Predictive Analytics for Sales Forecasting

Table of Contents

Introduction
What is Predictive Sales Analytics?
How is Predictive Sales Analytics Useful?
Common Predictive Sale Analytic Metrics
Challenges for Predictive Sale Analytics
How to Forecast Sales using Predictive Analytics?
Final Words
Topics in Predictive Analytics

Introduction

Over the decade, the inclination of business enterprises towards data science and analytics has increased tremendously owing to its cost-efficiency and work effectiveness. But the expansion of these concepts to sales forecasting and management is of much greater value. There are several use cases where Predictive Sale Analytics have created their mark. Reportedly, out of 1000 sales organizations globally, 53% have made it to the “high performing companies” with the effective use of data analytics form called Predictive Sale Analytics.

What is Predictive Sales Analytics?

The market being a dynamic space is full of innumerable opportunities and pitfalls. As a result, every business entity aims to identify these spots at the earliest and act rationally. As a result, the concept of Predictive Sales Analytics takes a productive shape here. It refers to the descriptive market study which includes sales diagnosing, prediction, and prescription. These insights are extremely crucial to unlock new market opportunities and pave the way towards the prospective pipelines.

For instance, today sales incline much towards lead scoring where cold calling to emailing is broadly in fashion. Honestly, the process does show good results but has an equal probability to turn upside down or might require a longer time to yield the expected output. So, how to bridge the gap? Can it be fixed with the Predictive Sales Analytic tool? Yes, because it works on the concept of predictive modeling where past behavior of the major stakeholder (Customer) is taken into consideration to determine future sales. Thus, it is a well-blended combination of data science and machine learning aiming to increase the accuracy of quality lead identification and reduce errors significantly.

Alongside, predictive sales analytics is a combination of five characteristics making it the prominent choice of individuals and business houses today.

A set of defined objectives.
Development of well-defined metrics and charts.
Concentrate on the behavior of interest.
Envisages efficient resource allocation
A collection of sufficiently integrated data.

How is Predictive Sales Analytics Useful?

As the name suggests predictive analytics plays a crucial role in forecasting sales followed by risk detection and sales operations optimization. Moreover, the most talked-about tool is suitable for all business sectors, retail, e-commerce, and manufacturing companies to measure the credit risk, detect fraud, and discover relevant factors influencing their business horizon.

Use Case	Description
Designing Best-suited Campaign for B2B and B2C	Predictive analytics focuses on historical sales, economic shifts, competitors’ approach, and consumer behavior to help derive the right strategy in the form of a campaign. The campaigns so designed are targeted towards SMART (Specific, measurable, attainable, relevant, and time-bound) goals for the sales team. Furthermore, accurate sale prediction helps to understand when and how to launch a campaign followed by inventory control, financial planning, and demand planning.
Error and Fraud Detection	Such a powerful analysis tool operates on the concept of Artificial Intelligence and Machine Learning. Thus, the process is nowhere tedious and complex. Instead, it leads to accurate sales report generation and leaves no room for human errors to exist. The more accurate report is, the better the strategy framework becomes, and consequently the annual turnover increases.
Customer Retention	In the customer-oriented market, predictive analytics acts as the savior by making it easier to understand the customer behavior. Conclusively, better customer loyalty programs, up-selling, and cross-selling strategies can be framed. Successful customer-oriented programs and goal-oriented after-sale services help to retain customers in the long run. Hence, it becomes concrete base for sustainable growth of the organizations.
Right Price Fixation	Over the long years of operations, B2B sellers have decided commodity prices based on their experiences, but the market today is highly flexible and dynamic. Therefore, it is essential to deploy sophisticated pricing tools like predictive sale analytics to ensure the right price fixation. The predictive analytics allows industries to arrive at viable trade-off prices through required foresight on prices too. Furthermore, the dynamic deal scoring strategy also strengthens sales representatives by providing all relevant information and to lead a well-guided campaign.
Shorter Sale Cycles	The sale enablement software built to provide accurate predictive analytics help to shorten the sale cycles. It clearly defines the potential of the buyers and fuels the understanding towards an optimal lead qualification process. As a result, the sales velocity increases and time spent reduces taking the organization closer to the desired goal. So, no more extra time allocation for the tedious administrative work and long wait to close the deal. Furthermore, the time wastage on the repetitive task also gets reduced and better efforts are directed towards lead generation.
Lead Generation	Since sales and customer retention is the utmost aim of the organization. Therefore these enterprises focus on strengthening their customer base through lead generation. With the rich and real data analysis structure, the business entities are able to score good leads through the right customer targeting.. Thus, predictive analytics is an effective way to convert prospects into customers through automated sales processes.

