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Explore how you can adopt Big Data to ensure effective lead generation. Learn interesting insights into the use of Big Data in marketing. Click here to read on.
Since the introduction of Big Data, most marketing activities have become data-driven. Big Data is used to boost consumer engagement and loyalty, optimise your website’s performance, or make pricing decisions. Its implementation in marketing has been a game-changing affair.
That's because marketers are using AI and machine learning to skim through vast amounts of data and to derive conclusions. They can even use predictive analytics to determine what actions consumers and prospects are likely to take in the future. Now, mentioned below are multiple ways Big Data has enhanced the process of lead generation.
1. Understanding the consumer decision journey
Gathering knowledge about the decision journey of the consumers is critical for developing strategies to get new consumers and retain the existing ones.
By incorporating Big Data into the marketing strategy, companies can make a substantial impact in the areas discussed below.
- Customer engagement: Big Data can provide insight into not just who your consumers are, but what they want, where they are, how they want to be engaged, and when.
- Customer loyalty and retention: Big Data can assist you in discovering the influences of loyalty and what compels them to come back again and again.
- Marketing optimisation/performance: Big Data can help you detect and measure the progress of your marketing activities across multiple channels. Also, it can optimise marketing programs through measurement, testing, and analysis.
2. Allows successful discovery of promising opportunities
To find more opportunities, it’s vital to build a data advantage by incorporating appropriate data sets derived from your company. Depending on the mass analysis of those data, however, is often a recipe for failure.
Analytics experts need to move beyond broad goals and get down to a level of meaningful specificity. They must use digital information to target buyers and use the analytics to learn more about target consumers.
Modern marketers can find the more granular level of details, like which websites consumers visit most often, which social media channels they use most frequently, and which buttons they click on a given website. This way,the “ideal buyerpersona" can easily be targeted with Big Data.
In this case, analytics tools like Cyfe or Sprout Social allow marketers to check the analytics. These tools provide clear insights into the number of engagements and impressions that the posts have garnered on social media.
3. Personalising customer experience
With every business leaning towards content marketing, there is an increasing need for creating meaningful content. This has resulted in quite a confusion among the consumers to find the right information depending on their needs.
By evaluating data about consumer behaviour and interests, organisations can create recommendation engines. These recommendation engines can offer content to the consumers with which they're most likely to engage. Using this method, brands will not only enhance the online experience of the consumers but also improve the in-store layout and marketing messages.
Netflix is an excellent specimen of a big brand that leverages Big Data analytics for targeted advertising. Nobody's a stranger to the effects of Netflix, whether it’s on students or middle-aged people.
Having over 100,000 subscribers, the platform gathers huge data, which is important for achieving the status that it enjoys. If you’re a subscriber, you already know how they send you suggestions for the next movie you should watch. This suggestion is provided using your past searches and viewed data.
4. Start small and achieve big
When it comes to implementing Big Data into marketing, your approach should be to use small but selective data. This will be convenient in terms of addressing the immediate needs of your lead generation and conversion campaigns. This will assist you in avoiding the complexities of managing the overload of data.
As you move ahead with your marketing campaigns, you can keep adding layers of data to expand the campaigns further. Over time, you will be able to generate better leads and sales conversion.
5. Delving into the emotions of the consumers
It’s a general notion that data analysis in marketing and sales will only highlight the analytical results. However, with emotional intelligence being a crucial element of human communication, data science is considered a complementary tool. With the rapid advancement in technology, we now not only can decipher human emotions but also interpret them and revert to them.
With the phenomenon of sentiment analysis, organisations can gauge the emotion behind written feedback by evaluating it. This technique can be adopted to assess the communication regarding your brand on social media platforms, blogs, and review sites to understand the collective consumer opinion towards it.
“The use of chatbots is also quite beneficial in deciphering the consumer emotions as the entire communication history can be easily recorded and assessed," states Bruce Kelly, an expert from Allessaywriter.com.
6. More accurate testing
The fact that we're able to accumulate and assess an abundance of data will be helpful to conduct much more conclusive testing. This is because the algorithms of the future will focus on all sorts of additional data, like visitors' search histories, to provide us with more conclusive test results.
For instance, variant A of your landing page might work best amongst a younger generation, while variant B works best with older consumers. Marketers can use insights like these to personalise and make their site attractive to different target audiences. This way, they’re serving up the different variants based on what previous data reveals the consumer is most likely to engage with.
7. The implementation of semantic search
Semantic search denotes the method of searching in natural language terms rather than in the short burst of keywords that we're typically used to.
Big Data and machine learning make it extremely convenient for search engines to understand what a consumer is looking for. So, the smart marketers have started to include semantic search into their site search functionality to enhance the user experience for their visitors.
One prominent example of semantic search has to be Walmart, which has adopted text analysis, machine learning, and synonym mining to enhance the accuracy of their site search. As per Walmart, incorporating semantic search to their website has accelerated the conversion rate by 10-15%. For a brand like Walmart, that ultimately adds up to millions (if not billions) of dollars.
8. Extending human efforts coupled with machine-driven analytics
If marketers want to derive accurate conclusions from a massive pile of data, they'll need the assistance of a machine for processing it. Because of this, digital marketers work in tandem with machines to assess data and to make decisions based upon it.
But irrespective of how much technology evolves, there will always be the need for a human to monitor it. That is even more applicable when it comes to the complicated field of Big Data analytics. No human can approach this alone, and neither could any single piece of software. The combined efforts of the two are far more powerful.
9. Big Data enables more targeted ads
Big Data enables digital advertisers to target consumers with more personalised ads that they most likely want to see. Google and Facebook—the dominant players in digital advertising—have been creating and delivering more appealing ads in non-intrusive ways.
These personalised and targeted ads are all based on the data the consumers provide about what they’re doing, liking, saying, sharing, etc.
10. Taking a holistic, company-wide approach of Big Data
While channeling the power of Big Data should definitely be an initiative for the marketing department, educating the other departments of the company about how Big Data relates to them can work wonders.
Sure, marketers should be assessing all the data, but they should also attempt to bridge the gap with the IT department, letting them know exactly how it’s implemented.
Moreover, they should make the expenses of Big Data initiatives clear to the accounting department as well. In fact, front-line employees should be equipped with all the relevant data for greater personalisation of the consumer experience.
Since its inception, Big Data has evolved considerably, but there's still plenty of potential for marketers to take advantage of in this case. So, it's essential for marketers to understand that each organisation's Big Data repository is just as vital to their marketing efforts as their email list.
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