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AI Monks Response:

Business analytics has opened doors to a whole new world and raised decision making to a radically different stratum. Today, any organization makes an informed decision by slicing, dicing, and dissecting the data. But will this analysis hold any value if we completely ignore the business aspect of the problem at hand? We don’t think so. Analytics consulting is a functional role that aims to maintain a balance between business and hardcore analytics with an intention to deliver value-added analytical solutions.

Let us understand the role of an analytics consultant.

Usually, when we talk about consultants, the first thing that strikes our mind is an external expert who visits a company to solve a specific current or potential problem. And they leave the company(clients, as they are called) once they have done the task. However, there is another role that may not be very explicit, but plays a somewhat similar role; an internal consultant.
The external and internal consultants are substantially similar in the kind of work they do. Whereas, the significant difference lies in the way the company engages or hires them.
External consultants are usually employed for specific projects; while, the internal consultants are full-time employees.
Apart from how the company hires the external and internal consultants, almost all other functions stay the same.

Globally, corporate spending on analytics consulting had seen a surge of $43 billion in 2017. Companies evenly split the investment between developing in-house capabilities and spending on external consultants. With 91% of executives convinced that analytics have already generated substantial value for their firm, we could expect a rise in both.

Let’s talk about some of the projects that consultants work on:

1. Automating reports or developing dashboards from scratch for a company using tools such as Tableau, PowerBI, Qlikview or custom-dashboards using scripting languages such as Python
2. Identify avenues to improve logistics performance of a logistics company (or logistics unit of a manufacturing or retail company)
3. Reduce the inventory held by a retail chain
4. Analyze the campaign performance of a company
5. Reduce the customer churn for a telecom client
6. Identifying the target customer personas/segments
7. Reduce the uninstall rate for app-driven companies
8. Cannibalization effect on sales of one store due to the opening of another store
9. Impact of A/B tests
10. Analyze the results of sampling or experimental designs

We can notice through the list mentioned above that all these projects start with a robust descriptive and diagnostic analysis.

• The diagnostic analysis helps in identifying the root causes of areas where the company is not doing good.
• Whereas, the descriptive analysis helps in identifying and assessing the performance of the company.
• Once you understand the overall context and underlying problem, you should move to predictive analysis.

Hence, for anyone aspiring to become an analytics consultant, it is essential to develop a strong understanding of descriptive and diagnostic analysis. Having identified the problem, the next step is to identify and provide a solution to the company. Now, this solution can come in the form of process changes or by building a predictive model that can help the company understand the potential situations that can arise beforehand.

Therefore, it becomes essential for an analytics consultant to have a basic understanding of statistics and predictive modeling techniques. As a consultant, you may not be required to go very deep into statistical analysis or in-depth mathematics of predictive models, but it’s helpful to know the basic principles.

Analytics Consultant Skills

Let’s further understand what skills an analytics consultant will require:

Soft Skills

• Ability to breakdown the business problem into small problems
• Ability to read and comprehend data
• Good communication skills
• Great presentation skills (Powerpoint)

Technical Skills

• SQL
• Any visualization tool such as Tableau, PowerBI, Qlikview or any other
• Any ETL tool such as Alteryx
• Excel

Programming Language

• R or Python

Statistical Concepts

• Sampling
• Hypothesis testing
• Data distribution
• ANOVA
• Different types of data

Read: Impact of Recession on Data Science Industry

How can you acquire these skills?

1. Soft Skills

Developing soft skills is more like running a marathon and not sprint. “Practice makes perfect” is not just a saying. You can develop these skills with practice over a period of time. The best source to develop these skills is case studies. Practice case studies as much as possible.

• Form a group with friends, pick up case studies from whatever source you can, and get into it.
• Try to solve them end-to-end as if you were presenting to an audience.
• Right from breaking down the problem statement, analyzing data to creating presentations – do all of that.
• Find yourself a mentor who can guide you in improving your analysis and presentation.
• Present the analysis to your mentor as if you were presenting to the CEO of your company. That will prepare you for real-world scenarios.

