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Marketing, Sales, and HR Is being a data scientist the only hope?

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Sales, marketing, and HR have been among the most profitable industries in the 21st century. But there have been some hidden downfalls that you may not be aware of.

On the other hand, The covid-19 pandemic has heavily disrupted marketing resulting in the layoff of many employees.

Due to this most of the sales and marketing professionals are struggling and freshers are confused at the same time. So, are the sales and marketing careers approaching a dead end!!

Obviously no.

There’s no need to be concerned as the saying goes, there’s always a solution for every problem.

Starting your career in Data Science and AI might be your one-stop solution to begin your career for breaking into the marketing and sales industry.

Data science is the newest craze, and it’s swept the marketing world as well.

This blog will help you in taking the necessary steps toward launching a career in the same.

First, let’s have a look at a few cases. One prime example is how Coca-Cola lost its market to Pepsi. It was one of the biggest sales disasters of all time. Coke even tried changing its formula but still couldn’t up to its game. This shows the tough and competitive nature of the industry that can cause people to change their opinion about the industry on the whole and not good opinions at that.

But if you think that the competition was only between two separate companies, I suppose you might be wrong. Competition can exist within the same company as well. For example, Ford came out with Ford EDSEL, a new car performing great in the market. So what was the problem? _It came during the economic recession. _The new car was much more expensive than ford’s previous models in the mercury line without offering anything new or revolutionary; therefore, it started to die down.

Especially after the pandemic, faulty marketing strategies caused a lot of small businesses and even bigger chains to close down because they did no good to their business. This resulted in the unemployment of many people, and some left their jobs without having a fallback plan. So what is the solution to survive in this industry? _Data Science! Applying DS techniques to sales and marketing can be a game-changer. _

Becoming a data scientist is never a bad idea. It is very in the now and is considered to be the sexiest job of the 21st century by the Harvard Business School. DS is a very lucrative subject no matter in which domain you apply it to. Data Science Courses fare well in the market owing to their importance in the coming times where every single thing will be driven by data.

Still not convinced? Let’s see why DS is necessary for the sales and marketing sector.

How is Data Science Used in Sales and Marketing?

Data science is the key to transforming multi-source data into actionable insights that improve the fundamental content. By gaining more data-backed insight, companies can transform their business strategies to maximize their market value.

McKinsey reports that 72 percent of fastest-growing B2Bs say their analytics help them plan sales, compared to half of those who are slowest growing. Their analytics are highly effective, they claim. Data science can be used in many aspects so that repetition is not a problem in the sales sector.

  1. Analysis of customer sentiment

Customer emotional analysis can be used to extract emotions from communication. This allows us to understand emotions and use this understanding in our business. The algorithms are used to analyze sentiment. They can be used to assess the general attitude towards texts on social media, blogs, and review sites for text mining. With just a click, automated sentiment analysis techniques allow real-time insight. These tools highlight the subtext of comments, taking facts, emotions, and general views into account. These emotions can also be broadened beyond the general classification of positive, negative, or neutral observations.

  1. Maximization of customer lifetime value (CLV)

Intelligent enterprise decisions are made based on the value of customer relationships. CLV is a measure of a customer’s profit over the entire term of their brand relationship. The lifetime value of your customers will give you a good idea of the future perspectives of your company.

There are many sub-matrics that can be used to measure these metrics: gross margin, frequency, order value, and so on. These metrics are used here. Intelligent algorithms are able to monitor, compare, and calculate any changes in data. You will maximize the lifetime value of your client with all these measures.

Here you will find customized recommendations, newsletter campaigns, and client loyalty programs. It would be best if you increased the measurements. These steps are easy: Take a few measurements, compare them, then determine the weakest metrics and then repeat.

  1. Future sales prediction

Specific data is required for the prediction model. This data includes the number and type of customers acquired, lost clients, average sales volume, seasonal trends, as well as season trends. It is important to know your sales expectations – as changing conditions can dramatically affect sales – before you make any decisions.

These data are used to search for patterns in sales forecast systems. These patterns are used to determine the general trends in the pipeline to make forecasts more precise.

