May 7, 2019

How does Big Data Algorithms help Marketers

Data-driven marketers use algorithms and machine learning techniques to find patterns within data sets that help them gain insights into customer behavior, interests, tastes, preferences, and purchase history. This helps them figure out what types of products or services will be most appealing for a target audience as well as the best kind of promotional offers to use for marketing campaigns. Here we will discuss how Big Data algorithms help marketers make the right decisions.

Written by

Harsh Gupta

The idea to use data for marketing purposes was not new at all but the approach to it has changed over time together with other technologies. The ability of marketing departments to acquire, process, and analyze huge amounts of data in order to make decisions about their customers is sometimes referred to as Big Data Marketing. This term actually covers two major areas: predictive analytics through machine learning methods (e.g., classification or regression) and social network analysis (SNA).

A big advantage of using Big Data Analytics in Marketing projects is that it helps make sense of large chunks of data quickly within an organization instead of waiting days or weeks until they receive reports from 6–12 different stakeholders.

The marketing analyst role is uncertain by nature due to the dynamic changes that constantly happen in eCommerce and other related fields. In addition, it takes a lot of time for marketers to understand how their actions impact user behavior and revenue. That’s why they need to base their decisions on data-driven results whenever possible rather than basing them on gut feelings or assumptions. Big Data Analytics can help Marketing teams work more efficiently and be more productive as well as provide them with the best practices and clear insights needed for decision-making purposes.

Big data refers to a set of data that exceeds the processing capacity of conventional database systems. The data is generated from high-speed, transactional systems such as website clickstreams, GPS signals, RFID (radio frequency identification), and machine-to-machine (M2M) communication such as automated meter reading (AMR), telematics, etc. It has been estimated that every day we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created within the last two years alone! This rapid business expansion produces a lot of data that is not always managed properly.

In the coming years, as more and more devices become smart and connected to each other, the amount of data being generated is expected to grow exponentially. Data from smartphones, tablets, wearable technology (e.g., fitness bands), cameras, games consoles, TVs, etc. will be captured by companies to analyze consumer behavior patterns or send alerts when something needs attention (Netflix), or even predict demand (Amazon’s M-Pesa).

Marketers are becoming increasingly reliant on their ability to process large amounts of this data quickly enough in order to gain a competitive advantage over others. The key benefits for marketers leveraging big data include understanding customer behaviors/patterns better; predictive analytics; engaging through personalized interactions; being proactive instead of reactive, thus increasing revenue and reducing costs. The challenge is to derive value from data before competitors do so.

Algorithms are the key tools used by marketers to convert raw data into actionable insights. These algorithms help in addressing questions like:

Who’s my customer?

What should I sell them next?

How should I price this product for maximum sales?

What ad should we show our customers right now?

Where can we improve our margins or revenue? Computer and mathematical models provide decision-makers (marketers) with the capability to make more informed business decisions as they can handle large amounts of information at once and also allow computers to interact with humans through natural language processing (NLP) to enable productive decision-making sessions.

A/B testing is another great example where machine learning can be employed today to conduct experiments at scale across different audience segments and provide customers’ specific information about their experience and help improve future campaigns and improve conversions. For instance: Even though you may not know much about your customer, data mining can help to extract useful insights about them. For example, you want to send a campaign to the laptop users in the age group 18–25 years old who earn more than $100K and are likely to use Search Engines daily for purchasing any product. On average when compared with all laptops users this segment makes 25% more purchases every quarter. So using algorithm data mining techniques it is possible to determine which customers in this segment are most likely to be interested in your offer and send the mailers accordingly.

An area where algorithms are used for predictive modeling is Sentiment Analysis. This will help marketers in determining which product people are talking most about, their likes & dislikes, etc based on social media conversations. They also use similar techniques to find out any competitor’s info who is making better marketing campaigns.

Companies are also trying to predict future demand of their products using algorithms so that they can start production of those beforehand and maintain stock shortage to avoid explosion of demands during the pre-sale time or public sale time etc. For example ‘Crowdflower’ announced a new tool later this month through which eCommerce websites can forecast how many products one would expect to sell on a certain date based on historical sales data.

Moreover, with an increase in social media usage by consumers companies are able to get feedback about their products directly from customers. The study was conducted after analyzing 200 million tweets sent between June 2012 and February 2013 by 2 million Twitter users who had tweeted at least once about a smartphone in the past 30 days. The study yielded some interesting results:

1. iPhone users are more likely to be female

2. Android users are more likely to be male

3. iPhone users have an average of 35 followers

4. Android users have an average of 132 friends

5. In general, iOS users mention Apple twice as much as Android users mention Google when tweeting about smartphones

Based on this data, it is clear that with the help of Big Data technologies, marketers can make very precise decisions regarding their campaigns and promotional offers in order for them to reach a wider audience and increase sales in a specific product line or market segment in a timely manner.

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