Benefits of doing Win-Loss Analysis
Win-Loss Analysis is a method of evaluating, comparing and contrasting the reasons for why a sale was won or lost. A similar approach that is popular is Lost-Sale Analysis.
Learn How to perform Win-Loss Analysis.
In order to appreciate the importance of Win-Loss Analysis, we will look at the ‘Benefits of doing Win-Loss Analysis’ below.
Approaching the Goal as an Opportunity and not a Problem
Quite often, whether it is do with conversion % improvement or revenue improvement, Sales Directors focus on identifying the reasons for the lost sale. They want to know about the root causes for the problem. The reality of lost sales is that, there may not be a specific negative point for sale not happening. The advantage with Win-Loss Analysis is to look at the situation through the lens of not only lost sale but also sale won. What did we do right and how?
Drawing Comparisons
By studying both Won and Lost deals, we can draw comparisons that is very useful to identify winning approaches. It can also present deficiencies the sales process. When done across sales team members, we will get to know how to align specific customer & staff personas, how to develop peer-support models, etc.
Developing Unbiased and Result Oriented Culture
Action plans so prepared by studying both won and lost cases with Win-Loss Analysis lays the foundation for an unbiased and result oriented culture among sales staff. As team members are able to appreciate the differences between Won and Lost deals, they recognize that this exercise isn’t about witch hunting. Everyone in the sales team would have some success and some failures. Thus Win-Loss Analysis, when done systematically and regularly can become a powerful coaching tool.
Won Customers are Emotionally Connected to Brand
One of the biggest advantages or benefits of applying Win-Loss Analysis instead of doing Lost-Sales Analysis is that, Won customers are emotionally connected to your brand as they recently made a purchase. They are more willing and forthcoming to share their feedback, opinions, likes, dislikes and choices with us. In simple words, we will get a good mind share from them. On the contrary, lost customers are most likely not to open up and share genuine reasons.
Check out Why doing Lost Sale Analysis is useless?
How To Perform Win-Loss Analysis.
Unlike Lost Sale Analysis, Win-Loss Analysis aims to take a comprehensive approach to studying the reasons for improving the sales process by evaluating orders Won and orders Lost.
Learn more about Why doing Lost Sale Analysis is useless? and the Benefits of doing a Win-Loss Analysis
Here are the steps to perform Win-Loss Analysis
- Collect a sample of both customers where we won orders as well as lost orders. Make sure you select the right ones
- Do some homework about each of those cases such as customer profile, persona, sales time lines and customer journey
- Seek interviews with customers
- Hone your interview skills as questioning, probing, listening, observation (non-verbal) skills
- Keep the interviews crisp
- Validate customer satisfaction with respect to product, features, price, finance options, sales experience.
- Probe and understand decision drivers such as consideration set, decision makers, influencers, objections etc.,
- Don’t put words into customer’s mouth and don’t expect perfect answers
- Also don’t assume that there’s always a problem for non-purchase
- Record your observations
- Study them later for patterns – both between and within Win and Lost Cases
Importance of Data Quality & Data Governance for Sales Analytics
The strength of analytics insight is a direct function of the data quality and Sales Analytics is no exception to this. In fact, in sales the dependency on external factors for data collection is so high, whether its client, customers, channel partners, marketing, operations etc., that sometimes it is impossible to produce any valuable insight from sales data.
Many of you reading the above paragraph would conclude something like this – ‘That’s why I keep telling all this analytics isn’t relevant for sales, it’s the customer relationship that matters!”. Wait, if the data or information in the sales system is inaccurate, forget analytics, it impacts business performance and that hurts the organization and you. Organizations spend more resources and effort on sales for lower return on investment. Burdens of poor communication and wrong decision making are bonus. So poor data quality in sales is not an excuse but a reason to pursue sales analytics.
When it comes to data quality, inferior quality and arrangement of data makes it difficult to perform substantial analytics. Other times, data is usable but in functional or process silos. For example: Data of one particular channel is good.
In general data quality can be assessed using the following attributes:
- Accuracy
Consistency - Completeness
We are never going to get 100% good data, as a rule of thumb, to start with, if 66% of your data is error free, some useful insights and inference can be drawn out.
