Data-Driven Marketing: How Big Data Helps Make Business Decisions

Ways to use big data in marketing

Data plays a central role in the market research and strategy formulation stages of marketing. It includes data from various sources, enabling marketers to establish groups with the same preferences and behavior. With this, the marketing team focuses on each group by personalizing offers and messaging. For instance, a marketer who wishes to eliminate ad waste may segment the target audience and tailor the ads.

A recent study summarized the most popular objectives among marketers. These include to:

• Increase customer loyalty (46%)

• Grow conversion rates (43%)

• Optimize the customer’s path to their decision to buy a product (42%)

• Grow engagement on the various types of content they post (35%)

• Define the best marketing channels (28%)

• Do away with ad waste/increase ad performance (21%)

• Make sense of other disparate data (16%)

• Enhance multi-touch attribution (11%)

Marketers at your company may choose to use one or several of the objectives above to develop working strategies. For instance, they may opt to segment your company’s target audience to eliminate ad wastage. They may also segment the target audience to grow engagement and increase loyalty by personalizing the messaging to the audience.

Key challenges of data-driven marketing

While beneficial, data-driven marketing is negatively impacted by a few challenges. These issues can be divided up into three categories: data collection, data organization, and data analysis.

Earlier, we noted that some marketers couldn’t speedily capture data on customer preferences due to outdated modeling. This modeling relates to obsolete systems that constrain the ability to gather and organize new kinds of data. They also include algorithms that were not initially built to analyze large volumes of data.

Data collection challenges

In gathering data for marketing purposes, marketers strive to use only high-quality data, i.e., data that results in actionable insights. This becomes a challenge with each passing day as new technologies emerge that, when used, generate even more data. As a result, marketers and the companies they work for have to deal with large volumes of data generated every day, some of which may not be useful.

Further, the high-quality data must be up-to-date to enable the company to address any concerns or work on trends as soon as they emerge. For the data to be useful, it should not have any missing links – it should be complete.

Data organization challenges

Data that marketers can use exists in either structured or unstructured form. While they can easily use readily available tools to organize the former, they can’t apply the same to the latter. Internet-based sources such as company websites, social media platforms, and e-commerce sites contain unstructured data that is harder to organize.

Secondly, the fact that marketers and companies collect data from different sources presents a second challenge. High-quality data is not easy to find because it is highly fragmented and exists as part of a messy whole. Marketers, therefore, have to first bring it together in a process known as harmonization and then separate the high-quality data from the noise.

Data analysis challenges

Regularly updated data is problematic and leads to large volumes of data, some of which may be irrelevant to the marketing campaign. This is despite being beneficial in the sense that it leads to real-time and up-to-date data.

At the same time, such data becomes obsolete at an unprecedented rate. This, therefore, means that companies that are unable to extract useful, actionable insights from the collected data fast enough may struggle to deliver desirable customer experiences across all touchpoints.

Web scraping for data-driven marketing

Fortunately, technological advances have led to robust and reliable public web scraping solutions, such as scraping with Python. These tools have in-built capabilities to address most of these challenges.

For instance, they undertake large-scale data extraction, convert the unstructured data into structured data, access relevant websites without getting blocked, deliver location-specific data, and handle any website changes. Additionally, some solutions feature web crawlers that scour the internet looking for real-time data, which the web scraper then extracts.

Web scraping, therefore, eases data collection and organization, which, in effect, also simplifies data analysis. In this regard, companies and marketers should invest in public web scraping technology, such web scraping API, provided by reliable service providers. Such an investment will ensure that they are on top of existing and emerging data, especially regarding data-driven digital marketing.

Wrapping up

Big data, which relates to large data sets on human behavior and preferences, offers actionable insights that influence business decisions. For instance, it enables a company to segment its target audience. It can then develop tailored data-driven digital marketing campaigns that appeal to the segment’s preferences and interests. Simply, big data facilitates personalization and marketing strategy formulation.

The existence of voluminous data, while beneficial, presents challenges relating to data collection, organization, and analysis. However, public web scraping solutions can help marketers and companies deal with these challenges.