Background:
A leading independent terminalling company wants to achieve seamless delivery during build and operate and thus improving client centricity
Condition:
- Frequent surprises due to complex stakeholder network and client dis-satisfaction
- Budget and timeline overrun on large projects
- Lack of accountability for red-flags
Big 3 Actions:
- BD process was not formally defined. BD skills were missing and there were no timelines or deliverables
- Client experience during the Build (NPD) and Operate phases were not measured. Stakeholder management was only at Sr. Leadership level
- Clear Measures of Success for Client Journey were defined
- End to End Process was established with involvement of all internal members (200+)
- RACI for all tasks with internal SLAs was established.
Background:
An ITES arm of a big software company which works with enterprises across industries wanting to improve its client relationship
Condition:
One of their large account ($1Bn) wanted to terminate their enterprise contract due to issues with ITES services. Hence main focus was to prevent the churn by short term fix and long term actions.
Big 5 Actions:
- Based on Client Interviews, employee interview and discovery, identified 4 actions to be immediately initiated (Service quality, Analysis, Client review framework, Org structure change). Facilitated the agreement of action with clients
- Identified delight opportunities to clients by performing Horizontal/Vertical synergy studies and subsequent prioritization roadmap
Background:
A leading business services firm provides B2B telecom Solution and services in India wanting to improve responsiveness in pre-sales
Condition:
- Clients are unhappy after first month bill. Payment disputes and escalations leading to retail brand damage
- Benchmarking study identified company is far behind all competitors in RFQ or leads
Big 5 Actions:
- After gemba, interviews and process-walk-thro three broad issues came out.
- Process not capable to respond to leads and RFQs at speed
- Pre-sales process is broken
- Post feasibility study – accountability issues
- The entire End-to-End business process spanning all regional teams was re-engineered and later digitized.
- Mentoring of re-engineering project teams Leadership workshop on customer experience & client handling across the BU
Meeting Service Level Agreements (SLAs) in B2B organizations can be difficult for a variety of reasons, including:
- Complex products or services: B2B companies often offer complex products or services that require specialized knowledge or expertise to maintain or repair. This can make it difficult to meet SLAs for repair or maintenance times.
- Long sales cycles: The B2B sales cycle is often longer than the B2C sales cycle. This can lead to delays in the delivery of products or services and can make it difficult to meet SLAs.
- Limited resources: B2B companies may have limited resources to devote to meeting SLAs, such as hiring additional staff or investing in new equipment.
- Lack of standardization: Different customers may have different expectations for service and support, which can make it difficult to standardize SLAs across all customers.
- Dependence on third-party vendors: B2B companies may rely on third-party vendors to provide certain products or services, which can make it difficult to control and meet SLAs.
- Limited visibility: B2B companies may have limited visibility into the performance of their products or services after they are delivered to the customer, which can make it difficult to identify and address issues that are impacting SLA performance.
If your are looking for improving ways to meet your SLAs, please contact us
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There are several common ways to improve the existing account growth in B2B companies, including:
- Building strong relationships: Building strong relationships with existing customers is essential for growing their accounts. This can involve regularly communicating with customers, understanding their needs and goals, and going above and beyond to meet their expectations.
- Offering additional products and services: Another way to improve account growth is by offering additional products and services to existing customers. This can help to increase the value of each account, and can also help to cross-sell and up-sell additional products and services.
- Providing excellent customer service: Providing excellent customer service is also key to improving account growth. This can include responding promptly to customer inquiries, addressing customer concerns, and resolving problems in a timely and efficient manner.
- Offering customized solutions: Offering customized solutions to existing customers can help to increase the value of each account. This can include tailoring products and services to meet the specific needs of each customer, and developing specialized solutions that are not available to other customers.
- Implementing customer retention programs: Implementing customer retention programs can help to improve account growth. This can include loyalty programs, referral programs, and other incentives that encourage customers to continue doing business with a company.
- Regularly gathering customer feedback: Regularly gathering customer feedback is another way to improve account growth. This can include conducting surveys, focus groups, and other research to understand customer needs and preferences. This feedback can be used to improve the company’s products or services, and to develop strategies for retaining and growing existing customers.
They are all prescriptive. The question is, which one of these is relevant to your organization and why? That clearly depends on your current state, ground level challenges and growth aspirations. Only by implementing relevant strategies, B2B companies can work to improve the growth of existing accounts and drive sustainable growth for the company.
