# Salespeople Performance Evaluation with Predictive Analytics in B2B
## 4 Work Methodology
### Data Preparation
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Field** & **Sample Value** & **Min** & **1 st Quartile** & **Median** & **Mean** & **3 rd Quartil** & **Max** \\ \hline Talent & Good & Not Performing: 373, Good: 269 and Outstanding: 53 & & & & & \\ \hline Growth\_All\_Products & \(-\)46 & \(-\)1790 & \(-\)26.5 & 37 & 138.4 & 205.5 & 12,617 \\ \hline Customer\_Base\_All\_Products & 4 & 1 & 7 & 14 & 15.97 & 22 & 83 \\ \hline Base\_Line\_All\_Products & 78 & 0 & 40.5 & 204 & 633.4 & 631.5 & 12,443 \\ \hline Growth\_Percent\_All\_Products & \(-\)0.59 & \(-\)1 & 0.17 & 0.18 & 3.17 & 1.08 & 566 \\ \hline Target\_All\_Products & 0 & 0 & 120 & 272 & 405.7 & 560 & 6200 \\ \hline Target\_Achievement\_All\_Products & 0 & \(-\)293 & \(-\)0.09 & 0.11 & \(-\)1.07 & 0.66 & 88 \\ \hline N\^{\prime}\_Opportunities\_created & 0 & 0 & 2 & 10 & 17.83 & 24 & 202 \\ \hline Average\_N\_of\_Opportunities\_per\_customer & 0 & 0 & 1 & 1 & 1.13 & 1 & 16 \\ \hline N\^{\prime}\_Months\_with\_growth\_above\_0 & 3 & 0 & 3 & 6 & 5.87 & 9 & 12 \\ \hline N\^{\prime}\_Different\_Products & 0 & 0 & 1 & 1 & 1.30 & 2 & 6 \\ \hline Grow\_with\_Different\_Products & 0 & 0 & 0 & 0 & 0.35 & 1 & 1 \\ \hline Ocean\_FCL\_Import\_Target & 0 & 0 & 0 & 101 & 220.90 & 300 & 2250 \\ \hline Ocean\_FCL\_Import\_Growth & 0 & \(-\)2193 & 0 & 0 & 72.88 & 87 & 3423 \\ \hline Ocean\_FCL\_Import\_Base\_Line & 0 & 0 & 0 & 37 & 303.80 & 233 & 12,199 \\ \hline Ocean\_FCL\_Import\_Growth\_Percent & 0 & \(-\)1 & 0 & 0 & 1.85 & 1 & 110,429 \\ \hline Ocean\_FCL\_Import\_Target\_Achievement & 0 & \(-\)185.50 & 0 & 0 & 0.03 & 0.41 & 88 \\ \hline Ocean\_FCL\_Export\_Target & 0 & 0 & 10 & 100 & 177.50 & 240 & 6200 \\ \hline Ocean\_FCL\_Export\_Growth & \(-\)46 & \(-\)1790 & \(-\)7.5 & 0 & 35.07 & 56 & 3224 \\ \hline Ocean\_FCL\_Export\_Base\_Line & 78 & 0 & 0 & 28 & 279.50 & 195.50 & 8413 \\ \hline Ocean\_FCL\_Export\_Growth\_Percent & \(-\)0.59 & \(-\)1 & \(-\)0.18 & 0 & 2.27 & 1 & 517.67 \\ \hline Ocean\_FCL\_Export\_Target\_Achievement & 0 & \(-\)293 & \(-\)0.03 & 0 & \(-\)0.99 & 0.33 & 27 \\ \hline Reef\_Export\_Target & 0 & 0 & 0 & 0 & 4.94 & 0 & 2000 \\ \hline Reef\_Export\_Growth & 0 & \(-\)771 & 0 & 0 & 2.48 & 0 & 1923 \\ \hline Reef\_Export\_Base\_Line & 0 & 0 & 0 & 0 & 16.41 & 0 & 5585 \\ \hline Reef\_Export\_Growth\_Percent & 0 & \(-\)1 & 0 & 0 & 0.10 & 0 & 46.8 \\ \hline Reef\_Export\_Target\_Achievement & 0 & \(-\)2 & 0 & 0 & 0.02 & 0 & 9.36 \\ \hline Ocean\_FCL\_Cross\_Trade\_Target & 0 & 0 & 0 & 0 & 1.49 & 0 & 229 \\ \hline Ocean\_FCL\_Cross\_Trade\_Growth & 0 & \(-\)320 & 0 & 0 & 2.41 & 0 & 706 \\ \hline Ocean\_FCL\_Cross\_Trade\_Base\_Line & 0 & 0 & 0 & 0 & 4.07 & 0 & 1270 \\ \hline Ocean\_FCL\_Cross\_Trade\_Growth\_Percent & 0 & \(-\)1 & 0 & 0 & 0.24 & 0 & 45.56 \\ \hline Ocean\_FCL\_Cross\_Trade\_Target\_Achievement & 0 & \(-\)3.7 & 0 & 0 & 0 & 0 & 3.43 \\ \hline Freight\_Management\_FCL\_Target & 0 & 0 & 0 & 0 & 0.76 & 0 & 530 \\ \hline Freight\_Management\_FCL\_Growth & 0 & \(-\)595 & 0 & 0 & 23.99 & 0 & 12,391 \\ \hline Freight\_Management\_FCL\_Base\_Line & 0 & 0 & 0 & 0 & 24.07 & 0 & 4858 \\ \hline Freight\_Management\_FCL\_Growth\_Percent & 0 & \(-\)1 & 0 & 0 & 0.24 & 0 & 55.50 \\ \hline Freight\_Management\_FCL\_Target\_Achievement & 0 & 0 & 0 & 0 & 0 & 0 & 0.51 \\ \hline Reef\_Import\_Target & 0 & 0 & 0 & 0 & 0.11 & 0 & 24 \\ \hline Reef\_Import\_Growth & 0 & \(-\)186 & 0 & 0 & 0.91 & 0 & 172 \\ \hline Reef\_Import\_Base\_Line & 0 & 0 & 0 & 0 & 3.11 & 0 & 501 \\ \hline Reef\_Import\_Growth\_Percent & 0 & \(-\)1 & 0 & 0 & 0.34 & 0 & 155 \\ \hline Reef\_Import\_Target\_Achievement & 0 & \(-\)0.65 & 0 & 0 & 0 & 0 & 0.58 \\ \hline Remaining\_Products\_Growth\_Percent & 0 & \(-\)389 & 0 & 0 & 0.64 & 0 & 408 \\ \hline \end{tabular}
\end{table} 
Table 3: Table with classification statistics. In order to train the model, below evaluations and transformation were applied to the 695 classified rows.