D099 Module 5

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Unit 4:

27 terms rtho How does sales analytics using CRM data contribute to a company's sales practices, strategies, and management decision-making?Sales analytics not only computes the success of recent sales activities and forecasts but also determines the trends and possible results of future ones. A typical example of a sales analytics activity is to measure sales target performance for a sales team and then use the past performance to set goals for future sales quotas.With this type of data, the sales department can relate sales activities to revenue and other performance outcomes, set objectives for the sales team, and create a sales plan.Define sales analytics and discuss the 2 significant roles sales analytics plays for a sales team.Sales analytics distinguishes data from several perspectives, such as understanding which customer segments to serve, scoring and prioritizing sales leads according to the likelihood of closing a deal, measuring the likelihood that a customer account will generate revenue, and determining customer retention.

• from an internal perspective, it can be used for segmentation- the organization

can use data analytics to analyze and prioritize its customer base and prospects in order to target them more efficiently.

• from an external perspective, it can be used to validate the company's value

assertion to the customer- the data can prove or disprove whether what the sales team is saying and offering the customer in terms of value is really being delivered.

Describe the 4 steps in the sales analytics process or workflow.

• Collection: this step determines the business objective and perform actual data

collection (gathering factual data) using the prescribed methods.

• Processing: this is where the data must be sorted and organized for analysis-

can be done with spreadsheets/data models that separate the data into categories so that the relevant data can be identified.

3. Analysis: now that the data has been sorted/separated into relevant

information, they can further be organized into charts or graphs to facilitate visualization and analysis.

• Interpretation: 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.Describe the 4 levels of sales data analytics and explain how data analysis supports decision making efforts.

1. Descriptive analytics: 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- can be useful in the sales cycle.

2. Diagnostic analytics: the historical data is measured against one another to

answer the question and can give insights into dependencies and help identify patterns. Provides valuable insights for organizations because it helps them understand which decisions impact performance.

• Predictive analytics: uses data to identify past patterns to predict the future

and can help decision makers to predict the outcomes of a decision before it's implemented. Using these probabilities, decision makers can calculate the expected value of alternatives once risks and benefits are taken into account and is particularly useful when there is a high degree of uncertainty.

• Prescriptive analytics: a framework that moves data all the way from collection

to interpretation and decision making, or the point where the data drive action.Gives a laser-like focus to answer specific questions.Describe the steps in the workflow for prescriptive analytics.

• The data is acquired and processed.
• Hypotheses are formed after running the data through analytical software.
• Initial actions are taken while hypotheses are being tested.
• Hypotheses are proven or disproven, and prescribed actions are driven by the

results.List and describe the 3 Vs of Big Data.Volume is 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.Velocity is the speed at which data are being sent and collected. How data are transported has evolved with the use of social media and requires analysis that must be continually updated with the new data that are being received.Variety is various data forms; data has moved from traditional structured data forms found in relational databases to new unstructured data forms.What is the internet of things (IoT) and how does it relate to big data and data analytics?IoT is a system of devices, appliances, and machines that are interconnected through the internet and can identify themselves to other devices and networks.There are more opportunities for generating extensive amounts of data that can be gathered and analyzed in detail, providing insight into consumer behaviors and product usage patterns.

Describe at least 3 ways that Big Data can help organizations address business activities.

1. Product development: developing predictive models for new products by

classifying major characteristics of past and current products and modeling the relationship among those characteristics and the success of the offerings.

2. Customer experience: as companies compete for customers who have access

to so much information, they need a clear view of their customers' experience, both to improve the experience and to target the right customer. Businesses can provide personalized offers, reduce customer loss, and handle issues proactively when they take action driven by the results of big data analysis.

