When it comes to devising a brand strategy, there’s no getting away from the unparalleled power of big data. Whether your brand’s aim is to tap into real-time user behavior data for personalization, or cloud-based data tools to better measure performance against competitors, big data offers more comprehensive insights from which an impactful strategy can be drawn.
Big data can be widely diverse, too. The quality of brand insights that can be taken from datasets can revolve around operational metrics, financial, or customer sentiment. Crucially, it can help businesses to better understand their target audience, improve internal processes, and to create a winning brand identity that leaves a positive impression wherever it goes.
Furthermore, data can help produce a range of different results for users, helping to solve problems, analyze performance, classify processes, and build knowledge of what impact different marketing practices can make on customers.
But how can businesses utilize big data specifically in order to form a flawless branding strategy?
Defining big data
Big data is a term that refers to richly complex data sets that can be created and compiled via a range of sources and is liable to grow significantly over time. At its core, big data offers high volume, large scale velocity, and a wide range of variety. When compiled correctly, big data can be so large that businesses are dependent on artificial intelligence programs as a means of interpreting the insights that the data can show.
There are three fundamental segments in which big data can be categorized:
Structured data: This refers to data that can be readily utilized for analysis, and comes with indicative columns and rows within databases. Structured data is easy to interpret, and is capable of quickly being mapped into predetermined fields.
Semi-structured data: Referring to data that doesn’t have a distinctly categorized database but comes in an analytically friendly format like XML data, semi-structured data can still hold plenty of use for businesses looking for faster insights.
Unstructured data: This is wholly unorganized data that has no formal model for categorization and doesn’t come in a database-friendly format. This means that unstructured data may arrive in a PDF, Word, TXT, or media log file format which can be more difficult to interpret.
Because of the vastness of big data, to achieve the best results, marketers must immediately look for ways in which to access and categorize the quantitative insights that can be provided. In this regard, the adoption of big data is just as much about incorporating the technology into business processes as it is about utilizing such insights.
Adapting big data for marketing
The potential for incorporating big data into the realm of marketing is clear. Rich insights give marketers the power to discover new prospects and win customers at a scale that’s never before been possible.
Audience behavior can be analyzed and acted upon in real-time, while master data management (MDM) and data integration can combine to offer an easy-to-digest view of data throughout a swathe of both internal and external sources. These unified holistic insights can empower marketers to make the right calls to win more customer engagement over time.
Today, leading tech software from AWS, Azure, and Google have been key in delivering big data analytics in a more digestible manner in comparison to unstructured data, and although there are still challenges between accessing data and effectively interpreting it, platforms from Talend, Marketo, SAP, and Netsuite are ever-evolving solutions to help businesses to transform their branding strategy.
Utilizing data to run targeted campaigns
To ensure that your branding strategy is as focused as possible, it’s essential that you amplify your reach to your target market. In order to do this, it’s essential that you have access to accurate, qualitative, and up-to-date data.
It’s in this area that big data truly excels. When utilized correctly, big data has the power to provide an accurate real-time picture of your target audience. In order to access these insights, it’s important to consider the types of data that you can use. These include:
- Demographics
- Social listening
- Third-party behavioural
- Conversion insights
- Geographic
- Psychographic
In correctly utilizing insights from these datasets, it’s possible to better segment your customers and to develop your branding strategy in order to resonate more meaningfully with them.
These insights can then be applied to your website that you’re looking to utilize, and can become standardized across your advertising channels, social media, landing pages, email campaigns, and blog content.
While traditional data is often largely generalized and unfocused, big data can help your brand look at customers on a more individualized basis. From looking at individual demographics, locations, personal likes and dislikes, businesses can gain a more comprehensive understanding of their customer profile and market directly to them.
Branding-level insights in action
For General Mills CMO Mark Addicks, qualitative data challenged how their business linked different products together, helping General Mills to form more targeted brand relationships and to generate more relevant content based on how customers interacted with their products.
“Initially, data was used simply to drive sales: advertising on the right day of the week, adding precision on when to engage, knowing what offer to put in front of whom, which pie to focus on for which part of the country.” – Mark Addicks
Furthermore, big data analytics are capable of transforming brands beyond targeted offers. Data has the power to deliver better audience segments that can reveal shared concerns and needs that many businesses don’t fully recognize. For instance, data can help to identify the needs of customers who are acting on cholesterol guidance from physicians. “People worry about taking their eye off the brand when you get that granular,” Addicks says, but he adds that when brands address a specific recurring customer pain point, it “drives sales, but also links to the broader brand positioning.”
Big data and social listening
There’s no getting away from the potential of data generated within social media platforms. Social media hosts the world’s largest collection of unstructured data, and this means that brands can excel by interpreting and acting on social listening.
Statistically, only 3% of useful data is tagged online, and a far lower percentage is actually analyzed. However, social listening platforms like Brandwatch, Sprinklr, Digimind are all strong solutions that promise to get to the bottom of all the qualitative data produced across different social channels.
According to statistics, some 500 million Tweets are sent each day, and 720,000 hours of content is uploaded to YouTube daily – this indicates just how much your customers are talking when they’re not interacting directly with your brand. Although social media data can be discovered manually, social listening tools can be a great time-saving solution in gauging audience sentiment and uncovering where your key brand engagements are occurring online.
This is where big data excels the most. At its best, data can generate a continuous feedback loop for brands. Parameters can be set up to effectively analyze whether a specific brand marketing strategy is working, and whether it’s having the right impact on customers. With the correct utility of big data, marketers can simply shift from one approach to another in a continuous trial and improvement process until they identify how best to resonate with their targets.
The internet is a big place, and it’s sprawling with user opinion and customer advice that can be acted on. With the help of big data, your brand strategy can be fine-tuned in a way that’s never before been possible.
Cover image: fabio