If you are looking for ways to improve your account growth, click here
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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….
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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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Sales Problem Solver’s Skills
We all are problem solvers at our own level. Handling challenges, hurdles, persistent issues and alleviating them is what differentiates a great sales manager from the rest.
Tools and techniques for solving problems are well established for a long time now and there are various methods, techniques and tools that can be used. For example, Lean Six Sigma, TQM, Agile, Scrum, Design Thinking, etc are few such frameworks. Various sectors have seen success with these methodologies. However, when it comes to functional success, business development and sales is a unique function in that, it is highly dependent on external factors. Even many internal factors are not within good control of the sales organization. Thus, there are certain unique challenges that sales managers face when they try to apply such proven problem solving methodologies for sales problems.
More than a structured approach or framework for solving sales problems, the first thing Sales Managers should develop is a Sales Problem Solving Mindset. The skills that support this mindset are described below. Each of these skills can separately add value to every sales manager’s performance. When applied in the form of a structured problem solving framework, it becomes very powerful.
Problem Framing Skills
The first step in solving business problems is knowing exactly what is the problem that has to be solved. This might sound like a no-brainer. But don’t underestimate the power of clarity. If the organization wants to improve its market share in a particular segment, the problem may have to be framed keeping in consideration points such as – Is our market share very low compared to peers, how long has it been this way, what have we done and why have they failed, what is the customer’s perception about us?, etc. After sufficient exploration, we may narrow down and define the problem that we wish to solve as “How can our employees engage with our channel partners effectively?” instead of “How to improve our Market Share?”
Sometimes, we can define the problem and then collect data, but on other occasions, we have to do the reverse. Thus framing a problem for a given scenario is a skill in itself and Sales Managers need to master this.
Data Collection Skills
In order to permanently solve any problem, we need to go to the root cause of the problem, and use data or facts to validate really if a particular cause is the reason for the problem. Often, Sales Managers are plagued by reasons that their team members use to justify their poor performance. But these have to be validated with data. Unfortunately, the problem in sales is that required data is not often available. Sales managers should develop necessary skills to understand what data is needed to validate a given problem or cause and how to gather that data. Sometimes surrogate measures will have to be used and sometimes they have to live with surveyed data. Thus the ability to know what data is needed in a given scenario and how to gather that is the second skill needed for Sales Managers wishing to solve business problems.
Quantitative Analytical Skills
Today’s sales enablement systems such as CRM, ERP, etc provide enough data to get started with some basic sales analytics. However sales managers need to upskill themselves in analytics. They need to know how to perform necessary analysis from the data available. While Sales Analysts are well versed in crunching numbers and preparing charts, many Sales Managers are not even comfortable with relevant formulae and charts in Excel. The ability to work with data, using visualization methods to validate association or relationship between various factors, use statistical tools to identify sources of inconsistency in performance & factors driving it, ability to identify known best performers from data, using probability principles to validate demand-supply problems in sales and statistical association/correlation studies to identify causes are all essential analytical skills for sales managers.
Qualitative Discovery Skills
To solve most sales problems, an augmented approach of using both Data and ‘Gemba’ is needed. There are several aspects such as field observation, validation or audit of sales standards across the sales process, partner sales rep skill and will validation, qualitative evaluation of customer objection handling, benchmarking competition best practices, picking up verbal and non-verbal clues, etc., that require strong discovery skills that sales managers need to develop.
Experimentation Validation
The willingness to accept that not everything that we are doing is going click & that too the very first time is the basis for experimentation validation. When solutions are put in place, often sales managers are anxious to see the results right away. In reality, many of our action plans are unique to the circumstances and the mindset to see it as a series of experiments and set logical check points, learn the lessons and move forward to refine the action plan or solution is any skill that sales managers need to acquire too.
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1. Improve Recruitment Effectiveness
HR analytics lets HR make better decisions on the basis of historical information of employee performance. For example, if data suggests that some of your best talent have certain education background, hobbies or profile, you will be able to screen profiles from the candidates pool and get those who are most likely to be successful. This would mean lower cost of recruitment, reduction attrition in future and better business results. The availability of online databases, applications, profiles in social media and career directories, documents, etc. today enables how we can improve the effectiveness of recruitment and easily learn more about applicants.