If you are keen on improving your ability to read and comprehend data quickly, read as much as possible. Understand what are the different KPIs companies track regularly. Learn about different use cases that different companies work on. This will help you in developing data understanding.

2. Technical Skills

Understanding of technical skills is vital because you want to work on data eventually. From extracting to cleaning to manipulation to analysis, you should have expertise in all four areas. In a lot of scenarios, you will be given access to SQL systems where you will have to query data yourself; hence SQL becomes essential. DO NOT disregard SQL – it’s one of the most important skills if you want to work in the world of data.

• Alteryx, as an ETL tool, can help you carry out basic data transformation and data manipulation easily. Knowledge of a visualization tool can help you analyze data quickly and present it beautifully and insightfully.
• Learn any one of two languages (R or Python). They help significantly in building predictive models and much more complex data analysis where your other options may not work out. Hence they come very handily.
• Basic statistical concepts can help you in developing an understanding of the data collection process. Additionally, it can help you work on different types of datasets, such as sample data, survey data, experimental data, etc. Hence, learning these basic statistical concepts is essential.

3. Latest Happenings

Additionally, staying updated with the latest happenings in the industry would help you stay ahead of the game.

Conclusion

Summing up, we could ascertain that Analytics consulting is one of the most financially rewarding and coveted profiles today. Analytics consultants are in hugely sought after by global giants like Accenture, HP, Dell, BNY Mellon etc. Sound domain knowledge, rich experience and exceptional communication skills paves the way for it.

Read AI Bytes by AI Monks on stay on top of analytics and data science industry insights.

The post Do you want to become an Analytics Consultant, too? appeared first on AI MONKS.

1.      Audience

Audience is at the core of your presentation. You need to understand your audience, their skill-set and their focus area before you start working on your presentation. If you audience is Chief Technology Office or Data Scientists, you may talk about technical jargon and machine learning models; however, if your audience is Finance or Marketing professionals, then talking about Random Forest or Gradient Boosting will be complete alien to them.

2.      Facts

Stick to facts to the extent possible. In the case where you are presenting you opinion, that should be backed by facts and logic. A lot of times, people tend to bring in their bias and judgement based on their experiences. The whole idea behind using data is to remove bias and get a true sense of business. Don’t get carried away by emotions, judgments and experiences – always support your conclusion and opinion with facts.

3.      Structure

Create a skeleton of how and in what manner are you going to create your presentation. When I have to create a presentation, I always start with a clean slate and start adding titles to ‘Table of Contents’ or create a slide and write what you are going to cover in each of those slides. Once you have created a skeleton and added high-level points for each of the slides, you can go through each of the slides and prune to make the final structure. This will help you in achieving three objectives:

1. This will limit your tendency to provide redundant information.
2. Your information flow will be more coherent and organized .
3. Likelihood of missing out any key point will be reduced.

You need to ensure that data points and analysis are coherent with each other; one point should lead to another – in data analysis, one analysis should lead to another analysis.

4.      Exhaustive

Collectively, your presentation should be complete in itself – anyone going through your presentation for the first time should get clear understanding of the project, background, approach, results and impact, along with proper reasoning and explanation. This was covered in detail in the first part of this series: Why Storytelling is One of the Most Important Things for a Data Scientist?

5.      Action Title and Call-outs

Action title is one of most important components of the slides that I make. If I were to explain the entire slide or key takeaway from the slide in a single line, it should be reflected in the action title. In the scenarios where slides are too loaded with text or numbers, senior level executives don’t prefer to go through each of the points; instead, they are more interested in key takeaway from the slide. If you can’t express the entire slide in one line, you need to work on that slide, either it has too much information or too less of an information. Call-outs should be used to highlight any kind of outlier or abnormal behavior. You may just highlight/border the text with a different color and provide explanation for the same. In cases where you want to explain some behavior/trend in a graph, create a call-out and write your explanation for the behavior. It comes very handy while we are doing diagnostic analytics.