Signs of good data are reflected in the overall mindset and data governance associated with sales. For example, when key data domains have been defined and created central data repositories and further when integrated, accurate, and common data is maintained in a central warehouse with an eye to look for new metrics and data, the quality of data is likely to automatically improve.
Tactically, there is nothing like collecting the data right, first time. Rules & tools for automating the data collection, preventing duplicates, ensuring quality of meta data, assigning accountability, creating data management hierarchies, etc. are some best practices.
However all of this depends on what type of data we are dealing with. Imaginery or audatory data handling are quite different and AI algorithms are available that can be deployed and used for data quality validation or even improvement of quality.
Tools are available for performing the following operations to improve data quality:
- Cleaning remove incorrect or duplicates or modify the values
- Parsing and standardization break the data into components and bring them to a unified format.
- Matching integrate or merge similar data
Profiling to score the quality using patterns, exceptions, and anomalies - Monitoring to control data quality.
- Enrichment to use external data to improve internal data quality
Here are some of the popular Cleaning tools :
OpenRefine (Google Owned), Trifacta Wrangler, Drake & TIBCO Clarity
Sales Analytics Framework
The Analytics Framework for Sales aims to support a long term strategy of applying analytics in sales and to integrate seamlessly with remaining part of the organization’s analytics strategy.
There are 3 important pillars for effective deployment of Sales Analytics Framework. A framework like this, when implemented consistently, will shift Sales Analytics from a tactical business enabler to strategic business enabler.
There are two important prerequisites that act as a foundation for the 3 pillars and at least one over-arching philosophy to derive best out of the Sales Analytics Framework. Let us look at what are they:
Business Benefit of deploying Sales Analytics Framework:
Business Growth
The ultimate business objective of using Sales Analytics to help meet organizational long term and short term goals such as Market Share, Revenue Growth, Profitability, Customer Retention, Cross-sell, Upsell, Referral business, etc
Overarching Philosophy:
Change Management & Data Based Culture
Ultimately leaders play a big role in deciding how the outcomes of analytics are put in practice during the decision making process. Businesses have their priorities that are time sensitive and hence sales directors play a big role in deciding how these would be actually applied. Driving a culture of data or fact based decision making within the organization should be one priority when it comes to sales analytics. The benefits of sales analytics grow multi-fold when cultural change occurs.
The Pillars:
Data Management
The first and foremost success factor in Sales Analytics is the ability to acquire, clean, organize, integrate, describe, share, govern and store data to be used for analytics. Data is usually acquired from various sources. Most organizations hit a road-block right at this stage of the journey. The next step is to clean and integrate data from various sources so as to enrich its value before performing required analytics.
Sales Analytic Processes
The core sales analytic processes include the ability to enrich the data through advanced analytics & data science and to provide, visualization, exploratory data analytics (EDA), business insights to drive sales, develop predictive models such as propensity, lead scoring, up-sell, cross-sell prediction, etc. The ongoing optimization and deployment of models in the production environment and their regular refinement are all integral to Sales Analytic Processes. The element of focus for us in Sales Analytics Processes is to answer some of the design questions keeping in mind the above end states, such as Measure of Success of Analytics Program, what analytics are we going to perform, what data and models are we going to use and how will we share the results. Learn more about How can Sales Managers leverage Analytics?
Decision Engines
How do analytics capabilities deliver business value. Traditional outcomes of analytics decision engines were linked to information delivery and visualization enabled through Online Analytical Processing (OLAP)and data mining. On the other spectrum, insight discovery and integration to decision making processes are capabilities that add business value.
The Prerequisites
Sales Analytics Infrastructure
To start with organizations can work with minimal infrastructure for analytics, but to scale analytics capability, definitely investment in infrastructure is a must. Analytics Infrastructure, not restricted to Sales Analytics, includes databases and data warehouses, statistical and data mining systems, scoring engines, grids and cloud storage ,etc., The question that new entrants frequently ask is, is it not possible to start Sales Analytics without these. The answer is that it is possible. With SaaS, it is possible to put together low cost infrastructure to get started with sales analytics. In addition to storage and handling, software tools required for sales analytics are also part of the assets. While there are standard and custom tools, the best to start would be Tableau, R, Python, SAS, RapidMiner, Orange, etc. Learn more about Guide to Selecting Sales Analytics Software Tools
Sales Analytics Talent & Capability
Quite often this is the most ignored area in analytics and is also a pitfall. Organizations get neck deep in creating analytics infrastructure, acquiring talent to manage these, and analytics talent with IT capability to deploy these tools, but they fail to understand that business users are really the ones who need upskilling. If they fail to recognize and resist the usage of analytics in their daily work management, Sales Analytics will only remain a pilot project to showcase. Lead More about What specific analytical skills are needed for Sales Managers in the era of Analytics?