Similarly, we can use online databased and career directories to build profiles and job descriptions based on how other organizations define such roles and the availability of talent pool in the market. This is higher success rate during not only recruitment but also in retention
2. Build Productive Workforce
Using historic data of employee performance and specific conditions that led an employee to performance better, HR managers can using Clustering Models to put together teams of like minded employees where every individual performs it his/her best. Similarly, inconsistent performance, spikes or drops in performance can help HR analysts identify key drivers for such pattern.
3. Reducing Attrition by Predicting it
This is one of the most widely used application or example for HR Analytics. By using historic data related to employees it is possible to used Machine Learning (ML) classification models is very accurately predict employees who are most likely to leave the organization. This is called as Predictive Model for Employee Attrition. The model provides the propensity or probability that an employee would leave in near future. This data based approach can replace RAG (Red/Amber/Green) colour codes that HRBP’s use to classify employees based on high flight risk.
4. Performance Management
Linking Performance to Pay is a ever green topic in HR. With performance data that goes beyond performance rating, C&B professionals can build statistical models to validate if the increased compensation and benefits to an individual can result in justifiable business performance improvement. Further data analytics can be used to profile employees based on the value they see various benefits provided by the organization and personalize the package.
5. L&D Effectiveness
L&D can play pivotal in enhancing business performance and building a future fit workforce by using the data to identify training needs, establish quantitative effectiveness measures for L&D interventions and statistically prove effectiveness of the program. For example, using wearables, L&D professionals can capture real time data of employees heart rate, to ascertain the effectiveness of the learning module covered in training. Data can be used to design effective intervention.
And most importantly, L&D can rescue themselves from the perception of being providers of different career development programmes that deplete a large part of the company’s budget.
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In the recent years, most business functions have undergone a transformation because of the power of Big Data, Cloud Storage and Analytics. The Digitization wave that is sweeping the industry now is nothing but an outcome of the synergy of various technology developments over the past 2 decades. HR is no exception to this. HR Analytics and Big Data have provided the ability to HR leaders to take “intuitions” out of their decisions, that have been the norm before and replace that with informed decisions based on data. The use of HR analytics has made official decisions more promising and accurate.
For this reason, today many companies invest tremendous resources on talent management tools and skilled staff including data scientists, analytics and analysts.
Nevertheless, there’s a lot more to do in this area. According to a Deloitte survey, 3 out of four businesses (75%) believe the data analytics use is “important” but only 8% think that their organisation, is strong in analytics. (The same figure as in 2014).
HR Analytics can touch every division of HR and improve its decision making including Talent Acquisition and Management, Compensation and Benefits, Performance Management, HR Operations, Learning and Development, Leadership Development, etc. .
Most organizations today sit on a pile of data, thanks to HRMS & Cloud storage. However, in the absence of a proper HR analytics tool or necessary capability in HR professionals, these useful data or information we are talking about might be scattered and unused. Organizations are now getting to accept that Analytics is more about capability and less about acquiring fancy technological tools.
A HR Professional with right Analytics capability can interpret and transform this valuable data in useful statistics using HR and big data analytics to insights. HR will determine what to do on the basis of the results until trends are illustrated. The impact of HR metrics on organisational performance is analysed using analytics and that can enables leaders to take proactive decisions.
HR Analytics can also help in addressing problems that organizations face. For example, High performers exit an organisation more often than low-performers, and if so, what leads to that turnover? Data based insights can empower business leaders to take right decisions regarding talent rather than mulling over intuitions or finger pointing between HR and Business.
Here are 6 big Benefits of HR Analytics
Improve HR alignment to Business Strategy
It is very common to see that HR function in isolation vis-a vis the business. If you don’t agree with me, find out what business leaders do when HR slides are put up in Management Committee presentations and what HR head does when Business slides are put up? Most HR metrics, processes, & policies are benchmarked with respect to industry and competition, but very rarely they are aligned to hard hitting reality of their own business. For example, just by aligning HR metrics to business metrics, such as HR Cost per Revenue or HR Cost per unit sold, Revenue per employee, Average Lead time to productivity, HR professionals can take the first step towards better alignment to business strategy.
Complex decisions regarding the hiring, employee performance, career progression, internal movements, etc have direct impact on business strategy. When HR Analytics can provide insights on which employee is most likely to be productive in a new role, who is most likely to accept a internal job movement based on historic data, how long is it likely to take to close a critical position, based on data, HR seamlessly aligns with Business needs and strategy.
