Big Data analysis has transformed the marketing tactics of companies. You might ask how? To be honest, 90% of all the data that exist today have been created from 2017 only. Can you imagine the amount of research that has gone into the analytics part?
No wonder Big Data is being relied upon by both the small and large companies for lead generation. However, one big issue that is noticeable for most of the companies is the low volume of qualified leads, or leads that are likely to get converted. Only 10% of the leads turn into marketing qualified leads (MQLs).
To address this, ten ways have been listed out to improve lead generation using Big Data.
Table of Contents
The biggest change that is quite prominent today than 10 years ago is the usage of smartphones. Nearly 90% of the world’s population relies on these personal digital assistants. But, only 35% of the business has mobile-optimized websites. Thus, it has to be optimized accordingly. And, an expert needs to examine the behavioral flow of Google Analytics to gain detailed insight into the traffic.
Big Data analysis leads to proper SEO. When the experts place the right keyword in the right place, along with the CTAs, it results in further engagements. This results in the possibility of lead generation. Furthermore, search engines like Google direct visitors to pages that consist of relevant information.
Customer Experience Personalization
The need of the hour is meaningful content creation for the ultimate customer satisfaction. To do this, companies need to take the help of Big Data analysis. Following visitor signup, customer data is transferred to a centralized database. And, their activities are monitored like the products they are viewing, their preferences, etc. Based on this, real-time recommendation engines are modified.
Lead generation is possible only by creating profiles or personas. For instance, it has been observed that when buyers perform online research, 41% read user reviews, 84.3% check out business websites, and 77% use Google search. Based on this data, an expert can decipher the patterns followed by the visitor and optimizes the recommendation settings accordingly. In this way, companies can improve marketing messages and in-store layouts.
Lead scoring is a crucial aspect of lead generation these days. Data is analyzed on prospective customers in order to properly gauge the validity of the customers. Moreover, ranking is based on their perceived value. Experts need to monitor whether the prospect is interacting with the brand or not. Based on this, the lead scores are changed. This is how companies ensure quality leads.
68% of the companies have cited lead scoring as a top revenue contributor. Based on the interaction of the customers, lead scores are changed, and it is made more relevant.
Studying Marketing Funnel
The marketing funnel is a term that is used to refer to the visualization for understanding the process of turning leads into customers. The idea is to cast a huge net to rope in as many leads as possible. Following this, nurturing is conducted on the prospective customers through the purchasing decision, narrowing down these candidates in each stage of the funnel.
This concept is used by all the big establishments like Amazon, Walmart, eBay, etc. As far as Data analysis is concerned, you need to collect data from lead management, email marketing, website analytics, and customer care to study how a prospect is moving in the funnel. 37% of companies claim that it is the toughest part of any analytics.
Just having exabytes of data at our disposal won’t suffice. Experts need to conduct a lot of experimentation and testing. In fact, they should accumulate all sorts of data related to the leads or potential customers. This would help when the algorithms of the future would focus on all sorts of additional data.
For instance, variation is essential so as to cater to the younger and the older generation. Thus, a website’s landing page might have two variants. Moreover, data testing for variant creation can be done using Hadoop, where datasets can be processed easily using unconventional methods.
When you look for a product on Amazon, you will see that even before you type in the full word, the search results appear. Similarly, when you look for information on Google, it anticipates your needs before you type in the sentence. This is known as semantic searching. It helps in enhancing the user experience and in generating leads. Based on Big Data and AI readings, experts can figure out the words visitors look for.
Imagine you visit an e-commerce website, and you start looking for products. Would you prefer the one who reads your intention and displays the required result? Or would you opt for a website, where you have to type in the full words? Amazon even incorporates long-tail keywords to reduce the typing effort of the customers.
In this age, digital advertising is of the utmost essence. Marketers use social media platforms like Instagram, Facebook, and YouTube for organic engagements and lead generation. Since Big Data offers a detailed insight into customer trends and choices, advertisements can be effectively created to target the right audience.
In addition to this, the experts consider the credit card data, newsletter subscriptions, and polls to create a relevant advertisement. Data-driven promotion is either strategic or embedded for 78% of the marketers.
Improving Supply Chains
Lead generation can be achieved from all franchises. The supply chain uses Big Data analysis and other quantitative processes to gain more customers. Moreover, it also helps in retaining the customer base that has been acquired by the company. In this regard, machine learning can be put to good use to derive insights regarding customer behavior.
In addition to this, lead generation can be enhanced by exploring the different online channels. Businesses can visualize resource planning for all supply chain systems, by exploring the various avenues.
Reading Customer Emotions
Big Data, AI, IoT, Cloud Computing together sum up Supply Chain 4.0. In order to generate more leads, companies need to analyze the psychology of the customer to understand their decision-making. Here, deep learning plays a pivotal role along with the data garnered from the Big Data analysis.
Sentiment analysis can be used to modify marketing campaigns or introduce new customized products. As people find that they are getting the desired products and services readily, they will gain trust in that company. Furthermore, chatbots can be employed to collect further customer feedback.
Big Data is useful for customer segmentation. It is done on the basis of the data collected from the moment customers visit the website. Moreover, it is carried out on the basis of shared traits and buying habits of the customers. An advanced analysis framework is used for behavioral analytics called Cohort analysis. It is so efficient that it will predict what the customer will buy and when.
When Uplift Modeling and Cohort analysis are put together, it enhances customer experience and offers companies detailed insight. Thus, the prospect of generating more leads increases.