10 Reasons why sales teams are reluctant to embrace sales analytics
Well, everyone knows about the importance of analytics. Many sales professionals sell ‘Analytics and BI’ products and services, yet they fail to embrace it. It’s quite surprising that they recognize its importance, its relevance to their growth and survival, but don’t get into action.
If you are a Sales Director or Sales Manager, have you ever wondered why this negligence?
Here are top 10 reasons for this reluctance:
1. Data is not available
The first and foremost reason that Sales Managers give is that they don’t have ‘those’ types of data in Sales. It’s difficult to collect – partners don’t cooperate, systems don’t have functionality to capture, it is not important in our business, etc. This is a show stopper.
2. Data is not readily usable
Data provided has many empty fields, some times even incorrect, only some partners or clients share information, etc., and so it’s not possible to use this data to perform Sales Analytics. Cleaning the data is time consuming and Sales Managers feel they don’t have resources to do it.
3. Not able to go beyond plotting the charts
Many Sales Managers are proficient enough in excel to plot some basic charts. But they are unable to go beyond. The challenge is that sales organization has been traditionally ignored when it comes to analytics related upskilling of talent.
4. Don’t know what analysis to perform
Many times, it is important to frame the problem and look at the data to identify the reasons for the problem. Sometimes, it’s exactly the reverse – Look at the data to even define the problem. So a structured problem solving approach is needed by Sales Managers and many of them aren’t’ skilled in that.
5. Not able to link analysis outcomes to ground reality
Some of them, especially the Sales Analysts, are able to perform good quality analysis, but no matter how good they are in sales analytics, one question is never answered – Why is a particular phenomenon occurring? The answer to this question will only come by linking the analysis to field observations. Unfortunately, when the analysis doesn’t provide anything useful, teams loose interest.
6. Not be able to convince stakeholders with the data analysis
Many Sales Managers find it easy to allow the Sales Directors to lead the conversation, lead the analysis and direct them on what actions have to be taken. Proactively analyzing the data comes with the risk of defending the analysis and insights, so identified. Sometimes it’s going to make them unpopular among their bosses. So it is understandable that there is a natural resistance to it. But another way to see it as a lack of storytelling skills based on Data Analytics.
7. No one is asking for it
Organizations that are laggards in Sales Analytics still work the traditional way. Their Business Development Head, Sales Directors, & Senior Managers aren’t data savvy and prefer to manage sales without much use of data and sales analytics. In such organizations, sales managers who rely on sales analytics will sooner or later be left with no motivation to pursue it.
8. It is time consuming and that time is better spent in the field
Analytics is a skill and any skill needs practice, if one wishes to master it. In the initial stages, lower proficiency means more time in learning. Many Sales Managers are restless and feel it’s a mere waste of time and energy to crunch numbers and look for insights, especially when action is happening out there in the field!
9. Able to meet targets without such analytics
Some sectors and organizations are lucky to be in the sweet spot. Their products and services have high demand and less supply. But history suggests that this is only a momentary state. Disruption happens everywhere, no industry or organization is spared. If not today, for tomorrow’s survival, sales managers need to upskill in Sales Analytics and learn to adapt. Failing to do so is mere complacency.
10. Company already has CRM
This is probably the most common explanation we hear from Sales Managers. Sales Analytics is a skill not a software tool. Traditional CRMs were only a workflow system carrying a repository of sales data but many of them have now acquired BI and analytics capabilities. But, general purpose inbuilt charts and analytics, may or may not address the problem of a particular territory or scenario. So effective Sales Managers do leverage the Sales Analytics from CRM and other sales enablement tools, but they make a point to apply their own sales analytics skills, over and above that to alleviate their problems.
