Contributed by: Nallaperumal K
LinkedIn Profile: https://www.linkedin.com/in/nallaperumal-k-5b6490129/
Analytics in Marketing
Marketing, in general, is referred to as the management process responsible for identifying, anticipating, and satisfying customer requirements profitably.
Now. Let’s take a look at What is Marketing Analytics?
Marketing Analytics is a broad range and is an essential tool or strategy that is used to unlock the customer’s relevant insights, increase the ROI (Return on Investment), profitability, and to make the brand perception popular among the audience or the end-users.
Another important aspect of marketing analytics is that you can convert the business to a profitable means, by implementing the right analytics, by discovering new areas of development, uncovering unknown markets, new end audiences, and areas of future marketing, and much more. Data-backed customer insights can be used to enhance marketing efforts at every stage of the process, and one of the most effective tactics is using analytics along with other business inputs and relative techniques.
Many Marketing life cycle techniques are used. But, in our case, we are going to see the most predominant one which is being used widely by firms. We will be seeing how the Marketing analytics are implemented or imputed into each stage. The following life cycle is a prominent and interrelated one. It goes as a one large continuous loop.
We will see how analytics helps us in each branch of marketing:
- Need for unmet actions
- Trend in Markets
- Market Size
a) Need for unmet actions
Any business is always about the need to meet the end requirement or business needs of a customer. If a customer need is unmet then it will eventually lead to dissatisfaction. To accommodate this, we need to analyze whether the needs of a consumer can be accomplished. We can analyze this through quality surveys, getting constant feedback, and if it is a product, then a constant review.
Google analytics/trends can be used to trace the progress, and based on the results, necessary feedback can be provided.
b) Trend in Markets:
In any business, the movement of business should always be monitored. In this process, there should be constant monitoring in trends like whether the particular trend in the business is moving upward or downward or stagnant. Based on the results, the necessary steps should be taken. Another important concept to note here is the optimization of marketing campaigns to find out the exact needs.
This trend can be found by exploring different experimentations such as statistical techniques like t-test, and other tests. This helps us in finding out the effectiveness of a new market product.
Customer surveys and any area of focus will help us to identify the current trend in the markets. Other factors such as randomness – sudden spike or dip should also be considered.
c) Market Size:
The market size is another key factor. It is very important to be considered because any business can jump to conclusions if we do not consider this aspect. Based on the size, a business can segment the market into small, medium, and large. Adding to the previous point, lead segmentation for campaign nurturing can also be done.
This market size could be anything, like the number of units produced (in case of a manufacturing company), the amount of data they are processing in case of a service-based company.
Big data helps marketers gain a better understanding of their customers and market size. The more marketers know about their size, the more they will be able to optimize spending and improve the user experience.
Taking a deep dive into the current financials, Nasdaq, current market share, could be considered for market size consideration.
The primary focus here is that we should focus on and identify who all are our competitors. We should identify our current position concerning the competitor and it is recommended to do a SWOT analysis to find out the area of weakness of the competitor, strength, threat and we should be in a position to overcome that. Here the current market share of the competitor, business journals, newspapers, annual or quarterly reports.
- Pricing and Demand Forecasting
- New Customer Acquisition
- Upselling and Cross-selling
a) Pricing and Demand Forecasting:
If we could predict the price that the customer would like to pay for a new product or service pricing, analytics comes into picture. Equipped with historical purchase, behavior, and leads data, businesses can better understand exactly what customer needs and wants are. In this context, we should also consider whether there is a product fit for the end-users. Another consideration would be the current market and its sensitivity, and the audience’s reaction towards the product. This will act as a valuable addition to the customers, as they are willing to pay less for a product with enhanced features.
Demand forecasting is also similar to pricing, but with the only difference is that here, with the help of past data for a particular product, we will be forecasting what is likely to happen in the future. We will be using techniques such as Time series forecasting, an ML model. The catch here is that the data has to be clean else there will be a skew in prediction.
b) New Customer Acquisition:
In any business, it doesn’t grow by itself – the addition of new customers helps us to gain a better position in the market. There are many metrics to find this out like –
- Customer Acquisition Cost (CAC)
- Marketing Percentage of CAC
- Lifetime Value Prediction (LVT)
- Ratio of LTV and CAC (Lifetime Value: Customer Acquisition Cost)
- Time to Earn CAC back
- Where the market originated
- Marketing Influencing percentage.
The above ratios or KPIs (Key Performance Indicators) can be tracked constantly using a dashboard on a weekly/monthly or quarterly basis.
c) Upselling and Cross-selling:
Using the data available about the customer behavior, a business can either upsell or cross-sell or combine both to increase the profit.
Upselling: If a customer decides to buy insurance of saying premium 10K every quarter- the business agent can recommend the features or end benefit for going for a high premium of say 25K or 50K instead of initial payment and then make the customer go for a higher premium from the decided premium he/she wishes to buy.
