Difference between revisions of "ANLY482 AY2016-17 T1 Group6/Midterm Progress"
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Our sponsor, Trustsphere is a software company that provides relationship analytics solutions. Their products deliver insights that help clients across the globe improve key business issues including sales force effectiveness, enterprise-wide collaboration and corporate governance. The company engaged our team to utilize our technical and analytical capabilities to help them understand and tackle their '''business problem of little growth in sales and a longer than ideal sales cycle'''. | Our sponsor, Trustsphere is a software company that provides relationship analytics solutions. Their products deliver insights that help clients across the globe improve key business issues including sales force effectiveness, enterprise-wide collaboration and corporate governance. The company engaged our team to utilize our technical and analytical capabilities to help them understand and tackle their '''business problem of little growth in sales and a longer than ideal sales cycle'''. | ||
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While the field of Sales Analytics has received plenty attention in the past, recent studies reveal that few companies have also delved into the area of Sales People Analytics. Salespeople communications to potential clients, especially in the B2B sphere, are wholly relied upon for marketing the company’s product. Furthermore, Steward et al. (2010) found that higher-performing salespeople also regularly activated their internal company networks, to coordinate a team of experts tailored to serve a particular customer. Just sales figures to evaluate salespeople performance covers a very narrow perspective as it disregards cycle time and in-progress pitches, therefore our team has defined our scope as to '''analyze the sales team’s internal and external communications to gain insight into their relationships with internal and external parties and to identify the sales stages that act as bottlenecks in the sales process'''. | While the field of Sales Analytics has received plenty attention in the past, recent studies reveal that few companies have also delved into the area of Sales People Analytics. Salespeople communications to potential clients, especially in the B2B sphere, are wholly relied upon for marketing the company’s product. Furthermore, Steward et al. (2010) found that higher-performing salespeople also regularly activated their internal company networks, to coordinate a team of experts tailored to serve a particular customer. Just sales figures to evaluate salespeople performance covers a very narrow perspective as it disregards cycle time and in-progress pitches, therefore our team has defined our scope as to '''analyze the sales team’s internal and external communications to gain insight into their relationships with internal and external parties and to identify the sales stages that act as bottlenecks in the sales process'''. | ||
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'''B) Staff List''' | '''B) Staff List''' | ||
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:3. SOCIAL NETWORK ANALYSIS | :3. SOCIAL NETWORK ANALYSIS | ||
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*Analyses collaboration trends, that is, what department do salespeople interact with more, for example does interacting with C-Suite employees correlate with better performance? | *Analyses collaboration trends, that is, what department do salespeople interact with more, for example does interacting with C-Suite employees correlate with better performance? | ||
*Examines overlap trends, to see if multiple salespeople interact with the same client, and whether abnormalities exist within the overlap. | *Examines overlap trends, to see if multiple salespeople interact with the same client, and whether abnormalities exist within the overlap. | ||
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:a. Hot Relationship: Last contact was made less than 3 days ago | :a. Hot Relationship: Last contact was made less than 3 days ago | ||
:b. Cold Relationship: Last Contact was made more than 3 days ago | :b. Cold Relationship: Last Contact was made more than 3 days ago | ||
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<u>2. Hot & Cold Relationships</u> | <u>2. Hot & Cold Relationships</u> | ||
:a. Strong Relationship: Above average number of emails exchanged AND is a hot relationship | :a. Strong Relationship: Above average number of emails exchanged AND is a hot relationship | ||
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Revision as of 20:43, 4 December 2016
Home | Team | Project Overview | Midterm Progress | Final Progress | Project Management | Documentation |
Our sponsor, Trustsphere is a software company that provides relationship analytics solutions. Their products deliver insights that help clients across the globe improve key business issues including sales force effectiveness, enterprise-wide collaboration and corporate governance. The company engaged our team to utilize our technical and analytical capabilities to help them understand and tackle their business problem of little growth in sales and a longer than ideal sales cycle.
While the field of Sales Analytics has received plenty attention in the past, recent studies reveal that few companies have also delved into the area of Sales People Analytics. Salespeople communications to potential clients, especially in the B2B sphere, are wholly relied upon for marketing the company’s product. Furthermore, Steward et al. (2010) found that higher-performing salespeople also regularly activated their internal company networks, to coordinate a team of experts tailored to serve a particular customer. Just sales figures to evaluate salespeople performance covers a very narrow perspective as it disregards cycle time and in-progress pitches, therefore our team has defined our scope as to analyze the sales team’s internal and external communications to gain insight into their relationships with internal and external parties and to identify the sales stages that act as bottlenecks in the sales process.