Improving Your Organization’s Sales Analytics Capability
With the advent of Big data and Analytics, every single discipline of business entities have seen tremendous benefits derive out of data based insights. While Analytics has penetrated Operations, Supply Chain, Marketing, HR, and IT, one of the biggest beneficiaries that directly contributes to the top line of the organization is Sales Analytics. Marketing Analytics & Marketing Automation has ensured high return on investment for the marketing costs, increasing reach and quality of marketing communication. The demand so generated through effective marketing has to be converted to business volumes. This is where Sales Analytics plays a big role. Sales processes are quite complex, involving various partner organizations, internal functions, several legal and regulatory requirements. Adding to this complexity are factors such as engagement of off-role employees, high staff attrition, competition activity and economic conditions.
Sales Analytics plays a significant role in this pursuit. The dynamics of sales data and the factors mentioned above, demand that Sales Analytics be approached a little differently from other analytics programs such as Supply Chain or Operations Analytics. Traditional focus has been on acquiring an efficient Sales Analytics Solution, Sales Process Automation and Sales Intelligence technology tool. There are a variety of products in the market with incredible capabilities such as SAP Business Objects, Oracle Business Intelligence, Salesforce Wave analytics, Alteryx, Tableau, Qlik Sense Enterprise, Sisense, Microstrategy, etc.,
However, now-a-days, the business benefits from Sales Analytics are not usually constrained by technology capabilities rather by other factors such as:(arranged in no specific order)
- Data Quality & Data Governance
- Conviction of Sales Leadership towards data based selling
- Sales Analytics Talent & Capabilities
- Cross-functional Alignment
Unfortunately, leaders spend too much energy in selecting the right technology tool only to realize their team members aren’t ready for a heavy dose of data driven insights or ecosystem isn’t ready.
Data Quality & Data Governance
It’s no secret that sales data quality is a challenge as it involves partners, customers and external factors. Though hand-held devices, cloud storage and real time analytics tools have made eased things, no one can disagree that getting data that is reliable isn’t easy in sales. Further, many times it is difficult to audit its authenticity. Read More about the Importance of Data Quality & Data Governance for Sales Analytics.
Conviction of Sales Leadership towards data based selling
Sales is considered an art and building relationships with customers is considered as an enabler. Many Sales leaders don’t believe in looking beyond sales numbers. Metrics and data that provide insights and help us identify drivers aren’t given too much importance. Without this conviction, it is very difficult to see any real ROI for Sales Analytics investments. In fact, this is a show stopper. Read More about the Tips to build Sales leadership buy-in for data based selling through Sales Analytics
Sales Analytics Talent & Capabilities
In order to fully leverage Analytics and Big Data, Sales leaders need to build skills & capabilities among sales teams that include Quantitative Skills, Data or Fact based Decision Making & technical skills, Data based Problem Solving which will help them to decide what data is needed in any given scenario, framing hypotheses and validating the hypothesis through data. Read More about What specific analytical skills are needed for Sales Managers in the era of Analytics?.
Cross-functional Alignment
Prima facie, Sales Analytics seems to be an internal matter of the Sales function.However, true and meaningful Sales Analytics will require strong alignment of various functions such as Marketing, Supply Chain, Manufacturing, Quality, Product Development, Service, Business Planning, HR, etc. Studying the association of cross functional factors on Sales or Business Outcomes such as Market Share, Revenue, Profitability is very important. This means cross functional data sources will need to be analyzed in Sales Analytics. Read More about Non-Sales Data & its value in Sales Analytics.
Thus Building Organization’s Sales Analytics Capability needs a thoughtful & comprehensive approach.
Guide to Selecting Sales Analytics Software Tools
Organizations that aim to make the most out of Analytics to drive business and top line focus on Marketing Analytics and Sales Analytics. While the penetration and the adoption of Analytics in Marketing is almost as old as the adoption of analytics in business, Sales Analytics is an area that holds tremendous potential.