6.      Frame of Reference

Provide a frame of reference while emphasizing your analysis or results. For example, if I were to say, “sales of my company this year were US 110 Mn”, this standalone statement may not give me complete information. A better statement would be “sales of my company this were US 100 Mn, which represented YoY growth of 15%.” Second statement creates more impact in the mind of audience. Let’s take another example – “because of improved accuracy, revenue impact of the model will be US 10 Mn” vs “because of improved accuracy, revenue impact of the model will be US 10 Mn, which is 5% of our FY2018 revenue.” Bringing out these frames of references at the right places in your presentation make them more impactful.

7.      Icons

As it is said that visuals are always better than text, so why not use that to our advantage. Microsoft Office offers beautiful and rich collection of Icons that we can use to highlight certain points. For example, if I were to explain the scale of project that I worked upon, below are two methods I can use:

Method 1:

“The complexity of the project can be gauged from the fact that we had to deal with 1,000 Trucks, 100 million data records, 100,000 trips per year.”

Method 2:

Which method would you choose?

8.      Smart Art

MS Office provides some wonderful smart arts which you can use to present your points in a visually appealing manner. For example, if you want to present the entire project flow or different stages of your engagement, you may use process flow smart art. You may create your own smart arts using icons and shapes. In another example, let’s say you want to present a process which has data consolidation, followed by analytics layer and ending with a visualization layer – you can use a combination of icons, shapes and smart arts.

9.      Color Scheme

Be very careful of the color scheme that you use. Most of the companies would have their defined color scheme; in such cases you should adhere to the prescribed scheme. In case there is no prescribed color scheme, make sure that you don’t use very light or very bright color. Keep a consistent theme with your colors; if you want to highlight certain points, use same color across your presentation. For instance, you may use red color to highlight negative points or green color for positive points.

10.  Independent Feedback

This is an additional step which comes in when you have done everything at your end. Basically, once you have completed your presentation you should get it reviewed by a third person and take an independent view (this may not be possible in cases where you are working on highly confidential projects). Independent views will give you a clarity if you have covered all the aspects in terms of the presentation being exhaustive and coherent. Ensure that you are open to receiving feedback and inputs and justify them with objectivity, wherever required.

Learn everything about RPA for FREE in this course – Introduction to Robotic Process Automation (RPA)

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In my last five years of working in analytics and consulting, I have come across this phrase thousands of times:

“The story is not coming out properly.

We need to work on story first, rest can be done easily.”

Why so much emphasis on story? After all, in data science and analytics what matters most is the accuracy of the model, isn’t it?

Are you sure?

Let’s figure that out.

Let’s assume that you have worked on a churn prediction model in your organization and you have to present the entire engagement to your CEO and CFO, who don’t have any background of the project. Seems easy? Since it’s a data science project, all you have to do is tell them about the model and the accuracy, that’s it. Is it?

Let’s take different scenarios of presenting our results and see which one you will prefer.

Approach 1:

“We have developed a churn prediction model with an accuracy of 82.7%.”

Approach 2:

“We developed an ensemble of random forest and logistic regression to predict the likelihood of a customer churning out in next three months. We have achieved an accuracy of 82.7% using the ensemble. The previous model that was based only on logistic regression had an accuracy of 71.1%.”

Approach 3:

“Our North India zone was facing the challenge of high churn rates. A lot of old customers were churning out from our network. We undertook this engagement with the objective to predict customers who are likely to churn in next 3 months. The existing model lacked the accuracy that we wanted to achieve. For this, we used internal customer data and complemented it with external social media and demographic data to develop an ensemble of model. We developed a random forest model with 1000 trees and logistic regression which is based on maximum likelihood estimation. This model gave us an accuracy of 82.7% which is better than that of our previous model.”

