Marketing and Sales Analytics

Big Data is the most effective strategy for marketing and sales since the Internet entered the buisness world around two decades ago.But the REAL challenge is to understand its actual functioning.

 Organizations are day to day challenged with huge amounts of data, organizational hurdles, frequently fluctuating customer behavior, and immense competition. New technologies and newly emerging channels and platforms have been responsible for a tough environment. Simultaneously, the importance of data and digital technologies has shown a clear cut path into customer needs and behaviors.

USE CASES

Brand analytics

Brand analytics compares the quality of your brand  to your competitors. Your brand is not a mere logo or a commercial status – it’s a connection that bonds you to your customers. It’s imperative to understand  customers reactions to your brand as this will influence your decisions  and strategy.

 

You can assemble such data from anywhere where your customers are discussing your brand, such as customer service conversations, sales conversations, online forums, blogs, review sites, and social media.

The internet is a rich source of information regarding how people feel about your brand and your business. People love to share so tap into this rich vein of information.

Marketing And Sales Channel Analytics

Marketing and sales channel analytics allow you to assess the different channels available to you and establish which are the most effective. It is possible that you will reach different areas of your market through different channels but  it is wise to know which ones are working and which are less likely.

 

For the current marketing and sales channels, and for any  unused channels you will need to set some conversion rate goals so as to know what you want the channel to achieve.  

Pricing Analytics

Pricing analytics  involves analyzing price sensitivity in market segments and is especially useful in highly competitive markets where everything that can be done has been done.

 

Pricing analytics requires data mining and the development of forecasting models and algorithms. It also often involves multiple, concurrent business experiments that can be run quickly and easily so you can measure what is likely to happen with each price change.

 If you use pricing analytics to improve revenue, it's important to remember to improve the value you give to customers who are willing to pay more.

Competitor analytics

Competitor analytics is important for marketing and strategic planning to understand their strengths and weaknesses and identify opportunities to exploit and threats to navigate. 

There are different ways of gathering competitor data, such as business journals and newspapers, annual reports, product brochures and marketing activity. You could even have an employee, friend or family member to buy a product or service from your key competitors and assess their experience.

 

 The most useful tip for competitor analytics is to do it but practically most don't.

Non-customer analytics

 Non-customer analytics is the understanding of people who are currently not your customers and what they think about your product, services or brand. By identifying who is not buying from you and their reason for doing so, you can expand your market to include those individuals.

 

 If you want to know why people are not buying your product or service, you need to ask them: interviews, questionnaires and focus groups can help.

 It can be remarkably easy to get feedback from people who are not your customers using the power of social media.

Market trend analytics

 Understanding of market trend is important to understand whether the market is trending up or down.

 

To figure market trends, you can run business experiments or scenario analysis to see what the market would look like and how it would impact your business in either a growing, stagnating or growth market. Customer surveys and focus groups can also help.

External variations, such as change in legislation and social expectations of the masses also should be borne in mind.

Demand forecasting

Predictive analytics is the field that forecasts demand. It helps to analyze the quantity of a product or service that consumers apprehend  to buy.

 

It is not based on guesses or past sales data or current data from test markets. Analytic techniques such as time series analysis can be very useful here.

The data used for demand forecasting must be precise. If  not , the results can be devastating and ruinous.

Unmet need analytics

Unmet need analytics is the process of figuring out whether there are any unmet needs around your product or service or within your market which you could meet to increase customer satisfaction and revenue.

 

Useful tools for unmet need analytics include product reviews, qualitative surveys, focus groups and interviews. You could also use tools like Google Trends to help identify what customers are searching for.

Market size analytics

Market size analytics is the process of working out how large the market is for your products and services, and whether there is sufficient growth potential.

 

The size of the market is measured in terms of volume, value  or frequency. Useful data includes government data, trade association data, financial data from competitors, and customer surveys.

 Just because a market is large doesn’t mean it’s profitable – especially if most of the customers that want a particular product or service already have one and are unlikely to want another

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