Sales Analytics Framework has several components and software is one of its key components. Here’s a ‘Guide to Select Sales Analytics Software Tools’:
Differentiation between Sales Analytics and Sales Reporting/Sales Intelligence
While selecting Sales Analytics Software Tools, there needs to be clarity and not ambiguity about what we wish to accomplish from the tool. Many misunderstand Sales Reporting and Sales Intelligence tools to be capable of performing advanced analytics such as Predictive or Prescriptive Analytics. While existing tools are constantly adding new functionality, it isn’t in the remit of reporting tools to perform advanced analytics.
It is good to start with reporting and intelligence tools and ripe the full benefits of better reporting before exploring advanced analytics capabilities for sales.
For example:
- SQL Server Reporting Service can be used to build dashboards for sales data for sales process management
- Sisense is a reporting tool with vast visualization capability for data that can used to manage the process
- Tableau Desktop Tool and Microstrategy Tool are capable of performing a variety of visual analysis such as trend analysis, study relationship between various factors or segments, etc that enable decision making. The purpose is to enable the users to discover insights, answer questions to drive performance.
- Alteryx Analytics Tool and TIBCO Spotfire are capable of performing advanced descriptive and predictive analytics such as scatter plot, association analysis, logistic regression, decision tree, PCA without programming. Both Alteryx & TIBCO are recognized as leaders in Gartner’s magic quadrant 2020 for data science and machine learning platforms.
Please note that there are many tools for BI in the marketplace, and I have just highlighted few to give an idea of capabilities that one may consider while selecting tools. This is not a comprehensive description of capabilities and exhaustive list of suppliers.
Customizing General Analytics Tools Vs going for Pre-built Sales Tools
There are many General Analytics Tools which have wide capability and interesting features such as the ones that you saw above. Because of the vastness of the functionality and flexibility that these tools bring-in such as accessing multiple data sources and customizing the output, they can consume additional effort to configure, selection of right metrics, and visualization required. In simple ways, implementation time can be high. On the other hand, there are tools which are built for Sales, and so they come with standard libraries of metrics, visualization charts and analytics charts that are relevant to sales.
For example:
- SAS Office Analytics and QlikSense are strong self-visualization tools for data discovery that have powerful features but are general purpose BI tools. They can be used in any where, not only Sales. For organizations that are already invested in such tools, adding Sales BI would make economic sense.
- On the other hand, MoData is a Sales Analytics specific tool for the B2B segment which is capable of creating dashboards, scorecards/performance reports, visual analysis, predictive analysis and performing sales forecasting. They have 100’s of sales specific visualization charts, thus reducing the implementation time.
Analytics Integration with Sales Systems Vs in-built Analytics functionality of Sales Systems
While we have touched on various reporting, BI and Analytics tools that can be integrated with the existing sales IT tools easily, as all tools come with the capability to integrate with multiple data sources, another option is to consider in-built analytics functionality of Sales Systems.
Many organizations that venture into sales analytics already would have sales systems (sales enablement tools) such as CRM, marketing automation, lead management, client onboarding, etc. And many of these tools are jacking up their analytics capabilities as well.
For example:
- Salesforce, a powerful CRM engine is adding AI capabilities for sales as in Salesforce Einstein.
- On the other hand, Xant is an end to end sales engagement tool that covers Sales Automation, Reporting, Intelligence, Customer Scoring, etc that can be seamlessly integrated to existing sales systems such as Salesforce, Microsoft, SAP,etc.
Learn More about Features to look for in Sales Analytics Tool
Channel Management Metrics and Sales Analytics
Whether B2B or B2C, nowadays growth based organizations have multiple channels to access their customers. Large organizations across sectors such as Automotive, FMCG, Consumer Durables, BFSI, Retail, e-Commerce, Telecom, IT & Tech have both direct sales as well as channel partners. Many B2B sectors such Industry IT, capital goods, aviation, chemicals also have channel partners.
Managing the performance of the channel, channel partners performance and improving overall channel effectiveness is a significant success factor for both the principal and the partner. Towards this, the role of analytics, particularly Sales Analytics is vital in achieving business goals and in making consistent progress.
