Data Analytics helping to gain competitive advantage over opponents in marketing
Businesses are in pressure to increase their marketing efficiency and results of their product/services. This pressure is result of customer expectations and increase of competition in business world. The amount of available data is also growing and companies collect the information across their organization and industry. This will help them to analyze the improvements needed and check the trends in sales whether they have increased or decreased which will result in finding the potential gap in the market. So, data analytics has become an important tool across organizations to enhance their marketing. This information is accessed by business analysts and business users to create business value and competitive advantage. There are many advantages as using this data can help companies saves money, improve their efficiency and develop new marketing strategies which differentiate themselves from the fellow competitors. Data analytics can be describes as the technology and technique to analyze the data and draw conclusion about them to help organizations make informed business decisions.
Six steps to start marketing using data analytics
1. Be strategic
Trying to analyze all the data at once may be a risk. Instead narrow your focus on the metrics which will provide more about product insights.
2. Understand your business and how data is related
After narrowing you focus on the metrics, you need to understand your business i.e. focus on your target audience and your revenue model. Make sure that the focus on these two elements will all affect the relevance of different metrics.
3. Be objective
Analyzing the data unbiased should be followed all throughout the process. Selective thinking or taking decisions on confirmation bias can result in statistical errors while considering others hypothesis. This will be fatal mistake which will cost your business and try to think objectively to search for the relevant data which confirms our thought.
4. Look data from all angles
Analyzing the data by questioning ourselves like “what is happening” or “why it is happening” or “what if this happens” will give all the perspective of the data we have which leads us to more information. As we start our data analysis, we should avoid stereotype thinking or tunnel vision to realize how many incorrect assumptions we made.
5. Analyze different metrics together
Considering all the metrics available and looking through them thoroughly will help to get more insights of the product.
6. Don't treat everyone the same.
It is very important to understand how your product works on a whole and take each and every metric into consideration to know which part of the product is doing well and which part needs to be improved. So the idea not to treat each metric will give you better insights of the product and also the room for improvisation.
The Product we want to demonstrate using these six steps is Credit Card. We see a lot of offers are being promoted by Credit Card companies. Some companies promote same offers such as cash back bonus, miles etc and some companies have different promotions such as unlimited credit line. They do a lot of background work using data analytics to promote their product to the customers.
• Collecting the data and analyzing at once may put you in risk instead just try to narrow your focus on the kind of promotion you want to use to attract the customers. If you are concentrating on a demographic region to promote your product make sure you get your data correct and analyze the data in such a way that you the correct product to promote. For example, if you are looking to promote your credit card in Oklahoma City. Try to find the trends in Oklahoma City such as people who use credit cards, will they go for cash back bonus or miles etc. Take that into consideration and promote your product in that city
• After you get your data on which promotion you need to focus in that demographic region then try to find other metrics such as which age group uses the product most or whether the male or female users are using the product. Narrowing these types of metrics will help you to find out the target audience and also helps to understand your business model.
• Analyzing the data objectively also helps the marketing to take it to next level. Unbiased decision making should be followed throughout the process. Statistical errors may occur when you take others hypothesis or ideas without personally checking the data. This may result in collapse of the product in the area you want to succeed. Example, if the cash back bonus is very popular among 18-24 years young women and there might some people who want to promote same cash back bonus for the same age young men. Without analyzing the data we cannot offer the promotion to those young men. This may result either success or a catastrophic failure for the product.
• Looking through all the angles also helps in successful marketing of the product. Credit cards are designed in such a way that they have different metrics to be considered by the user before applying for the card such as user considers the free APR intro period, APR rate after the intro, annual fees, offers such as cash back bonus, reward points, miles etc. Making sure to provide the best card to the customer will be the primary objective of the credit card companies. None the less no credit card company will provide all the features stated above but using data analytics they can provide as many features as they can to build or attract the customer to get the card.
• As we discussed in the above point, combining different features on credit card may increase the chances of the customer to get the card. Let's take a student who is applying for a credit card, his thoughts before getting the credit card would be to get a credit card without annual fee, less APR and long no APR intro period. To these features if we can add cash back bonus and referral bonus the chances of getting that card for that student will be high.
• Not every customer is the same. After using data analytics, we may find that credit cards in the age of 25-30 in a certain demographic region are more popular. This doesn't mean that every person who falls in that age group uses a credit card. So having the data and metrics analyzed would play an important role to find the target customer.
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