smooth(method = "lm", formula = y ~ x, data=subset(df_sales, centrality>=0.30),

se = FALSE, col = "purple") +

theme_classic()

Figure 9: Attain quota and centrality, subsets of collaboration effects

This clearly depicts the negative effects of quota attainment with increasing centrality within the organization. (Higher centrality means employee is more central to data flows of information with networks.)

Then we included centrality squared in the data set, as a quadratic relationship, to achieve a better model fit.

Attain Quota OLS Linear Regression Model:

Figure 10: Regression analysis of effects on attained quota %

Interpretations on the coefficients:

Centrality — significant, the likelihood of attaining quota goes up.

I(centrality²) — significant, the likelihood of attaining quota goes down.

meetingcount — significant, the likelihood of attaining quota goes up.

Scatterplot of Interior Meeting Count and Attained Quota %:

Figure 11: Meeting count — interior and attained quota %

After 1,000 meetings, there appears to be a tapered slope of productivity in attaining quotas. This is consistent with the centrality and email count variables. (Note: Many sales employees attained their quota without meeting with interior departments.)

Conclusion:

An increase in collaboration may lead to increased productivity, but only up to a certain point. Too much collaboration will likely lead to a decrease in productivity, due to lost time obtaining objectives.

Based on the insights from this analysis, I can recommend a few solutions to increase quota attainment. There may be opportunities to improve individual sales processes through more structured sales protocols.

Further analysis of higher performers could shed light on why their sales processes produce better results. The team could use data analytics to better predict which customers are more likely to purchase, based on historical data, and allocate more time to those customers. That information can be summarized and reported to all other sales employees. This may be one path to improving quota attainment and increasing sales department-wide.

For further analysis, additional data I would like to have is quota attainment for the other departments, not just sales. I would run an additional analysis to see if centrality, email and meeting count impacts other (non-sales) departments and their ability to reach their objectives.

Sources: Polzer, Jeff T., et al., (2018). Sarah Powers at Automated Precision Products, Harvard Business Review.