For managers who are responsible for Channel Management and partner performance, such as Territory Sales Managers, Area Managers, Regional Managers, National Sales Managers, Account Managers, Dealer Management, Sales Analyst, Sales Planners, Business Heads here are a set of important metrics that they can monitor regularly. Once enough data is available, extensive sales analytics can be performed by channel sales managers using simple tools such as Excel to come up with insights for revenue growth. As the dynamics of Channel Partner Management for B2B and B2C are very different, I have split this into two groups – B2C Channel Partners and B2B Channels Partners
The type of metrics include Business Performance Metrics, Product Performance, Network Performance, Partner Profitability, Partner Sales Operations, Partner Staff Effectiveness, etc.
Region/Territory Level (B2B and B2C):
Following are a set of metrics that can be tracked at the Area Office, Region Office or Head Office Level to evaluate the performance of various channel partners and draw comparison between various channels.
- Market Share – by Category, by Segment
- Conversion % – by Lead Source, by Segment, by Model
- YoY Revenue Growth
- Network Penetration against potential
- Product Mix against competition
- Gross Margin – by Product
- Partner Cannibalization
- Product Cannibalization
- Contribution – by Category, by Segment
- Cross-sell and Up-sell % – by Channel, By Segment
- Customer Acquisition Cost – By Channel
- Repeat Business Revenue to Total Revenue – By Channel
- Carry over stocks
- Unfilled Staff Positions
Partner Level Metrics for Product Based Businesses:
In sectors such as Automotive, FMCG, Consumer Durables, Retail, Telecom, Technology products, etc, the trade channel includes Distributors, Dealers & Retailers who are appointed by the principal. In addition to the above, companies may appoint Modern trade partners such as large format corporate, e-commerce and have company owned outlets. When it comes to managing partner performance, below metrics that cover areas such as Partner Profitability, Operations, Inventory Management,Staff development are relevant for any Area or Sales Manager.
- Days to Break Even
- Profit per employee
- Sales Productivity – by person, by Segment, by model
- Conversion % – by Lead Source, by Segment, by Model, by person
- BTL Activity Productivity – by activity type
- Sales Process Satisfaction
- Overdue Customer Delivery
- Enquiry to Sale Lead Time
- Avg # of follow ups before lead drop
- Order cancel rate
- Average Leads per salesperson
- Sell through rate
- # of Stock Outs – by Model-Color
- # days of inventory
- Carry over stocks
- Inventory Funding
- Interest Coverage Ratio
- Pending incentive disbursal
- Staff attrition %
- Unfilled staff positions
- Product training adherence %
- Win-Loss Pareto
- Customer Retention %
- Cross-sell and Up-sell %
- Repeat Business Revenue to Total Revenue
Partner Level Metrics for Services Based Businesses:
In sectors such as IT products and services, industry IT, capital goods, aviation, chemicals, etc appoint resellers, franchisee, business associates in addition to direct selling done by the brand. When it comes to managing partner performance, below metrics that cover areas such as Partner Profitability, Operations, Inventory Management, Staff development are relevant for any Channel Partner Managers.
- Days to Break Even
- Profit per employee
- Sales Productivity – by person, by Segment, by model
- Conversion % – by Lead Source, by Segment, by Model, by person
- Campaign Productivity – by activity type
- Sales Process Satisfaction
- Enquiry to Sale Lead Time
- Avg # of follow ups before lead drop
- Order cancel rate
- Average Leads per salesperson
- Sell through rate
- % of Leads accepted by principal
- Pending incentive disbursal
- Staff attrition %
- Win-Loss Pareto
- Avg Cost of Acquisition
- Customer Retention %
- Cross-sell and Up-sell %
- Repeat Business Revenue to Total Revenue
Once a management routine to measure and report these metrics have been established, performance improvement can be achieved through advanced sales analytics such as:
- Trend Analytics – impact of seasons, month, week, day of week, time of sale
- Market basket analysis for bundling products
Factors resulting in poor conversion - Factors influencing Profitability and Contribution
- Effect of Sales Campaigns and BTL (Below The Line) promotion activities
- Factors driving known best performers
- Best combination for upsell and cross-selling
- And many more….
Sales Organization, including Sales Director, Account Managers, Channel Managers, Regional Managers, Area Managers, Zonal Managers, Territory Managers are constantly challenged to break the sealing – go beyond their best performance in the recent past. While they adopt different strategies to achieve business goals, the availability of data or information about the customers, their needs, channel partners, capability and capacity, supply chain efficacy, pricing insights, competition activities, below the line (BTL) activities has improved in the recent decade due to sales process automation, ERP, CRM and other sales enablement tools.