Creating meaningful HR Processes
Not long ago, HR was marred with policy paralysis. Organizations had HR policies for everything. Processes were built for those policies and not for people who would use, manage or benefit from it. HR Automation is many ways has helped organizations their standardize processes. Whether it’s about Leave Approval, Employee Escalations, Reimbursements, Payroll, etc.
When we have meaningful data that provide us insights about processes, we will be able to take decisions that matter the most for our employees. For example, an organization introduced flexible working hours for its executives just because everyone in the market is doing that. And because few employees asked for it. Few weeks into the few flexi working system, surprisingly, they found that most employees wouldn’t avail this benefit. Data suggested that 90% of employees commute to work using company shuttle as the organization is located in an industrial suburb. So just by looking into data, organizations can build processes that are meaningful rather than what is an industry norm.
Another popular example is that of Google reducing the number of rounds of interviews based on data, thereby improving candidate experience, interviewer experience and cutting down on the lead time to hire.
Enhance Employee Experience
Insights from the data across employee lifecycle can help HR managers emotionally connect with employees, build personalization , etc., For example, if an employee struggles to comply with certain HR policies, data of can provide timely insights on how the organization can support the employee in bettering his experience during the tenure thereby creating a win-win situation.
Improve HR Effectiveness
Data insights from HR analytics can suggest to us which candidates are likely to get selected, which are likely to perform well, if selected, thereby enabling the business to increase its performance and success rate. Such insights can be used in not only hiring, but in career progression, retention, learning and development, etc., For example, it would be an invaluable insight if HR can suggest which employees are likely to perform together without conflict, if business wants a put a new team together.
Reduce HR related costs
HR Analytics can help HR managers identify blind spots as far as leakage is concerned. For example, how much increment should we give a candidate, what are the increment slabs that the organization have so as to keep employee attrition within certain level, and so on.
Build a Great Place to Work
Ultimately, it is every HR head’s dream to build an organization that employee’s love to work for -One where employees wake up every morning and say, ‘here’s another great day’. Instead of being a copy cat and experimenting with what works for other best employers in your industry or country, delving into data can cull out insights on what your employees love, relish and dislike.
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Is there a difference between Six Sigma and Lean Six Sigma?
Lean and Six Sigma are close cousins in the process improvement world and they have lot of commonalities. Now we will talk about the difference between Six Sigma and Lean Six Sigma.
Six Sigma uses a data centric analytical approach to problem solving and process improvements. That means, there would be time and effort in data collection and analysis. While this sounds very logical to any problem solving approach, there can be practical challenges.
For example, some times we may need data and analysis to be even prove the obvious. That is lame.
On the other hand, Lean Six Sigma brings in some of the principles of Lean. Lean is largely a pragmatic and prescriptive approach. Which implies that we will look at data and practically validate that problem and move on to prescriptive solutions.
Thus combining Lean with Six Sigma, helps in reducing the time and effort needed to analyze or improve a situation. Lean will bring in a set of solutions that are tried and tested for a situation. For example, if you have high inventory, that Lean would suggest you to implement Kanban.
Lean is appealing because most often it simplifies the situation and that may not be always true with Six Sigma. However the flip side to Lean is that if the system have been improved several times and reached a certain level of performance and consistency, Lean can bring out any further improvement unless we approach the problem with Six Sigma lens, using extensive data collection and analysis.
Looking at the body of knowledge of Six Sigma and Lean Six Sigma, you will find that Lean Six Sigma courses following tools:
- Cost of Poor Quality
- Lean Principles
- Definition, Origin, Principles & Goals of Lean
- Value, Value Stream, Concept of Muda(Waste) & Categories of Waste
- 7 Types of Wastes, How to Identify them, & Waste Identification Template
- Value Stream Mapping (VSM), Symbols, Benefits & Procedure
- Push System, Pull System, Single Piece Flow, 5S, Kaizen, SMED, Poka-Yoke
- Types of Poka-Yoke – Shut Down, Prevention, Warning, Instructions
- Heijunka & Visual Control
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Background
An automobile manufacturer wanted to identify key independent measures (lead measures) which have to be improved to be successful in critical success factors of strategic initiatives across supply chain and manufacturing.
Solution
- Identified an exhaustive ‘Metrics Value Tree’ consisting of lag and lead measures across supply chain and manufacturing
- Established the relationship with critical success factors of strategic initiatives
- Established data collection & measurement rigor
- Supported the deployment of digital dashboard
Results
- More than 20% increase in success rate of strategic initiatives
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