Approach 4:

“Our North India zone was facing the challenge of high churn rates. For the past four quarters, churn rates were over 10% each quarter which was leading to a revenue loss of around USD 100,000 per quarter. A lot of old customers were churning out from our network.

We undertook this engagement with the objective to predict customers who are likely to churn in next 3 months. By estimating this probability, we would want to focus on customers (through offers, promotions or improving customer relationship) who have high likelihood of churning out.

Though we already had a model for the same, but the accuracy provided by the model was not helping us much. So, we developed a new algorithm that combines two machine learning models and provides us better accuracy (82.7%) than the previous model (71.1%). For developing this new model, we used internal customer data and enriched it with external social media and demographic data to develop an ensemble of model. The overall impact of this model is estimated to be around USD 75,000 per quarter.”

Which approach would you choose if you were to explain the engagement to your CEO and CFO?

Approach 1? Hell, No…

Approach 2? No…

Approach 3? Umm… May be

Approach 4? Yes, certainly.

Now, one can always argue that approach 4 is nothing but providing complete details about the project – background, data, algorithm, output and impact.

That’s what storytelling is.

Five Key Aspects of Storytelling

One of the most important aspects you need to consider while providing all these details is your ‘audience.’ Understand who your audience is. If your audience is Chief Data Scientist then you should go into technicalities of the model; however, in this case our audience is CEO and CFO. They would be least interested in the underlying model, rather, they are more concerned about the revenue/cost impact it will have on business.

I will define storytelling as “presenting different aspects of a project in such a manner that they are exhaustive, coherent, succinct and audience-friendly.”

Now, let’s understand different aspects of a data science projects and learn how to present them in your story.

Any data science or data analysis project would essentially cover five different aspects.

Can you relate this with the ‘Approach 4’ mentioned above? Is there any other Approach which covers all the five aspects?

No, right?

When you are working on large datasets and your aim is to develop a model that improve accuracy, it is not uncommon to lose sight of the larger picture. That’s why while presenting your results, you should keep the above framework in your mind and build your entire story accordingly.

Your model may be among the best models in the world but if you can’t convince business users about it, they will always have their apprehensions in implementing that. So, you should give due attention to first and last aspect of the above framework – ‘Background’ and ‘Impact’. These two aspects will help business users understand the criticality and importance of your project.

If you like this post, we are sure that you will also like – Presentation for Data Science Project – 10 Points to Master

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Supply chain analytics is a building block for operations-heavy company. The supply chain function is responsible for managing the purchase of raw material, inventory, vendor management, negotiations, among other things. Hence, it becomes important to learn about supply chain analytics, and we believe there is no better way to learn from real-life supply chain analytics case study.

Rajesh is a manager of a retail store in Timbaktu. Rajesh has been facing a problem of low service level for the past few weeks. He has been given a target from his boss to improve the service level to 95% or above from current levels of 80%. Since Rajesh’s appraisal rating depends on the service level that he is maintaining, he took this target very seriously and started in-house analysis to identify the problem.

He collected the sales and procurement data for analyses. During his analyses, he wanted to see if something can be improved to increase the service level to 95%. While doing the analyses, he noticed that while their sales forecast are quite in line with their demands, the problem exists with their reorder level. They are not able to order the right quantities from the vendors.

Some of the data points that Rajesh collected are as follows:

Stock is delivered from the vendor to the retail store in 4 weeks after placing the order i.e. lead time is 4 weeks. Demand is normally distributed with a mean of 100 units a week and a standard deviation of 10 units.

Also, Rajesh is also interested in knowing the reorder level if he wants to increase the service level to 98 per cent.

Could you help Rajesh identify the right reorder level?

If you have any other way of solving this case study, feel free to share your solutions with us at info@aimonks.com. If we like your solution, we will publish it on our website.

You may also like Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business

Disclaimer: Please note that this case study has been written with a sole purpose to impart information. Any resemblance with a true business scenario is purely coincidental and AI Monks should not be held responsible for it.

