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Sales Management - D099 > Module 5: Sales Analytics > Flashcards

Module 5: Sales Analytics Flashcards

1
Q

CRM analytics

A

A systematic electronic analysis of customer data to improve decision-making

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2
Q

sales analytics

A

The process of generating insights from sales data to identify, model, understand, and predict sales trends and forecasts from large data sets

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3
Q

revenue

A

The total amount of money generated by the sale of goods or services related to a company’s primary operations before expenses

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4
Q

big data

A

Large, complex data sets that require nontraditional data processing software

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5
Q

segmentation

A

The process of dividing a market of potential customers into groups, or segments, based on different characteristics

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6
Q

Sales Analytics General Process

A

Collection
Processing
Analysis
Interpretation

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7
Q

Collection

A

n this step, you determine the business objective (what you want to measure, what question you want to answer, or what methods will be used to collect the data) and perform the actual data collection using the prescribed methods. Collection methods can be simple, as in surveys and online forms, or more complex, as in organizational databases where customer information is entered. There should be an emphasis on gathering factual and accurate data so that decisions based on these data are valid.

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8
Q

Processing

A

The collected data are usually unstructured and can contain irrelevant information. Thus, these data must be sorted and organized for analysis. Usually, this is done by using spreadsheets or data models that separate the data into categories in columns and rows so that the relevant data can be identified.

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9
Q

Analysis

A

Now that the data have been sorted and separated into relevant information, they can be further organized into charts and graphs to facilitate visualization and analysis. In this step, you may realize that you need more data or that the data you pulled are not useful and other data should be extracted.

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10
Q

Interpretation

A

Once the data analysis is completed, it is time to interpret the results. At this point, data can be used to guide business decisions, to provide information as to the next steps, or to inform a best course of action. The interpretation phase is where the data are used to answer the questions posed at the beginning of the process.

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11
Q

Levels of Analytics

A
  1. Descriptive analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics
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12
Q

Descriptive analytics

A

A preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis

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13
Q

Diagnostic analytics

A

A deep look at data to attempt to understand the causes of events and behaviors

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14
Q

Predictive analytics

A

The use of data to identify the past patterns to predict the future

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15
Q

lead scoring

A

A numerical calculation used to rank the prospects of a perceived value to the organization

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16
Q

Prescriptive analytics

A

A type of data analytics that factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy

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17
Q

Prescriptive Analytics Workflow

A
  1. The data are ACQUIRED and processed.
    2 . HYPOTHESIS are formed after running the data through analytical software.
  2. Initial ACTIONS are taken while hypotheses are being tested.
  3. Hypotheses are PROVEN OR DISPROVEN, and prescribed actions are driven by the results.
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18
Q

Big Data

A

high-volume, high-velocity, and high-variety information assets that require innovative information processing for enhanced insights and decision-making. The challenge with big data is that it is…well, big!
Volume, Velocity, Variety

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19
Q

Volume

A

associated with the amount of data available that includes anything from customer transactions to scientific data. Data sets that are so large will not fit into one information processing system. This led to the development of distributed file systems that integrate data across multiple systems known as distributed computing.

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20
Q

distributed computing

A

A model in which components of a software system are shared among multiple computers

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21
Q

Velocity

A

the speed at which data are being sent and collected. How data are transported has evolved with the use of social media, for example, and requires analysis that must be continually updated with the new data that are being received.

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22
Q

Variety

A

the various data forms. Data have moved from traditional structured data forms found in relational databases to new unstructured data forms. Multimedia (images, videos, audios), biometrics (fingerprints, facial recognition, mouse clicks moved by your hand), and graph data (connections between people) are forms of unstructured data that are actively collected.

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23
Q

structured data

A

Any data that reside in a fixed field within a record or file, including data contained in relational databases and spreadsheets

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24
Q

relational databases

A

A database structured to recognize relations among stored items of information

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25
Q

unstructured data

A

Information that either does not have a predefined data model or is not organized in a predefined manner. Unstructured information is typically text-heavy but may contain data such as dates, numbers, and facts

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26
Q

internet of things (IoT)

A

A system of devices, appliances, and machines that are interconnected through the internet and can identify themselves to other devices and networks

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27
Q

market segmentation

A

The process of defining and subdividing a large homogeneous market into clearly identifiable segments having similar needs, wants, or demand characteristics

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28
Q

ideal customer profiles (ICP)

A

A categorical description of a customer that would benefit immensely from a company’s offerings and provides significant value in exchange

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29
Q

360-degree customer view

A

A process of collecting aggregated data from various customer touchpoints for complete understanding of the customer and to guide interactions with the customer

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30
Q

data captured to create a 360-degree view of the customer

A
  1. Fit Data

2. Behavioral Data

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31
Q

Fit data

A

are attributes about the customer, demographic data for B2C customers and firmographics for B2B customers. Demographic data capture measurable consumer information such as age and income levels. Firmographics for B2B customers can include industry type, revenue data, number of employees, or any information related to the customer.

