A company's CRM account includes a huge amount of business-related information, including customers' interests and buying patterns, sales cycle duration, rate of completion of sales activities, and sales velocity. A thorough exploration of this data can give users insights into patterns which will play a pivotal role in their decisions related to marketing investments, managing inventory, campaign budgets, and responding to market needs.
CRM offers many analytic tools that help users decode business data into simpler graphical forms that are easy to understand and can be shared with peers across the organization. One of these tools is Zones, where data points are plotted on a graphical area to represent the most optimal to least optimal conditions in the form of zones.
In zone analysis, the darkest zone represents the best or the most optimal data point and the lightest zone indicates the least optimal data point.
Let's look at some business scenarios to understand how zones represent data points and help with analysis.
Revenue generation pattern in the sales team
Zone analytics can be useful for analyzing a team's performance. You can quickly identify which sales personnel close the most high value deals and which ones close fewer high value deals. This analysis can also give you a good idea of the type of deals your company usually lands. For instance, if most of your sales people are generating low revenue, it means the deals that they usually get are lower value deals. You can look at ways to improve your existing lead generation processes or revisit your customer engagement strategy to help get higher value deals.
Overall sales turnover and monetary gains
Zone analytics can indicate how your sales turnover performed. You can plot the number of units sold and the total profit generated to find which products that were purchased most often, which shows which are in high demand among your customer base. These insights can be helpful for maintaining inventory stocks and also revisiting customer redressal or support tickets to find out why some products are less popular.
Region-wise booking pattern
With zone analysis, you can identify patterns in bookings based on region. For example, if you work for a travel agency, you can see the number of bookings for a particular destination and take proactive measures to optimize the rise or decline in demand. If the number of bookings for a destination is increasing, then you may need to contact the hotels there to let them know about the increase and confirm they have sufficient capacity so that reservations are not cancelled at the last minute. Likewise, if you see a decline in bookings, you can let the relevant partners know so that they can plan accordingly and your arrangement with them can continue to be cost effective.
Types of zone analytics
There are two types of zone analytics which represent data points differently on the graph.
In the standard graph, data points are scattered across the graphical area based on their position on the x and y axes. Depending on the way zones are configured, a data point can fall on the darkest or lightest zone, indicating more or less optimal conditions respectively.
The advanced graph indicates the number of occurrences of each data point by the size of the data point on the graph. For example, if the average revenue generated by the deals closed by a particular salesperson is higher, the data point for that salesperson will be larger, while a smaller data point will appear for a salesperson who closed deals with a lower average revenue.
Using plot area to indicate the number of occurrences makes it easier to understand the graph.
Configuring zone analytics
Configuring zone analytics involves the following steps:
Choosing the object for analysis: The data that you want to analyze can be stored in either a standard or custom module. Select the module which contains the data you want to analyze.
Grouping the data: The data that needs to be analyzed must be grouped using specific parameters. For example, leads can be grouped by their status, industry, or time of creation. Likewise, deals can be grouped by their closing date, stage, or type. Standard and custom date and picklist field values can be used to group the data.
Filtering data types: Criteria such as leads from North America, leads from the IT industry, deals that need analysis, or deals created in January, can be used in the criteria filter if you need to analyze a specific subset of the data. If criteria are defined, only the records that satisfy the criteria will be included in the analysis.
Defining X- and Y-axis parameters: The data points are plotted on the X- and Y-axes. The parameters for these axes can be record count, total amount, minimum revenue, or average of any currency. Standard and custom date, time, and currency fields will be listed here.
Defining positive and negative instances: Depending on the data that is being analyzed the increase or decrease in number must be defined as positive or negative. For example, when analyzing the number of tickets resolved, an increase should be considered as positive growth. However, when analyzing the number of complaints or cases submitted, a decrease should be considered positive. The objective is to define the position of the optimal zone, based on which the actual data zones are plotted.
Setting zone split: The zones can be split equally or unequally. In an equal split, the zones are plotted by joining the X and Y-coordinates at equal distances, so that each zone has an equal area. However, if you want the zone areas to be divided unequally, you can set custom values to define the X and Y axes to determine the zone area.
Custom values can be entered for the X and Y-axes to set the plot area of the zones. This can be useful if you want data points that would otherwise be less optimal to be considered as better or well performing data. For example, if campaign popularity is plotted across product lines and the marketing team is already aware that surveys are not as popular among your customer base, they can use custom values for the zone analysis so that the surveys fall in the least popular or lightest zone rather than showing no data points due to lack of optimal values.
To configure zone analytics
- Go to the Analytics module.
- Click Add Component and choose Zone.
- In Choose Zone Component Style, select Standard or Advanced.
- In the Zone Configuration page:
- Enter the Component Name.
- In Object to be Analyzed, select a module and choose a grouping style from the drop-down list.
- Click +Criteria Filter and select fields, if required.
- In Measure, select X and Y-axis parameters from the drop-down list.
- Choose Consider increase in value as Positive or Negative.
- In Zone Split, select Split Equally or Define Custom Values.
- If you choose custom values, enter values in the X-axis and Y-axis fields.
- Click Save.