The post Supply Chain Analytics Case Study: Improving Procurement for a Retail Store appeared first on AI MONKS.

Over the past few years, retail analytics has played an important role in driving business performance for retail organizations. Be it reducing costs, improving top line or optimizing process, retail analytics has played an important role in all facets of an organization. In order to learn the benefits that analytics can provide to retail organizations, case study is one of the best methods. This post – Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business – has been written with an objective to help you experience actual business problems that companies face.

Fashionista, Inc. is a brick-and-mortar fashion retailer with 100+ stores across the USA. The retailer has been doing a good job for the past 10 years in acquiring customers and has maintained pace with the new and latest trends over the years. The company has focused a great deal on tying up and partnering with popular brands and innovative labels over the years. Though the company has been doing good and has an EBITDA margin of 15%, which is one the highest in the industry, two quarters back, the company decided to introduce automation and digitization in all its stores.

As a part of its digitization strategy, the retailer decided to do a pilot project for three months at 5 of its stores. The project aimed at capturing the number of walk-ins (potential customers) at each of the stores at any point in time across all days of the week for the time the store was opened (10:00 AM to 10:00 PM). The company later, aggregated the data at an hourly level for each of the stores to see the number of walk-ins at each of the stores in an hour.

On further analysis of this data,  the company came up with surprising insights that the average conversion ratio (the number of transactions made during an hour vs the number of people entering the store during the same hour) is way below the industry standard. The client initially thought that the people coming in a group of 2 or more usually end up buying in a single transaction; however, the same was denied by multiple store managers. The reason put forth by store managers was that Fashionista runs a very lucrative loyalty program for its customers and hence, multiple customers in one group usually tend to buy separately so as to take benefit of the loyalty program. Moreover, this is also encouraged by store managers to increase the customer base in their CRMs.

When the CEO got to know about this, he decided to do a detailed study on this, and identify the reasons and potential opportunities to improve. He wanted to answer one question in particular – do we have adequate staff in stores to cater to customers who walk-in?

If yes, then there are other reasons for low conversion rate. He also thought that if we are adequately or over-staffed, can we reduce in-store staff and save some cost?

If no, what is the optimal number of in-store staff at each of the five stores? Can we also hire staff on contractual basis for few hours on certain days to manage the under-staffed situation? OR hiring full-time staff makes more sense?

Another question he wanted to seek an answer was should he keep the store up and running during certain times when conversation rates are abysmally low?

The CEO thought of reaching out to management consultants who can help him provide clarity on his thoughts and decide the strategy ahead. Can you help the CEO in solving the problem at hand and provide him potential savings or revenue improvement estimates?

Additional information:

The stores currently deploy staff depending on the area of the store. For every 200 sq ft, the company employs one full-time employee. The salary of staff is USD 3,000 per month for a shift of 8 hours, six days a week. For every additional hour that the staff works, the company has to pay, USD 10 per hour extra. Additionally, each of the stores has one store manager with a monthly salary of USD 4,500 for a shift of 8 hours, six days a week. For every additional hour that the store manager works, company pays him USD 24 per hour. Store manager has weekly off on every Monday, hence, there is no store manager on Monday. On an hourly basis, contractual staff can be employed at USD 12 per hour.

The rental and fixed cost of the store is decided on monthly basis and can’t be reduced further.

Below are details of the five where pilot was carried out.

Following datasets have been provided separately.

• Transaction details, aggregated at hourly level, at each of the five stores
• Walk-ins, aggregated at hourly level, at each of the five stores

If you want us to review at your solution and publish on our website, send your solutions at info@aimonks.com.

Here’s another case study for you to try: Supply Chain Case Study: Improving Procurement for a Retail Store

Disclaimer: Please note that this case study has been written with a sole purpose to impart information. Any resemblance with a true business scenario is purely coincidental and AI Monks should not be held responsible for it.

The post Retail Analytics Case Study: Helping Fashionista, Inc. Improve Business appeared first on AI MONKS.