  1. Churn Prevention

Sales professionals are now able to anticipate when clients will purchase their next product. It is also possible to predict when consumers will stop buying. Customer churn is the percentage of customers that have stopped using the product or bought it again. Machine Learning algorithms can be used to identify patterns and features in customer behavior, communication, order, and order.

  1. Inventory Management

Effective inventory management is essential for retailers to ensure that sales rise but supply remains stable. To achieve this, supply chains and inventory chains must be thoroughly examined. Machine Learning algorithms can analyze and provide detailed supply data and identify patterns and correlations. An analyst then evaluates this data and provides a strategy to increase revenue, timely delivery, and inventory management.

  1. Cross-sell recommendations

All companies use cross-sales to increase their revenue. For clients who wish to buy over-the-counter, offering complementary products is a good idea. Buyers have the option to buy a product that is superior to what they are used to when upselling.

The algorithm analyzes transaction sales data to determine if the products were purchased together. Therefore, data science’s role is to provide transaction and CRM data along with factual advice. These algorithms help to decide which products can be promoted or put in the catalog.

  1. Merchandising

Rotating goods allow customers to retain their products’ freshness and quality, while appealing packaging and branding attract attention.

Marketing algorithms include data sets to gather insights and create priority customer sets that account for seasonality, relevance, and trends.

  1. Optimizing the price

To optimize price, models can be used to analyze how demand changes with inventory costs and manufacturing costs to determine the best price at different price levels. These models can also be used to adjust prices for particular customer segments. Client satisfaction is directly affected by price optimization.

  1. Chatbots – salespeople

Sales data science is best applied to bots, not salespeople. Chatbots automate consumer interactions and save time-solving problems. Modern chatbots can interpret customer messages using sentiment analysis algorithms.

Chatbots can also send hundreds of messages per second simultaneously. The selling bots are extremely efficient. Chatbots can offer better customer service in certain situations. They are able to process requests instantly. A bot can save you money.

  1. Augmented Reality Implementation

Augmented reality provides a great outlook on sales implementation. Augmented reality is a way to give clients a more realistic buying experience, especially for online retailers.

The first use of virtual reality is to enhance product and shelf navigation in shops and online. Virtual fitting rooms are also available. Customers have the opportunity to meet with the product, which increases their chances of purchasing it.

MNCs Currently Using Data Science

Airbnb

Airbnb is a great example of data science applied to marketing. They hired a data scientist right from the beginning. There were seven people on the team at the time. Since the founder recognized the potential of data science to accelerate company growth, it has been a priority. This means that all levels have been adequately explored, and problems as well as opportunities.

Netflix

Keeping its subscribers coming back is one of the top priorities for a content subscription service like Netflix. Netflix’s recommendation engine is designed to serve exactly this purpose. It recommends films and series based on the viewing history of similar users. Although the initial effect on the user is positive, enriching, and helpful, the ultimate goal of Netflix’s recommendation engine is to keep them subscribed month after month.

Spotify

Spotify is similar to Netflix in that it aims to keep its subscribers happy by offering new and interesting ways for them to discover music. There is a significant difference between Spotify and Netflix in the amount of content they offer – which is necessary considering the differences in content types.

Meta

Meta, previously known as Facebook data science is multi-layered. They not only have their own insights to analyze and take action but also offer marketing tools and insights for the thousands of businesses that market through their platform. It is essential that they have effective strategies that work for customers.

Google

Google, like Facebook, aims to provide a high return on investment for its business-owning customers. Google will provide these services for small businesses that do not have an in-house data scientist. It is our goal to make data and analytics as easy and intuitive as possible.

What is the advantage of using Data Science in Business?

There are many perks to using data science in the sales and marketing industry. Some of them are listed below.