Common Predictive Sale Analytic Metrics

The sales metrics also known as KPIs (Key Performance indicators) represent the data relating to the company’s sales team and individual’s performance. Thus, preparations of an accurate set of metrics through predictive sales analytics act as a bridge to attain long-term goals and growth. In comparison to conventional approaches, predictive analytics provide more reliable source data to frame strategies.

Depending on the experience and maturity of the sales team, predictive sales metrics are bifurcated into three main categories varying from simplest to most complex structure.

Metric Level	Metric Name	Metric Description
Basic Level	Sales Quota	Quota refers to the overall sale goals of an organization from a stipulated time frame.
	Goal Attainment	It is a comparative study of prevailing gap in the sales quota and actual attainment.
	Pipeline Coverage	The relationship between total amount of sales opportunities and set quota.
Semi-advanced Level	Historical Data	It is an overall performance analyses of the sales team in respect to conversions.
	Regular Data	The collection of activities performed by the team to achieve the set targets, both offline & online.
	CRM Score	Being based on data science concept , it helps to determine which section needs major attention.
Advanced Level	Sales Linearity	An average time duration required to convert prospects into clients, it is defined in terms of week or months.
	Deal Slippage	It refers to an ability of the organization to study the relevant cause of a failed deal, and implement required strategy in the next quarter.
	Define Next Pipeline	The consideration of out-quarter metric helps to develop and aggressive growth plans and achieve desired targets

The most common four phased sale metrics focus on sales activity, sales pipeline, lead generation and sales productivity. The essential elements of these forms of metrics, which are crucial for tracking and forecasting, are as follows:

Activity	Metrics
Sales Activity	Calls and emails Scheduled Meetings Referral Requests Social Media Engagement Proposals Demonstrations
Sales Pipeline	Length of Sales Cycle Value of Pipeline Value of Sales Open and closed opportunities Conversion Rate Average Contract Value
Lead Generation	Volume of new prospects Lead Response Time Dropped leads Frequency of lead follow-up. Qualified Leads Customer Acquisition Cost (CAC)
Sales Productivity	Time selling Data entry time Content Creation Quantity of Sales tools Sale collaterals Performance of high quality leads

Challenges for Predictive Sale Analytics

Regardless, being an effective tool, predictive data analytics still remains unexplored in the highly competitive world. According to the study, merely 30% of the organizations consider data analytics for sale forecasting. Thus, there are still 70% of the marketing and sales departments across the world choosing conventional methods over an AI-based Initiative. More than half of the marketers clearly admit, they lack the required resources and knowledge to use advanced technology. As a result, the coordinated efforts of the management and subject matter experts are required to bridge the gap.

Additionally, the market scenarios changing at a faster pace results in a huge data explosion. Therefore, it becomes complex for the decision-maker to channelize these available data into actionable insights. So, how to do it? The answer would be to launch a goal-oriented data collection campaign which makes it easier to focus on relevant data and avoid irrelevant ones. Moreover, the advanced record-keeping technologies today have emerged as the savior to facilitate sales forecasting.

The availability of innumerous tech stacks and tools often leads to dilemmas. Thus, it is best to concentrate on your respective business goals and budgets to simplify an elongated list and pick the right best fit for the business health. The selection of the technology shall also depend on the skill set of the team members. Since, several organizations consider the modern approach of sales forecasting as less transparent; therefore it is essential to align people with the process in a suitable format too.

How to Forecast Sales using Predictive Analytics?

The sale forecasting is the crucial part of the development plans of the business, therefore requires sincere consideration to address the prevailing gaps. As a result, with the urge to make research and development processes fully automated, the experts have made use of the digitized human tools (AI-based) to study and analyze the customer-oriented market. Followed by the collection of data through automated processes, it is enriched with additional manually fetched data from inside and outside the organization. These inbuilt applications provide real-time data control and collection. Thus, sales forecasting has become more reliable and people can stay updated with the ongoing trend, future prospects, and past experiences.

Some of the prominent predictive sale analytics software are as follows:

SQL Server Reporting Services
Salesforce Wave Analytics
Tableau Desktop
IBM Sales Performance Management
SAS office Analytics
Micro strategy clear story data
Oracle Business intelligence
TIBCO Spot fire
SAP Business Objective, etc.

Final Words

Earlier, such analytical studies were conducted using the traditional methods which often resulted in human error. Thus, to fetch better precision and accuracy the concept of technically advanced analytics method came into existence. Though initially, the process takes time, yet the accuracy it results in is worth the time invested. Recently, the technologically advanced method of forecasting sales is becoming the talk of the sales and marketing town. As a result, the necessary steps are taken to bring predictive analytics to life. Thus, the enterprises are equipped with better information which helps to detect early threat signs and keep the best foot forward. Consequently, business houses get sufficient time to establish progressive market health for themselves with timely predictive sales analyses.

Topics in Predictive Analytics