This post is a continuation of the first post Sales & Marketing Analytics – Top Analytics Projects in Retail Industry. In the first post, we talked about the function which is at the forefront of every business and has a direct impact on the top line of the company. This post talks about the other functions of the business which may not be at the forefront but forms the backbone of the business. Operations and supply chain are as essential functions, if not more, to the business as the sales & marketing. Sales & marketing focus on selling products to consumers; while, operations and supply chain focus on producing those products and ensuring that the products are accessible to consumers.

There are five different sub-functions that we will discuss under operations and supply chain function. Then under each of the sub-functions, we will talk about top analytics use cases to make the business more data-driven.

The five sub-functions in operations and supply chain are as follow (refer to the image):

1. Demand Planning
2. Procurement
3. Inventory
4. Logistics & Transportation
5. Vendor Management

Moving a step further, let’s look at the top supply chain analytics use cases spread across these five sub-functions.

A) Demand Planning

1. Forecasting

B) Procurement

2. Reorder Level Identification

3. Price Variance Analysis

C) Inventory

4. Inventory Rationalization

a) 9-Box Analysis

b) Variability (sigma/mu) Analysis

5. Stock-out Prediction

D) Logistics and Transportation

6. Fleet Monitoring

7. Network Planning

8. Route Optimization

E) Vendor Management

9. Vendor Scoring

10. Vendor Consolidation

Let’s now talk about each of the use cases in detail and understand how and why they are relevant in the business context.

1. Forecasting:

Forecasting is one of the most essential parts of demand planning. Forecasting sales (based on primary or secondary or both datasets) is essential to maintain a balance between meeting consumers’ demands and not get bogged down by excessive inventory. Traditionally, forecasting has been done through rudimentary approaches or based on one’s judgement. However, with advancements in technology and sophisticated algorithm, it is possible to do forecasting at SKU level with decent accuracy.

2. Reorder Level Identification:

Once you have identified (forecasted) the number of goods you need to procure, identifying the right reorder level is essential to ensure that the production doesn’t stop because of stock-outs, as well as ensure that working capital is not blocked because of incorrect orders. Historically, procurement managers have been identifying reorder levels at a product category level based on their judgements; however, now it has become convenient to calculate reorder level for each item that is procured. Moreover, based on historical data one can also identify the demand distribution curve that different products have and orders for each can be placed accordingly using statistical techniques.

3. Price Variance Analysis:

This may not be a very common use case, but this essentially provides procurement teams to identify cost savings opportunities. Price variance analysis means analyzing which all items were procured from which all vendors and at what costs? Are there any significant disparities in unit economics? Are we buying the same item from different vendors at different prices? Is there a business rationale for a higher price? Is this an indication for kickbacks or any kind of frauds by the procurement function? This analysis, though mostly descriptive and statistical in nature, plays an important role in identifying cost savings opportunities and uncover any fraudulent activities that may be taking place.

4. Inventory Rationalization:

This is one of the most common and most useful use cases of analytics for the operations function. Any amount of unsold inventory is blocked working capital for the company. The unsold inventory doesn’t just use the limited amount of space that you may have in your store/warehouse, it also has a significant impact on your working capital. Imagine you are a billion-dollar retail enterprise and you find out that you have an inventory worth USD 25 million lying in your warehouse which has not been sold for over a year now. Do you know how much money are you losing because of that? If you were (or if you already have) to borrow that amount of money from a bank at 6% interest rate per annum, you will be paying USD 1.5 million as interest. Assuming your net average profit margin for products in that USD 25 million inventory is 8%, your total margin would have been USD 2 million. Now, after a year you have already lost USD 1.5 million as interest, you are just left with USD 0.5 million as your profit which is 2% margin. That’s why inventory rationalization is one of the most common and most sought-after use cases in the industry.