However, without sufficient analytical skills to analyze this data and draw meaningful insights, results are not guaranteed. Sales Reporting and Business Intelligence (BI) tools provide dashboards and scorecards that enable Sales Managers to analyze the information for constructive decision making. But that’s just the least value one can extract from the data.
Business Outcomes that can drastically improve through enhanced sales analytics skills are:
- Improvement in Market Share
- Improvement in Product Contribution and Gross Margin
- Seasonal Plan – Forecasting, Pricing & profitability improvement
- Effectiveness of Promotions
- Distributor/Dealer/Retailer Profitability
- Improvement in Conversion %
- Penetration of fast moving products
- Improvement in Cross-selling and Up-selling
- Network Expansion planning
- Optimization of Resource allocation
- Reduction in stock-outs
- Sales staff performance improvement
Here’s a list of Sales Analytics skills and difficulty level that Sales Managers need in the era of Analytics:
| Difficulty Level | Analytics Skills |
| Beginner | Data Summarization |
| Beginner | Data Visualization |
| Beginner | Segmentation Analysis |
| Beginner | Trend Analysis |
| Intermediate | Time Series Forecasting |
| Intermediate | Data pattern analysis |
| Advanced | Hypothesis formulation and Hypothesis testing |
| Advanced | Data clustering |
| Advanced | Conjoint Analysis |
| Advanced | Predictive analysis and modelling |
How can Sales Managers leverage Analytics?
When it comes to analytics, its best use is in directly helping the sales managers perform better in their job. A good sales analytics program should provide insights on the KRIs of sales managers, so that they can work smarter and deliver results consistently.
The good news with analytics is that everything is data or fact based. There is no ambiguity, no ifs and buts. Sales Managers don’t have to cut a sorry figure in front of their Sales directors in reviews. The reasons and justifications they give will be backed by data or facts.
Hence Sales Managers can leverage Analytics in the following areas:
Market Share
Identify categories, products, territories for improvement, factors that drive improvement, where company is losing market share, region wise causes
Revenue Growth
Identify categories, products, channel partners, territories with degrowth or not meeting plan, data or evidence based reasons such as competition pricing, stock outs, conversion %, etc
Gross Margin Improvement
Identify fast moving products that have low contribution to profits, identify focus products to prioritize
Conversion %
Conversion % trends, Pipeline analysis by product, categories, channel partner, monthly trends, factors impacting conversion % such as product availability, training, followups, pricing, etc
Channel Partner Profitability
Analysis of Partner Profitability, factors driving break-even such as inventory holding, staff cost, productivity, etc
Sales Productivity
Sales productivity trend, variation, factors for variation, staff experience, product knowledge, peak hour volumes, sales tools availability, etc
Promotion
Measure of success of promotions, % conversion, selection of duration, location and type of promotion, factors driving success of promotions, ROI computation
Network expansion
Plan for network expansion on geo spatial data of product density, competition density
Sales Satisfaction
Sales satisfaction trend by product, by channel partner, by customer type, factors to improve satisfaction
Learn more about 5 Reasons to Up-skill Sales Teams on Sales Analytics
5 Surprising benefits of Sales Analytics
Sales Analytics can help Sales Directors to deliver business performance such as revenue growth, market share, etc. Having precise fact or data based reasons can help them take the right decisions at the right time.
But in addition to the above primary benefit of sales analytics, here are 5 surprising benefits of adopting sales analytics for any organization:
Execution Culture
The famous saying ‘What gets measured, gets done’ drives home the point that insights derived from Sales Analytics can cut down on a lot of planning, (over)reaction, corrective action, product recalls, course corrections, reviews, wrong hires, etc., and gets the focus back to action. Sales Directors and Managers can deliver on their promises to the company’s board and senior management. Ultimately, in the long run, the culture of the company will shift towards one of execution and results, moving away from excuse, uncertainty, ambiguity and complexity.
