How Data Analytics Impact your Business
Hello everyone! Long time no write, I just wanna share my experience about data-driven can make your business growth, definitely with good quality data. I think we are familiar about data-driven, speak by data, social media analysis or anything else which intersect data analytics. Anything about how to gain your business revenue, it should be more deeply analysis on your data. Data analytics would help you to identify ambiguity your data, performance your company, validation about anything that you company missing and let’s try to fix it by the data! So, this is a some way to analyze your external or internal data to compare with another business or your competitor.
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” — By Geoffrey Moore, an American Management Consultant and Author
Social Media Analysis
The first section, I wanna share how to get external data from twitter, instagram, trend on google and example what the data should be compile in this analysis. Let’s see how to do it!
Scraping Twitter and Instagram
In this section, you can scraping the social media platform that you have (maybe twitter or instagram) to know how big is your business brand or how well your company and product is known by consumers. This way to get your data.
Twint is an advanced Twitter scraping tool written in Python that allows for scraping Tweets from Twitter profiles without using Twitter’s API — Twint Documentation
Instascrape is a lightweight Python package that provides an expressive and flexible API for scraping Instagram data. — Instascrape Documentation
With twint and instascrape you no need API keys for scraping all data that you want on both. The json file that you get is all about your brand or company has mention, likes, or retweet by the users on the twitter and many more data. Instagram data that you get such as the users, post likes, searching hastag, etc. All data that you need on both package, you can more deeply explore on their documentation package. I will attach the source of documentation package.
After you get the data, this is my idea for take some insight with the data.
- Analysis the user profile (if they have any description on their profile). So, you can grouping by customer profile who need your product.
- Analysis how big your company brand or product compare with the competitor. Total followers, average likes by post, average or total mention, total retweet, trend your product in google trends. You can search and analyze your product searching trend by customer in google trends platform.
- Analysis comments, and tweets to know about users need, what they saying about our product or services this is good or bad, maybe you can know about how customers love your product or not. If you fascinated about that you can explore with sentiment analysis that I already write in this link.
Internal Data Analysis
The second section, is all about data internal that you have in your company or business. You might be start on which data you need for the analysis to be carried out. This is example data to be more deeply analyzed.
- Transaction Data
Transaction data would help you to calculate profit on your retail (for example), how much transaction do you have in one week or by month, and what’s the favorite product. You can create reporting about the transaction data in infographics.
- Customer Data
Profiling customer is very important. With profiling your customer the company or business can targeting consumers according to the segmentation that has been done. If you need insight about the customer segmentation you can accessed in this link.
That’s two examples that you can more deeply analyze from internal data to get more insight from your data. You can also make a machine learning (if needed) to give your business sense and predictive analysis. The transaction data could be created the association rules from each transaction, recommendation product, forecasting sales on several month, sentiment analysis (if you have a review for each transaction on product) and many more. The customer data could be analyze by creating segmentation customer, segmentation product, targeting some customer and create marketing strategy for gain your product and business.
Conclusion
How to compile external & internal data? and what can be done with both? My answer is the strategy canvas. You can create strategy canvas for analyze our competitor. After we have external and internal data, try to define the factor that would we compare. This is an example analyze strategy canvas the product of competitor based on internal & external data.
Strategy Canvases provide a simple way of visualising how your competitors attract customers, and/or how your customers choose the product or service they buy in your category. This allows you to differentiate yourself by choosing a different combination of factors on which to compete. — strategiccoffee.chriscfox.com
The factor would be compare is a brand, price, services, quality product, stock product and advertising. After that, try to give score for each factor, just remember you can assume the score by data that you have. An example for brand factor , your competitor have 50K followers on instagram, average 5000 likes for one posting and 1500 visit instagram for 6 hours. You can compare it from your data external by create weighted matrix score and try to calculate it.
So much assumption of course in this step to weighed the matrix score, but we could know with that matrix score what factor that your company missed compare the competitor. Your brand win at price factor, and the rest are factors that you should to fix it. Brainstorming with relevant department would give a best strategy for handle all factors that you lose than competitor. You can make better decision for growth your business and develop product with data analytics that has been done. Create great report for visualize every you present your insight to another department. So, you, your team and ‘the data’ have a big master plan for beat the competitor.
That is all from me, I hope you can take the insight from this discussion. There are still many mistakes and shortcomings in every analysis that I do. This analysis not perfect at all, and I’m not good at marketing too. Still so many marketing tools to combine or colaborate with data that we have. Maybe this discussion would be one of your reference.
For more detail about this discussion, the code, and more visualize you can reach my github by following this link https://github.com/Anugrahn. Feel free to ask, and lets start discuss guys!
Thank you, I hope you enjoy it guys. See you on the next stories. Have a nice day! :)
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