5. Stock-out Prediction:

According to Retaildive.com, retailers are loosing around USD 1 trillion annually because of out-of-stocks (Link). Identifying which SKUs are likely to stock-out at what time is the key to handle this problem. This is not a stand-alone use case but has to be carried out with efficient forecasting and right reorder level calculation (Use Case 1 and 2). Avoiding stock-outs does not just help in preventing lost sales, it increases customer loyalty as well. If a customer visits a store and every time she finds what she is looking for, the likelihood of repeat visits increases considerably.

6. Fleet Monitoring:

Fleet monitoring is mainly a diagnostic exercise that companies carry out to understand the movement of their fleet, esp. in the cases where companies have owned or leased fleet. This helps companies in evaluating if their vehicles are under-utilized or over-utilized or adequately utilized. Also, the performance of fleet at a trip level, route level and by a driver can be assessed. Furthermore, the analysis at a trip level can help companies identify performance improvement opportunities in their fleet movement by following best practices.

7. Network Planning:

Network planning is an optimization problem carried out by companies having a presence across multiple-states or large geographical area. The objective of this problem is to identify the right locations of warehouses or distribution centres from where a company can ship goods to its distributors or dealers. In this optimization problem, the objective function is to minimize cost while meeting all the SLAs (service level agreements) signed with dealers. This exercise is usually carried out by companies once a quarter or half-year, depending on the growth trajectory, or when new locations are to be identified.

8. Route Optimization:

Route optimization is again an optimization problem but mainly carried out on run-time for truckers delivering goods to different locations. Route optimization is an exercise carried out to identify which route should a vehicle follow to deliver goods to intended locations. The inputs that go into this problem are the delivery loads (or orders from different locations), truck capacity, the distance of delivery locations from distribution centres and time constraints (defined from SLAs). Basis these inputs, our objective is to define a route which a vehicle should follow such that the cost is minimized, and all the deliveries are made within the stipulated time.

9. Vendor Scoring:

Vendor scoring, though descriptive in nature, is a very useful application of analytics for companies to score their vendors based on multiple parameters such as quality of products and service, adhere to service level agreements, the price charged for products and services, the criticality of a vendor in the overall functioning of the business. This is mainly carried out by companies which have thousands of vendors (manufacturing companies, for instance). This helps companies identify their top vendors objectively and develop strong relations with them to ensure business continuity.

10. Vendor Consolidation:

Vendor consolidation is another cost-saving technique followed by a lot of companies (primarily by companies having thousands of vendors, as in the manufacturing sector). The objective of this exercise is to identify if there are any opportunities to reduce the number of vendors that the company may have and by giving more business to a limited set of vendors, negotiate better prices or terms of business. Terms of business could mean shorter lead times or longer payment period. The latter part will help the company optimize its working capital.

All the projects listed above may not essentially be data science and machine learning projects, but one thing to note here is that the simple descriptive analytics can also provide multiple use-cases for companies which have a significant impact on business operations. Also, with the volume of data that is being generated these days, it is becoming more and more essential to use analytics tools even for slicing and dicing data.

We would love to hear your experiences with any of these projects. If you have done any other project that is not listed here, we would be happy to learn from your experience. Please free to reach out to us at info@aimonks.com.

The post Supply Chain Analytics – Top 10 Analytics Projects appeared first on AI MONKS.

Banking analytics seems to be a promising solution in such a case.

The financial services industry is one of the industries which has embraced analytics across the entire customer lifecycle. Right from identifying products for a customer to designing promotional campaigns, data science has been adopted at each customer touchpoint. From underwriting to delinquency, financial institutions are adopting data science and analytics across different functions. In this post, we have highlighted the top 10 analytics projects used by the financial services industry to attract and retain customers. You may try your hands on any one of these projects.

Lead prioritization:

Lead prioritization is used where ticket value is considerably high and resources to sell are limited. In the BFSI sector, the ticket may refer to a high-value loan (home/auto/personal/corporate/etc.) or a policy or an investment scheme. Accordingly, customers can be segmented into different categories based on their likelihood to buy the product. Lead prioritization helps in allocating limited resources to potential leads with a higher chance of a conversion. The more technical term for this is ‘customer propensity modelling’.