Improved Team Trust, Bonding Resulting in Lower Employee Attrition
When decisions are made based on facts or data, there is transparency in the system whether it’s to do with promotions, incentives, reviews. Mis-communication, in-appropriate feedback, unnecessary discussions, finger pointing, etc come down drastically, implying that the team starts trusting their managers, peers and subordinates more. The positive of this would be seen in high collaboration, sharing of responsibilities, delegation of authorities, faster execution, lesser employee attrition.
Sales Managers Become Better Coaches
When there is data available on not just the performance numbers, but factors that lead to poor performance, managers will have better insights to coach their employees. Their coaching will be specific, timely & personalized. They will also have more time on-hand to guide, engage and improve the leadership skills of their team members.
Lower Sales Cost & Effort
With Sales Analytics, sales initiatives such as below the line (BTL) promotions, staff hiring, forecasting, inventory management, customer requirement gathering, etc., would be more precise resulting in fewer trial and error, which is typical of new initiatives in sales. This will lower the sales cost and effort for the organization.
Higher Channel Partner Loyalty
When the principal is firm on its actions and results are consistent with the plan, channel partners start trusting the brand and loyalty is automatically built. For example, when the principal puts a lot of weight beyond a new product, builds inventory and asks channel partners to commit more, but later the product fails to create the anticipated impact, channel partners loose hope. With Sales Analytics, channel managers can use insights from data to manage the engagement and performance of channel partners effectively. With time channel partner’s trust on the brand’s promise becomes solid frutifying into lifelong loyalty.
5 Reasons to Up-skill Sales Teams on Sales Analytics
The last 2 decades experienced a sea-change when it comes to how organizations handle business development. While ERP and CRM put process rigor and discipline to the sales management process, the era of Data Analytics, Big Data and Cloud Computing have empowered sales managers with data that can enable them to make informed decisions. The future looks exciting with 5G mobility, Machine Learning, AI and Quantum Computing bringing in more and more information & insights, instantly for decision making. However, such opportunities come with their own share of risk and threat. Sales Managers who used to bank on their interpersonal skills, negotiation skills, etc., to deliver stellar performance have to accept that without adequate up-skilling on Sales Analytics, their extinction is indispensable.
Here are top 5 reasons to up-skill sales teams on sales analytics:
1. Analyze Data Independently and Save Time
Many sales managers lack sufficient data analytical skills. The skill sets are vastly across organizations. Some can’t make charts in excel while others are more versatile. It is not an aberration to state that most of them cannot independently analyze data to come up with breakthrough insights. They are either dependent on the sales analyst or their managers to provide them an action plan, given a scenario.
Today, ample data is available in finger tips and business intelligence (BI) tools are also within reach of every employee. Analytics savvy sales managers utilize these resources to independently analyze the data to define problems, identify factors driving the problem, unearth patterns in the data for planning and execution purposes. They not only save time due to reduced dependency on others but are also more confident because it’s backed by data or facts.
2. Ask Right Questions and Take Informed Decisions
Many times sales managers deal with ambiguous situations such as lack of information on competition’s plan, customer’s behaviours, economic drivers, product acceptance in the market, etc. Asking the right questions, at the right time, to the right person gets the right answers. Those who are skilled in Sales Analytics will analyze the data to understand the situation better, frame questions or problems that are specific and relevant to the circumstances. Without doubt, this will enable them to become better decision makers.
3. Identify Opportunities/Root Causes Early and Improve Business Performance
Locating an oasis in a desert is much easier with a satellite imagery. It can pinpoint exactly how to get there faster too. Solving problems in sales and unearthing business opportunities are much easier with data analytics. Sales managers with Sales Analytics skills are 3 times more likely to tap on opportunity or solve a problem than those without such skills.
4. Communicate and Convince Stakeholders
Sales folks are brave hearts who deal with objections and resistance day in, day out. Whether it is a client, business partner, sales director, internal stakeholders such as supply chain, product development, marketing, etc, they are more likely to convince such stakeholders with insights, charts, evidence, etc than otherwise. Storytelling, backed with data and analytics is more likely to get buy-ins, approvals, endorsement and support.
5. Become a Future Fit Business Leader
The business world looks very different from it was 2 decades ago and be assured it would be even more different in the years to come. Digital transformation is being led by data, analytics, cloud computing and fast networks. Sales and business leaders are expected to only be an active user of data and analytics but become a power user to survive in future..
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