32
Q

Behavioral data

A

capture what the consumer has done with the company. This type of data fits into three categories: intent, engagement, and relationship. Intent is about what the customer is doing, such as conducting web searches and liking posts on social media. Engagement data capture interactions of the customer with the company and include website visits and email opt-ins. Relationship data are actual interactions with the sales force or anyone in the company through meetings, service calls, and various other interactions.

33
Q

firmographics

A

Descriptive attributes of firms that can be used to aggregate individual firms into meaningful market segments

34
Q

Lead scoring

A

A numerical calculation used to rank the prospects of a perceived value to the organization

35
Q

cold calls

A

Sales calls to a person without any information on that person’s interest or interactions with the company or product

36
Q

qualified lead

A

An individual who could become a potential customer, based on predefined criteria and identifying information

37
Q

Lead nurturing

A

The process of developing relationships with buyers at every stage of the sales funnel and through every step of the buyer’s journey

38
Q

upselling

A

A sales strategy aimed at generating more sales by inducing more expensive items, upgrades, or add-ons to make a more profitable sale

39
Q

cross-selling

A

A sales strategy aimed at generating more sales by suggesting additional, related, or complementary items to a buyer who is already committed to making a purchase

40
Q

customer lifetime value (CLV)

A

A prediction of the net profit attributed to the entire future relationship with a customer

41
Q

linear regression model

A

A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables

42
Q

sales forecasting

A

The process of estimating future sales

43
Q

Personnel changes

A

Variations in the number and dynamics of the sales team can significantly impact sales results. For instance, if a salesperson leaves, sales will probably decrease until he or she is replaced and the new salesperson gets trained.

44
Q

Economic conditions

A

The economic climate is an essential factor to consider when forecasting. A strong economy usually delivers prospects who are confident and willing to buy. On the other hand, a weak economy can lead to slower sales cycles and reluctant prospects who may need more reassurance and encouragement.

45
Q

Competitor changes

A

Unforeseen changes in the competing market can significantly impact a sales forecast. For example, if a competitor decreases prices or adds value to a similar product, your sales team may have a more difficult time closing deals. They may need to offer more significant discounts or add more value to the deal with added features or services, which then impacts the forecasted revenue.

46
Q

Product changes

A

Generally, changes in products translate to more sales because of improved product features, more attractive pricing, or other benefits. These types of changes can help sell more and faster. Conversely, if products become more expensive without significant improvements, or if there are issues with production or deliveries, this will negatively impact sales. Sales teams must be informed about planned product changes to prepare and modify selling strategies to meet sales goals.

47
Q

statistics

A

A discipline that concerns the collection, organization, display, analysis, interpretation, and presentation of data

48
Q

artificial intelligence

A

An area of computer science that emphasizes the creation of intelligent machines that work and react like humans

49
Q

Data for predictive sales analytics

A

Historical data, data from the web, behavioral information, demographic information, transactional data, social media, real-time data feeds, third party databases

50
Q

Predictive Analytics

A
51
Q

Predictive lead scoring

A

his is one of the primary uses of predictive sales analytics. It recognizes trends in the customer journey and uses them to predict where the customer is in the sales pipeline. Sales teams can prioritize opportunities based on the likelihood each lead will close. This information will direct the team as to which steps to follow to close more deals and the best method to utilize in order to attain higher closure rates. Lead scores are updated regularly, and the sales manager is informed about the buying positioning of each prospect. With the use of this function, sales managers can set priorities for the sales team in order to close more deals.

52
Q

Predictive forecasting

A

Based on historical sales performance and the current state of the sales pipeline, predictive analytics can depict the outcome of the current deals in the pipeline for each salesperson. These projections help the sales team and manager determine which prospects to pursue immediately and which to follow up with later.

53
Q

Predict customer attrition

A

Predictive sales analytics can raise awareness with sales managers about customer attrition, based on satisfaction, usage, and historical trends. With this information, sales managers can set customer retention strategies and direct the sales team accordingly.

54
Q

Sales performance monitoring

A

Predictive sales analytics assist sales managers in customizing their coaching strategy with their sales team. After identifying shortcomings, sales managers can use analytics to measure and analyze future events from which they can help salespersons overcome their shortcomings and achieve their targets.