Customer lifetime value (CLV):

Customer lifetime value is a value that the customer is likely to provide to the bank in his entire relationship with the bank. This helps the bank in evaluating the investment and effort that the bank should put in to acquire and retain the customers. Lead prioritization and CLV estimation serve a similar purpose of identifying top leads and customers, respectively.

Predicting the life event of a customer:

Based on a customer’s bank statement or credit/debit card statement, one can predict future life event of a customer. Since the banks already have this data for customers, data procurement will not be a task for the bank. Based on the predicted life event, relevant products can be offered to customers. An important point to note here is that the tagging of transactions will play an important role in determining the accuracy of the model. Additionally, in the case of salary accounts of customers, looking at the salary trend of the customer can provide information about a recent promotion or a job change.

RFM modelling:

This is a customer segmentation technique based on three parameters – recency, frequency and monetary. Recency refers to how recently the customer has purchased a product; frequency refers to the frequency of purchases by the customer; while monetary refers to the money value of purchases by the customer. Customers in each segment would usually possess similar attributes and can be served with similar kind of promotional offers.

Cross-selling/up-selling:

Cross-selling/up-selling refers to selling additional products to existing customers. These products could be different from the ones that the customer has already bought or top-up of existing products. An example of cross-selling for a bank is offering a credit card to a customer having a savings account with the bank; while, an example of up-selling is increasing (topping-up) the insurance cover of the policy-holder. Again, in this case, if you can predict the current and future life event of the customer, the probability of cross-selling the right product increases considerably.

Learn about Sales & Marketing Analytics – Top Analytics Projects in Retail Industry

Next best offer:

Next best offer is used for improving promotional efficiency and increase conversion rates at different customer touch points. Based on a customer’s historical behaviour, the company can send targeted promotions to which the customer is likely to respond. This is another method of cross-selling based on a recommendation engine. Again, having details of customer’s current and expected life events can improve the likelihood of offering the right product.

Sentiment Analysis:

Carrying out a sentiment analysis on social media posts can provide detailed insights to customer feedback. Not just social media, sentiment analysis can be carried out on support emails, app reviews or any other customer touchpoint. Furthermore, this can be extended to carry out a lot of other analysis such as emotion analysis, key negative/positive themes, topic modelling, etc.

Delinquency prediction:

NPAs – Non-performing Assets: this term has become very common in the banking world because of rising defaults in recent years. An account is considered NPA if it defaults on its payment schedule. Default (or delinquency) prediction is one of the most common and useful use cases in the industry. Companies, almost on a daily basis, are trying to improve their models to predict delinquent customers. This helps banks to control the revenue (principal + interest, both) loss against an issued loan.

Do you know restaurant industry is using analytics to gain a competitive advantage? Read Restaurant Analytics – Top Analytics Project Ideas to know details.

Predicting the probability of a customer to renew insurance (or any other) policies:

This is very useful for insurance companies. Since the insurance companies don’t have access to customer’s banking history, achieving accuracy becomes challenging only with internal data points. The model for this can be enriched by adding external data points related to the customer’s current life stage. If policy issuer is the same as the bank where the customer holds his account, the prediction model can be enhanced with customer’s banking data.

Churn Analysis:

The technical term in banking parlance for this is ‘balance transfer.’ Balance transfer refers to transferring of live loan account by a customer from one bank to another, mostly because of better interest rates or improved service. Churn analysis provides knowledge about customers who are likely to transfer the balance to other banks. Subsequently, as a remediation measure, the bank can take corrective measures such as building a strong relationship with customers or offering them other products at discounted prices to prevent customer churn.

BFSI sector provides a plethora of other customer analytics projects that you can take up.

Have you worked on any of these projects or maybe, a different one? We would love to hear your views and approach to carrying out these projects. Don’t forget to share your experience with us at info@aimonks.com.

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