For this project, our team is working with two sets of data provided to us by TrustSphere:
A) Daily email communication data (main dataset)
- This dataset contains year-to-date (up till 31 August 2016) records of daily email communication data of all 19 Trustsphere sales people across the globe. This data includes the following variables:
- Date: Includes the date and time of a particular email being sent
- Originator address: Sender email address
- Recipient address: Receiver email address
- Direction: Nature of communication (internal, inbound or outbound)
- MsgID: Unique message ID of emails sent
- Email Subject: Email subject header
B) Staff List
- The dataset lists all of TrustSphere staff (57) with the following variables:
- Name
- Hierarchy
- Department
- Position
- Location
We were also provided with a Relationship dataset, as mentioned previously in the proposal, which contained individual records of salespeople relationships – however we are not using this dataset in any of our analyses.
After repeated interaction with the Sales team, our team decided to divide the scope into the following sections:
- 1. RELATIONSHIP REPORT
- Analyses the number and strength of Internal and External Relationships Salespeople have developed over the analysis period.
- Takes into account the frequency and recency of emails exchanged by and with the sales person to highlight their communication and collaboration effort.
- 2. CLIENTS AND SALES STAGES
- Reports the sales progress for each account up-till 31st August. That is, how many account are active and what stage of the sales cycle they’re in.
- Evaluates the performance of each salesperson depending on how many accounts they have in each stage, their response trends in each stage, progress from historical communication indications etc.
- Provides a postmortem on inactive accounts, reporting on how long communications with a client lasted, what stage did communications end and which salespeople were responsible.
- 3. SOCIAL NETWORK ANALYSIS
- Analyses collaboration trends, that is, what department do salespeople interact with more, for example does interacting with C-Suite employees correlate with better performance?
- Examines overlap trends, to see if multiple salespeople interact with the same client, and whether abnormalities exist within the overlap.
- Examines how networks differ with relation to location, whether their lies a communication gap between teams based in different regions.
Area of Interest | Analyses | Purpose |
---|---|---|
Relationship Report | Data Transformation, Summary Statistics and Visualisations |
|
Clients & Stages | Data Transformation, Text Mining, Summary Statistics |
|
Salespeople Network | Social Network Analysis |
|
Following our exploratory analysis of the data and the scope and repeated meetings with the client we have established certain classifications and metrics that ease reporting of evaluations of the sales performance.
STAGES AND CLIENTS
1. Sales Stages
- a. Prospecting Stage: When a prospecting email is sent out to a prospective client. They may or may not respond.
- b. Meeting Stage: When the client has responded favourably to the prospecting stage and Trustsphere has a scheduled pitch meeting with the client.
- c. POC Stage: When the client has agreed to commission a product trial.
- d. After POC Stage: Follow ups, quotations, contracts etc.
The classification of these stages was provided to us by Trustsphere. Their commission structure rewards sales people after they get a client into the Meeting and POC Stage. Therefore all emails after the POC stage are classified into the the ‘After POC’ stage.
2. Active and Inactive Clients
- a. Active Client: Contact has taken place within the past 30 days
- b. Inactive Client: No contact has taken place in the past 30 days
3. Failed Prospects
- a. Failed Prospect is a classification that indicates what percentage of prospecting emails sent out did not make it to the meeting stage.
RELATIONSHIP REPORT
1. Hot & Cold Relationships
- a. Hot Relationship: Last contact was made less than 3 days ago
- b. Cold Relationship: Last Contact was made more than 3 days ago
2. Hot & Cold Relationships
- a. Strong Relationship: Above average number of emails exchanged AND is a hot relationship
- b. Weak Relationship: Below average number of emails exchanged AND/OR Cold Relationship
Now that our Datasets are ready our next steps are to -
- A. Calculating and Visualising Metrics from the above Dataset to gather Insights
- Using SAS, JMP and Tableau we will be calculating and visualising these metrics on a per month basis to determine time series and seasonality trends and identify any red flags in salespersons performance or sales cycle process.
- B. Create a Dashboard to report Insights to Sales Manager
- Using Javascript and D3 we will create a web based dashboard to report insights to the sales insights to the Sales Manager/Director. (Mock Up created on Powerpoint, Appendix B)
Now that our data is ready we are in process of computing the above mentioned metrics for example
- A. Relationship Score
The Relationship Report dataset was imported into SAS EG. The columns were standardized and the relationship score was computed using the formula mentioned in the report.
- B. Sales Stages