If some of your customers purchase a life insurance policy say X and later buy an accidental insurance policy- Y within say 3 months, then you can recommend an accidental policy while purchasing the insurance policy itself.
This is primarily done with the help of Collaborative filtering. It is primarily used for recommending products, services, and advertisements based on past variables, including buying behaviors. This filtering is common for upselling, cross-selling, and next-selling. Another technique namely Market Basket Analysis can also be used here.
- Churn Analytics
- Customer Lifetime Value and retention rate.
a) Churn Analytics:
Businesses use churn analytics to find out the rate at which the customer is likely to quit the product, service, or site. This part of the analytics should answer the following questions
- Are we losing customers? What is the reason behind it?
- If the business tries to increase certain attributes such as price or reduce the service provided. What is the likelihood that the customer would churn?
The answer to the above questions can be done by using a machine learning model such as – Logistic regression, Random Forest, Linear Discriminant Analysis. The business can also find out based on what features their customers are likely to churn and what is the remedy that has to be done to avoid the same.
b) Customer Lifetime Value and Retention rate:
CLV – It is nothing but the prediction of the net profit attributed by a single customer over the entire journey with the business (including the future). CLV is seen as an asset to any business. This encourages marketers to focus on the long-term value of the customers instead of acquiring customers with low value.
Retention rate is another primary factor which needs to be considered – This is nothing but the ability of the business to retain its customers, basically it is the technique used to retain the customers by taking necessary actions to avoid churn.
Some of the examples to increase retention rates are:
- Customer Feedback
- Constant Communication
- Customer Loyalty program
- Company Newsletters
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- Sentiment for Brands
- Marketing and Sales Channels
a) Sentiment for Brands:
The brand perception always matters. We should consider how our brand is being received by the customers. Word of mouth plays a huge role here. Brand perception helps the business understand where they stand. The sentiment of the brand (say a product or service) can be found out from customer feedback or customer service or online forums. We can also do a sentiment analysis to see the perception of our brand and its response to see whether it is positive, negative, or neutral. One of the key factors in driving any business is the brand perception either be it the logo or the product itself.
Twitter analysis on brand perception can also be done for this. Reviews on social media apart from twitter can also be considered.
b) Marketing and Sales Channels:
Many channels enable you to sell or market your products; they can be done online or offline. Here, the main goal is to find out how many people bought your product. As there are many segments and many mediums of marketing, it is essential to find out which channel or mode is performing better than the other. There is also a need to find out any unused yet potential channels. To do so, we can use conversion rates to click-through rates.
In this modern world, most of the marketing is being done online rather than offline. Some of the analytics tools to measure these are often referred to as digital marketing. Google Analytics is primarily used for this and a tool from Adobe. Adobe Marketing Cloud is also used to market online.
- Making the Product easily recallable
Making the Product easily recallable:
One of the strongest drivers in marketing is to simply recall the product or service. Many businesses have found a significant and consistent increase in sales once the product is made with a logo and tagline. Sometimes celebrity endorsement also plays a major role in increasing the sales. That’s the reason why the company’s sponsor for any international event by collaborating with another partner to increase their visibility.
For example, football is a famous sport in European nations. If you see the jersey of Manchester United – the Chevrolet logo is being used in the jersey at front. This provides a great opportunity for the car manufacturer.
Some of the measures that are used to identify the after-effects are:
- Website traffic before and after the match in our case
- Google keyword search
- Social engagement etc.
The above technique of online marketing analytics is called Digital Marketing.
That’s why awareness is also another metric that should be looked upon very keenly and to make our product or service visible to all our potential customers. (In our above example it is called Marketing in Sports analytics).
Apart from all of the above, another important aspect which I would like to emphasize here is Target Marketing.
Here, we should look at the possibility of targeting specific customers for a specific kind of product(preference) or services. We should analyze and look at the type of product or service that works better for certain leads/customers. This can be answered by using analytic techniques such as Clustering. Once you know which type of product or service resonates with a certain specific audience, and what is the best channel to reach them, you can customize either the product/service. When a customer receives higher-quality communication and a perfect product from an organization, this increases the probability of sales conversion. In turn, this will give us a high ROI.
- Enhanced decision making when they are backed by data.
- Improved Customer satisfaction and retention.
- It gives an upper edge over the competitors in business who still have not opted for marketing analytics.
- Increase in profit.
- One of the main challenges of marketing analytics is that it involves complex integration of data.
- The user or the analyst’s ability to use the data effectively.
- Data verification, Data validation, and reliability.
- Requires a strong understanding of “What happened before in the current market and data?”(i.e.) Comparison of before data.
Finally, by applying marketing analytics in organizations, risks can be significantly reduced because decisions will be made based on data, not merely any assumptions or any instincts and random guesses. It allows you to make more strategic planning, business decisions in a legitimate and informed manner. By doing Marketing analytics in any business – it will increase the likelihood or the probability of success.
Many successful e-commerce ventures adopt marketing analytics in various stages of the marketing life cycle. Some of the examples are Amazon, Airbnb, Myntra, Flipkart.
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