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  • Ashwin Devulacheruvu

Data Science in Sales and Marketing


Data science can play a very important and unique role in marketing, do you want to know how?, It is quite strange and hard to believe for people who are not able to think out of the box.


We can find patterns for a particular product/Service by analysing few people (minimum 100) as research participants to form a test data base from various locations with different Socio-Economic changes by covering almost all types of categories. With this we can decode what co-relates people to buy/use a particular product/service, we call this as insight. This insight can be used to build a prediction model with C4.5 Decision Tree which is the most accurate prediction model.


This model can be built on various platforms and we train the model with the test data initially collected for a particular product / service. Once the model is trained it automatically learns what category of people will be interested in a specific product / service.


Now the population data base with required location will be connected to the model to predict potential customers who are interested in a specific product / service.

The model will predict with accuracy on how accurate the model is predicting the customers with percentage wise, for example 82% i.e for every 100 people 15 members will not be interested in the product/service, 82 people will be interested in the particular product/service.


This type of model is called prediction modelling for sales and marketing, the local name for this type of marketing by win whispers is focused marketing with Data Science.


This type of Marketing with Data Science can be generalised to understand better (Data, Sales force, 2018).

Advantages of this type of modelling are as follows;


1. Increase Marketing Efficiency:

The more you know about the people in your target markets, the better your marketing will be. And there’s plenty to know. Their needs, challenges, income, locality and interests all play a role in shaping their buying decisions. With over two quintillion bytes of data generated every day, your marketing messages must stand out in the clutter, for that to happen, your messages must be more personal, targeted, and relevant than ever before. The key to getting results is having the correct data about the people you’re trying to reach. However, customer profiling is a time- and labour-intensive process. That’s where data science can help.


2. Reduce Financial Risk:

Difficulty in calculating marketing ROI is one of the biggest frustrations for every professional digital marketer. That’s because digital marketing isn’t as tangible as other parts of a business. It’s a multidimensional process with many different channels.

Sometimes even the smallest detail can significantly affect a marketing ROI calculation. Yet, marketers must measure a multi touch customer journey that’s the sum of many diverse, often difficult to measure, interactions.

To calculate marketing ROI more effectively, the key is to find and track which variables and outcomes are most relevant to your business. Your data-driven solution could increase the accuracy of your marketing ROI calculations by reviewing KPIs over time.


3. Predictable Revenue:

Sales forecasting is notoriously hard to do. In many cases, you’d have better luck tossing a coin. Few organisations are happy with the accuracy of their sales forecasts because they depend on gut feeling and countless spreadsheets. Sales forecasting is a challenge that’s ready for a data science solution. An example is IBM’s Budget, Authority, Need, and Time frame (BANT) process. BANT is IBM’s lead-scoring method. With this type of pipeline forecasting, each opportunity is fit into a stage of the sales process. Then, a percentage generates a probability-adjusted revenue prediction. This lets IBM build forecasts using data from four inputs: the dollar amount, stage, probability, and close date.


4. Long Term Profitability:

A process for creating the most positive customer experience is referred to as customer journey mapping. This process lets you map the customer journey – from their first impressions to a purchase, the delivery, and all the way through to repeat sales. This leads to mouth to mouth marketing which is directly proportional to long term profitability.



Conclusions:


The Future of almost every Business will be Data Driven, With the growing use of digital marketing, it won’t be long before everything is connected digitally in some way. Data science gives you the tools to use the vast amounts of data you may already be sitting on.

Sure, you can still do many things manually. But when all your competitors are using data science, will you be able to keep up?

Through determination to transform digitally, CEOs, CFOs, and other organisational leaders can use advanced data science tools and methods to make their collective vision a reality and lead their company into the future. Since there’s no doubt the future will be data driven, isn’t it better to get in the game sooner rather than later?

while most companies are swimming in data, they don’t know how to translate it into a more positive customer experience. Data science can help.


References:

1. 4 Ways Data Science Is Transforming Sales and Marketing, Part 1. (2018). Retrieved 2 December 2019, from https://rtslabs.com/4-ways-data-science-is-transforming-sales-and-marketing-part-1/


2. KNIME | Open for Innovation. (2019). Retrieved 2 December 2019, from https://www.knime.com/


3. What is Predictive Modeling ? - Compare Reviews, Features, Pricing in 2019 - PAT RESEARCH: B2B Reviews, Buying Guides & Best Practices. (2019). Retrieved 2 December 2019, from https://www.predictiveanalyticstoday.com/predictive-modeling/

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