55
Q

Benefits of Predictive sales analytics

A

improved productivity and efficiency of the sales cycle
effective management of sales leads
increased accuracy of sales forecasts and predictions of future revenue
improved timing and relevancy of sales messaging
effective customer relationship management with improved customer retention and customer experience
achieved sales targets with improved performance of sales team

56
Q

Conversion ratios

A

A performance metric that measures how good a salesperson is at moving customers from one stage in the selling cycle to the next

57
Q

activity goals

A

A metric that measures how many sales calls of each type a representative has to make in a certain period of time

58
Q

win–loss analysis

A

A review of how well a salesperson performed given the opportunities he or she faced

59
Q

The following are some pre-sales activities sales managers may consider measuring using CRM sales analytics:

A

New opportunities—Tracking new opportunities helps measure the strength of the sales pipeline. Sales managers can measure the number of opportunity calls, new opportunities added, and the number of proposals sent, acted upon, and approved.
Prospecting—Sales managers can measure their sales team’s ability to pick up new leads and enter the sales pipeline. Number of prospecting calls, new leads, and cold emails sent can be measured using performance metrics.
Product demonstrations—In order to set goals for the sales team, sales managers can measure the total number of product demos that led to sales and the number of product demos by each salesperson.
Meetings and calls—Sales rely on meaningful customer interactions. It is important that sales managers track the number of calls, length of calls, number of meetings, and post-meeting engagements with customers.
Post-sales engagement activities are the interactions that take place after a sale, such as product demonstrations and phone meetings. Monitoring these activities helps retain customers.

60
Q

The following are some post-sales activities sales managers may consider measuring using CRM sales analytics:

A

Tickets—The number of tickets a customer generates can direct sales teams to address his or her issues proactively. The higher the number of tickets generated by a customer, the more the risk of canceling the product.
Billing records—Patterns found in billing records may indicate the likelihood a customer may cancel or keep the product.
Cross-sells and upsells—Based on the purchasing activity of cross-sells and upsells, sales and marketing managers can plan campaigns and set objectives.

61
Q

win rate

A

The actual revenue versus the revenue estimate

62
Q

close rate

A

The number of deals closed versus the number of opportunities in the pipeline

63
Q

Sales growth

A

This KPI measures change in sales units or revenues over a period of time, such as by month, quarter, or year over year. Sales managers can revise sales plans when sales growth has fallen below targets for growth. Also, given growth estimates, a sales manager can change orders and inventory on hand.

64
Q

Sales target

A

Managers can track the extent to which performance goals were met. Goals may include measures such as revenue, number of active accounts, units sold, and sales by region. Sales managers can adjust sales quotas for salespeople and teams, reward high-performing salespeople and teams, or assign more sales force resources to customer segments or underperforming product lines.

65
Q

Sales opportunities

A

This metric Identifies underperforming accounts with potential for additional sales, and non-customer accounts that are similar to existing customer accounts. This KPI can direct sales managers to assign sales force resources (number of sales representatives, travel budget, promotional resources) to top sales opportunities identified, set the size of sales force to assigned territories, and set sales quotas.

66
Q

Sales to date

A

This KPI compares current sales to that of the same time last year. Sales managers can take corrective action when sales are behind anticipated levels. Sales incentives can be identified to make improvements before the end of a quarter. This metric is typically shared with public relations and investor relations departments.

67
Q

Product performance

A

Managers can track sales by product or product line. They can make recommendations to divest underperforming products or invest additional sales resources if products should have a competitive advantage.

68
Q

Lead response time

A

This KPI measures the amount of time it takes a salesperson to respond to an identified qualified lead.

69
Q

Lead conversion rate and new accounts open

A

This KPI measures the percentage of leads that are converted to customers and may be compared by salesperson, sales tactic, product line, customer category, or other points of comparison. Sale managers can use this metric to calculate ROI and customer acquisition costs. They can also realign sales force focus on stages of the sales funnel where prospects are lost.

70
Q

Sell-through rate

A

This metric measures sales as a percentage of total inventory. This measure can assist management in making adjustments in inventory levels, orders placed, and other supply chain management decisions.

71
Q

Cannibalization rate

A

Cannibalization rate

72
Q

Quote-to-close rate

A

This KPI measures the average time to close sales deals. This helps the sales manager advise salespeople on whether stages of the selling cycle should be shorter or longer to enhance the closing rate.

73
Q

Average purchase value

A

This is the average dollar value of purchases. It may be calculated by customer type, product line, and sales representative. Sales managers can identify the most valuable customers and target them with a loyalty and retention program.

74
Q

Customer acquisition cost

A

This is the average cost to convert a lead to a paying customer. Managers can determine which customers’ profitability does not warrant acquisition costs and eliminate similar prospects from sales initiatives.

75
Q

Average profit margin

A

This is the average profit (revenues minus costs) across sales categories by sales representative, sales team, product line, and customer type. Sales managers can identify whether costs are too high for the achieved profit margin and take steps to reduce costs, such as providing incentives offered in closing negotiations.

Sales Management - D099 (12 decks)

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