  • Spend less time and money trying out marketing strategies that fail
  • Only target the most valuable customers
  • Increase the lifetime value of a customer
  • Learn quickly from customer feedback
  • You can predict which products or services will be most popular in the future
  • Refine your digital advertising
  • Convert more leads with cross- or up-selling
  • Increased Security.
  • Allows companies to calculate the Return on Investment or ROI of a marketing campaign
  • Information for marketing departments about which marketing strategies are effective
  • It can give a picture of target consumers. It can give a company a picture of its target consumers.

Now that we have covered the whys and hows, let’s see where and what kind of a job you can get in the Sales and Marketing department as a data scientist.

Data Science Jobs in Sales and Marketing

In India, the average salary for the position of Data Scientist in the sales and marketing sector, as reported by LinkedIn, is Rs 8,50,000.

Based on Experience

  • Beginner (1-2 years)-₹ 6,11,000 PA
  • Mid-Senior (5-8 years)-₹ 10,00,000 PA
  • Expert (10-15 years)-₹ 20,00,000 PA

Based on Role

  • Data Scientist – ₹ 8,00,000 PA
  • Data Science Engineer – ₹ 9,76,133 PA
  • Data Analyst – ₹ 6,02,784 PA

Based on location.

  • Mumbai – ₹ 7,88,789
  • Chennai – ₹ 7,94,403
  • Bangalore – ₹ 9,84,488
  • Hyderabad – ₹ 7,95,023
  • Pune – ₹ 7,25,146
  • Kolkata – ₹ 4,02,978

Where can Learnbay Help you?

Learnbay provides some of the best Data Science courses in Bangalore. It especially provides Data science and AI courses for working professionals. It also provides you with various domain electives, sales, marketing, and HR being one of them. Let us see about this elective in a bit more detail.

Learnbay’s teaching approach

  • Our methods are completely practical-oriented. This means you will learn through project work and other practical activities.
  • You can choose any two domains to learn with.
  • The module is separated into modules for each specialization, so it is easy for you to understand the concept in the order of precedence.
  • This domain elective will also learn tools like Keras, Hadoop, MongoDB, Pytorch, TensorFlow, Seaborne, and OpenCV.

Projects

  • Sales Prediction (Sales Domain)

Big-Bazar like companies employs this Machine Learning model in order to identify the characteristics of stores and products that are most important for increasing sales. Certain characteristics have been identified for each retailer and product. This Machine Learning project aims to build a predictive model that will determine the sales of each product at a particular retailer. It involves determining the future or present-day sales using data such as past sales, seasonality, and economic conditions. This model can predict sales on a specific day if it is given a set of inputs.

[Sales and Marketing Forecasting Dataset from Kaggle](https://www.kaggle.com/harrimansaragih dummy-advertising-and-sales-data)

  • Resume Parsing(HR Domain)

All businesses face challenges in hiring the right talent. The challenge is made more difficult by a large number of applicants, especially if the business has high labor costs, is growing rapidly, or is subject to high attrition rates.IT departments lack the ability to access new markets. A typical service company will hire professionals who have a range of technical skills and business expertise to solve customer problems. Resume screening is the process of selecting the best talent from many applicants. Large companies don’t have the time to read every CV. Therefore, they use Machine Learning algorithms for Resume Screening.

Resume Screening criterion Dataset from Kaggle

  • Keyword Generation for social media ads(Marketing Domain)

Keyword generation can be defined as the task of automatically identifying a set of terms that best describe the subject matter of a document. This is an important technique in information retrieval systems (IR). Keywords simplify and speed up the search. Keyword generation can be used for text classification and topic modeling. S.Art and al extracted keywords to determine patent similarity. Keyword generation allows you to automatically index data, summarize text, or create tag clouds using the most representative keywords.

Kaggle dataset for keyword generation or extraction

Conclusion:

Throughout this blog, you have seen Data has become the cornerstone of all industries including HR, Sales, and Marketing. Data science techniques help sales leaders to manage their businesses efficiently, focus on viable plans, create leads, improve customer experience. This adoption of big data analytics is differentiating winners from the rest across sectors, resulting in an increase in the demand for skilled data professionals. Thus, You can undoubtedly build a rewarding career in this industry to secure a high-paying job.

Hope you found this blog informative enough.

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