3. Operational efficiency: big data allows companies to analyze and evaluate

production, customer feedback, returns, and other issues to decrease outages and foresee demand.How can organizations utilize CRM data and analytics to segment their customer base and target specific customer profiles?With CRM analytics tools, organizations can analyze patterns and trends in the data in order to create a market segmentation of their customer base. Customers can then be segmented into different groups, such as those based most on potential business category, business size, location, and loyalty.Effective segmentation can also be utilized to develop ideal customer profiles (ICP) that reflect an organization's best customers, enabling it to target similar prospects and capture potential leads more likely to make a purchase.What is a 360-degree view of the customer? Describe the 2 types of data that is used to create a 360-degree view of the customer.A 360-degree view of the customer is a process of collecting data from various customer touchpoints for complete understanding of the customer and to guide

interactions with the customer. Can include: shortening the buying process,

improving sales interactions, deepening relationships with customers, and providing personalized and proactive customer service that can lead to improved customer satisfaction and repeat sales.

• Fit data: 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.

2. Behavioral data: capture what the consumer has done with the company. This

type of data fits into three categories: intent, engagement, and relationship.

Explain how sales data analysis is used to inform sales

practices in each of these areas: 1) Lead generation 2)

Lead scoring 3) Customer Lifetime Value (CLV) and 4) Forecasting and planning.1) Analytics can help identify customers who are likely to buy a company's products, detect which channels are optimal for contacting prospects, and determine which discounts and price points will most likely attract prospects.2) Lets businesses rank and qualify leads by the level of interest the leads show in their interactions with the company. Sales teams use lead scoring to find the most suitable new customers and save the expense of following unprofitable leads.3) CLV is the net profit of a future relationship with a customer. Higher CLV for each existing customer means that the customer can generate more revenue without the business having to invest more dollars.4) Sales forecasts allow organizations to make educated business decisions and predict short-term and long-term performances and provides companies insight into managing their workforce, cash flow, and resources.Explain the concept of integrating a CRM system with other organizational systems and processes. How does this integration foster efficiency, collaboration, and better customer experiences?CRM is an organization-wide strategy including customer-facing aspects as well as other aspects of the organization. The development and implementation of CRM and analytic tools have fostered greater efficiency and collaboration among sales, service, marketing, and other departments. Integrated systems and processes bridge departments together.

What is a CRM dashboard? How does it impact the behavioral expectations of salespeople?CRM systems often include a dashboard, which may include top sales performers for a variety of chosen metrics and other types of data that are tracked and visualized.For example, if a qualified lead is identified, the CRM system can track the time it takes the salesperson to follow up with the lead. When sales teams understand data are being tracked, behavioral expectations are set and can lead to timely customer interactions that convert a lead to a purchase.What are predictive sales analytics? Describe the 4 tools supplied to sales managers by predictive analytics to help them set objectives and priorities.Predictive sales analytics can make predictions of likely sales outcomes given a variety of factors, such as the use of sales activities and changes in pricing strategies. Also extracts information from data to predict trends and behavioral patterns.

1) predictive lead scoring: recognizes trends in the customer journey and uses

them to predict where the customer is in the sales pipeline. They updated regularly, and the sales manager is informed about the buying positioning of each prospect.2) predictive forecasting: 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.

3) predict customer attrition: raises 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.

4) sales performance monitoring: assists 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.Explain how sales data analytics influences sales management decision-making in each of the following

areas: 1) Predictive sales 2) Salesperson/Sales team

performance 3) Conversion ratio, win-loss analysis, and activity goals and 4) Key performance indicators.1) Explain the concept of conversion ratios.Conversion ratios measure how good a salesperson is at moving customers from one stage of the sales funnel to the next.

A 10:1 ratio means it took 10 leads for the salesperson to get one suspect who

agreed to move to the next step. A salesperson with a 5:1 ratio only needs to

pursue five leads to get a suspect. So, if the representative can make only 10 sales

calls in a day, then the salesperson with the 5:1 ratio will have produced two

suspects versus just one suspect for the other salesperson. As a result, the second representative will have more suspects in the pipeline at the end of the day.