Effective Marketing Through Data Analytics
Data science, Big Data, and Predictive Analytics are some of the hottest buzzwords in the market right now for good reason: It allows organizations and businesses to peel back the layers to see and understand why things work, why things happen.
Currently, the best application of these hot buzzwords are in the field of marketing, where businesses can confidently make marketing decisions and take action while being guided by the data at their very hands, which inevitably led to the birth of Marketing Analytics.
Today, Marketing Analytics is used to gauge website performance, consumer behavior, effectiveness of advertising & marketing campaigns, and identify which products and features are best loved by your target customers.
In order to properly understand your business, you first have to properly understand your customers well. Know their needs, their wants, their motivation, and make sure that you know how you, as a brand and as a business, can fulfill them. In order to achieve this, you’ll have to first approach the matter from a quantitative perspective.
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The Customer Decision Journey
Traditional marketing once believed that a customer’s buying process is funnel-like, alluding that the thought process happening during purchase is logical and methodical. Thanks to Marketing Analytics, marketers of today now that the funnel approach is unreliable and incomplete.
Today, the customer decision journey is described as more like an orbit, showing that the farther a customer is from the brand, the less likely the customer is going to purchase your product. Otherwise, the closer the customer is from the brand, the more likely they are going to purchase the product and create a ‘Loyalty Loop’.
Through Marketing Analytics, today’s marketers can rely on data to properly mix and match the proper marketing channels and messages to keep customers closer to the brand and get into the Loyalty Loop.
Marketing Analytics Tools and Techniques
Google Analytics, Facebook Insights, and KISSmetrics are some of the most common analytical tools used in digital marketing. They show necessary insights from data like the number of visitors to your page and the like. For a deeper look and understanding, crucial analytical tools like SQL and Excel are necessary.
Metric is an informative number about an aspect of your business. Marketing Analytics are generally about mining, analyzing, and using these metrics to aid business decisions. There are various techniques that allow marketers to use Marketing Analytics to the fullest:
- Determine Key Performance Indicators (KPI). KPI is a form of metric that is unique to your business strategy and field. Establishing Key Performance Indicators like conversion rate, average order value, and the average time a visitor spends on your page. This is necessary for you to determine your brand’s performance. It is usually recommended to aim 4-6 KPIs.
- Establish Normalcy. Establishing normalcy is all about knowing your average numbers on a daily, weekly, monthly, and yearly basis. This is important as it allows you to quickly check if something’s off and immediately address the problem before it blows out of proportion.
- Suspect Exceptions. Because you have already established normalcy, it is now easy for you to identify and inspect the figures that go beyond or below your brand’s normal range. Formally called outliers, these exceptions allow you to discover an underlying problem or an opportunity you should capitalize on.
- Compare and Contrast. By having all the necessary data at your disposal for a thorough marketing analytics, you can now compare and contrast various aspects of your business from different angles and be able to answer problems like why a certain product performs better over another product.
The Components of Analytics
Every information gained from analytics should consist of the following:
- Source. This is where the traffic data or the general figure originates. Example: Social networking sites, Email
- Value. Value generally answers the ‘how much’ part gained from the source. Example: The number of visits, the number of people who clicked
- Metric. Metric provides the effect or impact of the value to the business. Example: Revenue, Conversion rate, Average purchase value.
- Range. Range identifies the time, date, and intervals of the values and metrics. Example: Daily revenue, Weekly visits, Monthly conversion rates
- Segment. Segments are filtered sources generally classified into categories. Example: Desktop, Mobile, Local, International
The Analytics Information Template
The analytics information template provides an easy way to consume and provide information. It’s usually lined up according to this order: