Marketing, Sales, and HR: Is being a Data Scientist the only hope?
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.?
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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.
2.?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.
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3. 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.
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4. 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.
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5. 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.
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6. 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.
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7. 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.
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8. 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.
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9. 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.
?10. 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.
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.?
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