Challenges That Companies Face While Implementing Big Data Marketing Strategy

Presently, Big Data greatly influences the way the business world operates. Its impact is not merely restricted to marketing but extends to the way the daily activities of businesses are conducted. This has made Big Data analytics a significant tool for companies across the globe that will help them stay ahead of competition in their respective industries. However, merely because every business today needs Big Data solutions doesn’t imply that they can all successfully garner the benefits. The business realm has only started to gradually adopt Big Data trends and is dealing with multiple hurdles that stand in the way of successful implementation.

Here are the top four challenges businesses faces while applying Big Data in their marketing strategy:-

[Pull Stat]- “According to the Big Data Executive Survey 2017, out of 85% companies that are implementing data-driven strategy, only 37% have been successful.”

Incorporating Complex Data into the Customer Journey

In order to get productive insights from Big Data,the primary step is to understand its correlation with the buying journey of the customer. This becomes a challenge because the target audience typically switches between multiple channels before converting into paying customers. Moreover, it takes even longer to convert these paying customers into loyal clienteles.

Therefore, from brand awareness to revenue generation, marketers need to have an in-depth understanding of the customers buying journey. They have to gather as much information as they can from both online and offline sources. For example, a retail store can employ data-based POS systems to collect data from different stores and combine with the data collated from their social media profiles and website pages.

Surplus Data

Data companies have access to an indefinite valuable stack of actionable insights. However, with the digital universe doubling in size every two years, it has become quite challenging for the companies to make sense of all the data. Besides, accumulating data is not the hard part, its effective implementation is. In the haste to achieve principal position, companies gobble as much data as they can, which later results in data paralysis (data overload). To avoid this, they must consider the following steps :-

Narrow Down Data Sources

One of the basic things to do is cut down the source of data as much as possible. Look for minimal essential data sources that organizations can depend on to analyse the performance. Also, select a few data sources to analyse the crucial factors as well.

Filter the Narrowed Data

Next step is to filter out the irrelevant data that has nothing to do with business objectives. Marketers should be clear about what kind of data fit or doesn’t fit with the data analytics stream. This will ensure that no time is wasted on streaming irrelevant aspects.

Focus on the Critical Data Pattern

Again, the bedrock of data collection should be keeping only what matters.   Therefore, marketers need to identify and understand data patterns that clearly explain the company goals. Do Instagram likes are advantageous for the business? Do they drive in new leads or sales? Finding these correlations and sticking to them is very important to focus on the data pattern.

[Pull Stat]- “By the year 2020 the data created and copied yearly will hit the mark f 44 trillion gigabytes or 44 zettabytes.”

Breakdown of the Data

When implementing Big Data marketing strategy, it is crucial to have a thorough segmentation process to outline and distribute leads into entitled groups. This allows the companies to have a clear perspective of the groups that can be converted into more profitable ones. This can be done in the following ways:-

Define Objective

Defining the goal of the segmentation is the primary step here. What will be the process of segmentation? What is the purpose of it? Whatever the end-goal is, it should be well defined in advance to get the most effective insights into the key customer’s behaviour.

Focus on Relevant Parameters

For instance, when segmenting visitors of the website, the most important parameters would be the time they spend on the website, pages they viewed for the longest duration, visitors that went through more than a single page, etc. Hence, parameters like these should be identified beforehand to ensure relevancy in segmentation process.

The Process of Breaking Down

The last step is to identify the process of breaking down the parameters to get the desired data insights. Typically low, medium and high are used for the granular segmentation but it varies from the company to company.

Privacy Apprehension

Data privacy is one of the biggest challenges of implementing the data-driven approach in a company. Recently the Facebook-Cambridge Analytica Scandal involving data collection and management was in the much controversial limelight. Additionally, General Data Protection Regulation or GDPR has put various restrictions on how organizations can collect personal data from the customers.

While the regulations are limited to the European Union, other countries are soon likely to follow the course. Besides, consumers have also become extremely cautious about how they share their personal information on the internet. Therefore, if companies aim to get valuable data from the customers, they need to design reliable and trustworthy data collection strategies with following key points in mind:-

  • Transparency
  • First-party data collection
  • Thorough Customer awareness

From designing strategies based on trustworthy factors to precisely measuring the results, Big Data marketing strategy has changed the course of both, online and offline marketing to a great extent. Converting data into actionable insights is certainly a complicated process, however there many strategies that can help businesses turn their data into actionable strategies. Above points are four important challenges faced by businesses and tips on how they can solve these challenges to head in the right direction.